Innovating With Infrastructure: The Automobile Industry in India

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Innovating with Infrastructure The Automobile Industry in India

Sumila Gulyani

Innovating with Infrastructure

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Innovating with Infrastructure The Automobile Industry in India Sumila Gulyani

© Sumila Gulyani 2001 The findings, interpretations, and conclusions in this book are entirely those of the author and are not attributable in any manner to the World Bank. All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No paragraph of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1T 4LP. Any person who does any unauthorised act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The author has asserted her right to be identified as the author of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2001 by PALGRAVE Houndmills, Basingstoke, Hampshire RG21 6XS and 175 Fifth Avenue, New York, N. Y. 10010 Companies and representatives throughout the world PALGRAVE is the new global academic imprint of St. Martin’s Press LLC Scholarly and Reference Division and Palgrave Publishers Ltd (formerly Macmillan Press Ltd). ISBN 0–333–91580–1 This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. A catalogue record for this book is available from the British Library. Library of Congress Cataloging-in-Publication Data Gulyani, Sumila, 1965– Innovating with infrastructure : the automobile industry in India / Sumila Gulyani. p. cm. Includes bibliographical references and index. ISBN 0–333–91580–1 (cloth) 1. Automobile industry and trade—India. I. Title. HD9710.I42 G85 2002 338.4’76292’0954—dc21 2001033196 10 10

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Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire

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For my parents Urmila and Bansi Gulyani


Contents List of Figures and Tables

xii

List of Appendices

xiv

Acknowledgements

xv

Acronyms

xvii

Currency Exchange Rates

xix

1 Introduction

1

The infrastructure debate: practioners versus academics Moving beyond the debate

3 6

Developing a new analytical framework Supply-side variables – technological indivisibilities and institutional arrangements Impact – external economies and direct benefits Response – user-devised strategies to offset impacts of poor infrastructure Putting together the supply–impact–response framework The focus of inquiry and unit of analysis The Indian automobile industry The major case study: Maruti-Suzuki Structure of the book 2 Innovative strategies for tackling power problems The main arguments

6 8 10 12 14 15 18 20 23 28 29

The power problem and conventional solutions The power problem in India The government’s new approach: encourage private investment and self-generation World Bank’s critique of self-generation The Bank’s solution: unbundling and private competition

vii

30 30 31 32 32

viii Contents

Maruti upgrades from generators to captive plant Selecting gas turbines: a revolutionary technology Negotiating access to natural gas

35 36 37

Demolishing the myth of high self-generation costs

38

(a) Maruti’s price compares favorably with IPPs (b) Maruti’s low cost is not unique – a comparison with Nigerian and Indonesian firms (c) Self-generation meets demand for quality at low cost (d) Maruti’s energy expenditures are lower than other assemblers Self-generation as a preferred alternative Maruti’s power-sharing arrangements Supplying power to adjacent joint-venture suppliers Selling electricity to the state grid Expanding the customer pool India’s largest car maker runs an efficient electric utility Transmission and distribution system Billing and collection Tariff structure, sales revenue, and a “cross subsidy” for HSEB The Maruti model: features and insights How unreliable power affects supply chains and competitiveness Output losses and disruptions in production plans Loss of material and variation in product quality Inventories as a “solution” to supply-chain unpredictability Impacts cascade through the supply chain Conclusion 3 Effects of poor transportation on industrial competitiveness Transportation and competitiveness: insights from the literature Development practitioners’ view of India’s transportation problem The value chain as a determinant of competitiveness

39 43 44 47 49 51 52 53 54 55 55 57 58 59 60 60 61 62 62 63 77 78 78 80

Contents ix

Lean production, supply-chains, and just-in-time delivery Transportation systems: the missing variable in the competitiveness literature Combining insights from different strands of literature The total logistics cost equation as an analytical approach Case study: Maruti’s logistics costs Maruti’s supply and distribution chains and its transportation demand Effect of the transportation system on logistics costs Freight expenditures Cost of goods damaged in transit How the transportation system affects inventory levels Struggling to get lean: Maruti’s inventory problem The “fat” is in the supply chain The international supply chain is anything but lean The domestic supply chain: only local suppliers can deliver JIT Ford’s logistics plan and insights into transportation problems Inventories increase with distance: some quantitative evidence The correlation between distance and inventories Conclusions

81 84 85 86 87 87 88 89 89 90 92 93 95 99 99 102 103 105

4 Clustering as an infrastructure solution

112

Maruti’s localization strategy and the creation of a Delhi auto district

115

Maruti’s mandate, its location decision, and creation of a local supply base Encouraging suppliers to cluster near its assembly plant Facilitating localization: Maruti’s supplier park and “incentives package” Are the incentives exceptional? The “model” supplier-park deal and why government also wins Positive spillovers: Gurgaon as a diversified industrial district Summarizing the benefits of clustering

115 119 121 125 126 129 130

x Contents

Ford, Hyundai, and the growth of the Chennai auto district Ford’s location decision Ford’s transportation solutions: logistics planning and localization Hyundai’s localization strategy The emerging geography of production The geography of the US auto industry How and why the geography of production differs in India 5 The supply–impact–response framework Introduction Recapitulating and explaining the findings using the framework Solving the power problem Captive self-generation is a solution that works well Self-generation combined with power sharing works better Solving the transportation problem Transportation impacts: it’s not just freight costs that matter Assemblers’ response: logistics planning and clustering Comprehensive transport solutions are not easy to implement Identifying better infrastructure solutions Partnering with the state: better solutions, broader benefits The Maruti–HSIDC partnership offers lessons for industrial park development The Maruti–HSEB partnership creates a replicable power solution The state as a “developmental” deal-maker 6 Conclusions and policy implications Ameliorating infrastructure problems: a non-traditional approach

133 133 135 136 137 137 140 145 145 147 148 148 150 152 152 154 156 158 159 160 161 164 165 168

Contents xi

Proposed industrial infrastructure approach for India An alternative industrial targeting strategy

168 169

Notes

172

References and Selected Bibliography

188

Index

194

List of Figures and Tables Figures 1.1 1.2 1.3 1.4 1.5 2.1 2.2 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9a 3.9b 4.1 4.2 4.3 5.1 5.2

The supply–impact–response framework Vehicle production in India, 1984–97 Maruti dominates the Indian passenger car industry, 1988–97 Maruti – growth in production, 1988–97 Maruti’s export performance, 1992–97 Economies of scale in power generation, 1930–90 Maruti’s power-sharing arrangements Assemblers’ structure of costs – salience of the supply chain Suppliers’ structure of costs – again, the supply chain is key Maruti: days of inventory, by type, 1992–97 Maruti: C&RM against other inventories, 1992–97 Maruti: C&RM inventories, 1992–97 Maruti: inventory of goods in transit, 1994–97 Maruti: closing stock or buffer inventory, 1994–97 Maruti: reliance on imports and its effect on C&RM inventory, 1992–97 Ford – distance against delivery frequency and inventory SBL – distance against delivery frequency and inventory Location of Maruti’s major suppliers within the Delhi auto district Industrial development around the Maruti assembly plant, 1996 The emerging geography of production in the Indian auto industry The supply–impact–response framework Firms self-generate to counter power problems

xii

7 18 20 22 22 37 52 82 82 94 94 94 95 96 96 102 103 118 131 141 146 149

List of Figures and Tables xiii

5.3 5.4

Self-generation with power-sharing as a response to the power problem The transport problem and firm-level solutions

150 157

Tables 2.1 2.2 2.3 3.1 3.2 3.3 3.4 4.1

Captive plants compared to “fast track” projects and IPPs in India, 1996 Auto assemblers’ energy expenditures, 1996–97 Revenues and costs of electricity sold by Maruti, 1996–97 Maruti’s distribution of finished vehicles: cost of freight and travel time Maruti’s inventory performance compared to other assemblers SBL – inventories vary by product and distance of supplier Maruti’s logistics costs as a percentage of sales revenue, 1996–97 Value of Maruti’s purchases from suppliers in the Delhi district, 1996–97

41 48 58 90 92 104 105 119

List of Appendices 1.1 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 4.1 4.2

Study methodology Maruti’s power system – output, costs, sales, 1996–97 Maruti – financial analysis for 86 MW and 60 MW generation plants Equipment vendor’s quote for a 4 MW power plant for Arvind Mills Costs of self–generation in Indonesia and Nigeria Maruti – outbound freight logistics Inventory of imported against indigenous materials (Case – TVS Sundram Fasteners, 1996–97) Inventory of imported against indigenous materials (Case – Mark Auto) Regression results – inventories increase with distance Geography of purchases – data from first-tier suppliers in Gurgoan HSIDC invites applications for its IMT project

xiv

24 64 66 74 76 108 109 110 111 143 144

Acknowledgements This book started as a dissertation at the Massachusetts Institute of Technology. I found the process of researching and writing this book to be a far cry from the drudgery that is normally associated with such endeavors. For this I owe many people and would like to thank: Michael Piore for teaching me to enjoy theory. Alice Amsden for her inspiring iconoclastic approach. Dale Whittington for always challenging my ideas. Robert Ebel of encouraging me to take time from my work for him at the World Bank for academic pursuits. Ralph Gakenhiemer, Susan Helper, John Irving, Lisa Peattie, Karen Polenske, Kavita Sethi, Joseph Sussman and Judith Tendler for their constructive criticism and intellectual guidance. The Hugh Hampton Memorial Fund, the International Motor Vehicle Program, and the Department of Urban Studies and Planning at MIT for providing crucial research funding. Melba Jezierski for being my highly effective writing therapist at the dissertation stage and Joan Casey for editing it into a book. Donald Reisman for directing me to Palgrave, and Timothy Farmiloe, then Director of Publications, for accepting the manuscript. The CEOs and executives of the firms, government agencies, and industry associations in this study, for their incredible support and candidness. Mr R. C. Bhargava, then CEO of Maruti, for allowing me unrestricted access to this pioneering firm. For sharing their insights and providing crucial data, I thank O. P. Kadam, Rakesh Jain and Jyoti Dahiya at Maruti, and Jeff Body, Sandip Sanyal and J. Arun at Ford. My research in India would have been impossible but for the help that I received from my friends N. Bahri, Inder Chopra, Neeta Kukreja, D. Kumar, Alpana and Rahul Kirloskar, Vandana and Rajesh Mehta, Sujata and Murli Rananathan and Rajeev Aggarwal. For the invaluable discussions and debates over umpteen cups of coffee and glasses of wine, I thank my friends and colleagues: Mona Mourshed, Vinit Mukhija, Jose Olivera, Theo Seltzer, Monica Pinhañez, Ann Steffes, Jennifer Johnson, Gaurav Shah, Joaquin Herranz, Kathleen Wage, Jackie Bode and Kavita Sethi. For all of this and much more – especially for continuing to be my friends, philosophers, and guides – I am indebted to Nichola Lowe and Rashmi Taneja.

xv

xvi

Acknowledgements

A special thanks to my family – my parents, to whom this book is dedicated, for their unwavering support and for their direct participation in my research and data collection efforts. My sister Meeta for ensuring exotic breaks in Ladakh and Rio and for being my personal cheerleader. My brother Manish for making me aim for a seemingly impossible finish date. My sister-in-law Anu for making my Ottawa visits my most academically productive “holidays.” My niece Priya for being the most delightful distraction ever.

Acronyms ACMA AIAM BHEL C&RM CCGT CKD/SKD CNC DOS FOB GAIL GDP GT HSEB HSIDC IIP IMT IPP ITR JIT JV kV kW kWh MD MODVAT MOP MoU MSEB MW MWh NTPC PPA SEB SIDC

Auto Component Manufacturers’ Association Association of Indian Auto Manufacturers Bharat Heavy Electricals Ltd Components and raw materials Combined cycle gas turbines Completely knocked–down/semi knocked–down Computer numerically controlled Days of supply Free on board Gas Authority of India Ltd Gross domestic product Gas turbine Haryana State Electricity Board Haryana State Industrial Development Corporation Integrated industrial park Industrial model township Independent power producer Inventory–turn–ratio Just–in–time Joint venture Kilovolt Kilowatt Kilowatt–hour Managing director Modified value added tax Ministry of Power Memorandum of understanding Maharashtra State Electricity Board Megawatt Megawatt–hour National Thermal Power Corporation Power purchase agreement State Electricity Board State Industrial Development Corporation xvii

xviii Acronyms

SOC SOE TLC TPA TQM UPSEB

Social overhead capital State owned enterprise Total logistics cost Third party access Total quality management Uttar Pradesh State Electricity Board

Currency Exchange Rates

Year

Rupees per US Dollar Official rate

1990–91 1991–92 (liberalization starts in June 1991) 1992–93 1993–94 1994–95 1995–96 1996–97

Unified rate

17.95 24.52 26.41

(market rate:30.65) 31.36 31.40 33.46 35.50

Notes: The Indian financial year runs from April 1 to March 31. In 1992–93 India had a dual exchange rate system; in 1993 the rates were unified at the free market rate. Source: IMF Statistics as cited in World Bank, 1997. For ease of reference and because most of the data presented in this book is for the year 1996–97 and the first quarter of 1997–98, an exchange rate of Rs 35/US$ is used throughout. Calculations that deviate from this rule are duly noted.

xix


1 Introduction

Industrial firms in developing countries have to contend with acute shortages of physical infrastructure. Users in many of these countries face problems such as “brownouts and blackouts in power systems, intermittent water supplies from municipal systems, long waiting periods for telephone service connections, and increasing traffic congestion” (World Bank 1994a: p. 7). Not only has the quantity of public infrastructure stock and services failed to keep pace with demand, but the quality of the service that gets provided also is often poor. As a result, the subset of industrial users with access to public infrastructure still have to contend with highly unreliable and poor quality service. Nonetheless, industrial firms in developing countries do manage to produce and survive without access to infrastructure, such as reliable electric power and freight transportation systems. Indeed, in countries such as China and India, industrial firms appear to be not just surviving but also thriving, and often competing successfully in world markets, despite severe infrastructure deficiencies. This book examines the paradox of increasing industrial development and improving industrial performance in the face of severe – and, allegedly, worsening – deficiencies in physical infrastructure. It is important to resolve this paradox because it lies at the center of a debate between the new academic literature and development “practitioners,” in particular, governments of developing countries and agencies such as the World Bank. On the one hand, the new academic literature fails to show conclusively that good infrastructure correlates with better industrial performance, and the precise links between infrastructure and development remain open to debate (Gramlich 1994; World Bank 1994a). On the other hand, the practitioners – while noting

1

2 Innovating with Infrastructure

that the evidence from the literature is inconclusive – continue to believe that good infrastructure is critical for industrial competitiveness and economic growth, and are earmarking billions of dollars for additional infrastructure investments (e.g., India Infrastructure Report 1996; World Bank 1994a). That is, the practitioners are allocating scarce public and private resources to ameliorating the infrastructure “problem” without really understanding how and in what ways, if at all, infrastructure affects industry. This study offers a way to bridge this gap in our understanding and build a more differentiated view of the links between infrastructure and industrial performance. Specifically, it analyzes the infrastructure “black box” and focuses on the questions: How does poor infrastructure, in particular, unreliable power and inadequate freight transportation systems, affect the costs and competitiveness of firms? How do firms cope? The illustrative case study is the automobile industry in India, which has been growing rapidly at a time when the Indian government, international lending agencies, the media, and analysts have been emphasizing that the country is experiencing an infrastructure “crisis.”1 This analysis shows not only that infrastructure is a key variable affecting the performance of firms but also – and more importantly – how and through what mechanisms infrastructure affects industrial performance and competitiveness. Precisely because infrastructure is a critical determinant of performance, firms often devise ingenious solutions to infrastructure problems. Their solutions include, but are not limited to, self-provision of infrastructure. This study dissects some of these userdevised innovations – it analyzes why they were required, how they work, and the role that the government has played in facilitating their implementation and enhancing their effectiveness. Overall, this analysis helps improve our understanding of the links between infrastructure and industrial performance in three ways. First, it provides some insights into the nature of the infrastructure problem and demand for services from the perspective of industrial users themselves. These insights suggest how we might link infrastructure and productivity in academic studies, and how practitioners can better target the problem. Second, it reveals how industry copes with infrastructure deficiencies and which solutions work well and why. It, thus, identifies a set of non-traditional infrastructure solutions that policymakers can use in conjunction with more traditional ones. Third, it offers some lessons on how governments and industry can partner in the process of improving infrastructure provision and, thereby, in enhancing industrial development more generally.

Introduction 3

The infrastructure debate: practitioners versus academics Physical infrastructure, broadly defined, produces basic services without which primary, secondary, and tertiary productive activities cannot function. Physical infrastructure includes transport, electric power, telecommunications, water, sanitation, and waste disposal. It is often referred to as economic infrastructure to distinguish it from social infrastructure, which produces services such as health, education, and law and order. The discussion below – and, in general, this book – focuses on the debates surrounding physical infrastructure. Development practitioners – policymakers, governments of developing countries, and international lending agencies – believe that poor infrastructure is a key bottleneck to industrial development and economic growth (e.g., India Infrastructure Report 1996; World Bank 1994a). To ameliorate these deficiencies, developing countries invest about US$200 billion a year – four percent of their national output and a fifth of their total investment – in new infrastructure (World Bank 1994a). Nonetheless, development practitioners argue that this is not sufficient, that governments need to aim for higher levels of investment in infrastructure and for higher levels of quality in the services that are provided. India’s approach to the infrastructure problem is illustrative of how governments of developing countries tend to perceive and address the issue. The India Infrastructure Report (1996), a policy document prepared by a government-appointed expert committee, offers some insights. The report starts with the belief that “the availability of infrastructure is imperative for overall economic development.” It calculates that infrastructure investments need to increase from their current level of 5.5 percent of GDP to about 7.0–8.0 percent of GDP. In absolute terms, this translates into a target of US$330–345 billion in new infrastructure investments over the ten-year period 1996–2006. The report develops the policy approaches and mechanisms through which the government can achieve these investment targets. It singles out one sector of the economy – the industrial sector – for special targeting while the new approaches to infrastructure provision are being implemented. Such analyses and efforts by governments of developing countries are strongly supported and, often, led by international development agencies, such as the World Bank. Indeed, according to the World Development Report (World Bank 1994a), it is critical for developing countries to focus on infrastructure investments and capital stock and on the quality of infrastructure services in the economy because


“the adequacy of infrastructure helps determine one country’s success and another’s failures … (and) good infrastructure raises productivity and lowers production costs” (p. 2). By contrast, much of the recent academic literature appears to belie the notion that infrastructure is strongly correlated with industrial productivity or national growth. The following discussion delineates how the infrastructure issue is perceived in three different strands of academic literature, that is, the macroeconomic literature on growth and industrialization, the more micro literature on industrial performance, and a set of macro-level regression studies focusing on the correlation between infrastructure and industrial productivity. The new literature on industrialization in developing countries and macroeconomic growth pays minimal attention to physical infrastructure and its potentially important role in industrial investment, growth, and competitiveness. For example, few of the analyses of the East Asian industrialization “miracles,” such as South Korea and Taiwan, highlight infrastructure as a component of these success stories (e.g., Amsden 1989; Wade 1990; World Bank 1994b).2 Even mainstream neoclassical macroeconomics and growth theories appear to be moving away from their traditional emphasis on capital accumulation and infrastructure investment as the engine of economic growth. Indeed, the “new” growth theories stress the relative importance of human capital, technological ability, and knowledge spillovers as key variables that affect economic growth, and contend that these variables might help explain the lack of convergence between the growth rates of developing and advanced industrialized countries (e.g., Romer 1994; Solow 1994). Similarly, the literature on industrial performance and productivity, despite its micro level and firm-specific research, fails to identify physical infrastructure as a variable in determining performance. Rather, this literature focuses on “softer” variables – such as management attitude and aptitude, assembler-supplier relations, industrial relations, and inter-firm networks – and argues, albeit implicitly, over their relative salience in explaining industrial success and dynamism.3 For example, the lean production model identifies management attitude as the key variable affecting performance (Womack, Jones and Roos 1990).4 By comparison, the literature on industrial districts attributes the competitiveness of small firms, grouped in particular locations, to the existence of strong networks among these firms (Piore and Sabel 1984; Sengenberger and Pyke 1991). These networks – usually, horizontal relations between a core of more-or-less equal small enterprises – allow

Introduction 5

specialization and subcontracting which, in turn, induce efficiency and promote collective capability (see Humphrey 1995).5 A flurry of infrastructure studies, focusing on advanced industrialized countries, has failed to rekindle a broader academic interest in the infrastructure question, perhaps because the results have been ambiguous. Since David Aschauer (1989) started a debate by arguing that the slowdown in productivity in the United States during the 1970s could be explained by the slowdown in infrastructure investment, economists have conducted more than 40 macro-level regression studies. These studies focus explicitly on the links between infrastructure and industrial productivity and, generally, examine this relationship at the national, regional, or state level in advanced industrialized countries. Specifically, these studies deploy different econometric techniques to analyze the relationship between infrastructure (measured in terms of either investment or public capital stock) and economic growth or industrial output or production costs.6 In his review essay, Gramlich (1994) argues that these macro-level studies are inconclusive, that is, there is no persuasive evidence that public infrastructure is correlated with higher industrial productivity in advanced industrialized countries.7 In sum, there is an implicit conflict between the new academic literature and the development practitioners over the role that infrastructure plays in determining industrial performance and, by extension, the extent of industrial demand for public infrastructure. The new macrolevel academic literature on economic growth and industrialization in developing countries does not focus on the infrastructure question. And the micro-level literature on industrial performance, including that on lean production and industrial districts, does not identify infrastructure as a key variable in determining performance. Finally, recent academic regression studies that focus on the links between infrastructure and industrial productivity (primarily in advanced industrialized countries) are inconclusive. Overall, the new academic literature does not suggest that there are any strong links between infrastructure and industrial performance and does not shed much light on the types of connections that may exist between the two. The practitioners continue to assume that economic growth and industrial competitiveness suffer in the face of infrastructure deficiencies. They are, thus, focusing on how the quantity and quality of infrastructure can be improved by restructuring or altering service provision arrangements. Apart from financing additional infrastructure investments and trying to improve incentives for government-owned utilities,


the practitioners are also devising ways to encourage private firms to finance and manage these investments. In other words, the practitioners are spending billions of dollars in ameliorating supply-side constraints, creating new policies and institutional arrangements to facilitate infrastructure restructuring, and worrying about solving the infrastructure problem – especially for industry – without understanding the nature of the problem or the perspective of industrial users. Moving beyond the debate The practitioners and the academics are partially right. The practitioners are right in assuming that infrastructure affects competitiveness, but they may be making serious mistakes in estimating the extent of the infrastructure gap and in selecting their solutions, policies, and priorities. The academics are right in finding that public infrastructure and productivity may not always be strongly correlated, but they may be wrong in their interpretation of the result. This study reaches conclusions that stand counter to much conventional wisdom because it develops and uses a new analytical framework to examine the infrastructure problem and relies on a different methodology and unit of analysis to understand how infrastructure – in particular, electric power and freight transportation systems – affects the costs and competitiveness of firms and how they cope.8 Unlike previous studies that examine individual firms in different industries, this study focuses on firms in one industry and broadens the unit of analysis to include the supply chain of a firm. It empirically examines the infrastructure problems that industrial users face, how these deficiencies affect their performance, and the solutions that firms devise. It is through an inductive analysis of these data that this study attempts to reveal the multiple – direct and indirect – mechanisms through which infrastructure affects industrial performance, to explain how firms survive without access to public infrastructure, and to identify features of infrastructure services that play a particularly important role in determining competitiveness.

Developing a new analytical framework This section develops a framework that helps analyze, first, the mechanisms through which a deficient infrastructure service imposes costs on firms and, second, how we would expect firms to act given the problems in that service. This framework represents one approach to understanding, at the micro or firm level, both the impact of and

Introduction 7

response to inadequacies in different types of infrastructure services. In this book, I show how the framework works in the case of two infrastructure problems, that is, power and freight transportation; arguably, it can be used to explain outcomes in the case of other infrastructure services, such as water supply, as well. The framework has three components (see Figure 1.1). For a particular infrastructure service and a given firm, we need to examine the following three issues: (a) the “supply-side” variables that structure the provision of the service; (b) the “impact” that this service has on a firm, that is, the direct and external benefits/costs that it creates; and (c) the “response” or the strategies that firm devises to offset inadequacies in that service. Each of these components of the framework – for

Supply-side factors – Technology – Institutions – Service and equipment providers

Response

Impact

User or firm response to poor service

Direct and external costs and benefits

Influences firm demand for public infrastructure

Influences firm demand for infrastructure

Figure 1.1 The supply–impact–response framework: understanding a firm’s demand for infrastructure


convenience the “supply–impact–response” framework – is discussed in turn below. The framework is built by combining insights from early development theory with those from the more recent literature on infrastructure restructuring that examines alternative arrangements for supply of infrastructure services. Supply-side variables – technological indivisibilities and institutional arrangements The technology and institutional arrangements through which an infrastructure service is supplied together determine the quantity and quality of service provided. The technology embodied in production and provision determines the extent of technical “indivisibilities” and scale economies in that service. As we will see below, the concepts of indivisibilities, scale economies, and institutional arrangements lie at the center of both old and new ideas about how infrastructure should be supplied and the arguments over government versus market provision of services. We bring these concepts into our new theoretical framework and, unlike the existing literature, which uses them to make arguments about the supply side of the infrastructure equation, we use them to understand the demand side of the equation. The discussion below, therefore, leads to the first component of the theoretical framework and involves, specifically, an examination of how these concepts – the extent of scale economies and indivisibilities, and the institutional arrangements for service provision – shape industrial demand for infrastructure and influence user-responses to poor service. Infrastructure or “social overhead capital” can be defined as: … comprising those basic services without which primary, secondary, and tertiary productive activities cannot function. In a wider sense, it includes all public services from law and order through education to transportation, communications, power and water supply, as well as agricultural infrastructure such as irrigation and drainage systems. (Hirschman 1958) Hirschman (1958) argues, however, that the hard “core” of the concept of social overhead capital can probably be limited to transportation and power because the technology embodied in the provision of these services is characterized by technical indivisibilities (lumpiness) as well as by a high capital–output ratio.9 By definition, a commodity is indivisible if the minimum size at which it is available is large – for example, to carry any freight from New York to Chicago, a railroad

Introduction 9

must lay a rail track that is about 1000 miles long.10 The need for indivisible equipment (as in the railroad example) is the source of large fixed costs, and indivisible inputs by nature yield economies of scale and scope. A key problem with indivisibilities is that they serve as an impediment to efficient pricing and, in particular, make marginal cost pricing unprofitable. For early development theorists, the existence of indivisibilities and large scale economies in power and transport – combined with the fact that they produce large benefits for other industries and various sectors of the economy – creates an exceptionally strong argument for according high priority to these investments and also for government provision of these services.11 According to the more recent literature on infrastructure restructuring, two fundamental changes are forcing revisions in how infrastructure is perceived and how it is provided. First, changes in technology are reducing the economies of scale in such services as power generation and telecommunications, making it possible for multiple suppliers to coexist. Simultaneously, computers and information technology are creating unprecedented possibilities, such as the ability to create “spot markets” for electricity and to collect tolls electronically, thereby, making it easier to check free-riders and ameliorate other market failures in infrastructure provision. Second, innovations in institutional arrangements and regulatory frameworks – often pioneered by and tested in advanced industrialized countries – are making it possible to unbundle or separate different aspects of service provision and to introduce competition into various segments. In the case of electricity, for example, it is now both technically and institutionally feasible to separate generation, transmission, and distribution. Once unbundled, it is feasible to treat each service segment independently, to allow different public and private entities to own each of the components, and to introduce competition in many of these service segments. Thus, it is now technically and institutionally feasible to not treat different types of infrastructure, even core infrastructure, as a monolithic system with high indivisibilities and large scale economies. For proponents of restructuring, this means it is time to move away from public and private monopolies and toward infrastructure markets where multiple suppliers compete with each other. Changes in supply-side variables – new technological and institutional developments – are altering old perceptions and constraints associated with core infrastructure. The restructuring literature focuses on how these developments are driving or should drive a revolution in

10


who supplies infrastructure. But it fails to note that these developments are also affecting industrial demand for public infrastructure. For example, as scale economies fall, industrial users may find it cheap to opt for self-provision and lower their demand for publicly provided service. Similarly, new institutional and regulatory arrangements that facilitate unbundling and allow for a greater menu of supply-side solutions involving private participation also create the space for a new set of user responses; thus, it is possible for industrial users to enter into new types of contracts and infrastructure deals with other firms as well as government agencies. In sum, the first component of the supply–impact–response framework involves an examination of how changes in supply-side variables – the technology and institutional arrangements – affect industrial users, specifically, how these alter industrial demand for infrastructure and the strategies that industrial users can devise to cope with poor infrastructure. Impact – external economies and direct benefits Theories in development economics during the 1940s and 1950s emphasized that the external economies associated with infrastructure are key to understanding its importance and role in industrialization of developing countries. By contrast, the new literature tends to focus on quantifiable direct benefits of infrastructure. The “impact” segment of this framework includes both the direct benefits and external economies and shows how these together shape not only the demand for infrastructure but also the responses to inadequacies in service. Thus, a more complete examination of the type and magnitude of the benefits/costs forms the second component of the theoretical framework. Early development theorists, such as Rosenstein-Rodan (1963) and Rostow (1963), have argued that social overhead capital is a prerequisite for industrial investment, which, in turn, is the engine of economic growth. Their main rationale lies in their notion of external economies. In the narrow, modern sense in which they are currently used, external economies in production include the unpaid side effects of one producer’s output or inputs on other producers (New Palgrave 1987: p. 261). (For example, external economies arise in a case where a dam constructed by a hydroelectric plant eliminates flooding of farmers’ crop fields.) For early development theorists, however, external economies included both unpaid and paid (or price) effects – technological and pecuniary external economies, respectively – of producer activities.12 These theorists argued that the external economies

Introduction 11

of social overhead capital – that is, its complementarity with other industries or its forward and backward linkages13 – were particularly large relative to the direct benefits that these infrastructure investments created (Rosenstein-Rodan 1963; Scitovsky 1963; Singer 1984). Hence, “investment in social overhead capital is advocated not because of its direct impact on final output but because it permits and, in fact, invites directly productive activities [or industrial investment] to come in” (as summarized by Hirschman 1958). While early development theory emphasized the broader impacts or external economies of infrastructure rather than its direct benefits and impact on final output, much of the recent infrastructure literature tends to focus only on the direct benefits of more and better infrastructure services. This is particularly true for the macro econometric studies that focus on the links between infrastructure and industrial productivity.14 It is also true for the work done by practitioners. Although practitioners argue strongly that infrastructure has broad impacts, they themselves rely on narrowly defined direct benefits as the basis for selecting among different infrastructure projects. Further, because it is hard to assess all types of direct benefits, both academic analyses and practitioners’ project feasibility studies tend to measure only those that are most obvious and more easily quantifiable. For example, in evaluating the benefits of the stock of transportation infrastructure, such as roads, academic regression studies measure whether or not this stock is related to changes in a particular dependent variable, such as an industry’s unit costs of production (e.g., Nadiri and Mamuneas 1994) or the total industrial output in a particular state or region (e.g., Munnel 1990, cf. Munnel 1992). Similarly, practitioners, such as those at the World Bank, select transport projects on the basis of a single benefit, that is, savings in vehicle operating costs that are likely to result from improved road infrastructure. But both the academic and the practitioner are, usually, unable to capture or isolate the effects of other direct benefits resulting from attributes such as greater speed, freedom from seasonality, smaller risk of loss, and direct routing.15 For instance, these studies tend to ignore the possibility that greater speed and regularity of transportation can combine to reduce the average amount of inventory required per unit, and this, in turn, reduces the amount of tied up working capital. These are direct benefits, but they may not be captured in the variables that are measured. That is, a direct benefit, such as lower inventory costs, may not translate into a reduction in freight prices or savings in vehicle operating costs or even a reduction in the unit costs of production. In other

12


words, newer studies appear to take a narrow view of the benefits associated with infrastructure, and it is exceptionally narrow when compared to the view adopted by early development theorists. The assessment of “impact” – direct and external costs/benefits – represents the second part of the framework. Specifically, it involves identification of the types of direct benefits and external economies16 of infrastructure (or, conversely, the direct and external costs imposed by poor infrastructure) that affect industrial users, the magnitude of direct versus external costs/benefits, and whether certain kinds of costs/benefits are more pertinent than others at the firm level. Getting better quantitative estimates on costs or benefits, alone, will not, however, help us understand how it is that certain industries and areas continue to grow in the face of severe infrastructure deficiencies. For this, we need to examine the coping strategies that firms devise. Response – user-devised strategies to offset impacts of poor infrastructure The third component of the framework calls for an analysis of the responses, solutions, or coping strategies that industrial firms devise to limit the adverse impact of poor infrastructure. Such an inductive analysis is important not only because it is likely to shed light on how firms survive without access to reliable infrastructure but also because it is likely to reveal how firms perceive the problem, the relative importance they attach to different kinds of costs, and the priorities that they set in devising solutions. The literature provides limited insights into how firms are likely to respond to infrastructure deficiencies, and this component offers a point of departure from current thinking on infrastructure. In his classic The Strategy of Economic Development, Hirschman (1958) outlined two paths to providing infrastructure or social overhead capital – the excess capacity approach, in which infrastructure investment leads industrial demand, and the shortage approach, in which infrastructure investment follows demand. Hirschman argued against the then-dominant excess capacity approach and, hence, against the notion that infrastructure investment is a prerequisite to industrial investment. He presented the shortage approach as an alternative path to infrastructure development, and argued that infrastructure can follow industrial demand. The shortage approach to infrastructure, Hirschman argued, is more economical and less prone to mistakes. Poor infrastructure may not have a serious adverse impact on dynamic industrial centers (and industries) because firms in these areas will not fail to invest in

Introduction 13

infrastructure, such as power and transportation. Further, these glaring shortfalls in social overhead capital, and pressure from the firms with “large unmet demands,” will force government to invest in infrastructure and ameliorate the situation. Thus, the sequence in which infrastructure investment leads industrial investment could well be reversed without causing undue damage because industry will devise creative responses, such as opting for self-provision and/or forcing government to step in and close the gap between demand and supply of infrastructure. For Hirschman, then, development via the infrastructure shortage approach represented a self-correcting imbalance, and was unlikely to lead to industrial stagnation. In fact, an extreme outcome like stagnation is conceivable “only in a community whose behavior has become thoroughly irrational and where creative responses have been choked off” (Hirschman 1958: p. 97). In hindsight, we know that many developing countries have de facto followed a shortage approach, but that governments have not really been able to close the infrastructure gap (e.g., World Bank 1994a). Empirical observations as well as infrastructure literature suggest that self-provision is almost ubiquitous in developing countries, and that industrial firms invest in their own power generators, captive power plants, tubewells, and water treatment plants. This is seen as a problem, however, because these investments often entail high capital and operating costs. Some studies have found, for example, that the unit costs of self-provision are several times higher than the unit costs that result from efficient public utilities (World Bank 1994a; Lee, Anas and Oh 1996). Other forms of private provision – where unregulated private providers have entered the infrastructure business to fill gaps in government provision – also tend to entail high costs and inefficiencies. For example, water supplied by water vendors tends to cost users several times more than the prices charged by public water utilities (e.g., Briscoe et al. 1990; Whittington et al. 1991). Similarly, private operators of bus services often charge higher prices for providing service in areas with inadequate public transportation. Thus, both self-provision and many forms of unregulated private provision are considered more expensive and less efficient than efficiently provided public services. Overall, infrastructure shortages have been more severe and less selfcorrecting than those that Hirschman had in mind, and, according to recent literature, self-provision and certain other forms of private provision have been expensive and inefficient. Yet, developing countries continue to boast of dynamic areas and industries. Have the responses of industrial users, perhaps, been far more creative than even Hirschman

14


anticipated? Or has industry been able to force government to step in and provide infrastructure, but only in certain areas and industries? Are self-provision and other user-devised solutions, perhaps, more efficient and cost-effective than the literature admits? Do user-devised solutions create external economies and, if so, can firms act to internalize some of these benefits? It is these questions that the third component of the supply–impact–response framework attempts to answer. Specifically, the “response” component involves a detailed analysis of some user-devised solutions to gain an insight into the menu of potential responses to infrastructure problems and the extent to which these are, indeed, short-term, inefficient, and high-cost solutions. Putting together the supply–impact–response framework The three components of the framework – supply-side variables, the nature and magnitude of impact that a service has on a firm, and the response that a firm devises – interact to shape the outcome (Figure 1.1). That is, they determine together the extent to which deficiencies in an infrastructure service affect the performance of industrial firms. At the core of the supply–impact–response framework lies the following set of arguments or propositions: l

l

l

Supply-side variables. The ability of firms to respond depends on, among other things, supply-side variables. The supply variables also determine the nature and scale of adverse impacts on a firm because they affect the quality and quantity of service. Impact. The willingness of firms to respond to deficiencies in a particular infrastructure service depends, in part, on the extent of the adverse impacts – the direct costs and external diseconomies – caused by weaknesses in that service. The higher and/or more obvious the negative impacts, the stronger the incentives for a firm to devise a response to the problem. Response. The extent and effectiveness of firm responses, in turn, determine the net impact of poor service on a firm.

This framework, then, provides one approach to understanding how an infrastructure service is likely to affect an industrial user and to predicting how this user might respond to service inadequacies. Extending previous conceptual models Neither the pieces of the framework nor the complementarity among its parts is entirely new. Rather, two of its components – supply technology

Introduction 15

and costs imposed by poor infrastructure – build directly on ideas from the early and new literature. In fact, theorists use these two components together to make their arguments about how infrastructure should be perceived and how it should be provided. Thus, early development theorists argued for government provision because of indivisibilities in the technology combined with their notion of large external economies associated with infrastructure. More recently, proponents of restructuring argue that there is no longer a rationale for government provision because economies of scale have fallen and because direct benefits of infrastructure are large enough to encourage investment by private providers. It is possible to unbundle, privatize provision, and create markets for infrastructure services. The framework in this study extends previous conceptual models in three ways: First, it reintroduces old concepts and uses them in combination with new ideas and developments to analyze infrastructure issues. Taken together, these concepts offer a more complete framework for examining the links between infrastructure and competitiveness and for analyzing and understanding empirical observations, such as those in this study. Second, this framework includes an important concept that both the early and recent literature underrate – that industrial users devise strategies to cope with poor infrastructure and these are, potentially, highly innovative and efficient solutions. It is due to the introduction of this component – user responses to poor infrastructure – that the framework begins to lead to a different understanding of the infrastructure problem and possible solutions. Third, by opting for a user perspective, this framework reverses the way in which previous models set up the infrastructure problem and try to resolve it. Specifically, existing literature argues about the extent to which markets might fail in providing services and, hence, about how the supply side of the market should be organized to mitigate these failures. By contrast, this framework examines the types of problems that industrial users actually face when infrastructure provision is poor, and uses their understanding to identify the more debilitating supply side failures that need to be fixed. It then identifies some lessons on how these infrastructure problems could be fixed.

The focus of inquiry and unit of analysis The discussion in the previous section suggests that any study attempting to link infrastructure with industrial performance should try to

16


capture both the direct benefits and the external economies of infrastructure. This calls for an analytical approach that lies in between the micro and the macro approaches – that is, it needs to be larger than a set of individual firms to allow us to capture external economies, but smaller than a whole region or the national economy to allow for a good assessment of the direct benefits. Further, the analytical approach needs to be sufficiently micro to allow analyses of firm-level responses to infrastructure problems. In this study, then, the focus of inquiry is a particular industry, and the unit of analysis is the firm and its supply chain. This section discusses, in greater detail, the rationale for this analytical approach. In selecting industry as the focus of inquiry, I borrow from the literature on industrial performance and competitive strategy, which argues that firms compete within industries (and not in states or nations), and that competitive advantage is won or lost at the industry level (Porter 1990). The illustrative case in this study is the automobile industry in India. The next section presents a brief overview of the industry, but here it is important to note some of the reasons for focusing on automobiles. First, the auto industry has grown rapidly, in fact, significantly faster than Indian industry as a whole, despite severe infrastructure constraints. Second, there is no apriori reason for believing that this industry is exceptionally sensitive to the quantity or quality of infrastructure services and, hence, it might offer lessons that can be generalized to other manufacturing industries. Third, competition in this industry is getting increasingly intense, due to the entry of several world-class auto assemblers into the market, and this allows us the opportunity to examine whether and how infrastructure is a variable in determining differential performance among firms. Fourth, there is an extensive literature on the auto industry in developing and advanced industrialized countries that serves as an important base for comparing and contrasting the findings on the Indian auto industry. This last advantage is worth emphasizing. The existence of this literature means that we know rather well what we should expect to see in the Indian auto industry, and we can be relatively confident about the extent to which the findings in this study are unique or general. The unit of analysis in this study is the firm and its supply chain. The supply chain of a firm can be defined as the network of suppliers that provides the inputs – in particular, the parts and raw materials – needed to manufacture its product. A detailed firm-level analysis allows for a better understanding of the direct benefits of infrastructure, and an analysis of the supply chain allows for an examination of how

Introduction 17

infrastructure might affect the external economies in the system. The justification for selecting this unit of analysis comes, first, from the literature on international competitiveness and, second, from the auto industry itself. The literature on strategy and industrial competitiveness emphasizes that efficient and well-managed “value chains” are critical in determining competitive success in global industry (e.g., Porter 1990; Gereffi and Korzeniewicz 1994). According to Porter (1990), “a firm’s value chain is an interdependent system or network of activities, connected by linkages. Linkages occur when the way in which one activity is performed affects the costs or effectiveness of other activities.” The value chain, or “commodity chain” as it is referred to by authors such as Gereffi and Korzeniewicz (1994), comprises the sequential stages of input acquisition, manufacturing, distribution, marketing, and consumption.17 The better the firm organizes each of these stages and the greater the value that it creates, not just in each activity but in the chain as a whole, the better its performance and competitiveness. In the automobile industry, the efficiency and effectiveness of the supply chain, that is, the first stage of the value or commodity chain in which the inputs are acquired and organized, is particularly critical for good performance. This is because the automobile industry is characterized by complex supply chains where hundreds of suppliers provide the thousands of parts required in a single vehicle. This means that the supply chain accounts for a majority of the expenses of auto assemblers and its organization represents, perhaps, the most complex task in producing a vehicle (see Womack et al. 1990). In the Indian auto industry, for example, the supply chain accounts for about 62–78 percent of total expenditures by auto assemblers (see Chapter 3, Figure 3.1). Cost data for supplier firms shows similar trends. For example, for a sample of nine supplier firms, parts and raw materials procured from their sub-suppliers accounted for 49–84 percent of their total costs (see Chapter 3, Figure 3.2). In other words, differential performance of auto assemblers depends not just on the performance at their own plants but on the collective capability of their entire production network – in particular, on the efficiency of their supply chains. In summary, the focus of inquiry in this study is the Indian automobile industry and the unit of analysis is the auto assembler and its supply chain. Specifically, this study examines the mechanisms through which poor infrastructure affects the costs of auto assemblers and the efficiency of their supply chains, and how assemblers act to offset these adverse impacts. By examining supply chains, and not just plant-level

18


performance of individual firms, this study aims at identifying the nature and magnitude of at least a subset of the external economies associated with infrastructure.

The Indian automobile industry Recording an average annual growth rate of about 20 percent over the ten-year period 1987–97, the Indian auto industry has been growing significantly faster than the manufacturing sector as a whole.18 Within the industry, the passenger car segment has been growing the fastest, and total car production outstrips the production of tractors as well as the combined production of heavy, medium, and light commercial vehicles (Figure 1.2). The growth of this industry has accelerated since the government deregulated entry into auto production in 1993. Over the four-year period 1993–97, total vehicle production grew at 28 percent per year, and the passenger car segment grew at an average annual rate of 38 percent. By comparison, industrial production grew by 6.0 percent in 1994, 8.6 percent in 1995, and an estimated 12.0 percent in 1997.19 In 1996–97, passenger cars accounted for 41 percent of the total vehicle production in the auto industry, dominating other segments in production volume. The Indian government’s decision to deregulate the auto industry is part of a broader economic liberalization and reform agenda adopted

450 000 400 000 No of vehicles

350 000 300 000 250 000 200 000 150 000 100 000 50 000 19 8 19 4 8 19 5 8 19 6 8 19 7 8 19 8 8 19 9 9 19 0 9 19 1 9 19 2 9 19 3 9 19 4 9 19 5 9 19 6 97

0

Figure 1.2 Vehicle production in India, 1984–97 Source: Compiled from ACMA (1997)

Passenger cars Jeeps/utility vehicles Buses Trucks + LCVs Tractors

Introduction 19

in 1991 in response to a balance of payment crisis. By 1993, the government had not only deregulated entry into the auto industry but also had jettisoned the use of licenses to control output levels and significantly reduced import tariffs on auto components. Prior to these reforms, there were only four car assemblers in the country, and Maruti-Suzuki, the leader held a 62 percent share in the passenger car market.20 Following deregulation, 12 foreign firms entered the market. The players include most of the major car companies in the world, such as Mercedes-Benz, Ford, General Motors, Honda, Toyota, Fiat, Hyundai, and Daewoo. Together, these assemblers made commitments for new investments amounting to several billion US dollars. Analysts estimated that these investments would boost India’s annual production capacity to about 1.2 million vehicles by 2000, up from 325 000 passenger cars and utility vehicles in 1995. In contrast to the projected production capacity of 1.2 million cars, domestic demand was projected to reach only about 0.5 to 0.8 million cars in 2000.21 In general, supply is expected to exceed demand for cars widely, and increasing competition is forcing assemblers to restructure to lower costs and improve quality.22 The projected excess supply situation also means that assemblers have to compete successfully not only in the domestic market but also perhaps in international markets to utilize their production capacities fully and maintain economies of scale. To improve their competitiveness, auto assemblers are adopting proven strategies, such as lowering the cost of their assembly operations, rationalizing their supply base, moving from cost plus to target prices for components, and attempting to institute just-in-time production systems. The rapid and seemingly unfettered growth of the passenger car industry makes it a good candidate for examining the following kinds of questions: How do the infrastructure inadequacies plaguing the nation as a whole affect auto firms? Do these firms have different levels of access to good infrastructure? If so, does differential access to services play a role in determining differential performance of firms within the industry? To answer these questions, I examine and compare the operations of, and infrastructure strategies adopted by, several car assemblers in India – including Maruti-Suzuki, Ford, Hyundai, Daewoo, and Telco – and 23 auto component firms that supply one or more of these assemblers. The detailed case study is Maruti-Suzuki and its supply chain; the next section provides some background on this firm and presents the primary reasons for selecting it as the major case for analysis.

20


The major case study: Maruti-Suzuki

450 000

90 81%

400 000

75%

350 000 300 000

59%

75%

62%

80 70 60

250 000

50

200 000

40

150 000

30

100 000

20

50 000

10

0

0 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 India – production volume Maruti's market share

Figure 1.3 Maruti dominates the Indian passenger car industry, 1988–97 Source: Compiled from ACMA (1997) and company annual reports

Maruti's market share (%)

Total vehicle production in India (no.)

Maruti-Suzuki is India’s largest carmaker and has been the undisputed market leader in the passenger car segment since the mid-1980s. It has also maintained a stellar performance record since its inception. Created in late 1982 as a joint venture between government of India and the Suzuki Motor Company of Japan, Maruti began full scale production in 1984–85. Within two years, Maruti had gained a 50 percent share of the car market and its share has been steadily increasing. By 1996–97, Maruti had increased its share to an astonishing 81 percent in the face of increasing competition from several world-class auto assemblers that had entered the Indian market (Figure 1.3). For the government of India – the partner with 74 percent of the equity at inception – the main goal of creating Maruti was to push for the modernization and expansion of the country’s small and antiquated auto industry. In 1983–84, the year before Maruti started full scale operations, total car production in the country was about 47 000 vehicles, and these models were technically obsolete. Through the Maruti project, the government was aiming to introduce the latest car production technology, to increase vehicle production, and to develop almost from scratch a modern, indigenous auto components base for passenger cars. Maruti started production with a new Suzuki model and was required to increase output to 100 000 cars over five years. Further,

Introduction 21

it was mandated to increase the local content in its Japanese product to 95 percent over the same period, and for this it needed to create a domestic supply base within five years. Maruti is credited with having catalyzed and led the modernization of the Indian passenger car industry since its inception. Over the next seven years, investments in the passenger car industry increased, combined output grew more than threefold from 47 000 in 1984 to 190 000 in 1991, and several new models were introduced. At the same time, the (nominal) value of production in the auto components industry also increased three fold (ACMA 1997).23 By 1991, Maruti’s own production had grown to 123 000 vehicles, the local content of its best-selling model had reached its target level of 95 percent,24 and the assembler had increased its market share in the passenger car segment to 62 percent.25 In 1991–92, a balance of payment crisis caused the government to adopt a plan aimed at economic liberalization. The changing economic environment resulted in two major consequences for Maruti. First, in 1992, the government allowed Suzuki to increase its equity stake to 50 percent. This changed Maruti’s status from a state-owned enterprise (SOE) to a private firm that, despite 50 percent government ownership, did not need to follow bureaucratic rules – for example, on employment, salaries, and investment – by which SOEs have to abide. Second, by 1993, the national economic liberalization program and specific reforms, such as declining import duties on auto components and elimination of entry barriers, were transforming the nature and extent of competition within the passenger car industry. Specifically, several of the world’s major auto companies decided to enter the Indian market and are now competing directly with Maruti. In response to deregulation and increasing competition, Maruti adopted an aggressive expansion program. It more than doubled (almost tripled) its production over six years, increasing output from 123 000 vehicles in 1990–91 to 340 000 vehicles in 1996–97 (Figure 1.4). Over the same period, it increased its market share from 62 percent to 81 percent (Figure 1.3),26 and its sales revenues for the year 1996–97 reached US$2.2 billion. Maruti started exporting in 1992, and its export volume has risen slowly but steadily from 14 500 vehicles in 1993 to 35 000 vehicles in 1997 (Figure 1.5). Over the five-year period 1992–97, exports accounted for an average of about 10 percent of Maruti’s total vehicle sales and sales revenue. In 1997, Maruti was installing additional capacity that would allow it to assemble a half-million cars by 2000. The discussion above suggests that Maruti has performed exceptionally well in its first 15 years. The assembler continues to dominate the

Innovating with Infrastructure 400 000

40 35%

35

350 000

30%

30

300 000

25

23%

21% 250 000

20 16%

15

200 000 14% 11%

150 000

10 6%

5%

5

100 000

–2%

0

50 000

Production volume (no. of vehicles)

% Growth in production over previous year

22

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 0

–5

% Growth in production

Production (no. of vehicles)

Figure 1.4 Maruti – growth in production, 1988–97 Source: Company annual reports

40 000

Exports as % of total sales

18

19%

35,031

16 14 12

30 000

26,103 22,921

11%

25 000 11%

10 8

35 000

10% 20,862

9%

10% 20 000 15 000

17,187 14,566

6

10 000

4

No. of vehicles exported

20

5 000

2

0

0 1992

1993

1994

Exports as % total

1995

1996

1997

No. of vehicles exported

Figure 1.5 Maruti’s export performance, 1992–97 Source: Company annual reports

Indian passenger car industry despite increasing competition, and it is beginning to compete in world markets as well. For us, this success story raises the following questions: How did Maruti continue to grow in the face of severe shortages in public infrastructure? How, if at all, do infrastructure inadequacies affect Maruti’s competitiveness now that

Introduction 23

the firm is competing directly with world-class producers in both its home market and in world markets? What infrastructure problems affect Maruti and how does the firm cope? The study focuses on these questions and compares findings at Maruti with observations at other major car assemblers, in particular, Ford, Hyundai, Daewoo, and Telco.

Structure of the book Each chapter in this book is a self-contained piece and can be read independently. Chapters 2 and 3 focus on power and transportation issues, respectively. They analyze the problem from the perspective of industrial firms and examine the solutions that the firms have devised to offset infrastructure problems. Chapter 4 presents the “clustering” solution to the infrastructure problem and reveals some of the links between infrastructure and development of industrial clusters. The clustering strategy is presented in a separate chapter because it has implications for the nature and geography of industrial development and because it is a broader solution that can help offset the problems associated with different infrastructure deficiencies, including poor power, transportation, and water supply. Although Chapters 2, 3, and 4 discuss many of the concepts that underlie the supply–impact– response framework, these chapters can be read without understanding the framework itself. Chapter 5 returns to the supply-impact-response framework and uses it to bring together the empirical observations presented in Chapter 2, 3, and 4. Chapter 5 demonstrates how the framework serves as a tool for infrastructure analysis and for distinguishing between good and bad solutions. It highlights the role that governments have played in helping firms devise some superior infrastructure solutions. Chapter 5 also serves as an executive summary. Chapter 6 presents the broader conclusions and implications of the study. It suggests that governments can take a non-traditional approach to ameliorating infrastructure problems for industry and explores what an alternative strategy might look like.

24


Appendix 1.1 Study methodology The unit of analysis in this study is the firm and its supply chain within a specific industry. This unit of analysis allows for an examination of the direct and some of the indirect or external mechanisms through which poor infrastructure affects the performance of firms. By contrast, previous studies have tended to select random samples of firms from different industries; have examined only a subset of the direct costs imposed on firms by poor infrastructure; tended to ignore any external effects, including those on the efficiency of firm networks and supply chains; and have, often, aggregated results at the macro (state or national) level.27 This is an empirical study, and it takes an inductive approach. It is based on a combination of quantitative data on costs with qualitative data from semi-structured interviews with firms. Primary data was gathered from a total of 31 auto firms – 23 component suppliers and eight assemblers.28 The major case study is of Maruti-Suzuki – the largest and most successful passenger car assembler in India – and its supply chain. The study also includes less detailed case studies of Ford, Hyundai, Daewoo, and Telco. These case studies are designed to allow for some comparative analysis. All of the suppliers that were selected supply either to Maruti and/or another major car assembler. Of the 23 supplier plants, 13 are located near Delhi (nine in Gurgaon, one in Noida, three in Faridabad), six in Chennai, and the remaining four in Pune. (Table A1.1 presents a list of all firms included in the study.) Apart from auto firms, the study involved visits to and interviews with the following: l

l l

l

Government officials in local, state, and central agencies. Of the local/state agencies, the state industrial development corporations (SIDCs) in Haryana and Uttar Pradesh (UP) are particularly important. The central agencies and research institutes include the National Council for Applied Economic Research, Ministry of Urban Development, National Institute of Urban Affairs, and the Center for Policy Research; Senior managers of the Association of Indian Auto Manufacturers (AIAM) and Auto Component Manufacturers’ Association (ACMA); Managers of nine non-auto firms (in various different industries) to ascertain the extent to which some of the findings are industryspecific; and Four electric power utilities and one water utility.

Introduction 25

The major data sources used in this study are personal interviews, company annual reports, specific data that the firms provided upon request, data published by AIAM and ACMA, Government of India Statistics, and articles in Indian newspapers and magazines. The field work for this study was conducted in three phases: Schedule

Assemblers visited

Locations visited

Phase I

June–August 1996

Maruti Bajaj Auto Telco

Gurgaon, Haryana Pune, Maharashtra Pune, Maharashtra

Phase II

January 1997

Maruti Daewoo Motors

Gurgaon, Haryana Surajpur (NOIDA), UP

Phase III

July–November 1997

Maruti Daewoo Honda Ford Ashok-Leyland

Gurgaon, Haryana Surajpur (NOIDA), UP Greater NOIDA, UP Chennai, Tamil Nadu Chennai, Tamil Nadu

Note: The three phases of field research also included visits to supplier plants, in particular, those in New Delhi, the Gurgaon and Faridabad clusters in Haryana, and in Pune and Chennai.

Table A1.1 List of firms included in study S.no

Firm

;2) Assemblers (6; 1 Maruti 2 Daewoo 3 Honda Siel 4 FORD 5 Hyundai 6 Telco 7 Ashok-Leyland 8 Bajaj Auto Auto component suppliers (23) 9 1 Lucas-TVS 10 2 Lucas-FIE 11 3 Rane Brake Linings 12 4 Sundaram Brake Linings 13 5 Sundram Fasteners 14 6 Rane (Madras) 15 7 Sona Steering 16 8 Lumax 17 9 Dynamic 18 10 Ju-shin 19 11 Munjal Showa 20 12 Sun Vac 21 13 Bharat Seats 22 14 Mark Auto

Segment/main products

Location

Passenger cars Passenger cars Passenger cars Passenger cars Passenger cars Trucks, buses, cars Trucks, buses 2-wheeler vehicles

Gurgaon Greater NOIDA Greater NOIDA Chennai Chennai Pune Chennai Pune

Electricals Fuel injection equipment Brake linings Brake linings Fasteners, radiator caps

Chennai Chennai Chennai Chennai Chennai Chennai Gurgaon Gurgaon Gurgaon Gurgaon Gurgaon Gurgaon Gurgaon Gurgaon

Steering column Electricals Upholstery Sheet metal parts Shock absorbers Plastic mouldings Seats Fuel tanks, axles

By location

6

9

23 24 25 26 27 28 29 30 31

15 16 17 18 19 20 21 22 23

Krishna Maruti Subros Clutch Auto Vinod Industries AR Industries P&P Kiwi Bharat Forge Kirloskar Oil Engines

Non-auto firms (9) 32 1 Arvind 33 2 Reliance 34 3 Flex 35 4 Tata Consultancy Services 36 5 Altos 37 6 HFCL 38 7 Mehra Bandhu 39 8 Usha 40 9 Siemens Total firms (40) Utilities (5) 1 2 3 4 5

Ahmedabad Elec. Co Noida Power Co Ltd HSEB-Haryana State Elec Board UPSEB-UP State Elec Board Public Health & . Water Dept

Seats Air-conditioners Clutches Gear cutters Castings Turned metal parts Forgings Engines

Gurgaon NOIDA Faridabad Faridabad Faridabad Pune Pune Pune Pune

Textiles Textiles Flexible packaging Software

Ahmedabad Ahmedabad NOIDA Gurgaon

Communications equip. Communications equip. Readymade garments Energy saving equipment Software

Gurgaon Gurgaon Gurgaon Gurgaon Gurgaon

Private electric utility Private distribution Govt. electric utility

Ahmedabad Noida Gurgaon

Govt. electric utility

Noida

Govt. water utility

Gurgaon

1 3

4

2 1 6

2 Innovative Strategies for Tackling Power Problems

Many developing countries have been unable to provide their industries with the basic input of reliable electric power. Industrial users in these countries suffer from shortfalls in both the quantity and quality of power. The problem of inadequate supply arises, in part, from the inability of governments to invest at a rate that keeps pace with growing demand. In a country such as India, the quantity shortage or demand-supply gap means it is usually hard to get electric connections from public utilities. When firms do get connections, the “sanctioned load” is often lower than their demand, and they face frequent, scheduled and unscheduled, power cuts. The problem of poor power quality, or significant fluctuations in voltage and frequency, has an adverse effect on manufacturing processes and efficiency. Quality problems arise from both the poor condition of the physical infrastructure, especially the transmission and distribution lines, and the demand–supply gap (e.g., at peak demand the grid is overloaded and voltage in the entire system tends to drop). To offset the negative impacts of poor public power, industrial firms in developing countries are opting increasingly for self-generation. Selfgeneration is, however, widely considered an inferior solution. Although the precise critiques and reasoning differ, most development practitioners, power technocrats, managers of public and private electric utilities, and energy economists agree that self-generation is, at best, a “second-best” solution for the following reasons. First, selfgeneration is supposedly high-cost relative to efficiently provided public power, and it tends to use polluting fuels such as liquid hydrocarbons (World Bank 1996). Second, users often invest in excess capacity, which then sits idle, and this represents a waste of national resources (Lee, Anas and Oh 1996). Third, the exit of industrial users from public grids 28

Tackling Power Problems 29

hurts the financial or commercial viability of electric utilities in developing countries – industrial firms are often the only paying customers, and they help cross-subsidize provision to residential and agricultural users (World Bank 1996). Fourth, from the perspective of many energy economists, self-generation is not an optimal solution to the power problem; it is better for governments to encourage the emergence of markets for electric power and to pursue models similar to the one adopted by the United Kingdom. In this model, private power companies bid (in unit prices) to sell their power, there is a competitive “spot market,” and a public regulator sets the rules and incentives that are necessary for the market to work well. Few of these critiques take into account how industrial users themselves perceive the power problem and the efficacy of self-generation as a solution. This chapter aims to do just that. It presents a case study of Maruti’s self-generation system and analyzes how and why the firm devised this particular solution. It also presents less detailed case studies of Daewoo Motors and Arvind Mills. The main arguments This chapter makes three arguments. First, contrary to popular notions about the costs and technologies of self-generation units, Maruti’s power plant is cost-effective and efficient, uses clean and safe fuels, and represents an excellent technology choice. Maruti’s plant offers a unit cost that is lower than the cost of public power and provides power that is far superior in quality to that supplied by the public utility. Strikingly, Maruti’s low unit cost of generation is not unique; both primary and secondary data suggest that the unit costs of self-generation are far lower than previous studies found. This is because new technologies, especially gas turbines, are revolutionizing power generation, and the economies of scale are falling dramatically. Second, Maruti has developed innovative power-sharing arrangements, and the resulting system is more like that of an independent power producer or even a mini electric utility rather than a captive plant. Maruti’s success as a mini electric utility brings into question the notion that only firms that specialize in the power business should be allowed to compete in generation and distribution and that other industrial firms should be excluded or discouraged from entering these markets. Third, unreliable power has an adverse effect on the costs and efficiency of a firm and its entire supply chain. While inadequate power results in lost production time, poor quality power creates less obvious

30


but more debilitating problems, such as machine damage, loss of materials, and variations in product quality. Consequently, production volumes, manufacturing costs, and output quality are all affected adversely. Together, the Maruti, Daewoo and Arvind Mills case studies suggest that it is the demand for exceptionally high quality electricity (rather than a mere search for lower costs) that is driving the decision of certain industrial firms to self-generate and develop elaborate powersharing arrangements.

The power problem and conventional solutions This section sets the policy context and frames the debate over the power problem in India. It delineates the Indian government’s understanding of and solution to the problem. It, then, discusses the World Bank’s critique of the central government’s policies and the Bank’s recommended solution for the states of Orissa, Rajasthan, and Haryana. The power problem in India Power shortages have been a serious and recurring problem, and may continue for several years to come. At the same time, the financial cost of power subsidies also has been a large burden … it now is equivalent to at least 1.5 percent of GDP. (World Bank 1995b: p. 63) The power situation in India is widely considered to be alarming. The India Infrastructure Report (1996), a policy document commissioned by the Government of India, suggests three major reasons for concern. First, there is a chronic power deficit in the country. At the beginning of the Eighth Five-Year Plan (April 1992), the country faced an energy shortage of about 8 percent and a peaking shortage of around 19 percent. By the end of the plan period, the shortages in energy and peaking were projected to reach 15 percent and 29 percent, respectively.29 There is, thus, an urgent need to accelerate investment in power in both the public and private sectors, as “the costs of any further delay in the clearing of power projects will inflict incalculable costs to the economy” (p. 32, vol. 1). Second, transmission and distribution (T&D) losses in India are extraordinarily high, in excess of 20 percent in 1993, as compared to normal T&D losses of 8–10 percent. In some states, the losses are estimated unofficially to be as high as 40 percent, with non-technical losses – unbilled supply, theft and pilferage of power – running at 20 percent.30 Third, the finances of the state electricity boards (SEBs) have been


deteriorating steadily; commercial losses rose by about 50 percent from their 1992 level to Rs 63 billion (about US$1.8 billion) in 1995.31 Only two of the 18 SEBs are expected to reach the target of ensuring a minimum return of three percent on the value of fixed assets in use, as specified by the Electricity (Supply) Act of 1948. Overall, demand estimates suggest that an additional capacity of 84 000 MW is required over the ten-year period 1996–2006, requiring investments of about US$134 billion in new capacity and an additional US$9 billion for plant renovation and co-generation. The government is expecting about three-fifths of this investment to come from the private sector. The government’s new approach: encourage private investment and self-generation To attract private investments, the government began a transformation of the policies and institutions in the power sector in 1991. It allowed private investments in power generation and offered incentives to encourage them. Government policy now permits 100 percent foreignowned companies to set up power projects and repatriate profits, and it offers liberal capital structuring with attractive rates of return. Despite a multitude of project proposals and MoUs (memorandum of understanding), however, only a few private investments have been brought to closure. Indeed, from 1992 to 1997, private investments were expected to add a minuscule 1350 MW of generation capacity. Since October 1995, the Ministry of Power (MOP) has issued several new guidelines and incentives to complement the 1991 policy. In particular, MOP has issued a liquid fuel policy to facilitate the rapid installation of diesel engine generating units by the private sector. Such diesel units allow firms to self-generate and are – along with other types of self-generation units – referred to as captive plants because they are usually set up by an individual firm or institution exclusively to meet its own needs. In fact, government policy has traditionally ensured “captivity” by disallowing the owners of self-generation units or captive plants from selling power to other users. MOP’s new guidelines aim at reversing some of the previous policies against captive plants. MOP has suggested that state governments should facilitate the entry of self-generation units into the system by offering private investors an appropriate tariff at which they can sell surplus power to the grid, and by allowing third-party access (“wheeling”) for direct sale of electricity to other industrial units. In other words, these policies encourage self-generators to sell surplus power rather than keep their plants captive.

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World Bank’s critique of self-generation In its country report for India, the World Bank (1996) argues strongly against the Indian government’s policy of facilitating self-generation or captive generation (these terms are often used interchangeably in this chapter). The Bank notes that the government has been encouraging self-generation units where the user is likely to be, but need not always be, one or more industrial firms, and especially if there is a possibility that these self-generators will sell surplus power to the public utility. The Bank notes that self-generation units tend to have the following characteristics: the technology is based on liquid hydrocarbons as primary fuel (naphtha or fuel oil); plants are relatively small (a few megawatts); little investment for handling is required at the plant site; and the construction period is normally very short (less than two years). Decentralized generation may be the best option when connection to the grid is too expensive, for example, in isolated and remote load centers. In other instances, it may be a short-term quick-fix solution when the alternative is no power at all. The Bank emphasizes, however, that “in the long run, … [captive or self generation] cannot be considered an effective solution to the acute power shortage … afflicting India” (World Bank 1996). The Bank’s arguments against self-generation plants are as follows. First, their power generally costs more than that generated by large conventional power plants (particularly for base-load generation). Second, transportation of liquid fuels over relatively long distances and in large volumes may pose serious environmental and safety hazards. In addition, these fuels may have to be imported, which may further strain the port and transport infrastructure. Third, the captive plants supply industrial customers, that is, the segment that can most afford high electricity tariffs. This means that the SEBs are likely to lose some of their best clients, and their financial situation is likely to deteriorate further. Finally, any future structural, institutional, ownership, or pricing reform may become more difficult. For example, in an area with significant captive generation, the financial viability of electricity distribution would be lower because the extent of cross-subsidies would decline. Steeper tariff increases would need to be imposed on agricultural and domestic consumers that have traditionally been subsidized. The Bank’s solution: unbundling and private competition The World Bank’s broad strategy for reforming the electric power sector in developing countries, in general, can be characterized as follows. The sector should be unbundled or separated into three


businesses – generation, transmission, and distribution. Private competition should be introduced in generation and distribution. Arguments for maintaining a monopoly are tenable only in transmission, which should be managed by either a corporatized government agency or a private regulated entity. Regulation is the key role for government, and an independent, central authority should be established to regulate the firms and public agencies in each of the three components. In India, the Bank presents its ongoing Power Sector Restructuring Project for the state of Orissa as the model reform strategy that all state governments should adopt. This project began in 1996 (“effective date”), and implementation is expected to be completed by 2002 (“closing date”). The Orissa State Electricity Board – the public utility responsible for the purchase, generation, transmission, and distribution of power in the state – will be disbanded. Instead, the state will create an Electricity Regulatory Commission, the Orissa Hydro Power Corporation (OHPC), and a Grid Corporation (GRIDCO). Commercialization and privatization are keys goals that these entities will pursue. For example, GRIDCO’s domain has been divided into 10 distribution circles, of which three – accounting for a third of GRIDCO’s total load and consumers – were transferred to a private distribution company in September 1996. The aim is to fully privatize distribution by 2000. Overall, the Bank emphasizes that in India, Significant, comprehensive, reform is necessary. This means restructuring the power sector at the state level, along the lines, for example, of what has been undertaken in Orissa … There is a key or core set of changes that are required at the state level; short of them, attempts are not likely to yield long-term success. Other types of measures (e.g. encouragement of captive generation) will only be second-best initiatives with marginal and short-lived impact. (World Bank 1996: p. 47) With respect to generation, the Bank proposes the following. To increase generation capacity, governments should encourage investment by independent power producers (IPPs) – such as the Enron Corporation (USA), Hopewell (Hong Kong), and Reliance Power (India) – that build and operate generation plants and supply electricity to the grid under specific contracts with the government.32 Contracts with IPPs generally include power purchase agreements whereby the government commits to buying a specified amount of power at an agreed purchase price or formula. These contracts also include mechanisms to reduce the risk faced by the private investor; for example, a government may agree to guarantee the availability of fuel and/or bear the foreign

34


exchange risk by fixing the purchase price in, say, US dollars. According to the Bank, to identify the most competent provider and least-cost solutions, governments should rely almost entirely on competitive bidding to select IPPs for each power generation project. The IPP route not only reduces or eliminates the investment burden to be borne by government, but also brings the private sector’s managerial and technical expertise into the power sector. IPPs in the private sector operate the generating units as a business and have the incentives to improve the commercial, economic, and technical viability of the system.33 Arguably and from a different vantage point, a self-generation system or captive power plant could be considered an IPP – the entire investment comes from the private sector and surplus power could, potentially, be sold to the grid. The Bank’s critique of self-generation units suggests, however, that such a situation is problematic. From the Bank’s perspective, a key problem with captive power plants is that they erode the commercial viability or financial returns for private sector power firms that consider doing business in a particular area. Further, self-generation units are not managed by power experts and, given their scale and fuel choices, tend not to be least cost. Overall, captive power plants represent a short-term quick fix and are, at best, second-best solutions. Some problems with the Bank’s solution The key problems with the Bank’s first-best solution lie in its political economy, difficulties in implementation, and the fact that it will take a long time (at least 10–15 years) to fix India’s electric power problem if the government relies only on this broad restructuring strategy. First, as in most radical reform projects, the implementation of the Orissa project is suffering delays. An early Bank report commends the state government on overall progress and improving revenues, but notes, for example, that the Orissa Electrical Engineers’ Association has been obstructing implementation. For instance, the Association persuaded its members not to apply for jobs in GRIDCO, and obtained stay orders from the administrative tribunal, which delayed the transfers of engineers from the state electricity board to GRIDCO and OHPC. Second, although all states are aware of the Orissa reform project and that there are some positive results, only a few states have decided to follow suit. Haryana, Rajasthan, and Andhra Pradesh are attempting reform, but have made little progress. Other states are even further behind. Finally, as noted earlier, only a few of the many MoUs signed between the government and IPPs have been implemented. Although


the government has been trying to encourage private participation in generation since 1991, as of 1997 private firms were estimated to have added only 1350 MW of capacity. In other words, restructuring the Indian electric power sector as a whole is likely to take a long time. Even if we assume that the restructuring will be a runaway success eventually, it will be about 10 to 15 years before industrial, commercial, and residential users can expect good quality and uninterrupted electric power supply. Industrial firms cannot and should not wait for these long-term solutions to kick in. Indeed, the much-maligned captive power plants can play a critical role in alleviating the power problems faced by industrial users.

Maruti upgrades from generators to captive plant With an extraordinary 81 percent market share in 1996–97, Maruti is India’s largest car maker. Maruti was created in 1982 as a joint venture of the Government of India (the majority shareholder) and Suzuki Motor Company of Japan. When Maruti started full-scale production in 1984–85, its only significant infrastructure-related investment was in six power generators. These diesel generators, imported from Japan, were to provide a back-up capacity of 8 megawatts (MW), that is, to serve as an emergency source in the event of failure in supply from the Haryana State Electricity Board (HSEB). Within a few years, Maruti’s managers realized that their power generators were not enough. The public power supply was so unreliable that HSEB was serving as the back-up rather than as the primary source, and Maruti was running its generators almost continuously. The assembler needed more power, and more reliability in its supply. In 1990, Maruti started work on its own power plant, a 20 MW gas turbine (GT), which began operation in 1993.34 Planning for a second 20 MW generating unit started in 1993, and the unit came on-line in December 1995. A third 20 MW unit became operational in November 1997. In late 1997, a fourth 20–26 MW unit was in advanced planning stages. The capital costs of the first three units were about: US$19 million for GT-1, US$14 million for GT-2, and US$15 million for GT-3.35 Even as Maruti has been building up its power generating capacity, the electricity situation in the country and in Haryana has been worsening. The World Bank (1996: p. 48) notes that system losses in Haryana are reported at 33 percent, but are probably closer to 40 percent; the plant load factor on thermal plants averages 44 percent (well below India’s average); and every year 27 percent of the distribution transformers fail because of overloading. In addition, unserved demand in

36


Haryana has increased from 9 to 25 percent during 1993–96, representing a cost to the economy of at least US$530 million (World Bank 1996).

Selecting gas turbines: a revolutionary technology It was in 1990 that Maruti first decided to opt for a gas turbine that would be operated in co-generation mode. It selected a relatively new 1980s technology that is now credited with revolutionizing power generation and, indeed, the electricity sector as a whole (Hunt and Shuttleworth 1996; The Economist 1998). Hunt and Shuttleworth (1996) note that it is because of combined cycle gas turbines (CCGTs) that generation is no longer considered a natural monopoly. They argue that the generation portion of the industry had been thought of as a natural monopoly because of the economies of scale that could be obtained by purchasing large and more efficient plants. As the market size grew and the use of electricity increased, so did the optimal size of the plant. As Figure 2.1 shows, the optimal size of generating units increased through the 50-year period 1930–80. Then things were turned around. Technology imported from materials science and space programs made turbines much more efficient than they had ever been and, at the same time, the price of gas declined. As a result, the way was cleared for smaller and cheaper generating units to be built economically. Customers began to think about building their own plants and wanted to know why they could not switch to suppliers who would offer lower prices. The stage was set for the revolution and reform – in particular, the move toward deregulation and competitive markets – that is now under way in the electric power industry in various parts of the world. Gas turbines have several advantages. First, capital and operating costs are low. Second, gas is cheap and is also a clean fuel, much cleaner, for example, than coal. Third, if operated in co-generation or combined cycle mode, gas turbines produce not only electricity but also heat or steam or hot air that can be used for other industrial processes.36 Fourth, gas turbines are efficient (30–38 percent) and can achieve up to 60 percent efficiency if they are operated in a combined-cycle mode by adding a steam turbine. Full recovery of heat can further enhance their efficiency (Casten 1995). By comparison, coal-fired thermal power plants achieve efficiency levels of 29–34 percent, at best. Fifth, they are versatile – although natural gas is clearly the superior and cheaper fuel for CCGTs, they can be operated on a variety of different fuels, such as naphtha and high-speed diesel. It is relatively easy, and fast, to reconfigure these generating units to accept a different fuel.

Tackling Power Problems 37 Thermal plants

$/MW 1930 1950

1970

CCGT

1980

1990 50

200

600

1000

MW Figure 2.1 Economies of scale in power generation, 1930–90 Source: Casten (1995). Reproduced with permission

In other words, CCGTs are low-cost, efficient, and versatile plants for generating electricity that can simultaneously meet an industrial user’s steam or hot air requirements. CCGTs are the also technology of choice for new base load plants in the United States, and virtually all new capacity in Britain is in CCGTs (Tenebaum, Lock and Barker 1992). In Britain, all of the 20 000 MW installed since 1989 has been gas-fired (The Economist 1998). Negotiating access to natural gas Before deciding on gas turbines, Maruti needed to ensure access to gas. This was an issue because neither Maruti’s industrial area nor the capital city of Delhi had provision for piped gas. Further, gas was controlled by the Gas Authority of India Ltd (GAIL), which allocated the available supplies among competing uses. Most of the gas was earmarked for the production of subsidized fertilizer for the agricultural sector. Nevertheless, Maruti’s managers reached an agreement with GAIL that was facilitated by the following circumstance. GAIL’s trunk pipeline was ready and it had gas, but there was little off-take by downstream fertilizer plants, its intended customers.37 Maruti was willing and able to pay for the gas and associated distribution infrastructure, including the pipeline. Under the deal, GAIL would build a special extension from its pipeline to the assembly plant. Maruti would pay not only for

38


the gas but also for the dedicated pipeline in the form of a monthly lease fee for 10 years (that is, until 2002). Further, the gas would be supplied under a take-or-pay contract whereby Maruti would pay for a fixed amount of gas even if consumption was lower.38 In January 1997, the minimum gas charge was US$0.8 million per month and was scheduled to increase to US$1.1 million in March 1997.39 The pipeline lease fee amounted to an additional US$0.17 million per month.40 The agreement with GAIL represents an excellent strategic move by Maruti. A dedicated pipeline involves a huge capital cost and is not an option that GAIL offers regularly. Field interviews indicate, for example, that at least two other large firms in the area requested that the pipeline be extended to their factories, but GAIL denied the requests on the basis of insufficient gas supplies. The lack of gas is, at least partially, an issue of bureaucratic priorities and political economy. The government makes gas available, for example, to Reliance Industries, one of the nation’s largest private sector conglomerates, for their 50 MW captive power plant in Ahmedabad. And in October 1997, the Prime Minister of India announced a 400 MW gas power plant in the electricity-starved Faridabad industrial area of Haryana, just 20 km from Maruti’s assembly plant. In other words, there is natural gas, albeit in limited quantity, but there are broader energy policy questions regarding who gets the resource and for what purpose. (A discussion of this point and India’s energy policy is beyond the scope of this study.) Overall, Maruti appears to have devised a good technical solution to the power problem and used its political clout to ensure access to gas. Commenting on the overall efficacy of the solution devised by his firm, however, Maruti’s Japanese joint managing director (JMD) noted, “[this solution to the power problem] costs us more, but we’ve solved the problem” (personal interview, June 1996). Although the JMD appears to confirm the World Bank’s notion that captive power generation is more expensive, the discussion in the following sections demonstrate that this is not the case. To evaluate the claim that it costs “more,” we need to answer the following questions: Compared to what? What is Maruti’s cost of generation?

Demolishing the myth of high self-generation costs During 1996–97, Maruti’s average cost of self-generation was US$0.08 per unit for its 40 MW system as a whole. This includes the cost of operating two turbines (GT-1 and GT-2); the third came on-line in the next fiscal year. An analysis of Maruti’s operating expenditures on power generation in 1996–97 shows that the average variable cost was


US$0.047 per unit (Appendix 2.1). According to the manager in Maruti’s energy department, the fixed costs amounted to an estimated US$0.032 per unit. Calculation of fixed costs entails assumptions on the interest and depreciation rates and the life of the plant, and the final costs are relatively sensitive to these assumptions.41 As we will see immediately below, Maruti’s assumptions on these variables are conservative. The assembler used a short plant life and a high discount rate, which means that the fixed cost of US$0.032 may represent an upper bound. Although we will use US$0.08 as the cost, it is important to note that it may actually be somewhat lower. In October 1997, Maruti was negotiating with HSEB on a fair price at which it could sell its surplus power to the power-short state electricity grid. For these negotiations, Maruti’s energy department calculated the cost of electricity from two alternatives: (a) the annualized cost if its three gas turbines are operated as a 60 MW simple cycle plant; and (b) the annualized cost from an 86 MW combined cycle plant, assuming that a steam turbine of 26 MW is added to the system (see Appendix 2.2). For these calculations, Maruti assumed a (relatively high) discount rate of 12 percent, and a (relatively short) plant life of 15 years. Further, and in contrast to the calculations in the previous paragraph, Maruti built in a 16 percent rate-of-return on its equity. The results were as follows. A 60 MW simple cycle plant would result in a uniform (or “levelized”) tariff of US$0.093 or Rs 3.25 per unit. An 86 MW combined cycle plant would result in a lower uniform tariff of US$0.088 or Rs 3.07 per unit. Despite the fact that HSEB was likely to push for lower prices, Maruti started its negotiations with a unit price of US$0.088–0.093 (for simplicity, US$0.09) per unit. Thus, Maruti’s unit price of US$0.09 also appears to represent an upper bound, and it may be possible for Maruti to supply power profitably at somewhat lower prices. The next four subsections compare Maruti’s: (a) price offer of US$0.09 per unit to HSEB with electricity prices offered by new private and public sector power generation projects; (b) generation costs of US$0.08 per unit with the costs of self-generation that a World Bank supported study finds in its surveys of industrial firms in Nigeria and Indonesia; (c) unit costs with the price of public power, as well as the quality of power from these two sources; and (d) energy expenditures with those of five other major auto assemblers in the country. (a) Maruti’s price compares favorably with IPPs The World Bank (1994a; 1996) and the literature on power restructuring valorize generation projects set up by independent power producers. According to the Bank, while IPP projects – particularly, those

40


selected through competitive bidding – are an ideal way of adding capacity and ensuring that generation is competitive and efficient, captive power plants are high-cost and short-term solutions. The data presented in Table 2.1 tests this notion by comparing the price of power from IPPs with the price and cost of self-generation. It suggests that captive power plants set up by industrial firms such as Maruti can be as competitive as IPPs on costs and can offer some additional advantages, as well. Specifically, Table 2.1 compares Maruti’s power prices as of October 1997 with the capital costs and unit prices reported for “fast track” generation projects that were being negotiated in late 1996. The table includes projects that the central and state governments were negotiating with independent power producers, as well as some projects proposed by the National Thermal Power Corporation (NTPC), a public sector company. It shows that the average unit price from nine of these projects was US$0.07 (more precisely US$0.068) per unit – the same as what some authors (e.g., Lee, Anas and Oh 1996) estimate to be the internationally competitive price of power generation. At US$0.09 per unit, Maruti’s proposed price is somewhat higher. But Table 2.1 also shows that Maruti’s price is exactly equal to at least one of these competitive IPPs (CMS-Neyveli). And Maruti’s capital cost per MW is substantially (about 40 percent) lower than the average cost for 11 of these power generation projects. Specifically, for Maruti’s 86 MW combined cycle system (which entails a higher cost per MW than the 60 MW system), the cost is Rs 32 million per MW of capacity. By comparison, the average for the 11 projects was Rs 41.7 million per MW. NTPC is the only specialized power producer developing a project that entails capital costs as low as those of Maruti. As noted in Table 2.1, however, the average cost escalation for all NTPC projects completed in 1991–94 was around 70 percent. The final three rows of Table 2.1 present the unit costs (rather than price) as well as capital expenditures incurred by Maruti and two other industrial firms (Daewoo and Arvind Mills) for their captive plants. It suggests that all three firms are competitive on both capital and unit costs relative to the 11 fast-track power projects. Overall, Maruti’s self-generation system (and, arguably, similar systems developed by other industrial firms) compares favorably with IPPs for the following three reasons. First, Maruti’s system results in capital costs and unit prices that are highly competitive relative to those offered by various specialized independent or public sector power producers. Second, unlike most IPPs, Maruti and other industrial firms do

Tackling Power Problems 41 Table 2.1 Captive plants compared to “fast track” projects and IPPs in India, 1996 Project

1 2 3 4 5 6 7 8 9 10 11

12a 12b

13 14 15

Size MW (1)

Spectrum – Godavri 208 – NTPC – Faridabadb Torrent – Gandhar 654 GVK – Jegurupadu 216 AES – Ib Valleyc 420 Enron – Dabhold 695 Congentrix – Mangalorec 1000 NTPC – Kayamkulamb – CMS – Neyveli 250 NTPC – Gangadhar 648 Ashok-Leyland – Vizagc 1000 Average 566 (No of data points) (9) Maruti-Option 1 86 Maruti-Option 2 60 Industrial self-generation units Maruti 60 Daewoo Motors 37 Arvind Mills 4

Capital cost Rs Mill/MW (2)

Price of energy Rs/kW (3)

Price of energy US$/kWa (4)

36.0 30.0 42.7 35.2 48.2 44.9 50.8 32.0 45.0 35.3 58.1 41.7 (11) 32.0 30.2

1.87 2.12 2.17 2.21 2.39 2.40 2.59 2.61 3.10 – – 2.38 (9) 3.10 3.25

0.053 0.061 0.062 0.063 0.068 0.069 0.074 0.075 0.089

30.2 29.7 30.6

2.84 3.10 2.75

0.068 (9) 0.089 0.093 Unit cost (not price) of self-generation 0.080 0.088 0.079

Notes: a Assuming an exchange rate of Rs 35/US$. b Average cost escalation for all NTPC projects completed in 1991–94 was around 70 per cent. c Coal powered projects. d After removing costs of additional infrastructure, capital costs for Enron were Rs 36.5 million/MW. Sources: Rows 1–11, Data from Ministry of Power and Dabhol Power Company, as presented in Exhibit 1, Case 9-596-100, Harvard Business School (1996). Reproduced by permission. Copyright © 1996 by the President and Fellows of Harvard College. Rows 12–15, are based on data collected from the headquarters of the respective firms.

not receive any potentially expensive guarantees from sovereign and state governments that help reduce project risks and, hence, the unit prices offered by these projects. Third, generation projects undertaken by firms like Maruti come on-line much faster than those negotiated with IPPs. IPPs often take longer because they are larger and because they tend to involve protracted and controversial negotiations between the government and private sector firms. Insights from the Enron-Dhabol IPP The controversial Enron-Dabhol project serves as a case in point, albeit an extreme one. In 1991, the central government opened up power

42


generation to private firms. In June 1992, the Enron Development Corporation, a US energy company, signed a preliminary memorandum of understanding with the state government of Maharashtra to develop a project at Dabhol with a price cap of US$0.073 per unit. A detailed power purchase agreement (PPA) was signed in December 1993. In August 1995, however, the state government cancelled the project because it represented a “deal … against the interests of Maharashtra.”42 The US government denounced the cancellation, and Enron initiated arbitration proceedings in London, claiming US$300 million in damages. After protracted negotiations, a new PPA was signed in February 1996, which was approved by the central government in May 1996. As of late 1997, five years after the first MoU was signed, the Enron project had not begun commercial production. The PPA that the government signed with Enron in 1993 was at the center of this controversy. It included the following features. Enron would complete the project, with a base load capacity of at least 625 MW, within an agreed time frame or pay penalties to the government. The government would provide the land and all the infrastructure necessary for construction, including approach roads and power. The government would also build the transmission line required for off-take. The power would be supplied to the Maharashtra State Electricity Board (MSEB) under a take-or-pay contract valid for at least 20 years from the start of commercial production.43 The agreed tariff included a capacity payment and an energy payment, and both of these had a mix of the Indian rupee and US dollar components. The government was required to bear the exchange rate risk to cover all the US dollar payments at the prevailing exchange rate. Finally, the tariff would be increased to adjust for inflation – the rupee costs would increase at the inflation rate in India, and the dollar component would increase at the US inflation rate. The renegotiated PPA of 1996 led to a change in the fuel from liquefied natural gas to naphtha and a lowering of the price from Rs 2.42 to Rs 1.86 per unit, that is, from US$0.069 to US$0.053 per unit (Business India, January 1997). But the other guarantees or features – such as the take-or-pay contract, the cover against foreign exchange fluctuations, and automatic indexing of tariffs – remained the same. Although the magnitude of government support and guarantees tends to vary, such risk-reducing mechanisms are not exceptional, in fact, they are usually standard features in IPP contracts signed by governments of developing countries.44 In summary, the renegotiated price from the Enron IPP project is now lower than the US$0.09 at which Maruti would like to sell power,


but only with extensive government support to Enron, particularly in the form of purchase contracts and other risk-reducing mechanisms. Indeed, the Enron price excludes the cash expenditures incurred by state government on land and infrastructure for the project, and hides the fact that the various government guarantees may translate into significant financial costs (contingent liabilities) for the government. By contrast, if the state government were to buy power from Maruti, it would get it almost immediately, with minimal negotiations or transaction costs, without incurring any capital expenditures, and without bearing any foreign exchange risk. As we will see shortly, the Haryana State Electricity Board (HSEB) does just that and, at least for the time being, pays a tariff of only US$0.04 per unit. (b) Maruti’s low cost is not unique – a comparison with Nigerian and Indonesian firms At first glance, the fact that Maruti and at least two other industrial firms can generate at a cost as low as US$0.08 per unit is both surprising and unique. In fact, this finding seems to conflict sharply with the results of firm surveys in Nigeria (1988), Indonesia (1992), and Thailand (1992), conducted under the aegis of the World Bank. This set of studies – one of few empirical infrastructure analyses in developing countries – strongly supports the view that self-generation is expensive and inefficient. Anas and Lee, the primary authors of these studies, found that the average cost of privately produced power was 10 to 30 times higher than the price of publicly provided electricity (Anas and Lee 1989; Lee and Anas 1992; Lee, Anas and Oh 1996; Anas, Lee and Murray 1996).45 Specifically, the average cost of private provision by manufacturing firms in Nigeria was US$0.69 per unit (kilowatt-hour or kWh), and that in Indonesia was US$2.14 per unit. By contrast, the internationally competitive price of power was estimated to be as low as US$0.07 per unit – that is, at this price, independent power producers would enter the market and supply electricity (if government regulations were to allow entry). This gap between the international price and the cost of self-generation in their sample leads the authors to conclude that selfgeneration is expensive and inefficient. A closer examination of these data shows, however, that for firms with “larger” captive generation capacity,46 the cost was significantly lower. In fact, the least-cost generators had unit costs that were 10 to 30 times lower than the average unit costs calculated for a particular country. In Indonesia, for example, the average cost of self-generation

44


across a sample of 182 firms was US$ 2.14, but the cost of generation for 10 of these firms, producing 500–999 MWh of electricity, was only US$0.07 per unit, and for another 10 firms, producing over 2000 MWh, the cost was US$0.08 per unit (Lee, Anas and Oh 1996: Table F10, see Appendix 2.4).47 Anas and Lee came up with almost astronomical costs of selfgeneration because they averaged the costs over dramatically different technologies and sizes of electricity generation. To continue with the Indonesia example, the costs were averaged over the following spectrum of self-generation: on the one end were 48 firms that self-generated less than 4 MWh of electricity, and at the other end were 23 firms that generated more than 500 MWh of electricity (Lee et al. 1996: Table F10).48 In terms of technology, they appear to be aggregating small diesel or battery-operated generators for running light bulbs and/or a small machine, along with large captive power plants including, perhaps, CCGTs. If we disaggregate Anas and Lee’s data and examine the lower cost generators, we get the following results: 23 firms in Indonesia (13 percent of 182 firms) had unit costs in the US$0.06–0.08 range and 12 firms in Nigeria (7 percent of 164 firms) had unit costs in the US$0.07–0.08 range. Anas and Lee’s own data, then, reveal a total of at least 35 firms (10 percent of the combined sample) in Nigeria and Indonesia with generation costs similar to those at Maruti. In other words, Maruti’s generation cost of US$0.08 per unit is not an exception, and firms that install somewhat larger generating capacities can and do produce electricity at prices that an internationally competitive independent power producer would offer. Indeed, since Anas and Lee conducted their surveys in 1988 in Nigeria and 1992 in Indonesia, power generation technology has moved rapidly, making it increasingly more affordable for larger numbers of industrial users to install efficient and low-cost captive power plants. (c) Self-generation meets demand for quality at low cost How do Maruti’s costs compare with the price charged by the public electricity utility? In August 1996, HSEB raised the price of power for industrial consumers from US$0.07 per unit to US$0.09 per unit. At US$0.08 per unit, then, Maruti’s cost of generation is lower than the revised HSEB rate.49 We know, however, that most of Maruti’s costbenefit calculations regarding self-generation were done when the tariff for public power was US$0.07 or less. That is, Maruti decided to opt for self-generation even when the cost of public power was lower. This


is because there is a huge difference in the level of service from these two sources. HSEB’s power is not only inadequate in quantity but is also extremely unreliable because of voltage and frequency fluctuations and frequent unscheduled power cuts. By contrast, Maruti’s self-generated power is adequate both in quantity and quality and is, therefore, almost 100 percent reliable. Specifically, the capacity of Maruti’s own power plant exceeds its current demand, and its system supplies stable, good quality power. In its initial decision to self-generate electricity, Maruti was opting to pay slightly more than the price of public power but for a higher service level. Now that HSEB has raised its tariffs and the power supply situation in the state has worsened, Maruti actually pays less for better quality power and far superior service. Following are brief case studies of two firms that have opted for selfgeneration even though they are served by competent private power companies. This discussion: (a) supports the argument that selfgeneration is not expensive relative to public power in India; (b) indicates that technologies other than gas turbines may also offer relatively low costs; (c) highlights some of the reasons why quality of power is important to industrial customers with sophisticated machines; and (d) suggests that industrial users may opt for self-generation to meet their demand for very high quality power even when they are being served by more efficient private (rather than public sector) utilities and distribution companies. Case 1: Daewoo Motors Daewoo Motors of South Korea has an assembly plant in the Greater NOIDA industrial area, located in the state of Uttar Pradesh (UP) at the border of Delhi and only about 25 km from the center of the capital city. Daewoo started commercial production in 1995, with the intention of expanding its facilities to reach an installed capacity of about 100 000 vehicles per year. In 1997, Daewoo was setting up a 37 MW captive power plant with heavy furnace oil for fuel and parts from South Korea, Spain, and India. The capital cost was estimated at about US$31.4 million and the unit cost of power at US$0.088 per unit (almost equal to the prevailing price of public power in the area). This decision to opt for a captive plant is somewhat surprising because Daewoo was served by the Noida Power Company Limited (NPCL), a relatively efficient private distribution company that won the contract for this industrial area in 1993. The manager in Daewoo’s energy division acknowledged that service improved after NPCL took

46


over from the state electricity board (for example, faults in distribution cables and switches were attended to almost immediately, and their customer service was excellent). In addition, because Noida is designated a “priority area” by the UP government, the power outages are extremely low. In early 1997, for instance, Daewoo faced outages of only about 10–12 hours per month, as compared to several days per month in most other areas. The quality of power, however, continues to be a major problem. NPCL cannot control the quality of power that it gets from the stateowned transmission grid. The quality of power in the grid depends, in turn, on the quality of the transmission infrastructure and the extent of the mismatch between demand and supply – for example, at peak load, demand exceeds supply in most grid systems in India and the voltage drops for all users; at night, the reverse is often true. Having realized the limitations of not having its own generating capacity, NPCL was planning to invest in its own 100 MW power plant. Daewoo decided it could not afford to wait. Its assembly plant uses many sophisticated computer-numerically-controlled (CNC) machines that are sensitive to the voltage and frequency of power. For example, these machines can tolerate a voltage fluctuation of (plus or minus) 1.0 percent, but the voltage fluctuations in the power from the public grid tended to be in the order of (plus or minus) 12.0 percent. A high voltage fluctuation causes a CNC machine to shut down. If a machine stops, it tends to jam the flow of material, and the problem cascades through the system or plant. If a machine is processing a piecemeal job, such as machining a part, it could either suddenly restart and potentially damage the tool-head, or it would need to be reset by the operator. In the latter case, the machine operator may have to reload the part and reset the specifications of the job on the machine’s computer. While its captive plant was being installed, Daewoo leased 12 diesel generating sets of 1 MW each. The leasing company set up the generators and was responsible for their operation and maintenance. Daewoo was responsible for providing the fuel. The total unit cost of power from this system, including the cost of fuel, was US$0.093. Car assemblers such as Maruti and Daewoo demand a relatively large volume and high quality of power, and they can self-generate at a relatively low-cost. One could argue that self-generation is unlikely to work for industrial users with a smaller demand for power – after all, Maruti now has a 60 MW system with three turbines of 20 MW each, and Daewoo is planning a 37 MW system comprised of three units. However, Daewoo’s temporary power plant, comprised of small diesel


generators, also appears to result in a relatively low unit cost. The case of Arvind Mills presented below suggests that it is economical to selfgenerate even for a far smaller demand of about 4 MW, and that the economics turn strongly in favor of self-generation if the production process or machines demand high quality power. Case 2: Arvind Mills Arvind Mills is a major producer and exporter of denim cloth. The firm is based in the city of Ahmedabad in Gujarat state, in the western part of the country. By virtue of being located in this city, the firm is served by the Ahmedabad Electricity Company (AEC), one of only five private power utilities in the country.50 AEC has managed generation, transmission, and distribution in Ahmedabad since 1934 and is considered to be one of the most successful electric utilities in the country. Nonetheless, in 1997 Arvind opted for a diesel captive power plant of about 4 MW. The capital costs of this project were estimated at US$3.5 million and the total cost of generation at US$0.079 per unit (Appendix 2.3). Of this, the variable costs accounted for US$0.047 per unit and the fixed costs for US$0.031 per unit, strikingly similar to Maruti. AEC tried to negotiate with Arvind and to dissuade the firm from making this “unnecessary” investment. According to a vice president at Arvind, this persuasion did not work for the following reasons. Arvind has a highly automated plant, with CNC machines, in which the material flows continuously through different stages, including carding, drawing, dyeing, spinning, and finishing. Power problems affect both the quality and quantity of fabric, and inferior quality fabric has to be sold at discounted prices (reductions averaging about US$0.75 per meter) in the export market. The firm’s managers calculated powerrelated losses at the denim plant, which has a 19 million meter annual capacity, as follows. A single interruption in power supply affects the quality of 3600 meters of fabric and results in a loss of US$2700, and a single voltage fluctuation (dip or surge) affects 2500 meters of fabric and results in a loss of US$1875. This translates into significant cumulative losses, given that voltage fluctuations occur several times a day and power cuts are not uncommon. (d) Maruti’s energy expenditures are lower than other assemblers Does Maruti’s solution for power cost “more” compared to what other assemblers in the country or, say, in the United States pay? Based on the data presented in Table 2.2, the answer is negative. Table 2.2 summarizes

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Table 2.2 Auto assemblers’ energy expenditures, 1996–97 Assembler

Segment

Location

Maruti Hindustan Motors Ashok-Leyland Telco Bajaj Hero-Honda US auto industry as a whole (1987)

Cars Cars Buses/trucks Truck/bus/car 2-wheelers 2-wheelers

Gurgaon, Haryana Calcutta Chennai/Hosur Pune Pune Haryana

Energy as % of sales revenuea 0.5 2.4 1.5 2.2 2.5 approx. 0.8 0.6 b

Notes: (1) These figures are not adjusted for any differences in the energy demand and efficiency of the different assembly plants, and/or conservation measures that may have been adopted. (2) Figure (b) represents the proportion of inputs that the US auto industry as a whole purchases from gas and electric utilities, that is, 0.006 of every dollar of inputs for the auto industry comes from the energy sectors of the economy. It is not directly comparable to firm-level data presented, but is nonetheless indicative. Sources: a Annual Reports of the Companies; of Commerce 1987).

b

US Input-Output Accounts (US Department

the energy expenditures as a percentage of sales revenue for six major auto assemblers. Maruti clearly had the lowest energy expenditures, a mere 0.5 percent of its sales revenue. The next-best performer, HeroHonda, spent 0.8 percent of its revenues on energy, more than one and a half times as much as Maruti. By contrast, the remaining four assemblers spent between 1.5 percent and 2.4 percent of their sales revenue on energy, about three to five times as much as Maruti. Although figures from the US Input-Output tables (US Department of Commerce 1987) are not directly comparable to our firm-level data, they are nonetheless indicative. The US data suggest that for the auto industry as a whole, US$0.006 of every dollar of inputs comes from the energy sectors of the economy, that is, gas and electricity utilities account for 0.6 percent of the total inputs of the industry. As shown in Table 2.2, Telco and Bajaj – market leaders in their segments – spend 2.2 percent and 2.5 percent, respectively, of their sales revenue on their energy bills (not adjusted for any differences in the energy demand and efficiency of the different assembly plants, and/or conservation measures). Factory visits to these firms, located in Pune, reveal that they buy their electricity from MSEB, one of the best managed electricity boards in the country and one of only two of the 18 SEBs with a 3.0 percent rate of return on their assets. MSEB provides relatively reliable service, for which its customers pay higher rates. In


August 1996, Bajaj and Telco were paying about US$0.10 per unit compared to the US$0.08 per unit that it was costing Maruti to self-generate. The explanation for Hero-Honda’s relatively low energy expenditures (0.8 percent) appears to lie in the fact that it also self-generates electricity. Hero-Honda, located near Maruti, had applied for a gas allocation, but its request was denied by GAIL. Even without gas, then, HeroHonda appears to be spending less on energy compared to firms like Bajaj (its direct competitor) and Telco that rely on public power.51 In other words, self-generation is often cheaper than the price that industry is required to pay for public power and, as argued earlier, provides higher quality and more reliability. Self-generation as a preferred alternative In summary, for Maruti:

Proposed unit price of US$0.09 (cost;16 percent return) – the price that it is trying to negotiate from HSEB – is somewhat higher than the estimated internationally competitive price of power generation of US$ 0.07 per unit. Proposed price of US$0.09 is equal to or higher than the actual prices (ranging from US$0.05–0.09 per unit) offered by nine fast track power projects, but, in contrast to these IPPs, Maruti’s system does not entail any capital expenditures or potentially expensive guarantees by government. Generation costs of US$0.08 per unit are lower than the price of public power even though the quality of power is far superior. Low generation costs are not unique – indeed, there are very similar to those of about 35 other larger producers in Indonesia and Nigeria studied by Lee, Anas and Oh (1996, 1999); and Self-generation results in an energy bill that is one-third to one-fifth of that borne by four other major assemblers in the country.

The supply-side revolution Self-generation has become an increasingly viable, low-cost, and efficient option because of supply-side changes. Economies of scale have fallen due to advances in generation technology. These technological changes have been accompanied by the emergence of sophisticated technology vendors – such as BHEL, General Electric, Wärtsilä-Diesel – that design, build, and sell generators and captive power plants tailored to the needs of individual users throughout the country. Although the actual threshold

50


above which self-generation becomes a low-cost and competitive solution needs to be ascertained through additional empirical research, this study suggests that the threshold level may already be as low as 1 MW (1000 kW) of generating capacity.52 Other research appears to support this finding and suggests that economies of scale in generation are continuing to fall. Gas-fired turbines, in particular, are getting smaller and more efficient. For example, Allied Signal, a firm that makes gas-turbines, believes it can sell micro-turbines of 40–75 kW capacity, small enough to be suitable for a fast-food restaurant or a small office building; it was aiming to market its 75 kW turbine for US$35 000–45 000 in 1999, and for US$25 000 by about 2002 (The Economist 1998). These supply-side changes are encouraging industrial users to shift toward self-generation even in advanced industrialized countries where utilities provide reliable service. In the United States, for example, selfgeneration by industrial firms has increased fourfold over 12 years from about 100 billion kWh in 1984 to an estimated 400 billion kWh in 1996 (see Cambridge Research Associates, cited in The Economist 1998). In 1996, self-generation by American industry represented about 35 percent of total industrial demand, and more than half of this selfgenerated electricity was sold to utilities rather than consumed on-site. Given that utilities in the United States provide high quality and reliable power, the shift to self-generation can be seen as a search for lower costs. Some analysts argue that this is indeed the case, and also that in enterprises with stable demand it is almost always cheaper to generate on-site because there are no charges for transmission, distribution, or billing (The Economist 1998). The demand for quality In India, the decision of industrial firms to invest in captive plants does not appear to be driven entirely by the difference between the cost of self-generation and the price of public power, but to a greater extent by the need to ensure high-quality power. This is why firms are investing in self-generation even when public power is cheaper. Certain kinds of firms – such as those that rely on CNC machines, and those with continuous manufacturing processes – are more likely to be sensitive to the quality of power. The emphasis that such industrial firms place on quality of power has some implications for restructuring in the sector. It brings into question the notion that high or distorted industrial tariffs (designed to cross-subsidize residential and agricultural customers) are an important factor forcing firms to opt for self-generation. This study


suggests that simply reducing electricity tariffs for industry and/or introducing private sector firms in distribution may not help stem the exit of quality-sensitive industrial customers from the public grid. To dissuade such customers from exiting, the power system needs to ensure very high quality power. Even then, firms may prefer to exit the grid and self-generate at low costs, just as they do in the United States. Overall, even in advanced industrialized countries where utilities provide high quality and reliable service, industrial firms often find that the economics are in favor of self-generation. In developing countries, the economics tend to turn overwhelmingly in favor of selfgeneration because industrial users cannot rely on public/private utilities for either adequate quantity or quality of power. Therefore, industrial users are increasingly opting for self-generation. Indeed, self-generation may emerge as a preferred and first-best – rather than back-up or second-best – source of electric power. Maruti has developed an innovative power-sharing arrangement that indicates how the trend toward self-generation can be exploited by governments of developing countries to create broader social benefits. It suggests that the government can leverage private investments in selfgeneration to increase capacity in the public grid, and that power-sharing or “wheeling” among firms is one way to meet industrial demand for exceptionally high quality power. We turn now to this arrangement.

Maruti’s power-sharing arrangements In fiscal year 1996–97, when its installed capacity was 40 MW, Maruti’s generation capacity exceeded its power requirements by almost 100 percent. Maruti’s third and fourth generating units (costing an estimated US$29–35 million) will double its generating capacity to 80 MW. The primary purpose of these new units is to meet the requirements of Maruti’s planned expansion (Plant III).53 However, the planned increase in power generating capacity is expected to surpass the increase in demand from the new assembly plant. Why would a firm as cost-conscious as Maruti invest in expensive generating capacity substantially in excess of its needs?54 What does it do with its current excess capacity? Maruti has developed powersharing arrangements and is intentionally expanding capacity to help solve the power problems of its suppliers. In 1996–97, Maruti consumed 51 percent of the electricity that it generated and sold the surplus 49 percent to: (a) its joint-venture firms that are in its immediate vicinity; (b) the state electricity grid; and (c) other firms that supply

52


Power consumption in MWh

350 000 300 000 250 000 Sales to utility 200 000 150 000 Sales to suppliers 100 000 50 000 0 90–91

Maruti own use

91–92

92–93

93–94 (GT-1)

94–95

95–96 (GT-2)

96–97

97–98 (GT-3)

Figure 2.2 Maruti’s power sharing arrangements Source: Company data

components for its cars. This means that Maruti’s plant is no longer a captive unit that serves only its own demand (see Figure 2.2). Maruti has a different arrangement with, and reason for serving, each one of its three customer categories. Supplying power to adjacent joint-venture suppliers Maruti supplies power to five of its nine joint-venture suppliers (JVs). These are dedicated supplier firms in which Maruti holds some equity. Most of the JVs were set up in collaboration with Suzuki’s suppliers in Japan, and they manufacture key components or sub-assemblies, such as seats, fuel tanks, instrument panels, and sheet metal parts. Five of them share a boundary wall with Maruti’s plant and are considered part of the “Maruti complex.” The remaining four JVs are located within a 50 km radius. Since Maruti’s power plants began operating in 1993, they have been supplying power to all JVs within the Maruti complex. According to Maruti’s managers, it made sense to solve the power problems of its key partners along with its own, particularly, because their demand was relatively small. Although Maruti meets their full demand for electricity, these JVs together account for only a small fraction – 6.0 percent in 1996–97 – of its total generation. Maruti claims that it supplies electricity to its JVs on a “no profit, no loss” basis. Since March 1996, these JVs have been paying US$0.093 per


unit – about 16 percent more than US$0.08 per unit calculated previously as the total cost of generation. In other words, Maruti’s power tariffs allow it to recover more than its full costs from its industrial customers (we return to this point subsequently). It is unclear whether the JVs understand that Maruti is charging them somewhat more that its own generation cost, but they are well aware that the price is equal to HSEB tariff for industry. Like Maruti, the JVs are required to continue paying a minimum monthly charge for their HSEB connection. The JVs still consider this to be an excellent arrangement – efficient, reliable, and problem-free. Selling electricity to the state grid Since December 1995, Maruti has been selling power to the HSEB. Until October of that year, with only one of its 20 MW turbines in operation, the assembler was relying on HSEB to a small extent. Over the six-month period April–October 1995, Maruti purchased a total of about 2000 MWh of power from HSEB.55 Its second 20 MW gas turbine came on-line in December 1995, and in the first month, Maruti sold 2700 MWh of power to HSEB. This means that Maruti had recruited HSEB as a customer well in advance and did not idle its generating capacity at all. Since then, Maruti’s supply to the grid has increased dramatically. By August 1996, Maruti was deploying 20 MW, or half of its installed capacity, to supply power to the HSEB grid. This power is supplied 24 hours a day, and Maruti has been able to increase the supply to 23 MW during peak load periods.56 Given that demand outstrips supply by about 25 percent in Haryana as a whole, Maruti’s power provides invaluable capacity to the state grid57 – sufficient to meet about 30 percent of Gurgaon district’s industrial power requirement. During fiscal year 1996–97, HSEB purchased a remarkable 38 percent of Maruti’s total generation or about 90 000 MWh of electricity. Unlike in the United States and several other countries, this is a relatively rare arrangement in India. In fact, only after the October 1995 power-sector reforms did the government decide to allow and encourage private producers to supply power to public transmission grids. Within a month of this policy coming into effect, Maruti cut a deal with HSEB to buy its surplus power. Although HSEB is pleased to use Maruti’s excess power to supplement its own supply, it does not necessarily believe that it should pay the full cost. HSEB argues that the price of power from the national grid is lower, and that it can (theoretically) purchase power from the (capacity-starved) national grid at the government-established price of US$0.04 per unit compared to Maruti’s

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US$0.08 per unit. Indeed, HSEB would rather not pay anything for the power since it is like a sunk cost for the firm – Maruti has a take-or-pay contract for gas, prefers to run its power plants continuously (even though it operates only two full shifts at the assembly plant), and would, hence, incur the cost whether or not it supplies power to HSEB. Nonetheless, Maruti has managed to negotiate a rate of US$0.04 per unit with the HSEB. This appears to be advantageous for both parties but especially for HSEB, which gets access to additional power and better quality power at a price similar to that from the national grid. Maruti gets to recover at least part of its cost of power. Maruti, however, does substantially better than it may seem. The agreed unit rate of US$0.04 (US$0.043 to be precise) is approximately equal to the cost of gas per unit of electricity generated. Per the analysis in the previous section, this rate is sufficient for Maruti to recover about 90 percent of its variable cost of US$0.047 per unit. Thus, even the lowest tariff that it charges its most difficult (or most demand elastic) customer covers, at the very least, the cost of gas that Maruti has to incur, given its take-or-pay contract with GAIL. Supplying power to the state grid allows Maruti to achieve a high plant load factor and recover its sunk costs on gas. As noted earlier, Maruti is trying to negotiate better prices from HSEB, arguing that HSEB should view it as an IPP and sign a power purchase agreement. Expanding the customer pool Maruti is extending the customer base of its power plant to include its other (non-JV) auto component suppliers that are located within a 15–20 km radius. This strategy offers two advantages. First, as we will see later, solving the power problems facing its suppliers has benefits for Maruti. Second, the suppliers are willing and able to pay higher tariffs compared to HSEB, and this helps Maruti recover its full costs of generation. Initially, Maruti decided to connect these suppliers to its own system because it had some excess capacity. Now, Maruti is well aware that it is a low-cost producer of highly reliable electricity – a valuable input or resource – and is using this as part of a broader competitive strategy (discussed in Chapter 4). By March 1997, the number of component suppliers receiving power from Maruti had increased from 5–15, and this number continues to grow. Maruti expects to connect an additional 20 suppliers in the near future.58 In 1996–97, these firms accounted for 5.0 percent of Maruti’s total generation and paid the same tariff (US$0.09 per unit) as the JV firms. Visits to 11 component suppliers located near Maruti revealed


that nine were connected to Maruti’s power system, and two were awaiting their connections.59 Two of these are JVs that have been receiving power from Maruti since 1993, and another two suppliers received their connections in March–April 1996. An additional five suppliers received their connections between July 1996 and March 1997. The remaining two suppliers, located 15 km from the Maruti plant, signed up for a connection but have to wait until the system is extended to their area. A July 1996 visit to one of these JV firms – Mark Auto – provides some insight into how these firms perceive the Maruti solution to the power problem. Mark Auto had recently completed two new plants about 12 km from the first plant. It was awaiting a Maruti connection, although they are not joint-venture plants. In anticipation of this alternative power supply, it had not applied for an HSEB connection. According to Mark Auto’s managing director, the HSEB connection was not only difficult and expensive to obtain, it would also saddle his firm with a minimum charge that would be extra and unnecessary once they obtained a Maruti connection. The firm had simply leased generators for about six to eight months and intended to rely on this relatively expensive and cumbersome arrangement until the Maruti connection came through.60

India’s largest car maker runs an efficient electric utility This section examines key features of Maruti’s “mini-electric utility,” in particular, its innovative transmission and distribution arrangements, billing and collection system, and the tariff structure. It discusses why this is an excellent system and what it appears to suggest with respect to improving power supply for industry. Transmission and distribution system The implementation plan agreed upon by the component suppliers, Maruti, and HSEB makes Maruti directly responsible for the distribution system that is required to connect the suppliers in its immediate vicinity (1–2 km). After obtaining all of the requisite government clearances, Maruti put in 11 kV underground cables that connect the firms in its vicinity. HSEB is responsible for constructing a dedicated 66 kV, 12 km transmission line to connect Maruti to its more distant component suppliers. HSEB’s major contribution is that it will use its eminent domain power to acquire the right-of-way for this dedicated transmission line. The capital costs of the transmission line, including land compensation, will be borne by Maruti and its suppliers. Once built,

56


the transmission line will connect Maruti to a substation located at a supplier’s factory (Rico). Again, Maruti will be responsible for the underground cables required to connect other suppliers to the Rico substation – that is, the local distribution system. Implementation, however, has been slower than anticipated. Maruti deposited the requisite funds with HSEB in early 1996. In December 1999, HSEB was still a month away from completion. The project was delayed in part due to problems with land acquisition, and partly due to the fact that the transmission line crosses several jurisdictions and involves different HSIDC offices. This arrangement represents a case of power “wheeling,” which means that the electricity generated by a power producer is wheeled or transported over transmission lines owned or managed by a different entity.61 Normally, the power producer would pay a wheeling charge or a fee to the owner of the transmission line. In this case, however, the power is wheeled on dedicated transmission lines constructed and managed by the public sector but financed by the private firms that will use the system. These private firms have opted for the more expensive alternative of financing a dedicated line – rather than wheeling electricity over the HSEB grid for a small charge – to ensure that the quality of the electricity remains high. If Maruti were to input good quality power into the grid, its customers at the other end would not get the same good quality electricity. This is because, as mentioned earlier, the quality of power that a user receives depends on the quality of power in the grid as a whole, and/or the extent of the mismatch between demand and supply. Cost sharing and why firms find the system attractive Maruti and its industrial customers will share the capital cost not only for the distribution lines put in by Maruti but also for the dedicated transmission line that HSEB is constructing with the money deposited by the assembler. Further, Maruti’s tariff for its industrial customers is designed to recover both the fixed and variable costs of the generation system. These firms will pay Maruti directly for the initial capital costs and, subsequently, for the electricity they consume. In other words, the component suppliers are buying a share of the total power project in proportion to their demand. All the firms get access to better infrastructure, but only by paying the direct costs of the capital investments and service improvements. The following serves as an example of how and why the deal is attractive for Maruti’s component suppliers. It also provides some insight into


how the public electric supply system tends to work or, rather, not work. KML a joint-venture seat supplier located about 15 km from Maruti’s plant, started operations in 1994, but does not have an electricity connection from HSEB. There is a waiting list for these connections, and given that KML is located in a non-priority area (not, for example, a government-owned or designated industrial complex), it is not astute to wait around for one. Thus, the management purchased three 380 KV generators and started production. In October 1997, the firm was continuing to rely on these generators, which operate on high-speed diesel, for 100 percent of its consumption. These generators cost a total of about US$38 000 in 1994, and the firm estimates that the variable or operating costs (mostly fuel) in 1997 were about US$0.065 per unit. KML will be one of the firms connected to the Maruti system from the Rico substation. KML will pay an estimated US$14 000–20 000 as its share of the capital costs of the transmission line and generation units. In addition, it has incurred a capital cost of US$14 000 for transformers and cables required at or near its own plant. KML sees this US$28 000– 34 000 as the capital cost of connecting to – and buying a share in – the Maruti system. The firm will pay Maruti about US$0.09 per unit for the power, but knows that the prices will be adjusted upward as fuel and other costs rise. It is more than willing to incur these costs because Maruti has a proven track record in delivering good quality power and because Maruti has all the incentives to maintain quality and minimize disruption in service to KML. Any disruptions at KML translate directly into disruptions in Maruti’s own production plans because KML is one of Maruti’s two seat-suppliers and works on a just-in-time basis, which means that there is buffer inventory for only a few hours (we return to this point subsequently). Billing and collection Maruti is serving not only as a generating company but also a distributor, and is directly responsible for billing and collections. It is important to note that Maruti set up these systems in such a way that it runs no risk of non-payment and incurs little extra cost for collection of charges. This is because the firms’ electricity fees are deducted directly from the payments owed to them by Maruti. Maruti also has been successful in collecting from HSEB, despite the fact that, like most other SEBs, it is in poor financial condition. It has done so by establishing a revolving credit mechanism and threatening to cut off supply if HSEB defaults on payments – it seems that HSEB perceives this to be a credible threat (personal interview, R. C. Bhargava, February 13 1998).

58


Tariff structure, sales revenue, and a “cross-subsidy” for HSEB In 1996–97, Maruti consumed 51 percent of its total generation and sold the remaining 49 percent (about 116 000 MWh) to HSEB and its JV and non-JV component suppliers. Its industrial customers accounted for 22 percent of the total electricity sold and for 38 percent of Maruti’s revenue from electricity sales (calculated from Table 2.3). HSEB accounted for as much as 78 percent of the total electricity sold and 62 percent of the assembler’s sales revenue. Maruti charged its industrial customers US$0.093 per unit, or about 16 percent more than its generation cost of US$0.08. By comparison, HSEB paid a tariff of US$0.043, which was about 10 percent short of Maruti’s variable cost of generation of US$0.047 per unit. The higher industrial tariffs appear to help cross-subsidize the electricity supplied to HSEB. Indeed, calculations in Table 2.3 show that the current industrial tariff generated just enough surplus to cover the loss entailed in supplying electricity to HSEB. Specifically, given a cost of US$0.08 and a tariff of US$0.093, sales to industrial customers generated a “profit” of US$329 000. The variable cost of serving HSEB was US$0.047 whereas the utility paid only US$0.043, generating a “loss” of about US$310 000. That is, Maruti appears to have set the industrial tariffs to ensure “no profit and no loss” from its customer pool as a whole. It is worth noting that Maruti and its suppliers continue to contribute to a cross-subsidy pool even though they have largely exited from the public system. As HSEB’s customers, they were paying higher industrial tariffs that helped subsidize other customers. Under Maruti’s power-sharing system, the firms continue to pay minimum charges to HSEB and also contribute to the cross-subsidy pool in the form of

Table 2.3 Revenues and costs of electricity sold by Maruti, 1996–97 HSEB Consumption (MWh) Actual revenues (US$) Estimated total cost (US$) (@ $0.08/kWh:$80/MWh) Estimated variable cost (US$) (@ $0.047/kWh:$47/MWh) Loss on HSEB (US$) (Variable cost minus revenue) Profit on suppliers (US$) (Revenue minus total cost)

Suppliers

Total

a 90 213 b 3 929 866 c:(a*$80) 7 217 040

25 582 2 375 449 2 046 541

115 795 6 305 315 9 263 581

d:(a*$47) 4 240 000

1 202 354

5 442 354

e:(d9b)

310 134

f:(b9c)

Source: Compiled from data sets provided by Maruti.

328 908


cheap power. That is, the mechanism through which the cross-subsidy operates has changed. Overall, Maruti recovered the fixed and variable cost of generation on the power that it sold to its component suppliers, and appears to have designed a cross-subsidy that helped recover the full variable cost of the electricity that it sold to HSEB. In other words, Maruti does better at recovering the costs of its power infrastructure than, perhaps, most electric utilities not only in India but in the entire developing world. As the World Bank (1994a: p. 47) notes, public electric utilities in the developing world are, on average, net money losers – their revenues cover only about 60 percent of their total cost. The Maruti model: features and insights In summary, Maruti and HSEB have put together a service production, delivery, and collection arrangement that is innovative both in financial and institutional terms. First, the industrial users pay for the direct capital costs of improvements in (or expansion of) the physical infrastructure to gain access to improved service. Second, the tariff levels are such that the industrial users pay the full fixed and variable costs of power generation and the government pays at least the cost of fuel for surplus power directed to its grid. The industrial users appear to be cross-subsidizing HSEB and its poorer customers, which ensures that the arrangement is financially sustainable. Third, the billing and collection arrangement with its industrial customers ensures a 100 percent collection of user fees (or electricity bills) at little additional cost to Maruti. Fourth, the system allows Maruti to achieve a plant load factor of about 70 percent – comparable to the National Thermal Power Corporation, equal to or better than the best state electricity boards, and far better than the all-India average of 55 percent for the 19 SEBs (India Infrastructure Report 1996: vol. 3, p. 92). This case presents, perhaps, an ideal example of how the Ministry of Power’s 1995 guidelines allowing power sharing and wheeling can not only work but also result in a “win–win” situation. Indeed, the case stands in sharp contrast to the objections raised by the World Bank (1996) against these government polices and captive generation – in particular, the potentially high costs, reliance on bad fuels, and the opportunity for industry to avoid contributions toward cross-subsidies. Overall, Maruti’s system provides an important precedent for power sharing and industrial wheeling. The shared power is either distributed directly by way of underground cables or is wheeled on dedicated transmission lines managed by the public sector but financed by the private sector. This approach appears to be highly replicable,62 and it offers one

60


mechanism to meet the demand for very high-quality electricity by certain quality-sensitive firms, at least in the short and medium term.63 Power sharing and wheeling also may make it easier to ameliorate the power problem and catalyze reform in the power sector in India – easier, that is, as compared to waiting for a radical restructuring of public utilities and disbanding of the state electricity boards altogether.

How unreliable power affects supply chains and competitiveness Why does Maruti make such an effort to solve the power problems of its suppliers? Why not just solve the problems at its own plant? It is not sufficient to overcome simply the problems at its own plant because Maruti is not a vertically integrated firm. Rather, it represents an opposite industrial structure with the firm relying on its supply chain for almost three-fourths of all inputs by value. This means that Maruti’s own performance in terms of cost, productivity, and competitiveness depends on the costs, reliability, and overall performance of its suppliers. Indeed, the literature on international competitiveness argues that efficient and well-managed supply chains are critical in determining competitive success in global industry (Porter 1985; Gereffi and Korzeniewicz 1994). But, as the discussion below will show, unreliable electric power supply reduces the efficiency of Maruti’s suppliers and the reliability of its supply chain as a whole. To enhance its competitiveness, Maruti needs to find innovative solutions not only to its own power problems but also those plaguing the firms in its supply chain. Unreliable power adversely affects a supplier firm and, thereby, its customer Maruti in three ways. First, power outages and inadequate supply disrupt production at the supplier-level, which directly affects production planning at Maruti. Second, unpredictable voltage fluctuations and power cuts lead to material losses, variations in product quality, and damage to machines, which adversely affect the costs and performance of suppliers. And this, directly or indirectly, adversely affects the costs, quality, and delivery schedules of components for Maruti. Third, to hedge against these problems, both Maruti and its suppliers have to hold higher inventories. Output losses and disruptions in production plans To what extent might power-related disruptions in the production of upstream supplier firms affect production at Maruti? The following incident serves as an example. A visit to Maruti in the summer of 1996


revealed that the firm was producing 50 fewer cars than the daily target of about 1200 cars – a 4 percent reduction. According to a manager in the vendor development and component purchases department, one of their suppliers, located about 100 km away, had been facing a power cut of 40 percent for a few days – a situation that is not infrequent during summer due to excess demand. This firm supplies cast cylinder heads for engines. Casting is a continuous process with a very high energy requirement that cannot be met with individual diesel generators.64 As a result, the supplier could not meet delivery schedules for several days. Maruti used its inventories initially, but within a few days the total volume of production was affected. A small item, then, led to a 4 percent reduction in daily production. If this situation continued for a few weeks, Maruti would have lost about US$1.1 million per week in sales revenue.65,66 Loss of material and variation in product quality When a machine shuts down suddenly it can adversely affect product quality and lead to loss of material. As mentioned earlier, this problem is particularly acute in the case of sophisticated CNC machines (compared to manual machines) because they have low tolerance for variations in power quality. This means that even a significant voltage fluctuation can shut them down and a machine operator is often required to reset the computer and reselect the specifications for the job. Manufacturing that involves a continuous process is more prone to disruption losses than a batch process.67 In continuous process manufacturing, the losses tend to be higher because the cycle times are longer and production lots are larger. For example, to reach a specified hardness, high-precision gear cutting tools are heat treated in several sequential but continuous stages that can take up to 24 hours. Any variations in the temperature (or for that matter time of treatment) change the extent to which the tool hardens and result in variations that are likely to exceed specified tolerances for these tools. A power fluctuation or temporary outage during the process lowers the temperature and is likely to lead to a rejection of the entire lot of gear cutting tools. In the case of other components or products, such problems could lead to variation in the final quality of the product. Material losses and quality variations, together with lost production time, then, adversely affect not only the costs but also the quality performance and reliability of supplier firms, especially those that use sophisticated machines and continuous process manufacturing. Specifically, they can lead to unexpected delays in delivery of components and create quality problems (such as higher variation, higher

62


rejection rates) – that directly or indirectly affect the costs and performance of Maruti. Inventories as a “solution” to supply-chain unpredictability To hedge against the above problems and the unpredictability that they introduce into the system, assemblers have devised certain “solutions,” one of which (almost universally deployed in India) is to hold higher levels of buffer or safety stocks. This is considered justifiable because the costs associated with “stocking-out” – reducing production or stopping the assembly line because of shipment delays – tend to be higher than holding buffer inventories. While unreliable power is not the only explanation for high levels of buffer stock, it definitely is one of the key factors that introduces unpredictability in the supply chain.68 How high are the inventory levels? In 1996–97, Maruti, one of the better performing Indian auto firms in terms of inventory levels, held 30 days of total inventory, including 22 days of inventory for components and raw materials. These inventory levels are surprisingly high, given that world-class and globally competitive auto assemblers use lean production systems and work with only a few hours to a few days of inventory to minimize costs and improve quality and productivity. (Chapter 3 contains a detailed discussion of the inventory problem.) Impacts cascade through the supply chain In summary, unreliable power imposes direct costs on suppliers, which, in turn, raises the costs for assemblers. These costs are not immediately obvious or easily quantifiable. They arise to some extent from material losses and to a larger extent from quality problems and disruptions in the production of upstream suppliers that affect production at assembly plants. To hedge against these problems and the unpredictability that they introduce into the system, both assemblers and their suppliers tend to hold higher buffer inventories or safety stocks. This raises total inventories. Poor power, then, has a cascading effect on the supply chain, and one of the manifestations is that firms all along the chain tend to hold higher levels of safety stock. That is, power constraints have a direct impact on total inventory, which, in turn, constitutes a major inefficiency plaguing even the leading auto assemblers in India. Solutions to unpredictability created by poor infrastructure, such as unreliable power, often lie outside the direct control of firms. Indeed,


solutions to infrastructure-related problems tend to be more under the control of government. However, unless firms in the auto industry find innovative solutions to counter the infrastructure deficiencies that plague the nation as a whole, they may not be able to move to lean/just-in-time production systems and minimal inventories that are considered critical to lowering costs and increasing competitiveness. Maruti’s efforts at resolving the power problems of the suppliers in its vicinity appear to be one response to the problem. But what about its non-local supply chain? This question is addressed in Chapter 4, but the short answer is that Maruti is “localizing” its supply chain by encouraging its suppliers to locate within a radius of about 20–25 km of its assembly plant and become part of a “Maruti auto district.” Once they are located in its vicinity, Maruti can potentially hook them up to its own highly reliable power system. In fact, Maruti is using its ability to provide, and almost guarantee, adequate and good quality power as a key incentive to attract its suppliers to the area – and this “guarantee” of good electric power is one that no state government in India can match. As shown in Chapter 4, Maruti is using its innovative mini electric utility as a key component of its competitive strategy.

Conclusion Maruti’s gas turbines, and the power-sharing and wheeling arrangements with its component suppliers and HSEB result in a win–win situation for all players. The system provides Maruti with high-quality power, and the sharing mechanisms help it achieve high load factors and low generation costs. These arrangements also help Maruti reduce the unreliability in its supply chain and, thereby, improve its competitiveness. Its component suppliers get access to exceptionally high quality power and reliable service at prices that are similar to that from the public utility. HSEB gains access to additional electric power to supplement its capacity-starved grid. In contrast to a regular IPP project, HSEB has gained access to power without waiting for several years for the IPP to come on-line, with minimal negotiations or transaction costs, without incurring any capital expenditures, and without bearing any foreign exchange risk. HSEB and other SEBs would do well to lockin such sources of electric power with power purchase agreements similar to those that they would sign with IPPs. Indeed, the Maruti power-sharing arrangement represents a highly effective solution or model that policymakers and other firms can seek to emulate and replicate.

64


Appendix 2.1 Maruti’s power system – output, costs, sales, 1996–97 Apr. 1996 A B C D E F G H I J K L M N O P Q R

Maruti consumption – MWh 7 188 Sale to joint venture suppliers – MWh 1 020 Sale to other suppliers – MWh 848 Sale to HSEB – MWh 4 094 Actual generation (A;B;C;D) – MWh 13 151 Projected generation GT (219.30) – 20 844 MWh Standby diesel generator (actual) – MWh 915 Total projected generation (F;G) – MWh 21 759 Plant load factor (%) 60 Gas consumption – NM3 5 655 657 Gas charges – Rs/NM3 4.10 Expenditure on gas (J*K) – Rs ’000 23 207 Expenditure on diesel – Rs ’000 1 769 Gas;diesel expenditure (I;K) – Rs ’000 24 976 Fuel cost per unit (N/E) – Rs/kWh 1.90 Revenue from HSEB – Rs ’000 6 141 Revenue from suppliers – Rs ’000 6 072 Total sales revenue (P;Q) – Rs ’000 12 214 Projected peak load – MW Actual peak load – MW

Notes: (a) HSEB paid a price of Rs 1.5/kWh. (b) Suppliers paid a price of Rs 3.25/kWh. Source: Company data.

May 1996 11 138 1 163 967 5 079 18 348 28 718 672 29 390 62 7 876 519 3.68 29 003 1 312 30 315 1.65 7 619 6 923 14 542

June 1996 11 766 1 126 1 148 4 990 19 029 27 792

July 1996 12 677 1 267 1 198 5 363 20 505 28 718

96 3 27 888 28 721 68 71 8 484 507 8 964 003 3.47 3.44 29 434 30 826 210 19 29 644 30 845 1.56 1.50 7 484 8 045 7 389 8 012 14 873 16 056 40.7 38.1


Aug. 1996

Sep. 1996

Oct. 1996

Nov. 1996

12 165 1 033 1 298 5 445 19 942 28 718

10 877 1 151 1 006 5 144 18 179 27 792

8 483 1 100 939 6 204 16 726 25 013

8 535 1 105 1 022 10 189 20 851 27 792

25 28 743 69 8 677 606 3.46 30 039 86 30 125 1.51 8 168 7 575 15 743

5 27 797 65 8 328 346 3.48 28 956 18 28 974 1.59 7 716 7 010 14 727 45.7

46 25 059 67 7 596 755 3.71 28 154 118 28 272 1.69 9 306 6 627 5 933 35.0

Dec. 1996 9 045 1 103 975 14 021 25 144 28 718

Jan. 1997

Feb. 1997

8 977 1 133 1 052 3 954 15 117 27 792

8 518 1 041 854 12 664 22 658 25 939

Mar. 1997 10 408 1 046 986 13 064 25 583 28 718

793 990 2 002 1 187 1 187 28 585 29 709 29 794 27 126 29 905 73 85 51 84 86 8 870 391 10 484 715 3 100 316 9 320 198 10 398 583 3.03 2.94 2.87 2.78 2.69 31 600 35 443 14 819 31 965 38 504 1 911 2 185 26 482 2 439 2 827 33 511 37 628 41 301 34 404 41 330 1.61 1.50 2.73 1.52 1.62 15 284 21 031 6 228 19 946 20 576 6 912 6 755 7 103 6 158 6 605 22 196 27 786 13 331 26 103 27 181 46.7

45.0

66


Appendix 2.2 Maruti – financial analysis for 86 MW and Source: Company data Case I:

86 MW combined cycle power plant – 3 gas turbines and 1 steam turbine (Rs in crores) Year of capitalization 2000–01 1993–94 1995–96 1997–98

Description of item Capital cost of steam turbine Capital cost of gas turbine I and WHRB Capital cost of gas turbine II Capital cost of gas turbine III Grand total Capacity of the project Auxiliary consumption Capital cost of the project Completion period Plant life (years) Funding: debt to equity Debt Equity Long-term interest rate Short-term interest rate Return on equity Term of loan Moratorium on principal and IDC No. of installments per year O&M charges Depreciation Depreciation as per IT Act. Capitalized cost Discounting rate PLANT LOAD FACTOR

Capital cost 85.00 60.00 50.00 54.00

Capitalized cost 93.81 66.44 55.37 59.79 275.40

86 MW 9.5% for steam turbine and 1% for gas turbine. 249.00 crores 24 months for steam turbine and 18 months for gas turbine 15 70% 30% 16% 16% 16% 12 years 2 years 2 3% of capitalized cost and increasing 7.5% per annum 9.62% (up to 90% of the capitalized cost) 25% 275.40 12% 68.5%

(I) Drawndown schedule: Percentage of loan capital of gas turbines drawn IDC as a factor of total capital cost Percentage of loan capital of steam turbine drawn IDC as a factor of total capital cost

0–6 months 50.00% 1.40% 10.00% 0.28%

7–12 months 35.00% 3.78% 30.00% 1.40%

(II) Calculation of unit cost: YEAR

1997–98

Fixed charges 43.69 (a) Transportation 7.13 charges for natural gas to 31.12.02 (b) Depreciation 13.63 charges @9.62% (c) Interest on working 0.85 capital @16% (1 month fuel charges and 2 months of O&M expenses) (d) Interest on term 12.62 loan @16% (e) Return on equity 6.14 @16% (f) Add income tax 3.31 @8.62% (on equity portion)

1998–99

1999–00

2000–01

2001–2

2002–3

2003–4

53.62 7.34

51.62 7.56

62.79 7.79

73.68 8.02

62.48 6.20

53.13 0.00

17.47

17.47

21.98

26.49

20.48

20.48

1.10

1.11

1.16

1.22

1.24

1.27

15.60

13.37

16.60

19.55

16.17

12.99

7.87

7.87

9.91

11.95

11.95

11.95

4.24

4.24

5.34

6.44

6.44

6.44


60 MW generation plants

13–18 months 15.00% 5.55% 45.00% 3.50%

18–24 months

Total 10.73%

15.00% 5.18%

10.36% (Rs in crores)

2004–5

2005–6

2006–7

2007–8

2008–9

2009–10

2010–11

2011–12

2012–13

45.92 0.00

43.38 0.00

36.45 0.00

34.59 0.00

28.02 0.00

19.81 0.00

18.68 0.00

13.64 0.00

13.66 0.00

15.81

15.81

10.89

10.89

10.52

3.42

3.42

2.77

2.77

1.30

1.32

1.35

1.39

0.96

0.99

1.02

0.60

0.62

10.43

7.86

5.81

3.92

2.59

1.44

0.29

0.00

0.00

11.95

11.95

11.95

11.95

9.07

9.07

9.07

6.67

6.67

6.44

6.44

6.44

6.44

4.89

4.89

4.89

3.59

3.59

68


Case I cont’d YEAR

1997–98

1998–99

1999–00

2000–01

2001–2

2002–3

2003–4

75.31 68.46

75.83 68.46

77.79 68.46

80.00 68.46

80.87 68.46

81.80 68.46

Variable charges: 58.39 (a) Fuel charges natural 53.21 gas @ Rs 3.759/nm3 ) and HSD @ Rs 10.1166/litre (11 months gas and 1 month HSD) (b) O&M expenses 5.18 Total cost of generation (Rs in crores) Total no. of units generated (in crores kWh) Unit fixed cost (Rs/kWh) Unit variable cost (Rs/kWh) Unit cost (Rs/kWh) Discounting factor @12% Present value fixed cost (Rs/kWh) Present value variable cost (Rs/kWh) Present value total cost (Rs/kWh) Levellised fixed cost (15 years plant life) (Rs/kWh) Levellised variable cost (15 years plant life) (Rs/kWh) Levellised unit cost (15 years plant life) (Rs/kWh)

6.85

7.37

9.33

11.54

12.40

13.33

102.08

128.93

127.45

140.57

153.68

143.35

134.93

27.70

35.64

35.64

42.70

49.76

49.76

49.76

1.58 2.11

1.50 2.11

1.45 2.13

1.47 1.82

1.48 1.61

1.26 1.63

1.07 1.64

3.68 1.00 1.58

3.62 0.89 1.34

3.58 0.80 1.15

3.29 0.71 l.05

3.09 0.64 0.94

2.88 0.57 0.71

2.71 0.51 0.54

2.11

1.89

1.70

1.30

1.02

0.92

0.83

3.68

3.23

2.85

2.34

1.96

1.63

1.37

1.21

1.21

1.21

1.21

1.21

1.21

1.21

1.87

1.87

1.87

1.87

1.87

1.87

1.87

3.07

3.07

3.07

3.07

3.07

3.07

3.07

Case II: 60 MW Simple cycle power plant – 3 gas turbines

Description of item Capital cost of gas turbine I and WHRB Capital cost of gas turbine II Capital cost of gas turbine III Grand total Capacity of the project Auxiliary consumption Capital cost of the project Construction period Plant life (in years) Funding: debt to equity Debt Equity Long-term interest rate Short-term interest rate Return on equity Term of loan Moratorium on principal and IDC No. of installments per year O&M charges Depreciation Depreciation as per IT Act. Capitalized cost Discounting rate PLANT LOAD FACTOR

Year of capitalization 1993–94 1995–96 1997–98

(Rs in crores) Capital Capitalized cost cost 60.00 66.44 50.00 55.37 54.00 59.79 181.60

60 MW 1% for gas turbine. 164.00 crores 18 months for gas turbine 15 70% 30% 16% 16% 16% 12 years 2 years 2 3% of capitalized cost and increasing 7.5% per annum 9.62% (up to 90% of the capitalized cost) 25% 181.60 12% 68.5%

Tackling Power Problems 69 (Rs in crores) 2011–12 2012–13

2004–5

2005–6

2006–7

2007–8

2008–9

2009–10

2010–11

82.80 68.46

83.87 68.46

85.03 68.46

86.27 68.46

58.89 45.64

59.88 45.64

60.95 45.64

33.99 22.82

34.83 22.82

14.33

15.41

16.57

17.81

13.25

14.24

15.31

11.17

12.01

128.71

127.25

121.47

120.86

86.91

79.69

79.63

47.63

48.49

49.76

49.76

49.76

49.76

33.18

33.18

33.18

16.59

16.59

0.92 1.66

0.87 1.69

0.73 1.71

0.70 1.73

0.84 1.78

0.60 1.80

0.56 1.84

0.82 2.05

0.82 2.10

2.59 0.45 0.42

2.56 0.40 0.35

2.44 0.36 0.26

2.43 0.32 0.22

2.62 0.29 0.24

2.40 0.26 0.15

2.40 0.23 0.13

2.87 0.20 0.17

2.92 0.18 0.15

0.75

0.68

0.62

0.56

0.51

0.46

0.42

0.42

0.38

1.17

1.03

0.88

0.78

0.75

0.62

0.55

0.59

0.53

1.21

1.21

1.21

I.21

1.21

1.21

1.21

1.21

1.21

1.87

1.87

1.87

1.87

1.87

1.87

1.87

1.87

1.87

3.07

3.07

3.07

3.07

3.07

3.07

3.07

3.07

3.07

70

Infrastructure and Industrial Performance in Developing Countries

(I) Drawndown schedule: 0–6 months 50.00% 1.40%

Percentage of loan capitaI of gas turbines drawn IDC as a factor of total capital cost

7–12 months 35.00% 3.78%

(II) Calculation of unit cost: YEAR

1998–99

1999–00

2001–2

2002–3

2003–4

Fixed charges 43.69 (a) Transportation charges 7.13 for natural gas to 31.12.02 (b) Depreciation charges 13.63 (c) Interest on working 0.85 capital (1 month fuel charges and 2 months of O&M expenses) (d) Interest on term loan 12.62 (e) Return on equity @16% 6.14 (f) Income tax @8.62% 3.31

1997–98

53.62 7.34

51.62 7.56

49.63 7.79

47.65 8.02

37.60 6.20

29.40 0.00

17.47 1.10

17.47 1.11

17.47 1.12

17.47 1.14

11.46 1.16

11.46 1.18

15.60 7.87 4.24

13.37 7.87 4.24

11.14 7.87 4.24

8.91 7.87 4.24

6.68 7.87 4.24

4.65 7.87 4.24

Variable charges (a) Fuel charges natural gas @ Rs 3.759 nm3 and HSD @ Rs 10.1166/litre (11 months Gas and 1 month HSD) (b) O&M Expenses

75.31 68.46

75.83 68.46

76.38 68.46

76.98 68.46

77.61 68.46

78.30 68.46

58.39 53.21

2000–1

5.18

6.85

7.37

7.92

8.51

9.15

9.84

102.08

128.93

127.45

126.01

124.63

115.22

107.70

Total no. of units 27.70 generated (in crores kWh) Unit fixed cost (Rs/kWh) 1.58 Unit variable cost (Rs/kWh) 2.11 Unit total cost (Rs/kWh) 3.68 Discounting factor @12% 1.00 Present value fixed cost 1.58 (Rs/kWh) Present value variable cost 2.11 (Rs/kWh) Present value total cost 3.68 (Rs/kWh) Levellised fixed cost 1.06 (15 years plant life) (Rs/kWh) Levellised variable cost 2.19 (15 years plant life) (Rs/kWh) Levellised unit cost 3.25 (15 years plant life) (Rs/kWh)

35.64

35.64

35.64

35.64

35.64

35.64

1.50 2.11 3.62 0.89 1.34

1.45 2.13 3.58 0.80 1.15

1.39 2.14 3.54 0.71 0.99

1.34 2.16 3.50 0.64 0.85

1.05 2.18 3.23 0.57 0.60

0.82 2.20 3.02 0.51 0.42

1.89

1.70

1.53

1.37

1.24

1.11

3.23

2.85

2.52

2.22

1.83

1.53

1.06

1.06

1.06

1.06

1.06

1.06

2.19

2.19

2.19

2.19

2.19

2.19

3.25

3.25

3.25

3.25

3.25

3.25

Total cost of generation (Rs in crores)


13–18 months 15.00% 5.55%

Total 10.73% (Rs in crores)

2004–5

2005–6

2006–7

2007–8

2008–9

2009–10

2010–11

2011–12

23.33 0.00

21.93 0.00

16.14 0.00

15.43 0.00

10.00 0.00

10.02 0.00

10.03 0.00

5.26 0.00

2012–13 5.27 0.00

6.78 1.19

6.78 1.22

1.87 1.24

1.87 1.26

1.49 0.83

1.49 0.84

1.49 0.86

0.84 0.44

0.84 0.45

3.24 7.87 4.24

1.82 7.87 4.24

0.92 7.87 4.24

0.18 7.87 4.24

0.00 4.99 2.69

0.00 4.99 2.69

0.00 4.99 2.69

0.00 2.59 1.40

0.00 2.59 1.40

79.04 68.46

79.83 68.46

80.68 68.46

81.60 68.46

53.87 45.64

54.49 45.64

55.15 45.64

27.76 22.82

28.13 22.82

10.58

11.37

12.22

13.14

8.23

8.84

9.51

4.94

5.31

102.36

101.76

96.82

97.03

63.87

64.50

65.18

33.02

33.40

35.64

35.64

35.64

35.64

23.76

23.76

23.76

11.88

11.88

0.65 2.22 2.87 0.45 0.30

0.62 2.24 2.86 0.40 0.25

0.45 2.26 2.72 0.36 0.16

0.43 2.29 2.72 0.32 0.14

0.42 2.27 2.69 0.29 0.12

0.42 2.29 2.71 0.26 0.11

0.42 2.32 2.74 0.23 0.10

0.44 2.34 2.78 0.20 0.09

0.44 2.37 2.81 0.18 0.08

1.00

0.90

0.82

0.74

0.65

0.59

0.53

0.48

0.43

1.30

1.15

0.98

0.88

0.77

0.70

0.63

0.57

0.51

1.06

1.06

1.06

1.06

1.06

1.06

1.06

1.06

1.06

2.19

2.19

2.19

2.19

2.19

2.19

2.19

2.19

2.19

3.25

3.25

3.25

3.25

3.25

3.25

3.25

3.25

3.25

72


(III) Loan Repayment: Debt Servicing (Repayment starts after 6 months of commercial operation and paid in 19 semi annual installments) (A) Opening Balance (Considering 7&2 66.71 105.63 half yearly installments paid for GT-I & II respectively) at the start of financial year 1997–98 (B) Half yearly 4.67 4.67 installment paid Half yearly periods 1 2 (a) Gas Turbine-I 2.55 2.55 (7 installments already paid) (b) Gas Turbine-II 2.12 2.12 (2 installments already paid) (c) Gas Turbine-III (C) Interest on 5.34 7.29 term loan (D) Closing balance 62.03 100.95 at the end of financial year YEAR (IV) Depreciation as per the details below: (@9.62% upto 90% of capitalised cost) (a) Gas Turbine-I (4 yearly dep. charges claimed) (b) Gas Turbine-II (1;1/2 yearly dep. charges claimed) (c) Gas Turbine-III

1997–98

100.95

93.99

87.02

1998–99

80.05

1999–00

73.08

66.11

59.15

2000–01

52.18

2001–02

6.97

6.97

6.97

6.97

6.97

6.97

6.97

6.97

3 2.55

4 2.55

5 2.55

6 2.55

7 2.55

8 2.55

9 2.55

10 2.55

2.12

2.12

2.12

2.12

2.12

2.12

2.12

2.12

2.29 8.08

2.29 7.52

2.29 6.96

2.29 6.40

2.29 5.85

2.29 5.29

2.29 4.73

2.29 4.17

93.99

87.02

80.05

73.08

66.11

59.15

52.18

45.21

1998–99

1999–00

2000–01

2001–02

2002–03

2003–04

13.63

17.47

17.47

17.47

17.47

11.46

11.46

6.39

6.39

6.39

6.39

6.39

0.38

0.38

5.33

5.33

5.33

5.33

5.33

5.33

5.33

1.92

5.75

5.75

5.75

5.75

5.75

5.75

(V) O&M Charges as per following details:

5.18

6.85

7.37

7.92

8.51

9.15

9.84

(a) Gas Turbine-I (commissioned in 1993–94) (b) Gas Turbine-II (commissioned in 1995–96) (c) Gas Turbine-III (commissioning 1997–98)

2.66

2.86

3.08

3.31

3.55

3.82

4.11

1.92

2.06

2.22

2.38

2.56

2.76

2.96

0.60

1.93

2.07

2.23

2.40

2.58

2.77

(VI) Units Generated by turbines:

27.70

35.64

35.64

35.64

35.64

35.64

35.64

(a) Gas Turbine-I (commissioned in 1993–94) (b) Gas Turbine-II (commissioned in 1995–96) c) Gas Turbine-III (commissioning 1997–98)

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

3.94

11.88

11.88

11.88

11.88

11.88

11.88


45.21

38.24

31.27

2002–03

26.85

2003–04 4.42

18.02

2004–05

9.18

2005–06

2006–07

2.12

2.12

2.12

2.12

2.12

2.12

2.29 3.62

2.29 3.06

2.29 2.50

2.29 2.15

2.29 1.79

2.29 1.44

2.29 1.09

2.29 0.73

2.29 0.55

2.29 0.37

2.29 0.18

0.00 0.00

38.24

31.27

26.85

22.43

18.02

13.60

9.18

6.88

4.59

2.29

0.00

0.00

18

2.29

2007–08

2.12

17

2.29

2.29

12 2.55

16

4.42

4.59

11 2.55

15

4.42

6.88

6.97

14

4.42

13.60

6.97

13

4.42

22.43

19

2.29 20

2.29 21

2011–12

0.00 22

2004–05

2005–06

2006–07

2007–08

2008–09

2009–10

2010–11

2012–13

6.78

6.78

1.87

1.87

1.49

1.49

1.49

0.84

0.84

0.38

0.38

0.38

0.38

0.00

0.00

0.00

0.00

0.00

0.65

0.65

0.65

0.65

0.65

0.65

0.65

0.00

0.00

5.75

5.75

0.84

0.84

0.84

0.84

0.84

0.84

0.84

10.58

11.37

12.22

13.14

8.23

8.84

9.51

4.94

5.31

4.42

4.75

5.10

5.49

0.00

0.00

0.00

0.00

0.00

3.18

3.42

3.68

3.96

4.25

4.57

4.91

0.00

0.00

2.98

3.20

3.44

3.70

3.97

4.27

4.59

4.94

5.31

35.64

35.64

35.64

35.64

23.76

23.76

23.76

11.88

11.88

11.88

11.88

11.88

11.88

0.00

0.00

0.00

0.00

0.00

11.88

11.88

11.88

11.88

11.88

11.88

11.88

0.00

0.00

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

11.88

74


Appendix 2.3 Equipment vendor’s quote for a 4 MW power plant for Arvind Mills I

UNITS GENERATED Units generated/annum (assuming 7500 hrs operation in one year and 90% station load)

kWh

COST OF FUEL Fuel consumption at alt. terminals at site 0% tolerance and fuel of CV10 200 kcal/kg Total fuel consumed/annum Cost of fuel/annum (@6.8/per kg) Fuel cost per unit

GMS/kWh M tons Rs Rs/kWh

206.00 5562.00 378.00 1.40

III (a) (b) (c)

COST OF LUBE OIL Specific lube oil consumption Lube oil consumed/annum Cost of lube oil/annum (@60/per kg) Lube oil cost per unit

GMS/kWh M ton Rs Rs/kWh

1.00 27.00 16.20 0.06

IV

COST OF MAINTENANCE Maintenance cost per unit (includes cost of spares and manpower)

II (a) (b) (c)

270.00

Rs/kWh

0.20

Rs/kWh Rs/kWh

1.40 0.06

Rs/kWh Rs/kWh

0.20 1.66

Summary of direct cost of generation (a) (b) (c)

Fuel cost per unit Lube oil cost per unit Maintenance cost per unit (incl. cost of spares and labor) Direct cost per unit generated (a;b;c)

Note: Generation cost can be reduced by incorporating a waste heat recovery boiler. Source: Company data.


A

E F

Diesel power plant Cost with nil. import duty (100% EOU) at exchange rate (1FIM:7.2 INR) Transportation, insurance Subtotal Indigenous scope of supply (estimated costs) Mechanical auxiliaries/systems Storage tanks, piping, valves, air and exhaust ducting, chimney, heat tracing and insulation, structurals, cooling system Electrical auxiliaries/systems HV switchgear, aux. transformer, PCC, MCC, earthing, lighting and lightning protection, DC system, cables and cable trays Station support system Ventilation, air-conditioning, safety equipment, OH crane Services Detailed engineering, supervision of installation, training, etc. Civil works Erection and commissioning Subtotal Total cost 2% Contingency Total overall cost Cost/MW Average cost of interest @18% and cost of dep. @6% Fixed cost of generation (per unit) Direct cost of generation (per unit)

G

Total cost of generation (per unit)

B (1)

(2)

(3)

(4)

(5) (6) C

D

(000s) 70 000

1 000 71 000 12 000

10 000

4 000

3 000

15 000 5 000 49 000 120 000 2 400 122 400 30 600 29 400 Rs 1.09 Rs 1.66 Rs 2.75

Appendix 2.4 Costs of self-generation in Indonesia and Nigeria Table E 10 Average cost of own electricity power generation by size of own electricity production, Nigeria Own electricity production (1000 kWh)

No. of firms (N)

Mean fixed cost(a) Naira per kWh

Mean variable cost(b) Naira per kWh

1–4* 5–9* 10–19 20–49 50–99 100–199 200–499 500–999 1000–1999* 2000 and over* All

4 5 12 22 32 22 34 21 6 6 164

30.930 12.028 2.860 1.355 1.156 0.561 0.452 0.348 0.191 0.148 1.964

1.833 5.511 1.336 0.865 0.946 0.481 0.283 0.344 0.094 0.172 0.788

Total cost Naira per kWh 32.763 17.539 4.196 2.220 2.102 1.041 0.735 0.692 0.285 0.320 2.752

Total cost US$ per kWh 8.190 4.385 1.050 0.555 0.525 0.260 0.183 0.173 0.073 0.080 0.688

Notes: (a) Annualized capital value of generators and accessories. (b) Includes fuel, maintenance, parts, and labor. The authors also note that: * Means may not be representative because of the small number of observations in the cell; the exchange rate was 4 Naira to US$1 in 1987; the annnualized values of generators were calculated using an interest rate of 10 percent per year. Source: Reproduced from Table E10 in Lee, Anas and Oh (1996) and Table 6 in Lee, Anas and Oh (1999).

Table F 10 Average cost of own electricity power generation by size of own electricity production, Indonesia Own electricity production (1000 kWh)

No. of firms (N)

1–4 5–9 10–19 20–49 50–99 100–199 200–499* 500–999 1000–1999* 2000 and over* All

48 26 21 26 15 19 4 10 3 10 182

Mean fixed cost(a) Rupiah per kWh 4611.234 4598.524 3557.675 877.246 3580.996 411.714 372.451 47.547 16.809 74.133 2762.170

Mean fixed cost(b) Rupiah per kWh 3479.875 1740.849 1003.054 587.223 796.855 587.115 715.709 91.141 97.729 91.015 1520.404

Total cost Rupiah per kWh 8091.109 6339.373 4560.729 1464.469 4377.850 998.829 1088.160 138.687 114.538 165.148 4282.574

Total cost US$ per kWh 4.046 3.170 2.280 0.732 2.189 0.499 0.544 0.069 0.057 0.083 2.141

Notes: (a) Annualized capital value of generators and accessories. (b) Includes fuel, maintenance, parts, and labor. The authors also note that: * Means may not be representative because of the small number of observations in the cell; the exchange rate was 2000 Rupiah to US$1 in 1992; the annualized values of generators were calculated using an interest rate of 10 percent per year. Source: Reproduced from Table E10 in Lee, Anas and Oh (1996) and Table 6 in Lee, Anas and Oh (1999).

3 Effects of Poor Transportation on Industrial Competitiveness

Freight transportation systems in many developing countries are highly inadequate and inefficient. First, the physical infrastructure – ports, airports, and road and rail networks – is capacity constrained and poorly maintained. Second, the freight services provided by private and public sector operators tend to be limited in range, poor in quality, and often technologically obsolete. Consequently, industrial firms in these countries operate under a handicap relative to their competitors in advanced industrialized countries. However, neither the magnitude nor nature of this handicap is well understood (see, e.g., Diamond and Spence 1989; World Bank 1994a; Anas, Lee and Murray 1996). To bridge this gap in our understanding, this chapter empirically examines the costs imposed on auto firms by the poor freight transportation system in India and ascertains which of these costs the firms themselves find to be more debilitating. From the perspective of the development practitioner, the link between transportation and industrial competitiveness is relatively straightforward – poor transport systems raise the unit costs of freight, which means that industrial users have to pay more. By contrast, the argument in this chapter is that the cost of freight is only one of several direct costs that poor transportation systems impose. An inadequate transportation system also raises the damages incurred in transit, the total inventories that firms have to hold, and the ordering and overhead costs associated with managing material flows. Put together, these direct costs constitute the “total logistics cost” borne by a firm. The total logistics cost equation, then, offers a more comprehensive approach for calculating the direct costs of poor transportation. Getting a better quantification of the direct/logistics costs does not, however, contribute to an understanding of how assemblers perceive 77

78


the problem and the relative importance they attach to various cost components. To capture such information, we use interview data combined with an inductive analysis of the transport solutions and competitive strategies that the auto assemblers have devised. This inductive analysis of user-responses – presented below and in Chapter 4 – shows that poor transportation creates major problems by introducing unreliability and inefficiency in the assemblers’ supply chains. Poor transport systems thus hurt competitiveness not only by raising direct costs but also, and more significantly, by creating external diseconomies that adversely affect the efficiency of supply chains and entire networks of firms. This chapter develops an analytical approach for understanding the links between transportation and competitiveness, and then uses it to analyze the Maruti and Ford cases. It demonstrates the relative importance of different transportation-related costs by (1) assessing the magnitude of various components of Maruti’s total logistics cost, and (2) analyzing Ford’s logistics plan for its Chennai plant. This chapter also will show how fast and reliable transportation is a key ingredient in making the just-in-time (JIT) and lean production system work. While it is both intuitive and logical that JIT delivery systems are likely to require good transportation, little, if any, of the vast literature on the topic shows how and to what extent, if at all, the transportation system affects the implementation of and gains from JIT/lean production.

Transportation and competitiveness: insights from the literature The discussion in this section (1) outlines how the development literature perceives the transportation problem and the ways in which it might affect firms; (2) examines the literature on industrial competitiveness and lean production and shows how mechanistic issues, like access to physical infrastructure, remain peripheral; and (3) combines insights from these two bodies of literature with those from the logistics literature to show how transportation systems may, in theory, play a role in determining competitiveness. Development practitioners’ view of India’s transportation problem According to the World Bank (1995a), the Indian transport system is inadequate, and the problem is serious enough that it may constrain

Effects of Poor Transportation 79

economic growth. The Bank’s analysis can be summarized as follows. Road transport accounts for 60 percent of India’s inter-city freight traffic (ton-km). This represents a significant shift from the 1950s, when Indian Railways carried most of the nation’s freight. The railways lost their share of the market, analysts argue, because they shifted to a passenger dominant operation, because investment did not keep pace with either the demand for freight or passenger service, and because government monopoly meant poor service. As a result, road transport has emerged as the dominant mode for both inter-city freight and passenger traffic. However, the increasing demand for road transport also appears to exceed supply, and the country’s road and highway networks are considered to be highly inadequate. Road density is insufficient given India’s large size, and most “highways” are merely single-lane undivided roads that are poorly maintained. These shortfalls manifest themselves in increasing congestion, long and highly variable travel time for a given distance, and high vehicle operating costs. The Bank’s conclusion, then, is that “[India has] … a transport system that is currently saturated on the main road and rail links and the possibility arises that the capacity constraint of the transportation system may (together with that of the power sector) serve as a constraint on overall economic growth” (World Bank 1995a). The India Infrastructure Report (1996) concurs with the above analysis and estimates the magnitude of losses. This report notes that due to inadequate road networks commercial vehicles can travel an average of only 200 –250 km per day, as compared to 500–600 km per day in industrialized countries. The problem is compounded by congested sections, existence of railway level crossings, and collection posts for the “octroi” tax, all of which lead to abnormal delays and high fuel costs. The economic losses due to bad conditions on the main roads are estimated to be on the order of Rs 200–300 billion (or US$7–9 billion) per annum. Hence, “inadequate road networks have led to higher transportation costs which have … severely eroded international competitiveness of the Indian economy” (vol. 1, p. 39). Overall, the development literature suggests that poor transportation systems result in slow movement of freight and high unit costs. Perhaps because it is hard to estimate the value of time, this literature tends to focus more on freight costs as the major or proxy indicator for the costs/benefits of a given transportation system.69 From this perspective, the key problem with badly maintained and inadequate road networks is that they raise the cost of freight by (a) increasing the cost of operations and maintenance due to greater wear and tear and higher

80


fuel consumption; and (b) lengthening transit time, which, in turn, means that both labor (driver) and capital (the truck) are deployed for a longer period of time to complete a given delivery. This literature thus suggests that improvements in the transportation system would result in lower freight costs and greater competitiveness. By extension, however, in firms and industries for which freight costs constitute a small proportion of total expenditure, an increase in those costs due to poor transportation networks is unlikely to have a significant impact on competitiveness. One could speculate that freight costs are unlikely to constitute a major expense in the auto industry – relative to, say, labor and materials – and, therefore, that the transportation system may not be a variable that influences relative performance or competitiveness in this industry. The vast literature on competitiveness in the auto industry appears to support such a speculation indirectly – it shows little concern over issues such as freight costs and the adequacy of transportation infrastructure, and focuses on entirely different variables to explain differences in performance among firms. The value chain as a determinant of competitiveness The literature on industrial competitiveness emphasizes that efficient and well-managed value chains are critical in determining competitive success in global industry (e.g., Porter 1985, 1990; Gereffi and Korzeniewicz 1994). According to Porter (1990), “a firm’s value chain is an interdependent system or network of activities, connected by linkages. Linkages occur when the way in which one activity is performed affects the costs or effectiveness of other activities.” The value chain or “commodity chain” is comprised of the sequential stages of input acquisition, manufacturing, distribution, marketing, and consumption (Gereffi and Korzeniewicz 1994). The better the firm organizes each of these stages and the more value it creates not just in each activity but in the chain as a whole, the better its performance and competitiveness. As long as manufacturing firms have high levels of vertical integration, enhancing value in the chain is largely a task of improving performance of, and coordination between, various departments within a firm. As manufacturing firms and industries move away from vertical integration, however, and rely increasingly on other sources for key inputs, materials, and parts, more of the value chain begins to become “external.” In a production system with low levels of vertical integration, performance and competitiveness depend not only on the efficiency of a single firm but also on other firms in its network. The task


of adding value, hence, involves improving the efficiency of the external supply and distribution chains. The attempts by a firm to better organize its supply and distribution chains can be then seen as an effort to create – and then internalize – additional value in its external value chains.70 Viewed as such, the lean production system and valuechain management represent some ways in which firms create and internalize external economies, increase the value-added in their production network as a whole and, thereby, increase competitiveness.71 The importance of supply chains in the auto industry In the automobile industry, the efficiency and effectiveness of the supply chain – the first stage of the value/commodity chain in which the inputs are acquired and organized – is particularly critical for good performance. This is because the industry is characterized by complex supply chains where hundreds of suppliers provide the thousands of parts required for a single vehicle. Not only does the supply chain account for the largest expenditure, but its organization also represents one of the more complex tasks in producing a vehicle. According to Womack, Jones and Roos (1990) , “… a typical model is made up of more than 10,000 parts … organizing this enormous task [of design, production, and supply of these parts] is probably the greatest challenge in manufacturing a vehicle.” An analysis of cost structures of Indian auto assemblers and suppliers shows that the supply chain – or the components and raw materials purchased from suppliers – represents, by far, the single largest expense category. Figure 3.1 shows that the supply chain accounts for about 62–78 percent of the total expenditures of five major auto assemblers. Cost data from nine supplier firms show similar trends – the supply chains of these firms account for 49–84 percent of their total expenditures (Figure 3.2). For each of these firms, most of the value addition occurs outside their own plants and in their respective supply chains. Thus, the relative performance of auto assemblers and that of different component suppliers depends on the collective capability of their production networks, in particular, on the efficiency of their supply chains. Lean production, supply-chains, and just-in-time delivery Nowhere, perhaps, is the critical nature of the supply chain more evident than in the lean production paradigm. Lean production was pioneered in the auto industry by the Toyota Motor Company in the 1960s, and has been adopted widely by firms in diverse industries. Indeed, some proponents maintain that all enterprises should aim to

82


100

% of total expenditure

90 80 70 60 50

76.2%

61.7%

78.3%

40

67.3%

76.7%

30 20

Other expenses Depreciation Financial expenses Freight, forwarding and pkging Stores and spares Materials and components Wages, salaries, benefits Power and fuel

10 da

o

on

lc

er oH H

Te

d an yl ALe

st du in H

M

ar ut

i

an

0

Figure 3.1 Assemblers’ structure of costs – salience of the supply chain

100 90 80 70 60 50 74% 84% 57% 49% 49% 77% 71% 60% 40 55% 30 20 10 0

Other expenses Depreciation Financial expenses Freight, forwarding Stores and spares Materials and components Wages and benefits Power and fuel

M un ja

lS M how ar a Bh k A ut ar o So at S na ea t Lu Ste s m er ax in (L g i g C lu hts R tch ) an Au e t R an -Ma o Su dr e as nd Br a ra m ke Br Ln ak g e Ln g

% of total expenditure

Source: Compiled from company annual reports 1996–97

Figure 3.2 Suppliers’ structure of costs – again, the supply chain is key Source: Compiled from company annual reports 1996–97


be lean (Womack and Jones 1994). The section below outlines key features of the lean production paradigm, discusses why supply chains are important in this system, and starts to show why infrastructure may play a role in its implementation. Within the auto industry, a lean production system is considered to be the hallmark of – and almost a minimum standard for – a globally competitive auto firm. Lean production is considered necessary because it offers a proven means by which firms can cut costs and improve performance dramatically. According to Womack and Jones (1994), the lean system is one where: By eliminating unnecessary steps, aligning all steps in … a continuous flow, recombining labor into cross-function teams … and continually striving for improvement, companies can develop, produce, and distribute products with half or less of the human effort, space, tools, time and overall expense. (emphasis in original) The implementation of lean production involves three managementled changes: transformation of design, restructuring of assemblersupplier relations, and reorganization of production along the lines of just-in-time ( JIT) delivery and total quality management (Humphrey 1995). However, JIT delivery and low inventories – the famous Japanese kanban system – lie at the heart of lean production. Indeed, for Womack and Jones (1994), “relatively high inventories [constitute] a cardinal sin in lean production.” The gradual removal of the inventory “safety net” or buffer forces managers to identify and eliminate the root causes of recurring problems upstream and be more flexible in responding to demand fluctuations downstream.72 These efforts focus attention on improving the quality of inputs, keeping tight control over the production process, reducing lead and cycle times at every stage, reducing lot sizes and set-up times, and shortening product development cycles (Levy 1997). The result is continuous improvement in quality, productivity, and responsiveness. In other words, low inventories are not merely a means for lowering the financial or space costs associated with stock. Rather, low inventories are key drivers of the entire lean system and the associated gains in quality and competitiveness. Many lean firms require suppliers to deliver several times a day, and the deliveries tend to be tightly scheduled. In the leanest firms, such as Toyota, deliveries may be required to arrive within a narrow two-hour window. Parts are delivered directly to the assembly line to be fitted

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into the vehicle – they are neither tested nor warehoused at the assembly plant. This system requires that the suppliers, rather than the assembler, do all of the necessary pre-testing and certify that the parts are defect-free before they are shipped. Because there are minimal buffer inventories at the assembly plant, a few hours’ delay in delivery can stop the assembly line, and the costs of such a delay are prohibitive. This system is incredibly fragile, particularly because in a truly lean system every firm in the network is lean. The lean paradigm thus requires all participants and systems to be synchronized like clockwork and to perform unfailingly. Transportation systems: the missing variable in the competitiveness literature In the literature, inventory levels are key indicators of the extent to which a firm has implemented lean production. The lean production literature finds little evidence that large differences in inventory among firms can be explained by differences in distance between the assembly plant and component suppliers (and, by extension, the relative efficiency of transport systems in a country). Womack, Jones and Roos (1990) found, for example, that although the NUMMI assembly plant in California obtained its components from suppliers located 5000 miles across the Pacific, it was able to run with a two-day supply of parts. NUMMI’s inventory was higher than Toyota’s Takaoka plant in Japan, which obtained its components from suppliers located five or ten miles away, but it was significantly lower than the two weeks of inventory at GM’s plant at Framingham, Massachusetts. Further, these authors found that: the best-performing companies in Japan run the best-performing transplants in North America, suggesting that most of the variation observed is due to differences in management. (p. 87) At the same time, the best American-owned plants in North America show that lean production can be implemented fully by western companies, and the best plants in developing countries show that lean production can be introduced anywhere in the world. (p. 88) Overall, Womack et al. (1990) argue that the only significant difference between lean and non-lean firms is management attitude, and the key variables are mostly internal. In other words, external variables, such as distance between firms and the transportation system, have little role to play (also see Lieberman et al. 1995).


Combining insights from different strands of literature One could think of at least two reasons why the lean production literature fails to identify transportation or, alternatively, proximity as a key ingredient for successful implementation of JIT systems. First, the experience on lean production arises largely from advanced industrialized countries – Japan and, subsequently, the United States and various European countries – which tend to have excellent transport infrastructure (road, rail, air, and sea).73 Second, much of this research is aimed at advising management on how to improve performance, and variables that are entirely external to management control tend to stay peripheral to the discussion. What is surprising, however, is that the literature on transportation in developing countries – despite its efforts to show the critical nature of, and economic benefits associated with, additional transport investments – also fails to note that good transportation may be a key ingredient for efficient supply-chains and lean production, which, in turn, are deemed necessary for industrial competitiveness. This is, perhaps, because the (World Bank-led) development literature focuses more on calculating the extent of supply shortages and on removing supply-side constraints than on further understanding the nature of demand. On the demand side, this literature suggests only that poor transportation systems can raise the freight costs for users and that movement of freight is likely to be slow. By contrast, the literature on logistics begins to provide connections between the nature of industrial demand for freight and the supply of transportation infrastructure and services. Logistics is defined as the process of strategically managing the movement and storage of materials, parts, and finished inventory from suppliers, between enterprise facilities and to customers. Total logistics costs include freight expenditures and the cost of holding inventories and managing material flows. The transportation and logistics literature argues that these costs are often highly significant, and minimizing total logistics costs is an important goal that can lead not only to improved profitability but also to greater responsiveness and better customer service and, therefore, to increased competitiveness of firms. The task of minimizing logistics costs becomes even more important in the lean production system because it drives the changes that result in continuous (rather than a one time) improvement in costs, quality, and responsiveness. And if transportation systems directly affect logistics costs, then, we can begin to see the linkages between competitiveness and transportation infrastructure. We can also begin to see why the transportation system may have a significant impact on performance

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even in industries for which freight costs, in themselves, do not constitute a significant proportion of expenditure. In summary, reliable and just-in-time deliveries lie at the heart of the lean production system that is considered to be necessary for competitiveness, especially in the auto industry. The literature does not provide any clues as to whether or to what extent transportation can serve as an obstacle for industrial firms attempting to implement lean production. The development literature suggests that poor transportation systems can raise freight costs for industry. If we use this perspective, transportation constraints are unlikely to be much of a problem, at least for firms and industries for which freight expenditures constitute a small proportion of total costs. By contrast, the logistics literature begins to suggest why (and, indirectly, to what extent) the transportation system may be critical for industrial costs and competitiveness – poor transportation can raise total logistics cost, which includes freight expenditures and the cost of holding inventories and managing material flows. In other words, the logistics literature suggests that poor transportation systems may serve as an obstacle to minimizing total logistics costs and, hence, to the implementation of lean production. The total logistics cost equation as an analytical approach The concept of total logistics cost (TLC) offers a better understanding of the various direct costs associated with the transportation system. Specifically, Total logistics cost:freight cost;damages;inventory ;ordering/overhead costs;packaging As noted earlier, the development literature tends to examine the first two variables (freight costs and damages) and the lean production literature focuses only on the third (inventory costs). The main advantages of this TLC equation are that it: (a) brings the variables together, which permits a more comprehensive calculation of the direct costs and financial outlays; (b) allows an examination of the relative magnitude of the different variables; and (c) captures the net effect of a strategy – for example, increasing the frequency of deliveries may lower the inventory costs but raise the freight costs and perhaps the ordering and overhead costs. Although the above equation is more comprehensive, it still captures only part of the role that transportation systems play in shaping the costs and competitiveness of firms. First, firms can and do devise ways to reduce their total costs. In such a situation, the total logistics cost


will be low, and this can be interpreted erroneously to mean that the firm is less vulnerable to poor infrastructure or that the poor transportation system does not impose high costs. Second, the TLC equation does not provide any insights into how the assemblers perceive the transportation problem and the costs that it imposes – for this, we need qualitative data such as interviews and an analysis of transport solutions that the assemblers have adopted. Third, and perhaps more important, the TLC equation for a specific firm captures only the direct costs, not the indirect costs or external diseconomies created by poor transportation. To understand the external diseconomies, it is critical to examine the ways in which poor transportation affects the efficiency and reliability of the supply chain. One way to deal with this problem would be to examine total logistics costs for the supply chain as a whole, but the quantification would still need to be supported by qualitative analyses. For the rest of this chapter, we will look at empirical – quantitative as well as qualitative – data to ascertain how the poor transportation system in India affects auto firms and their supply chains. The first case study is of Maruti (section 3.2). It examines the assembler’s demand for transportation and estimates the freight and damage costs. It, then, discusses how, why, and to what extent the transportation system affects inventory levels at Maruti. Although it attempts to quantify various components of Maruti’s logistics costs, the main purpose of the case discussion is to highlight the mechanisms through which transportation systems affect costs and competitiveness. This analysis reveals that a surprisingly high proportion of Maruti’s quantifiable inventory costs are associated with its international supply chain. The second case study is of the Ford Motor Company and focuses on the domestic supply chain (section 3.3). It examines Ford’s logistics plan for enhancing the delivery performance of its domestic supply chain and how the assembler perceives the transportation problem. We, then, look at inventory data from a set of supplier firms to ascertain whether the findings at Maruti and Ford apply to other auto firms.

Case study: Maruti’s logistics costs Maruti’s supply and distribution chains and its transportation demand Purchase of raw materials and components is the single largest expense incurred by auto assemblers and their component suppliers in India (Figures 3.1 and 3.2). At Maruti – one of the least vertically integrated

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auto firms in India – components and raw materials account for 78 percent of total expenditure, or about 71 percent of annual sales revenue.74 In 1996–97, the value of these purchases amounted to US$1.2 billion, of which US$794 million (61.5 percent) was procured domestically. Most of this firm’s imported purchases – largely steel and components – originate in Japan. Almost all of the imported components are supplied in the form of completely-knocked-down or semi-knocked-down (CKD/ SKD) kits by Suzuki, which has a 50 percent equity stake in Maruti. Suzuki ships most of the components from a port near its major plant in Hammatsu through a reputable Japanese company. For its domestic purchases, Maruti uses 400 major suppliers located in the northern, western, and southern regions of the country; the most distant suppliers are almost 2500 km away. Maruti relies almost entirely on private truckers for its inbound freight from domestic suppliers and ports. Organizing the transportation and distribution of finished vehicles (outbound freight) is an extensive but less complex task. In 1996–97, Maruti shipped almost 340 000 finished vehicles, of which 35 000 (10.3 percent) were exported. On average, the assembler dispatched 1170 vehicles – worth an estimated US$5.7 million – per working day.75 Maruti’s export vehicles are transported about 1500 km by train from a railway station near its assembly plant to a station near the Nava Sheva Port in Maharashtra. For its domestic distribution, which accounts for 90 percent of total vehicle sales and sales revenue, Maruti relies on private trucking companies. In summary, Maruti has complicated supply and distribution chains, which involve the movement of inputs and finished goods of significant value as well as volume. The efficiency of the supply chain is important because it represents 78 percent of expenditure (71 percent of sales revenue). For the inputs that it imports, Maruti relies on ocean freight, which is a relatively slow mode of transportation. For almost all of its domestic freight, the assembler relies on the trucking industry and thus on the country’s inadequate road network. Given this scenario, we next examine the magnitude of Maruti’s freight logistics costs and the extent to which these are affected by transportation mode and system. Effect of the transportation system on logistics costs Maruti – and industrial firms in general – bear five kinds of logistics costs that are directly related to transportation systems and their efficiency (a) freight expenditures, that is, the direct cost or price of moving a shipment by a given transportation mode (road, rail, sea, air);


(b) damages and losses incurred during shipment; (c) cost of carrying inventories – at origin, in transit, at destination – as well as the costs incurred due to a “stock out” that may occur if a shipment is late; (d) ordering and overhead costs associated with managing material flows; and (e) packaging costs. Taken together, these costs comprise the total logistics cost equation set forth earlier; their relative magnitudes are discussed below. Note that, for simplicity, “ordering and overhead costs” and “packaging costs” are not discussed here. Further, only basic inventory carrying costs, as opposed to such related costs as warehousing and the use of extra land or space, are addressed. Freight expenditures Maruti’s freight bill accounts for a significant proportion of its sales revenues. For fiscal year 1996–97, Maruti spent US$45 million on outbound freight, that is, on the transportation and distribution of finished vehicles to domestic dealers and overseas. The cost of inbound freight – the transport of components and raw materials purchased in India and abroad – is harder to calculate because it is included in the delivered price of these items; disaggregated data on freight as a proportion of these prices or total purchases are not available. According to a purchase manager at Maruti, a conservative estimate for the total cost of inbound freight in 1996–97 would be US$25 million. In percentage terms, in 1996–97 outbound freight accounted for 2.7 percent of total sales revenue and inbound freight for at least an additional 1.5 percent. By comparison, Maruti’s wage bill for that year, including benefits, accounted for 2.0 percent of total sales revenue.76 Cost of goods damaged in transit Although data for total damages incurred during transit (inbound and outbound) are not available, data on outbound freight (finished vehicles) indicate the type and magnitude of losses. For fiscal year 1994–95, a total of 724 vehicles were damaged to the extent that they were not fit for sale. Of these, 560 vehicles were damaged beyond repair and had to be dismantled. The other 164 vehicles were returned to the factory for repairs and subsequently assigned to alternative uses within the plant (e.g., for testing purposes or in-plant transportation). To fix damages incurred during transit, an additional 118 vehicles needed to be repaired prior to sale. A relatively conservative estimate of the aggregate losses in 1994–95 on finished vehicles in transit is US$1.3 million (Bose 1995, see Appendix 3.1).77 This represents the net costs after the

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salvage value of usable components is deducted.78 An alternative way to estimate the loss is to calculate the forgone sales revenue. The number of vehicles unfit for sale due to in-transit damages represented 0.35 percent of total vehicles sold in 1994–95, and the value of these forgone sales amounted to an estimated US$3.1 million. How the transportation system affects inventory levels The literature on logistics indicates that both the length and predictability of travel or transit time affect inventory levels. Transit time, in turn, depends on distance, the speed of a chosen mode (air against sea against road freight), the quantity and quality of transport infrastructure, and the level of service provided by the shipping/freight company. Analysis of qualitative and quantitative data from Maruti indicate that there are two mechanisms by which infrastructure and transit time directly affect its supply-chain inventory. First, poor infrastructure increases total transit time, which translates into higher in-transit or “pipeline” inventories. That is, goods that have been dispatched by the supplier take longer to reach the assembly plant, and capital is tied up for more time. To estimate the inventory penalty that the inadequate road network imposes, we need to ascertain how much longer the transit time is for road freight in India relative to countries with adequate highway infrastructure. Table 3.1 represents the average travel time

Table 3.1 Maruti’s distribution of finished vehicles: cost of freight and travel time Destination

Distance Freight cost (km) (US$ per car)

Transit time (days)

Trailer Truck Trailer Truck Nava Sheva Port Bombay Chennai Bangalore Calcutta Delhi Jaipur Ludhiana

1466 1436 2502 2168 1515 30 280 392

101 99 173 150 120 5 22 29

94 92 161 139 112 5 20 27

6 6 9 8 8 1 1 1

5 5 7 6 7 1 1 1

Turnaround time (days) Trailer

Truck

14 14 21 19 19 1 3 3

12 12 17 14 17 1 3 3

Note: Prices are for new trailers and trucks (i.e. 1995 models or later). These are just marginally higher than the prices offered for older equipment. Source: Company data, 1996.


index developed by Maruti, based on actual truck arrival and departure data. For example, it takes an average of seven days to transport material by truck 2500 km from Maruti’s plant near Delhi to Chennai; the round trip takes an average of 17 days. Given that the one-way trip is roughly equal to the distance between Boston and Miami, it is evident that the transit and “cycle” (turnaround or round trip) times are relatively long in India.79 As a further comparison, it takes about 1.5 days (37 hours) for shipments to travel 2000 km between Valencia, Spain, and Ford’s assembly plant in Dagenham (London), United Kingdom.80 Second, with poor transportation infrastructure not only does freight stay in transit significantly longer than it does in countries such as the United States, but transit time is also relatively unpredictable. In fact, the logistics literature indicates that unpredictability may have a greater adverse impact on costs than the transit time, because it tends to raise the buffer or safety stocks required at origin and destination. Further, unpredictability also may cause the receiving firm to bear “stock-out” costs – costs that arise if the safety stock has been used up and shipments carrying replacements are late. The stock-out cost for auto assemblers is the cost of stopping the assembly line, reducing production, or altering the product mix scheduled for production. Maruti factors in unpredictability in transit time in determining the level of buffer inventories or safety stock it holds. For example, although the estimated average transit time from Chennai to Maruti is seven days, it may be anywhere between six to nine days, depending on road conditions and weather. According to a manager in production planning, a three-day variation in travel time requires a minimum of about three days of buffer inventory for most components procured from Chennai, in addition to an average in-transit inventory of seven days. Similarly, if goods ordered from Japan take between 35 and 45 days in transit, the firm will hold a minimum of 10 days of buffer inventory for these goods, plus about 40 days of in-transit inventory. Thus, even if Maruti and its suppliers could control all other aspects of procuring inputs – for example, ensure zero defect shipments and fully optimize their ordering systems – the long and unpredictable transit times would prevent them from achieving zero (or very low levels of) inventory. Unquestionably, long and unpredictable transit time raises both in-transit inventory and the buffer or safety stock held by Maruti. But how high are the inventory levels? How much of the total inventory or “fat” can be associated with the supply chain, and what proportion can be ascribed to the inadequate transport system? To answer these questions, we next examine Maruti’s inventories in detail.

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Struggling to get lean: Maruti’s inventory problem To enhance its competitiveness, Maruti has been trying to cut inventories and implement JIT/lean production systems. Figure 3.3 shows that its inventory levels have indeed fallen substantially over the 1992–97 period. The total value of inventories as a percentage of sales revenue improved from a high of 20 percent in 1992 to 10 percent in 1997; Maruti thus was able to halve the value of its inventories as a percentage of sales revenue over a six-year period. Although inventory levels are improving, they continue to represent a significant problem for Maruti, whether they are analyzed in terms of their capital value and associated carrying cost, or in days of stock.81 On average, the capital value of inventories amounted to 15 percent of the sales revenue over the six-year period 1992–97. The fact that such a significant amount of capital is tied up is especially problematic given the high opportunity cost of capital in India. The minimum interest rate for loans from commercial banks ranged from 14–19 percent over the 1992–94 period (World Bank 1996).82 Assuming an opportunity cost of capital of 18 percent (simple interest, for convenience), the cost of carrying these inventories amounts to 2 to 4 percent of total sales revenue for the period 1992–97. By comparison, wages accounted for 2 to 3 percent of sales revenue over the same period. Overall, the estimated inventory carrying cost was significantly greater than the labor costs for each fiscal year during 1992–96; only in 1997 did the costs for the two categories converge. Table 3.2 compares Maruti’s average inventory-to-sales ratio with that of five world-class assemblers. Maruti’s average inventory to sales ratio is 6.5 times higher than Toyota, 3.0 times higher than Nissan, and

Table 3.2 Maruti’s inventory performance compared to other assemblers

Period analyzed Inventory/sales (average)

Toyota Nissan GM Ford 1982–91 1982–91 1982–91 1982–91 2.3%

5.1%

8.1%

8.4%

Chrysler 1982–91 9.8%

Maruti 1992–97 15.0%

Note: The fact that Maruti’s data are averaged over a more recent period (1992–97) biases the comparison in its favor – that is, Maruti is likely to be even further behind if its inventory levels are compared to those that the other assemblers achieved over the period 1992–97. Source: Dyer (1996) for data on all assemblers other than Maruti. Maruti’s inventory is compiled from the firm’s annual reports.


1.5 to 2.0 times higher than GM, Ford, and Chrysler. Indeed, Maruti is likely to be even further behind than these data suggest because we would expect the big-three North American firms to show a substantially better inventory performance for 1992–97, as compared to 1982–91, as they have moved toward lean production. Another way to assess inventory performance is to examine the number of days of inventory. Total inventory level at Maruti fell from about 57 days of stock in 1992 to 30 days in 1997.83 Inventory of components and raw materials fell from 39 days to 22 days. While it is hard to find strictly comparable data on inventory levels at other assembly plants in the world, the following data are indicative. In 1991, Toyota held just one day of raw material inventory and Nissan held about three days of stock at its plant in Japan (Lieberman, Demeester and Rivas 1995).84 Similarly, according to a logistics manager at Ford, its Dagenham plant in the United Kingdom operated with an average inventory of one day or less.85 Therefore, Maruti needs to improve its inventory performance significantly and to work with as little as one to three days of stock in order to compare favorably with other lean, world-class assemblers. The “fat” is in the supply chain An examination of Maruti’s inventory during 1992–97 shows that the components and raw materials (C&RM) category – or, simply, the supply chain – accounts for 67 percent to 74 percent of the total inventory (Figure 3.3). Over the past few years, Maruti has been able to squeeze down its inventory in all other categories – such as work-in-progress and finished goods – to relatively insignificant levels. Thus, in its own factory and for procedures that are directly under its control, Maruti has been able to move toward lean systems. By contrast, it has been harder to make the supply chain lean. In each of the six years under review, C&RM inventory was significantly higher than all other categories combined (Figure 3.4).86 The average C&RM inventory over the six-year period was 32 days. Given that the average total inventory was 49 days, C&RM accounted for 65 percent of total. During 1992–97, then, as much as 65 percent of the total “fat” in this system was related directly to the supply chain for components and raw materials, and the capital value of these C&RM inventories was equivalent to 10 percent of average sales revenue. The C&RM inventory can be broken down further into two categories: in-transit goods and at-factory goods.87 Figure 3.5 presents the relative magnitude of these categories. During 1992–97, the average at-factory

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Days of supply

70 60

Finished goods Work-in-progress Stores, spares, tools and other with vendors (incl. C&RM) C&RM at factory C&RM in transit

50 40 30 20 10

15

27

21 20

23 20

13

17

5

0 1992 1993

1994

13

9

9

1995 1996

1997

Days of supply

Figure 3.3 Maruti: days of inventory, by type, 1992–97

45 40 35 30 25 20 15 10 5 0

40

38

35 28

30

29

22

19

18

15

Total C&RM All other inventories

15 8

1992

1993

1994

1995

1996

1997

Days of supply

Figure 3.4 Maruti: C&RM against other inventories, 1992–97

45 40 35 30 25 20 15 10 5 0

27 20 13

23

21 17

C&RM in transit C&RM at factory Total C&RM

20

15 9

13 9

5 1992

1993

1994

1995

1996

1997

Figure 3.5 Maruti: C&RM inventories, 1992–97

stock or buffer inventory was 20 days, and goods in-transit accounted for the remaining 12 days of C&RM inventory. In other words, a fourth of Maruti’s total inventory of 49 days was accounted for by goods that were on the road, or some other part of the transport network – dispatched by suppliers but not yet at the assembly plant. The average


Days of supply

25 20 15 15

10 4 5 6

4

Mar'94

Mar'95

0

2

2

5

5

1 Mar'96

1 Mar'97

Steel coils under inspection Indigenous consumables Indigenous C&RM-in transit/inspection Imported components etc Imported materials

Figure 3.6 Maruti: inventory of goods in transit, 1994–97

capital value of in-transit inventory for 1992–97 was equivalent to an extraordinary 4 percent of the average sales revenue. The international supply chain is anything but lean Further analyses reveal that imports rather than domestic inputs account for almost all of Maruti’s in-transit inventory (Figure 3.6). Domestic suppliers are responsible for delivering to the assembly plant, which means that most goods show up in Maruti’s account books after they reach the plant and, therefore, only as at-factory inventory. By contrast, imported goods show up as Maruti’s in-transit inventory from the time they are dispatched to the time that they reach the assembly plant, when they become at-factory inventory. The reason is that imports are negotiated on a “free on board” (FOB) basis, which means that the foreign supplier is responsible for the shipment until it is “on board” (a ship or airplane, etc.), and the buyer pays for insurance, freight, and import duties. The goods show up as the buyer’s inventory because the foreign supplier dispatches the goods only after payment in the form of a “letter of credit” from the buyer.88 By comparison, domestic suppliers are paid only after the shipment reaches the plant. Imports also account for a disproportionate amount of at-factory inventory. Figure 3.7 presents four years of data on “closing stock,” that is, the buffer inventory recorded on the last day of a particular financial year. In 1996–97, for example, closing stock for imported components was 8.0 days; the capital value of this stock was equivalent to 2.7 percent of sales revenue. By comparison, the level for domesticsource components was less than half as much and accounted for 1.2 percent of sales revenue. This is a surprising result – domestic inputs accounted for 69 percent of the consumption of components and raw materials but for only 35 percent of the total closing stock. Imports

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Days of supply

25 20 15

5

5

1.0

5

3.5

CStk – raw and general materials, paints CStk – components, indigenous CStk – semi-finished components, indigenous CStk – CKD/SKD components, imports

0.7 3.6

10 16 5

0.8

14

12

8

0 Mar'94

Mar'95

Mar'96

Mar'97

16 14 12 10 8 6 4 2 0

14% 12%

35%

36% 13%

35%

34% 10%

10% 8%

31%

1992

1993

32%

1994

1995

C&RM in-transit and at factory – LHS

1996

37 36 35 34 33 32 31 30 29 28

Imports as % of C&RM consumption

C&RM inventory as % of sales revenue

Figure 3.7 Maruti: closing stock or buffer inventory, 1994–97

1997

% Imports – RHS

Figure 3.8 Maruti: reliance on imports and its effect on C&RM inventory, 1992–97

accounted for 31 percent of consumption and 63 percent of the closing stock. This suggests that imports carry a high “inventory penalty.” Another way to understand the import penalty is to examine the inventory for imports and domestic materials relative to their respective rates of consumption (rather than in terms of total consumption and sales revenues which is what we have used thus far). According to a manager at Maruti, in August 1997, the buffer inventory for imports was 25 days against about 4 days (about one-sixth as much) for domestic purchases. This finding suggests that the more the firm relies on imports the higher its inventories. Figure 3.8 supports this finding by showing that the aggregate level of C&RM inventory is correlated to the share of imports in C&RM consumption. Why are inventories for imports higher? The literature on lean production suggests that the answer is likely to lie in the following variables (a) assembler–supplier relations,


(b) quality–standards achieved by the supplier, and (c) management attitude. In Maruti’s case, however, it is easy to eliminate these usual suspects. About 80 percent of Maruti’s imports are from Japan, mostly CKD/SKD kits from the Suzuki Motor Company. As Maruti’s key foreign supplier, Suzuki has had all the incentives to perform exceptionally well. First, since 1992, Suzuki has a 50 percent equity stake in Maruti. Second, Maruti is Suzuki’s largest operation outside Japan and, apparently, its most profitable plant anywhere in the world.89 Third, Suzuki has been Maruti’s teacher with respect to both total quality management and, more generally, lean production. Suzuki is, hence, likely to have kept its response time for Maruti to a minimum and at least met, if not exceeded, the quality standards established by Maruti. Finally, the fact that Maruti has been improving its inventory performance continuously in all categories over the last six years, suggests that one can eliminate “management attitude” as the explanatory variable as well. The key explanatory variable is transit time. Despite serious efforts to reduce transit time, it still takes about 35 days for an ocean freight shipment from Suzuki to Maruti. And this appears to represent a good performance, if we use Maruti’s negotiations with DHL as evidence. DHL, an internationally reputed shipper, could not offer an equivalent (let alone shorter) delivery time, or match or beat the prices that Maruti pays its shippers.90 Thus, 35 days represents almost a minimum transit time for a freight shipment from Japan to reach this interior plant in India. In absolute terms, however, 35 days is too long and translates into substantial in-transit and buffer inventories. In the Maruti case – due to its size and special relationship with its foreign supplier Suzuki – it is possible to ascribe almost all of the high inventories for imports to a transit time variable.91 However, most auto firms in India do not have such relationships with their overseas suppliers. They are likely to suffer from both slower response and longer transit time. That is, for most firms, imports are likely to carry even higher penalties than for Maruti.92 In other words, the international supply chain is not lean.93 The inventory penalty of imports – additional evidence from supplier firms Sundram Fasteners, an award-winning General Motors supplier, sets different inventory targets for imports and domestic inputs (Appendix 3.2). The firm uses a “stock turn” indicator to monitor its inventories; this is similar to an inventory–turn ratio, and the higher the number the better the performance. In 1996–97, the firm targeted a stock turn of

98


“eight times” for its imports, but was unable to achieve the target at either of its two main units. By contrast, the target for domestic inputs was 26 times – more than three times higher. The firms’ two factories (called P and K) started the fiscal year at stock turn levels of 13 and 14 for domestic materials, steadily improved their inventory performance, and exceeded the target in each of the last three months of the year. In March 1997, the P and K units finished with domestic stock turn levels of 32 and 35, as compared to the imported stock turns of 7 and 6 times, respectively. Inventory turnover ratios at Mark Auto, one of Maruti’s top 20 suppliers by value, show similar trends (Appendix 3.3). The data from April–September 1997 show ratios for domestic raw materials at least twice as high as those for imported raw materials. The best inventory turnover ratio for imports was 5.8, as compared to 15 for domestic materials. The poor inventory turnover ratios for imports pulled down the overall inventory performance of the firm. In summary, the supply chain accounts for about two-thirds of Maruti’s total inventory, but the international component (i.e., imports) accounts for a disproportionate share of this total. For instance, in 1996–97, imports accounted for about 31 percent of C&RM consumption, but an extraordinary 56 percent of Maruti’s total (not C&RM) inventory. By comparison, domestic components and raw material accounted for 69 percent of C&RM consumption but only 11 percent of total inventory. This suggests that reducing its reliance on imports would directly lower Maruti’s inventory levels and carrying costs. Data from suppliers such as Sundaram Fasteners and Mark Auto suggest that other auto firms in the country could also improve their inventory performance by reducing reliance on imports. This does not mean, however, that the domestic supply chain is highly efficient and that the inventory costs associated with it are small. Rather, almost all of the domestic inventory costs tend to appear as direct costs at the supplier level and as indirect costs or external diseconomies to Maruti, whereas those associated with the international supply chain show up as direct costs for the assembler. Although the costs associated with its domestic supply chain are indirect, Maruti considers these to be highly problematic. Specifically, Maruti finds that only “local” suppliers – those within a radius of 80 km or less – are able to deliver both frequently and on a just-in-time basis. That is, only with local suppliers does Maruti expect delivery frequencies of one per day or more, and operate on minimal buffer inventories (one to three days). This is not to say that all local suppliers are capable of JIT


delivery, but rather that distant suppliers, irrespective of their competence, are unable to deliver just-in-time. Maruti has also discovered that, in general, buffer inventories must be increased as distance to a supplier increases. In other words, poor transportation systems appear to make it difficult, if not impossible, for Maruti to implement JIT delivery systems in its non-local domestic supply chain. Data from Ford and other suppliers, presented in the next section, allow us to test whether these qualitative observations at Maruti are unique or general.

The domestic supply chain: only local suppliers can deliver JIT Ford’s logistics plan and insights into transportation problems Ford’s first integrated assembly facility in India, located in Chennai in the southern part of the country, started production in mid-1999. This plant has an annual production capacity of 25 000 vehicles which may be increased to 50 000 vehicles in the future. At the time of this study in August 1997, Ford was in the late stages of supplier selection and development, and had devised a plan for minimizing the adverse impacts of poor transportation systems on its supply chain. By January 2000, Ford had implemented most but not all aspects of its logistics plan. The discussion below focuses on the plan and the logic behind it, and thus offers an insight into Ford’s perspective on the freight transportation problem.94 About 45 of Ford’s 75 domestic suppliers are located in distant parts of the country such as Delhi and in the Pune/Mumbai area. According to Ford’s estimates, these non-local suppliers will account for about 30 percent of its total domestic purchases of components and materials. Based on its analysis of India’s freight transportation situation, Ford concluded that its non-local suppliers would not be able to meet the benchmarks or delivery standards that its suppliers in the United Kingdom or the United States are required to meet. Ford has, therefore, devised a rather radical logistics solution. Ford invited bids from a short list of internationally reputed logistics companies to serve as its total logistics partner in India. The task is to move material and components from approximately 45 distant suppliers to the Chennai plant in a manner that emulates as closely as possible a pure JIT system. Subsequently, the logistics partner may be asked to manage the warehouse at the assembly plant, to help Ford better meet its goal of minimizing total logistics costs.


While not committed to a particular logistics plan in 1997, Ford’s logistics department had been developing ideas as to what a good system might look like. One of the first principles that they were trying to revise is that of “delivered price.” In a system in which the assembler negotiates delivered prices, the supplier is responsible for finding the cheapest and most efficient transportation solution for its product and for ensuring that the assembler’s delivery schedules are met unfailingly. In this system, the in-transit inventory shows up in the supplier’s account books. The logistics team wanted to change this and move to a system where Ford and its logistics partner take charge of the shipment either at the supplier’s factory gate or at a collection point nearby. The logic behind this move is as follows. Suppliers cannot ensure JIT deliveries, because the travel time over large distances is both long and unpredictable. The travel time problem is aggravated by the fact that, given Ford’s relatively small volumes, most of its suppliers would ship infrequently (perhaps, once in two or three weeks) and tend to rely on less-than-truckload shipments. In such shipments, components are usually transported in trucks carrying completely unrelated products and making deliveries to firms that may not be located in the same area. A full-truckload shipment, in contrast, offers several advantages: (a) a single pick-up point and a single destination point, thereby, eliminating extra stops; (b) the option to ship in “pallets” (i.e., carry the shipment in special reusable crates rather than cardboard boxes); and (c) better ability to track a single truck and its loads as opposed to small shipments in several different trucks. An assembler such as Ford prefers a full-truckload shipment, but also wants frequent deliveries while keeping its freight bill to a minimum. Ford was thus planning to consolidate shipments from different suppliers into full-truckloads – a system referred to as “cross-docking”; the full truck would then be dispatched to Chennai. To facilitate cross-docking, the Ford logistics department envisioned a system of “regional hubs” in Delhi, Bangalore, and Pune. The regional hub created in Delhi, for example, would consolidate shipments from Ford’s 12 suppliers in this area. The suppliers would be required to have their shipments ready – manufactured, tested, and palletized – as per Ford’s specifications. Depending on the characteristics of these shipments, the logistics partner would configure or load the truck to maximize its carrying capacity and dispatch the truck as soon as it was full. The partner would probably also deploy state-of-the art logistics planning and technologies, and offer to Ford value-added services such as electronic tracking of shipments.


Although the actual frequency of deliveries from these hubs and the target inventory level at Ford’s assembly plant had not yet been finalized, this system offered several clear advantages: full-truckload shipments, more frequent deliveries, better tracking of shipments, and lower buffer inventories. However, the system would raise several other components of Ford’s total logistics cost. It would increase Ford’s own expenditures on inbound freight and its ordering and overhead costs for managing material flows. Moreover, it would force Ford to bear directly the full cost of carrying in-transit inventories; the fact that Ford takes control of the shipment at or near a supplier’s factory means that these costs will no longer appear on the supplier’s account books. Thus, Ford’s total logistics cost is likely to change as follows: Total logistics cost (?):freight cost (↑);cost of damages (↓) ;buffer inventory (↓);in-transit inventory (↑) ;ordering/overhead cost (↑) ;packaging cost (↓) As the above equation suggests, it is unclear whether the new plan will ultimately lower or raise Ford’s logistics costs, but it is likely to reduce these costs over the supply chain as a whole, that is, lower the sum of the logistics costs borne by the assembler and its suppliers. Although not all managers were convinced that this elaborate system was worth the effort, Ford decided to implement the plan as an important step toward creating a well-functioning and responsive supply chain. Ford clearly considers the task of improving the efficiency and reliability of its supply chain, and of reducing the external diseconomies that poor infrastructure creates, to be worth extra financial expenditures and significant managerial effort on its own part. Irrespective of whether the benefits of this strategy exceed its costs, the very fact that Ford has developed an alternative logistics system for its operations in India offers strong support for the main arguments in this chapter. Ford is attempting to throw out the traditional and wellentrenched principle that suppliers are solely responsible for ensuring that delivery schedules are met. Ford is acknowledging that the delivery performance of a supplier is not entirely controlled by that firm’s management – indeed, it is strongly affected by the efficiency of the transportation system and services on which the supplier firm relies. And, by taking direct responsibility for carrying in-transit inventories, Ford also brings into question the relatively widespread notion that in a system of just-in-time delivery and delivered prices, the supplier


alone bears the costs of in-transit inventories and a major portion of the transportation cost. In other words, the inefficiencies associated with poor transportation systems adversely affect all firms in the supply chain and directly or indirectly translate into higher costs/prices for the final good. Inventories increase with distance: some quantitative evidence The quantitative data presented below are indicative rather than conclusive, but they do support the observation that buffer inventories increase with distance and that only local suppliers are likely to deliver just-in-time. Inventory planning and delivery schedules at Ford Figure 3.9a presents data on delivery schedules and inventory levels for 19 of 75 (25 percent) of Ford’s major suppliers in India (these are planning figures used in 1997; Ford started production only in mid-1999). It shows, first, that there is a correlation between delivery frequency and inventory levels. Ford intends to hold buffer stock slightly less than or equal to the delivery frequency, that is, about six days of stock for a supplier that delivers once in seven days. Second, distance is a key explanatory variable – as distance increases, inventory levels rise and delivery frequency falls. Inventories at Sundram Brake Linings (SBL)

Days

Data from SBL, a second-tier supplier firm based in Chennai, provide additional evidence. Figure 3.9b presents data on 13 raw material/input 8 7 6 5 4 3 2 1 0

6

6

3 1 0

500

1000 1500 2000 Distance of supplier from plant (km)

Delivery frequency (days)

Inventory (days)

Figure 3.9a Ford (Chennai): distance against delivery frequency and inventory (n:19 suppliers) Source: Company data

2500

Days

Effects of Poor Transportation 103 35 30 25 20 15 10 5 2 0 0

30 15

15 15

10 6 4 4

3

500

6

6 1000

1500

2000

2500

3000

3500

Distance of supplier from plant (km) Delivery frequency (days) Inventory (days) Figure 3.9b SBL: distance against delivery frequency and inventory (n:13 suppliers) Source: Company data

suppliers, which account for 67 percent of SBL’s total domestic purchases. It confirms the observation that delivery frequency and inventory levels are correlated, and that distance adversely affects inventory levels and delivery frequency. SBL maintains lower buffer inventories for firms that are closer, and local suppliers are often the preferred source in that they get a larger share of the orders for a particular item. This is evident from Table 3.3, which presents the SBL data broken down by product. For example, SBL purchases its input of P.F. resin (a raw material) from three sources. It holds two days of inventory for the supplier located 10 km from its plant, four days of inventory for the supplier in Hyderabad (650 km away), and six days of inventory for the supplier in Bombay (1200 km). Further, the local supplier accounts for 60 percent of the total resin purchased by SBL. This pattern, whereby the local supplier is the preferred or major source, holds for all but one of the other raw materials for which data are available (see Table 3.3). The correlation between distance and inventories Purchase data from five auto component suppliers were compiled to create a data base on 136 sub-suppliers, showing the value of goods purchased from a particular firm, its distance from the buyer, the frequency of deliveries to the buyer, and buffer inventory that the buyer holds for that sub-supplier. A simple, ordinary least squares regression was conducted on this cross-sectional data, with inventory level as the dependent variable and distance as the independent variable. The regression results (see Appendix 3.4) show that the two variables are positively

104 Innovating with Infrastructure Table 3.3 SBL – inventories vary by product and distance of supplier S.no

Name of vendor

Main products

1 2

Arasan Aluminum Mepco

3

Deva Metal

4

Jeevandoss

5

Golden Products Alagar Farms FGP Ltd Up Twiga Golden Products Sudarsan Phenom. Ind-Ital Bakelite Hylam Tipco Sub-total purchase value

Aluminum powder Aluminum powder Aluminum powder Chrome oxide green Friction dust

6 7 8 9 10 11 12 13

% of reqm.

Purchase 1996–97 (Rs ’000)

Distance Freq. of from plant delivery (km) (days)

Inventory (days)

24

31 400

500

7

4

43

56 000

500

7

4

33

44 500

1500

10

10

50

10 350

2500

15

6

40

54 000

200

3.5

3

Friction dust

60

93 000

500

7

6

Glass fiber Glass fiber Liquid resin

80 20 70

275 200 35 280 112 000

650 3000 200

7 30 3.5

15 15 3

Liquid resin

30

52 000

200

3.5

3

P.F. resin P.F. resin

60 18

469 500 112 600

10 650

1 7

2 4

P.F. resin

22

96 700 1 442 530

1200

7

6

Source: Company data, 1997.

correlated – inventories do increase with distance. The t-statistics are significant, and “distance” explains about 19 percent of the total variation in inventory levels. Specifically,95 Inventory level (days):a;b distance (in km) I:8.64;0.0056 D (t-stats) (9.71);(5.63) R-square:0.19, adjusted R-square:0.185 The low R-square or “goodness of fit” can be explained, first, by the fact that the five “buyers” are at different stages of inventory management and JIT production. Thus, their inventory norms vary widely. At one end of the spectrum, a buyer uses inventory norms ranging from 2 to 15 days; at the other end, a buyer works with norms ranging from 7 to 60 days. Second, distance is only one of the reasons why these five


firms (which are first-tier suppliers for various auto assemblers) hold significant levels of inventory. For example, inventories vary by type of product, whether it is produced in small batches or large lots, and whether there are capacity constraints in the supply base for that product. Of these five firms, only SBL has focused strongly on inventory management and brought several of the variables that can be controlled by management under control. The result is that inventory levels at SBL vary by product, but for each product they increase with distance (see Table 3.3). As the other firms move toward better inventory practices and bring various variables under control, we would expect to see the explanatory power of the “distance” variable increase.

Conclusions The literature on transportation in developing countries suggests that the major problem associated with inadequate road networks (and poor transportation systems, in general) is that they raise transportation costs and thereby hurt competitiveness. Analysis of Maruti’s expenditures shows that the assembler, indeed, incurs significant freight costs. Table 3.4 shows that, in 1996–97, Maruti’s freight bill, including damages, accounted for 4.35 percent of total sales revenue. These freight costs represent a highly significant expense, given that

Table 3.4 Maruti’s logistics costs as a percentage of sales revenue, 1996–97 Freight cost TLC (Maruti) : 4.0%

Cost of damages

Carry cost of inventorya

; 0.35;;%

;10%b

Days of inventory

Total inventory of 30 days a) Outbound 2.5% ; 0.35;% ;N/A Finished goods inventory of 2 days b) Inbound – 1.5*% ; N/A ;3% “In-transit” components/ inventory of 9 days materials ;5% Buffer inventory of 13 days Supply-chain inventory cost (8%)outbound freight cost (2.5%)wage bill (2.0%)inbound freight cost (1.5%) Notes: a Assuming that opportunity cost of capital is 18 percent (simple interest). b Represents total inventory carrying cost (including that caused by the transport system). “N/A” – not available * Estimated


the assembler’s entire wage bill accounted for only 2.0 percent of total sales revenues in that year. However, as Table 3.4 indicates, freight is not the largest cost variable in the TLC equation – indeed, the estimated inventory carrying costs were more than double the total freight costs. The carrying cost of in-transit (inbound) inventories was about 3.0 percent and it alone exceeded the expenditures on inbound freight (1.5 percent) and those on outbound freight (2.5 percent). As compared to the traditional approach of examining freight expenditures and vehicle operating costs, the total logistics cost equation offers a more comprehensive insight into the direct impacts of poor transport systems on firms. It also suggests reasons why distance between firms can affect costs – increasing distance translates into increases in freight costs, in-transit and buffer inventories, and damages. As an analytical framework, however, the equation has certain limitations and offers only a partial understanding of the transportation problem. It appears to suggest that the key problem with poor transportation is that it raises freight costs and the financial cost of holding inventories – that is, the assemblers have to spend more. As the Ford case demonstrates, however, the more debilitating problem is that poor transport systems introduce or aggravate unreliability and inefficiency in the non-local supply chain. Although lean production may be only one component of a competitive strategy or one type of strategy, auto assemblers in India see it as a necessary if not sufficient condition for competitiveness. Their efforts at implementing lean production are affected adversely by distance to suppliers – inventories increase with distance and only local suppliers are able to deliver just-in-time. This finding stands in contrast to much of the literature on lean production in the auto industry. Previous studies have found that distance from – or proximity to – suppliers does not help explain differences in inventory levels and “leanness” at different assembly plants (Lieberman, Demeester and Rivas 1995; Womack, Jones and Roos 1990). The difference between their findings, which are based largely on research in advanced industrialized countries, and my observations can be explained as follows. Due to the poor infrastructure, transit time for road freight in India is longer and more unpredictable than in the United States or Europe, and it translates directly into higher inventories for auto firms. That is, the excellent road and rail infrastructure for freight in the United States and Europe reduces the importance of proximity in their auto industries, while the terrible infrastructure in India makes proximity crucial to implementing just-in-time production in the Indian auto industry.


The connection between inventories and distance or transit time becomes even more apparent in the case of imports. Imports usually arrive as ocean freight, a slow mode, and then have to go through congested Indian ports and, for firms located in the interior, through the inadequate road and rail networks. As a result, inventory levels for imports are extraordinarily high relative to those for domestic inputs. This finding brings into question the compatibility of two “prescriptions” that the management literature offers for improving competitiveness – “get lean” and “globalize and/or internationalize.” The lean paradigm helps banish “waste” from the organization, and an international supply chain allows a buyer to access the best or most competitive producer of a particular product in the world. This research suggests that, at the firm-level, these two approaches clash – that is, a firm has to make a trade-off between making its supply chain lean and making it global or international. Over time, we would expect to see lower inventories for products that are sourced locally compared to those purchased from suppliers farther away, and for domestic inputs relative to imports. This correlation between inventory levels and distance of the supply source is likely to survive as long as the speed and reliability of freight transport and, by extension, the quality of transport infrastructure, continue to be problematic. We know, however, that neither the speed of ocean freight nor the poor condition of the existing transportation and road network in India is likely to improve dramatically in the near future. Poor and/or slow transportation networks, then, appear to preclude almost completely the possibility of achieving zero inventories. At the very least, poor transportation systems are a critical bottleneck in implementing JIT delivery. Given the high cost of carrying inventories, and the fact that the JIT system is central to achieving the quality and performance gains associated with lean production, auto firms have to find solutions to the transportation problem to improve competitiveness. Almost all auto assemblers are adopting two strategies. First, they are making their supply chains less international and are aiming for a domestic content of 60–90 percent. Although this strategy is driven by the pressure to compete against Maruti’s low vehicle prices (attributable largely to its high domestic content), it benefits the assemblers’ logistics as well.96 Second, the assemblers are making their domestic supply chains as local as possible by “clustering” together with their suppliers and other assemblers. In Chapter 4, we turn to an examination of the clustering strategy and see that it is being driven by transportation constraints combined with the logic of lean production.


Appendix 3.1 Maruti – outbound freight logistics Table A 3.1a Maruti – damages incurred on finished vehicles during transit, 1994–95 Number of vehicles Total cost – US$ (’000) Vehicles needing repairs (a) Vehicles to be dismantled – Domestic (b) – Export (c) Sub-total dismantled vehicles (b;c) Loss:45% of cost (d) Total losses due to damages (a;d)

282

166.9

315 247 562

1484.6 930.6 2415.2 1086.8 1253.7

844

Source: Bose (1995).

Table A 3.1b Dispatch of vehicles for domestic sales (excludes vehicles for the local area and Delhi) 1992–93 No. of vehicles dispatched Transit damage index No. of accidents No. of vehicles returned Vehicles returned as % of dispatch Transit time index Turnaround time index

128 367 5.78 19 57 0.04 1.11 1.51

1993–94 146 570 5.16 49 147 0.10 1.064 1.379

1994–95 176 376 7.03 45 164 0.09 1.042 1.193

Source: Company data Notes: Transit damage index: No. of major damages per 100 vehicles shipped. Transit time index: ratio of actual transit time to estimated transit time (see Table 3.1). Turnaround time index: ratio of actual time taken for round-trip to the estimated round-trip time.

Appendix 3.2 Inventory of imported against indigenous materials (Case – TVS Sundram Fasteners, 1996–97) Total RM stockturn (all units consolidated) 20 19 18 Norm: 18 times 17 16 15 14.37 14 13.67 12.98 13 12.49 12.24 12.02 12.3 12 10.92 11 10.53 10 8.95 9.01 9 9 8.37 8 7 95–96 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Total RM stockturn (Padi & Kpm) 20 19 18 Norm: 18 times 17 16.12 16 15 15.04 14 13.27 13 12 12.67 padi 11 10 10 9 8 kpm 7 95–96 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Imported RM stockturn (Padi & Kpm) 12 11 10.17 10 9 8.35 8 Norm: 8 times kpm 7.02 7 6 5.6 5.87 5 padi 4 4.1 3 2 1 0 95–96 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Indigenous RM stockturn (Padi & Kpm) 39 37 35 34.65 33 32.09 31 29 27 Norm: 26 times 25 23 21 19 17 padi 15 13 14 11 13 9 kpm 7 0 95–96 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Source: Company data


Appendix 3.3 Inventory of imported against indigenous materials (Case – Mark Auto) Mark Auto – inventory of imported against indigenous materials (over a 6-month period – April–Sept.1996) 18.0 16.0

16.0 14.0

12.6

Inventory turnover ratio

14.8 12.0 11.4 10.0

10.4

10.5

9.3 8.0 7.1

6.3

6.0 5.8

6.2

5.7

6.7

5.6

4.0

4.5

4.9 4.1

4.0

2.0 0.0 Apr'97 May'97 Jun'97 Jul'97 Aug'97 Sep'97

Source: Company data

Raw materials, imported Raw materials, indigenous Components (all indigenous) Total (incl. WIP and all other)

Appendix 3.4 Regression results – inventories increase with distance SUMMARY OUTPUT Regression statistics Multiple R R-square Adjusted R-square Standard error Observations

0.438 0.191 0.185 8.712 136

ANOVA

Intercept X-variable 1

SS

MS

1 134 135

2407.632 10170.243 12577.875

2407.632 75.897

F 31.722

Significance F 1.0035E-07

Coefficients

Standard error

t Stat

P-value

8.644 0.006

0.891 0.001

9.706 5.632

3.420E-17 1.003E-07

Y-inventory level (days) held at buyer's plant

Regression Residual Total

df

Lower 95% 6.883 0.004

Upper 95%

Lower 95.0%

10.405 0.008

6.883 0.004

X-variable 1 line fit plot

60 50 40 30 20 10 0

Y Predicted Y

0

500

1000 1500 2000 2500 3000 X-variable 1 distance of supplier from buyer (km)

3500

Upper 95.0% 10.405 0.008

4 Clustering as an Infrastructure Solution

With the influx of several world-class auto assemblers into India and the fact that planned production capacity exceeds the estimated demand, competition is getting increasingly intense. All assemblers are attempting to implement lean production techniques to cut costs, improve quality, and enhance their responsiveness to demand. As argued in the previous chapter, however, poor transportation infrastructure is proving to be a serious obstacle in implementing lean/ just-in-time production. This is because inadequate transportation infrastructure raises both the length and unpredictability of transit time for freight, which translates directly into higher in-transit and buffer inventories. In other words, poor transportation infrastructure does not allow for the elimination of the inventory safety net, which is one of the key drivers of the entire lean system and associated gains in quality and competitiveness. To mitigate the adverse impacts of poor transportation infrastructure, Maruti, Ford, Hyundai, and Daewoo are requiring that suppliers locate in close proximity to their plants – a strategy that is referred to here as “clustering” or “localization.” This chapter shows how transportation constraints, together with the imperatives of lean production, are driving the assemblers to cluster. The clustering tendency also is being encouraged by government incentives, such as sales tax concessions and the provision of subsidized land. And, as the Maruti case study will demonstrate, the clustering strategy allows assemblers to simultaneously alleviate other infrastructure problems, such as the lack of access to basic on-site services and reliable electric power. The argument, then, is that clustering in the Indian auto industry is driven by a relatively straightforward and mechanistic logic – lack of infrastructure. This stands in sharp contrast to the literature on 112

Clustering as an Infrastructure Solution 113

industrial clusters, which emphasizes the importance of “softer” variables in explaining why some of these clusters have emerged and why they have been successful. The industrial clusters literature focuses on social capital and “relational” variables, such as “trust” and strong inter-firm networks emerging from family and community ties. To be more specific, this literature attributes the competitiveness of certain dynamic industrial districts (or clusters of firms in particular locations) to the existence of strong networks among these firms (Piore and Sabel 1984; Sengenberger and Pyke 1991). These networks – usually, horizontal relations between “a core of more-or-less equal small enterprises” – allow specialization and subcontracting, which, in turn, induce efficiency and promote collective capability (see Humphrey 1995). These networks “work” – that is, they allow for flexible specialization and cooperative competition – because they operate in an environment where there is greater trust among the players, there are familial and community rules that help enforce contracts, and there is lack of hierarchy. Together, these variables help explain the industrial success and dynamism of clusters, such as those seen in Italy. The case of the Indian auto industry suggests, at the very least, that by ignoring the issue of access to infrastructure the industrial districts literature may be overlooking an important variable in helping explain not only the emergence of industrial clusters but also their continued success and dynamism. This case also suggests some connections between two separate models of competitiveness – the supply-chain/lean production model and the industrial districts model. In both models, inter-firm linkages play a central role in determining and enhancing competitiveness. But the type of linkages tend to be vastly different, and their prescriptions for enhancing competitiveness tend to lie on opposite ends of the spectrum. One of the tensions between these models arises from the geography of the production network. From a supply-chain and lean production perspective, the variables that explain competitiveness and success are to be found somewhere within an industry or firm’s global supply chain. The literature on industrial districts examines the linkages among firms grouped in particular locations, and from this perspective the variables contributing to success are associated with that geographic area. Thus, the industrial districts model requires geographic concentration, whereas the lean production model can exist, at least in theory, without geographic proximity between firms.


Another tension arises from the role of “hierarchy” in the networks – the governance structure of the networks in the industrial districts model differs significantly from the lean production model. For Piore and Sabel (1984), the more dynamic industrial districts tend to be characterized by non-hierarchical networks or linkages between moreor-less equal firms. The external economies that develop in these networks are likely to be shared more equally among these firms. At the firm-level, however, there are few incentives to invest in and create external economies (for example, by investing in training, R&D, or infrastructure facilities); this task tends to require other institutions, such as community-based organizations and trade associations. By contrast, the lean production model tends to be hierarchical. The stronger firms in the supply chain create external economies by organizing and improving the efficiency of the supply chain as a whole. They also appropriate many of the gains and external economies that they create. By extension, it is because external economies and diseconomies get internalized in their supply chain that assemblers have incentives to make broader investments, including those in infrastructure. In the Indian auto industry, the tensions between the two models get reconciled in unexpected ways. First, clustering is the assemblers’ solution for reducing unpredictability and enhancing the efficiency of the supply chain. That is, the industrial cluster or district emerges from attempts to localize and strengthen the supply chain. Second, because there is hierarchy in the supply chain – the assemblers clearly have more economic and political clout relative to their suppliers – it is possible for assemblers to create their own industrial clusters and internalize some of the external economies they create. Indian auto firms are, then, trying to create additional value in their supply chains and to internalize some of this value by combining the geography of the industrial districts model with the hierarchy of the lean production model. Overall, clustering is a more comprehensive infrastructure solution because it goes beyond the particular plant and alleviates infrastructure problems over the supply chain as a whole. Further, it solves not only transportation problems but also those caused by deficiencies in other infrastructure services, such as water supply and electricity. Specifically, clustering helps overcome the transportation constraints that prevent implementation of just-in-time (JIT) production by reducing the firms’ need for and vulnerability to transportation infrastructure. Clustering helps resolve other infrastructure problems because it allows assemblers to use their political clout and own investments to improve the level of


provision in their area. For example, Maruti has been successful in improving infrastructure in its industrial area because it has been able to negotiate for better services with local and state governments, and because it has invested directly in power plants, local roads, and water treatment plants. All of these factors reduce supply chain unpredictability, much of which is induced by infrastructure deficiencies.

Maruti’s localization strategy and the creation of a Delhi auto district To ascertain the extent to which local sourcing is a competitive strategy or priority for Maruti, this section plots the geography of its domestic supply chain and examines the mechanics of how the firm is implementing its localization or clustering strategy. We use the Maruti case to make the following arguments. First, although its supply chain is highly local, Maruti is putting in a tremendous amount of effort into localization by requiring its suppliers to cluster near its own plant. Second, despite its political and economic clout, Maruti cannot implement its clustering strategy and infrastructure solutions without help from the government. Maruti has, therefore, cut a deal with the government, and the two partners are developing a supplier park that will provide adequate infrastructure. Third, the collaboration between Maruti and the government represents a highly effective approach to developing industrial parks – one that state governments can emulate and replicate. On a broader level, it offers insights into how governments can partner with industry to lure additional industrial investment to a particular location. Fourth, Maruti’s efforts at localizing its supply chain since the mid-1980s and its collaboration with the government have had positive spillovers – they have helped trigger Gurgaon’s industrialization and the diversification of its industrial base. In other words, this is not only a story about infrastructure and lean production but also a story of the genesis and development of a vibrant industrial cluster. Maruti’s mandate, its location decision, and creation of a local supply base To recap, Maruti was created as a joint venture between the Government of India and the Suzuki Motor Company of Japan, with the government holding 74 percent of the equity.97 When Maruti started full-scale production in 1984–85, the supplier base for car manufacturing was extremely small and low-tech, and it provided inputs


for a national output of about 46 000 technically obsolete vehicles. As a step toward modernizing the auto industry, the Government of India wanted Maruti to introduce a new model, reach a production level of 100 000 cars within five years, and increase the local content in its Japanese product to 95 percent over the same five-year period. In other words, Maruti was faced with the task of creating almost from scratch an Indian supplier base for passenger cars. Maruti’s location was not ideally suited for a large assembly plant and the task of creating a supply base, perhaps, because the managers did not have a choice in the matter. The Government of India owned a plant site and a building in the Gurgaon district of Haryana state, on the southern border of New Delhi, about 30 km from its central business district. The new company was to take over these facilities and expand or modify them to meet its requirements. Gurgaon was akin to an urbanized village and was predominantly agricultural. The nearest industrial cluster at Faridabad, about 20 km away, had two auto firms (Escorts and Eicher) that manufactured tractors, two-wheeler scooters and motorcycles and, therefore, provided some access to a potential component supplier base. By comparison, other clusters – for example, in Pune and Chennai – were more industrially advanced, had larger concentrations of auto assemblers and suppliers, and could have made Maruti’s task of building a supplier base easier. Overall, the Gurgaon location imposed certain constraints – it was not an industrial center, there were only a few auto firms and potential suppliers near the area, labor with factory skills was not easily available in the vicinity, and it was nowhere near a port that could facilitate imports while a domestic supply base was being created. Nonetheless, Maruti did succeed in creating a domestic supply base and met its relatively ambitious production targets. At the end of its first five-year period in 1989–90, Maruti produced 105 000 vehicles with a domestic content of 86 percent. Then, the management decided to go further, and by 1996–97, the firm was producing 350 000 vehicles and had increased its market share to an astonishing 81 percent. Maruti also managed to achieve an overall domestic content of 69 percent by value despite the introduction of several new models with high import requirements, and the relaxation, since 1993, of the government’s local content guidelines.98 Its older and best-selling model, the Maruti 800, boasts a domestic content of over 95 percent. The fact that Maruti has been able to rapidly expand its production and successfully enhance domestic content, offers strong evidence of a vibrant auto components industry. Indeed, between 1983 and 1997, the nominal


value of production in the auto components industry increased 14-fold, from Rs 6.4 billion to Rs 88.3 billion (ACMA 1997). Maruti is widely credited with having led the transformation, in particular, of the passenger car segment of the auto components industry. Not only is Maruti’s supply chain dominantly domestic, it is also highly local. During 1994–97, as many as half of Maruti’s top 106 suppliers, by value, were “local” – that is, they were located within 80 km of its assembly plant.99 These 55 major local suppliers together accounted for 51 percent of Maruti’s total domestic purchases of raw materials and components by value. A purchase manager at Maruti estimated that they have localized as much as 65–70 percent of their domestic supply chain – this is the result one is likely to get from an analysis of purchase data from all 400 suppliers (rather than just the top 106). Maruti has helped create a significant auto district in the area. The “Delhi auto district” (as it is referred to in this book) consists of an agglomeration of auto firms within a semicircular geographical area, with its center in Delhi and a radius of about 80 km. It includes firms located in several smaller agglomerations or industrial “clusters” in Delhi, and in the two bordering states of Haryana and Uttar Pradesh (Figure 4.1).100 The Delhi auto district – with its several vibrant auto clusters such as Gurgaon, Faridabad, Noida, and Okhla – is considered to have the most extensive supplier base for passenger cars and is well ahead of the supplier districts in Pune, Mumbai (Bombay), and Chennai. Delhi’s extensive supplier base is, arguably, one of the main reasons why both Daewoo Motors of South Korea and Honda Motors of Japan chose to locate their new assembly plants in this area. A more surprising achievement is the extent to which Maruti has succeeded in developing Gurgaon as a supply base. The Gurgaon cluster dominates others within the Delhi auto district (Table 4.1 and Figure 4.1). Gurgaon hosts 26 of Maruti’s top 106 suppliers. It accounts for 46 percent of Maruti’s purchases from the district and at least 24 percent of its total domestic purchase bill. In 1996–97, the assembler procured US$209 million worth of components and raw materials from the 26 firms in Gurgaon. By comparison, Faridabad – the more mature industrial cluster and older auto components supply base in the area – came in third in terms of value of inputs supplied. In Faridabad, 11 firms accounted for US$46 million of Maruti’s purchases. Maruti’s supplier firms also are very local in their purchasing (Appendix 4.1). Detailed data from five of Maruti’s major first-tier suppliers in the Gurgaon area show that, at least, 43–67 percent of their


0 5.5

13 km

0

8 Miles

4

H

UTTAR PRADESH

A D

R

Gurgaon-Maruti plant 26 firms, $209 million

E

L

H

I

Okhla; 6 firms; $20 million

Noida; 5 firms $128 million

Y

Faridabad; 11 firms; $46 million

Site for Maruti's Manesar supplier park

A

Ballabgarh

N Sohna

Rewari

Daruhera

3 firms; $11 million

A

Palwal

Bawal Figure 4.1 Location of Maruti’s major suppliers within the Delhi auto district Source: Created from Table 4.1

domestic supply chain is local. These figures represent minimum levels of localization of the first-tier suppliers because the data sets obtained in this study include only the major sub-suppliers.101 If these firms are at all representative, Maruti’s second tier of suppliers also is predominantly local. In other words, the assembler and its first-tier suppliers located in Gurgaon have backward linkages that are largely local. In summary, Maruti’s domestic supply chain is highly local – at least 50 percent, but more likely about 65–70 percent, of its domestic purchases are procured within a distance of less than 80 km from its plant. In the process of localizing its supply chain, Maruti has fueled the

Clustering as an Infrastructure Solution 119 Table 4.1 Value of Maruti’s purchases from suppliers in the Delhi district, 1996–97 Name of industrial area

Gurgaon, Haryana Noida, UP Faridabad, Haryana Okhla etc., Delhi Sohna, Haryana Others Sub-total for Delhi district (c) Total domestic purchases (d)

Maruti’s purchases (US$ million) (a)

No. of firms (b)

As % of purchases in Delhi district (a/c)

As % of purchases in India (a/d)

209 128 46 20 11 41 455

26 5 11 6 3 4 55

46 28 10 4 2 9 100

24 14 5 2 1 5 51

884

100

Source: Compiled from data on Maruti’s purchases from its top 106 suppliers by value, 1996–97.

creation and growth of a Delhi auto district that dominates other locations in production of cars and components. Given that locations like Pune and Mumbai are more industrially advanced and had a substantial lead in the auto industry prior to Maruti’s creation, makes the current dominance of the Delhi auto district an even more remarkable achievement. Maruti’s success in localizing also has helped reduce transportationrelated unpredictability in its supply chain. Given the short distance that freight has to travel and that the Delhi area has some of the best road infrastructure in the country, transit time is short and relatively predictable. At least for local-source inputs, the assembler can move toward just-in-time deliveries and low inventory buffers. Encouraging suppliers to cluster near its assembly plant The localization of Maruti’s domestic supply chain is anything but a coincidence. Rather, it is a key component of the firm’s competitive strategy, in the implementation of which Maruti is getting better and more aggressive. In the initial planning phase, Maruti earmarked an area along one edge of its plant for joint venture (JV) auto component plants. The first JV commenced production in 1987, and there were five of them in the “Maruti complex” by 1997. Maruti also encouraged suppliers that


needed to establish a dedicated or new manufacturing facility to locate in its vicinity. Initially, Maruti accepted that several of its suppliers located in distant cities, such as Pune and Chennai, would continue to operate from their home base. As Maruti’s production volumes grew, this arrangement was neither attractive nor necessary. Maruti began to negotiate with its more distant suppliers to invest in new or expansion projects near its plant, arguing that its production volume more than justified such an investment. Some of the larger suppliers did respond, but preferred to start with a branch assembly plant rather than a full manufacturing facility. The branch plants tend to be “quasi-local” in that they rely on their head office or main plant for parts, and for technical and managerial support. For example, Rane and Lucas-TVS, both from Chennai, started assembly operations near Maruti’s plant, but their key corporate functions, primary manufacturing facility, and R&D remain in Chennai, about 2500 km away. Similarly, Pricol – a supplier of instrument panels – has its main manufacturing facility in Coimbatore, in the southern state of Tamil Nadu, about 2400 km from Maruti. In 1987, Pricol established a branch assembly facility in Gurgaon, about 5 km from the Maruti plant. Coimbatore continues to serve as the corporate headquarters and to be the center for new product development, R&D, and manufacture of new products. Pricol will continue to supply directly from that location until production is fully stabilized. Once production stabilizes, the assembly operations are likely to be transferred to the branch facility in Gurgaon. Maruti finds that the branch assembly plants help overcome some, but not all, of the problems that distance creates. The quasi-local branch assembly plants perform better at meeting delivery requirements and inventory goals than do distant suppliers, but are not as good as the truly local suppliers. They are able to deliver usually at least once a day, and make it possible for Maruti to reduce both the inventory norm or guideline and the actual level of buffer inventories that it holds for their items.102 Maruti cannot, however, reduce inventory norms and buffers to the minimal levels associated with many of its fully local suppliers. For example, the inventory norm for headlamps supplied by Lucas-TVS’s branch assembly plant in Gurgaon was reduced from seven days in 1996 to three days in 1997. By contrast, for some of its genuinely local suppliers, such as Bright Brothers (garnish) and Mark Auto (fuel tanks), Maruti was able to reduce the inventory norm to one day. Further, while the inventory norm for headlamps is three days, Maruti holds less inventory for Lumax, its local supplier, than for the Lucas-TVS branch assembly


plant. Another limitation of branch assembly plants is that they do not help fully realize some of the other potential benefits of proximity, such as shorter lead times, quicker response, and greater technical exchange between the assembler and suppliers. Facilitating localization: Maruti’s supplier park and “incentives package” Clearly, Maruti would prefer its suppliers to have full manufacturing facilities (rather than branch assembly plants) in close proximity. Although it is currently the largest car assembler in the country, this is not proving sufficient cause to encourage all suppliers to move. With the entry of other world-class auto assemblers into the market, Maruti’s bargaining power with suppliers is eroding. Suppliers have an unprecedented opportunity to diversify their customer base – they do not have to meet a particular assembler’s demands and have little reason to make the relatively difficult decision to relocate, especially when the result of the impending competitive game among assemblers is anything but clear. In addition, the land market near the Maruti plant has become increasingly tight over the last few years, and several supplier firms argued that they could not invest in a new facility in the area because developed industrial land was either not available or was prohibitively expensive. Consequently, Maruti developed a package of four incentives, with assistance from government agencies, to attract suppliers to the area. A first and central feature of this package is well-located and fully developed industrial land subsidized by a government agency, that is, the Haryana State Industrial Development Corporation (HSIDC). Specifically, Maruti has negotiated with HSIDC to make prime land available to its suppliers at a government-established price that is substantially lower than prevailing market rates. This incentive of offering land at prices below market value needs some explaining. HSIDC develops and manages several industrial parks in the state. It uses its eminent domain power to acquire land at relatively low compensation rates, develops the land, and allots it to industries based on goals and criteria established by the state’s Ministry of Industries.103 It is also responsible for providing infrastructure services in these areas and, where necessary and possible, helping finance industrial projects. Its mandate is to run the operation on a “no-profit, no-loss” basis and enhance industrialization. Firms that want to locate in a particular industrial park apply for a plot, and those that fit the guidelines are included in the short list. The final allocation is by lottery. It is, in


effect, tantamount to winning a lottery because HSIDC sets the price at a level that covers land acquisition and development costs, but is still substantially lower than the prevailing market rate.104 In early 1996, Maruti reached an agreement with HSIDC by which the agency would earmark about 200 acres of land for the firm’s suppliers. Maruti solicited project proposals (the equivalent of a detailed “letter of intention”) from its suppliers and prepared a list of the preferred vendors that were eligible to obtain plots from HSIDC at the low government rate. A change in the national and state governments in June 1996, and subsequent policy changes, delayed Maruti’s project. Nonetheless, by May 1997, HSIDC had acquired 250 acres of land for the supplier park in Manesar, just 20 km south of the Maruti plant and connected to it and to New Delhi by a major national highway (NH 7). Plots, ranging from one acre to 25 acres, were carved out for 40 suppliers and account for 103 acres of the total plotted area of about 150 acres (the remaining 100 acres are for roads and other infrastructure).105 Maruti negotiated a final price of US$127 000 per acre, but only after carefully reviewing HSIDC’s estimated costs for acquiring and developing the land and concluding that it represented just a little more than actual costs.106 The prevailing market prices in the area in June 1997 were estimated to be about twice as high. A senior manager at HSIDC and the CEO of a supplier firm that bought several acres of land agreed that the prevailing market value was at least about US$230 000 per acre. In other words, Maruti negotiated a major (approximately 50 percent) subsidy on land for its suppliers. Second, Maruti and the firms that invest in the supplier park benefit from sales tax concessions. Although the Haryana state government offers more than one type of tax incentive, almost all of Maruti’s suppliers in the area have opted for a sales tax deferment plan.107 The firms investing in the supplier park are expected to opt for the same plan. Under this program, the new industrial unit charges its customers the full sales tax, but is allowed to retain this amount, without interest, for five years. For a supplier firm, this translates into a five-year interest free loan, equivalent to a certain proportion of its sales revenue. For example, if the sales tax on a product manufactured by a firm is 5.0 percent, and its annual sales revenue is US$1.0 million, the firm gets to retain US$50 000 each year for five years. At the end of the five-year period, the firm is required to pay a lump sum of US$250 000 (and no interest) to the tax department. For Maruti, the main tax benefit is that it can deduct from its local tax bill the sales tax that it pays on inputs procured from suppliers


located in Haryana.108 Local sales taxes paid on goods procured from another state cannot be offset, causing what public finance economists call “tax pyramiding” or the repeated taxation of inputs. Specifically, Maruti pays sales tax on all inputs and on the final price of the good, which includes the price of the inputs as well as the sales tax paid earlier. For goods purchased within the state, including those from the new supplier park, Maruti avoids paying tax twice on the input which, in turn, helps lower the final price of the product (or improves the profit margin).109 In 1996–97, Maruti received an offset of US$25 million on its total sales tax bill of US$59 million, reducing its payment to US$34 million (Personal interview, Deputy Commissioner, Gurgaon Tax Office, October 1997). The assembler received an offset equivalent to 1.5 percent of sales revenue and reduced its sales tax bill to 2.1 percent of sales revenues. Third, the firms located in the supplier park will, potentially, have access to a valuable input – reliable and good quality electric power. To counter the electric power problem in the area, Maruti supplies power from its own 60 MW gas turbine power plant to some of its component suppliers in its immediate vicinity (discussed in Chapter 2), and is planning to extend this facility to firms in the supplier park. Maruti has already built a strong reputation among its suppliers for providing reliable service and high quality power. Suppliers that are connected to Maruti’s power system rate it as an excellent arrangement – it has adequate generation capacity, is highly reliable, and offers better quality electricity at lower prices than the public utility. As noted in Chapter 2, however, extending its electric power supply to more vendors by way of a dedicated transmission line is proving to be slower and more cumbersome than Maruti anticipated. Maruti is not entirely certain that it will be able to provide electricity to its supplier park, but some of the firms that are moving there believe that they are likely to gain this access.110 At a minimum, these firms are relatively certain that electric power is unlikely to be a problem in the supplier park – either the government will ensure a reliable system or Maruti will step in to fill the gap. The implicit guarantee of reliable power is an incentive that few, if any, state and local governments in India can match – not only because the demand for electricity substantially exceeds supply and generating capacity in almost all states in the country, but also because few firms believe that the government is capable of delivering on such promises. Indeed, the Haryana government declared Gurgaon as a “power-cut-free” industrial area in 1992–93, but was never able to deliver on its promise. The implicit


guarantee of reliable electricity is, then, a key component of the incentive package that is encouraging Maruti’s vendors to locate in its new supplier park. Fourth, HSIDC and Maruti planned to have basic services in place before the firms began production, and to offer significantly better infrastructure and facilities relative to most other industrial parks. HSIDC committed itself to providing water supply, drainage, and sewage systems; treatment facilities for waste water and sewage; and other facilities, such as a conference center and a training center. By comparison, in many industrial areas even “basic” water supply and sewage services are either unavailable or inadequate, and firms have to supplement the public system or rely entirely on self-provision, for example, by investing in their own tubewells/borewells and septic tanks. Some firms believe HSIDC’s claim that the park will provide state-of-the-art infrastructure and facilities similar to those offered in world-class industrial parks (for example, a helipad, conference center, and skills development center). At a minimum, firms in this supplier park expected the basic services and local roads to be in place before they commence full-scale production. By contrast, infrastructure development in most new government-sponsored industrial parks takes place slowly, over 5–10 years, only a few “pioneer” manufacturing units move in early and have to, initially, survive without public provision of services. Although it is too early to evaluate the supplier-park project as a whole, the incentives package appears to have worked exceptionally well. Forty major suppliers signed contracts to buy land and paid 25 percent of the total cost in advance to HSIDC. About 30 of these firms were committed to US$138 million in new investments. Moving with uncharacteristic speed, HSIDC completed the land acquisition in May 1997, and within six months had completed the access roads and made water supply and electricity available for construction. Some suppliers started construction in January 1998 and were in full-scale production by mid-1999. Maruti has been actively involved in designing, negotiating, and pushing the package, but it has offered only one direct contribution, that is, potential connections for its suppliers to its own reliable electric power system. The other three, and somewhat more concrete, incentives – subsidized land, sales tax concessions, and on-site infrastructure – are provided by the state government, and appear to have been wrung from it. Has the government given away too much? Are the concessions that Maruti negotiated exceptional?


Are the incentives exceptional? Neither the subsidized land nor sales tax concessions for Maruti and its suppliers are exceptional or exclusive. Indeed, these are common or general incentives that the Haryana state government offers to various types of firms in different industries, provided they meet certain criteria. However, firms are not always willing and able to avail themselves of government incentives. For example, some subsidized land is in industrial parks that either are relatively distant or have little access to infrastructure. In a developed and well-located industrial area such as Gurgaon, the demand for subsidized industrial plots exceeds supply. The government has, thus far, used rationing techniques – targeting a set of industries, excluding potentially polluting firms, carefully screening proposals to prepare short lists, and allocating plots by lottery. Firms attempting to qualify for the subsidized land incur significant transaction costs for an entirely uncertain outcome – after they qualify, they have to win the lottery. In the supplier park negotiations, Maruti and HSIDC found a way to make the process work faster and better for all players (this point is discussed in the next section). Firms also are often unwilling or unable to take advantage of sales tax concessions and systems such as MODVAT (modified value-added tax). The government introduced MODVAT to help overcome problems such as pyramiding or multiple taxation in the case of excise duties. For the MODVAT system to work well for a firm such as Maruti, most (preferably, all) of its suppliers have to register as taxpayers. However, Indian tax regulations exempt small firms from registering for and paying excise tax – this is an incentives that the government offers to small industry. More than the fiscal benefits of avoiding excise duties, these firms appear to value not having to deal with government bureaucracy, and avoiding the transaction costs associated with filing taxes and hosting auditors. Indeed, analysts have argued that the option of not paying taxes combined with some of the other incentives offered to small industry are so strong (or perverse) that firms have chosen to fragment into several small units rather than grow into one large firm. Nevertheless, Maruti is convincing more and more of its suppliers to register, irrespective of their size and tax regulations. The argument that it has offered is this: first, the excise duty is paid not by the manufacturing unit but by its customer, who can, then, reclaim the tax under MODVAT; and second, once the supplier firm registers as a taxpayer, it can also claim offsets on taxes that it has paid on its inputs. In other words, MODVAT can work for the benefit of all these firms and


reduce the multiple taxation problem if each player, in turn, ensures that its sub-suppliers pay tax and provide tax receipts. By pushing this system, Maruti is lowering the final price of its product or increasing its profit margin. It is, simultaneously, helping the government achieve two of its key but highly elusive revenue goals – broadening the tax base and making the system self-monitoring. In sum, the incentives that Maruti has received from the government are not exceptional. What is exceptional is the extent to which Maruti has been able to make existing government incentives yield benefits for its suppliers and itself. Maruti has used its strong managerial ability to identify opportunities and its clout with both government and suppliers to actually convert these opportunities into benefits. The process appears to result in a win–win situation for the assembler, its supplier firms, and the government. The “model” supplier-park deal and why government also wins The Maruti–HSIDC supplier-park deal represents an effective approach or “model” for developing industrial parks that state governments can seek to replicate. This section focuses on the negotiation and implementation process and highlights features that have helped make existing government incentives and policies, targeted towards industry, work better.111 It brings into question some of the assumptions underlying recent policies that encourage state governments to enter into partnerships with private-sector real estate developers to develop and manage industrial parks in a more commercial or market-oriented manner. In response to Maruti’s request for subsidized land, HSIDC offered two sites. One site, located 70 km from the assembly plant, was developed and could be handed over immediately. The second site was 30 km away, but land development would take an estimated two to three years to complete. The assembler was looking for a site that was even closer than 30 km and could be developed in less time. Maruti was particularly interested in a site near IMT (Industrial Model Township), a proposed Indo-Japanese industrial park project. The IMT project was conceived as a state-of-the-art industrial park with exceptionally high quality infrastructure services and facilities, and was to be developed as a joint-venture between a Japanese development firm and Haryana state government. A highly publicized public–private partnership, the IMT was also a pilot for a new approach to developing industrial parks that represented a move toward commercialization and private sector involvement, and away from government dominance.112


A site of about 1750 acres of land had been identified for the IMT project, and Maruti kept pushing the government to acquire land in that area. The result of Maruti’s persistence was that the HSIDC identified a 250-acre parcel that was not pledged to IMT but required a no-objection from the project consortium. The IMT consortium was unwilling to grant approval and, instead, offered land in its own township. The negotiations between IMT and Maruti failed. From Maruti’s perspective, the IMT prices were high and, worse, the payment terms were unreasonable. Specifically, for a plot size of less than 10 acres (which is what 39 of the 40 suppliers had requested), the price was set at US$223 000 per acre. In addition, due to the insistence of the Japanese partner, the price and payments were pegged to the US Dollar. Thus, if the value of the Indian Rupee continued to slide, as was widely expected, the final cost of land would be prohibitive and would render the entire project unfeasible. Maruti went back to the Haryana government to identify land outside the radius controlled, directly or indirectly, by the IMT consortium. Early in 1997, for reasons that are not entirely known, the Japanese developer pulled out of the IMT project. According to a manager at HSIDC, the development firm pulled out because it was uncertain about the government’s ability to co-finance the project and doubtful regarding the commercial viability of this state-of-the-art industrial park.113 The Haryana government was interested in keeping its widely publicized project alive, however, and the Maruti supplier park seemed like an excellent start. HSIDC earmarked 250 acres along one edge of the IMT project and negotiated with Maruti on the terms and conditions that the suppliers would have to meet. The discussion below summarizes the selection process and the terms of payment and why these worked well for HSIDC. Selection process for supplier firms Maruti and HSIDC decided to manage the qualification and selection process jointly and make it transparent, relatively fast, and without a lottery. Developing the selection criteria was the first step, and the partners spelled out their goals and priorities. Maruti’s main goal was to have suppliers closer, and it decided to give priority to: (a) larger, out-of-state suppliers that were willing to establish a facility in the area, and (b) its suppliers in Gurgaon and Faridabad (especially the smaller ones) that needed to expand but were having difficulty in finding developed but affordable sites in close proximity to the assembly plant.


HSIDC’s goal was to attract industrial investment and catalyze development by using the supplier park as a “model phase” of the IMT. To do so, it needed to identify firms that were willing and able to invest immediately, rather than firms that would buy the land but defer investment in plant and equipment. Land-owing government agencies, including HSIDC, are extremely wary of these types of investors and tend to view them as speculators rather than industrialists. Various government policies, such as the (in)famous “land ceiling act,” designed explicitly to check speculation have had little, if any, success.114 Landowning agencies, therefore, consider one of their main tasks to be screening applicants in a manner that encourages genuine investors and discourages speculators. For HSIDC, Maruti’s involvement solved the “speculator screening” problem. All the applicants were “certified” as major Maruti suppliers, the assembler was endorsing only those projects for which it would be a direct and major customer, and giving priority to suppliers who wanted to start construction immediately. Although HSIDC did review all project proposals – in particular, data on estimated investment and sales, projected employment, and financing sources – it had a guarantee on their being both commercially viable and fast-track projects. HSIDC’s second major requirement was that these firms (or their processes) be “non-polluting.” Maruti agreed to this condition, advertised it as a requirement among potential applicants, and screened out proposals that would, potentially, violate the pollution norms established for the industrial park (for example, firms that do electroplating). Third, HSIDC wanted the firms to pay the entire cost for the land within a nine-month period – 25 percent as soon as they were selected and the remaining 75 percent in three equal installments three months apart. Within days of receiving the applications, HSIDC set a date for interviews. A six-member selection committee was formed with equal representation from HSIDC and Maruti. The interviews were scheduled over two days, and applicants came in for a 20–30 minute discussion to clarify any issues regarding project feasibility and financing sources. The list of selected firms was finalized almost immediately. By June 1997 – that is, within weeks of completion of the land acquisition – a majority of the selected firms paid the first installment for the land. For the manager of the Gurgaon HSIDC office, this represented a highly successful screening and allotment procedure. A significant and reliable cash flow was established upfront, ensuring sufficient funds to proceed with rapid implementation. The speed that HSIDC demonstrated in developing the Maruti supplier park was due partly to


this availability of funds. In other words, the project became selffinancing at an early stage and did not have to wait for the limited and piecemeal budgetary allocations from the head office (in Chandigarh) for its development. By contrast, infrastructure investments and development of most new industrial parks tend to start slowly, buyers tend to dribble in rather than arrive en masse, and these together lead to the industrial park getting stuck in a “low-level” trap. The result is that industrial parks tend to take more than 5–10 years to show signs of significant industrial activity and to go beyond a minimal level of infrastructure provision. From HSIDC’s perspective, developments at the Maruti supplier park challenged the skepticism of the Japanese developer regarding HSIDC’s ability to raise requisite financing for the project and the viability of the IMT project as a whole. HSIDC gained confidence that it can successfully develop the project on its own, without any foreign or local private sector partner. On January 5 1998, less than one year after the developer pulled out, HSIDC issued full-page advertisements in leading newspapers to formally announce its IMT project and invite applications (see Appendix 4.2 for clipping). A project long on the drawingboard had finally taken off. Apart from helping launch IMT, the Maruti supplier park is expected to play two additional and important roles in its growth. It will serve as the model or showcase for this Industrial Model Township, with Maruti’s suppliers investing millions of dollars in the area and some starting full-scale production in 1999. The supplier park will also play the role of an “anchor store” in a new mall, that is, boost investor confidence and help attract significant industrial investment (see Appendix 4.2). If the phenomenal growth of Gurgaon is any indicator, these expectations are more than justified. Positive spillovers: Gurgaon as a diversified industrial district Maruti has been cutting deals with state agencies not just on electric power and developed land but also on several other infrastructure issues. For example, Maruti contributed toward a new water and drainage project and has offered to finance and manage the expansion of the access road that connects the national highway to its plant.115 It is also negotiating with Indian Railways to construct a railway siding at the plant; the current loading station is 12 km away. Maruti is using these investments to mitigate infrastructure problems for its plant and for its suppliers in the area, and also to encourage crucial suppliers to relocate.


Maruti’s investments and its collaboration with the state are helping improve the infrastructure in Gurgaon and, thereby, creating positive externalities. On the one hand, Maruti is pushing public agencies to improve water supply, roads, and transmission networks. On the other hand, the assembler is contributing directly toward the capital costs of some of these investments, and it is using its generating capacity to supply power to the capacity-starved public grid. The improving infrastructure benefits not just Maruti and its suppliers but also other firms in the area, and it is attracting diverse firms to locate in Gurgaon. In this case, improving physical infrastructure is a major driver of and benefit from physical agglomeration. Unlike most Marshallian external economies, this benefit of agglomeration accrues to firms not only in related sectors but also in sectors that may be completely unrelated to each other in terms of inputs, markets, and technology. Since Maruti first started production in Gurgaon, the size and location value of this industrial area have increased dramatically. An estimated 1600 small-scale firms and 150 large- and medium-scale manufacturing units are located in the district.116 Together, these firms employ about 90 000 workers. Exports from Gurgaon include computer hardware, garments, air conditioners, sports equipment, auto components, and cars. In 1996, these exports amounted to an estimated US$325 million. Figure 4.2 shows the dense industrial development that has occurred around Maruti. Overall, the Gurgaon area is developing as an important and diversified industrial cluster. This is, then, a case where a large auto assembler and the public sector are together developing a diversified industrial district that has adequate infrastructure and goes beyond being a mere agglomeration of automobile firms. The deal-making between Maruti and public agencies – the state electricity board and the industrial development corporation – serves as a good example of how it is possible to use private interest and investment to achieve both public and private goals.

Summarizing the benefits of clustering For Maruti and its suppliers, this supplier park and, more broadly, the clustering or localization strategy offer three benefits: (a) better logistics and just-in-time delivery that will allow better implementation of lean production; (b) improved access to generalized inputs, particularly infrastructure services; and (c) sales tax concessions. In addition, the strategy potentially (and in the longer run) also offers many of the other

N MARUTI

INDUSTRIAL PLAN SECTOR 18, 19, 20, GURGAON Figure 4.2 Industrial development around the Maruti assembly plant, 1996


benefits that the literature traditionally associates with proximity – in particular, relational or collaboration benefits that the industrial districts literature emphasizes, and the emergence of Marshallian external economies. It is, perhaps, worth noting that while Marshallian external economies can be construed broadly to include all sorts of benefits of co-location, the emphasis is on benefits associated with technical spillovers and access to specialized inputs.117 That is, the literature on Marshallian external economies does not focus on access to generalized inputs, such as basic infrastructure services, as a major benefit or driver of co-location. Neither does the literature on industrial districts. From the government’s perspective, Maruti is helping the state succeed in its goal of developing successful industrial parks and enhancing industrial development (see Figure 4.2). For HSIDC, the supplier park helped start the IMT project and is expected to serve as an anchor development that will help attract additional industrial investment to the area. For Haryana’s tax department, Maruti and its suppliers are among the largest sources of tax revenue, and the clustering strategy helps expand the tax base. More important, perhaps, for both the central and state tax departments is the role that Maruti is playing in expanding the number of supplier firms that are registered under the MODVAT system. Indeed, if the assembler can convert a majority of the firms in its supply chain into taxpayers that demand and provide tax receipts, it will have helped the tax department achieve a textbook case of tax reform – one that results in wider tax base and a selfmonitoring, transparent, and more effective system. It is important to emphasize that the incentives offered by the government are either self-financing or require no financial outlays. At worst, they defer for five to seven years the revenue benefits or sales tax that should accrue to the government with the expansion of the industrial base. That is, the Haryana incentives package is revenueneutral in the short run, and revenue-enhancing in the medium to long term. Overall, the state appears to have devised an industrial recruitment strategy that works rather well and brings into question some of the prevalent notions in the public finance literature – that “location contests” or competition among governments to attract industrial investment are either ineffective or, when they do work, serve as a drain on the treasury and are often financially unsustainable (see, e.g., Wheeler and Modi 1992; Chapman et al. 1995). If this clustering strategy has such significant benefits, are other firms and state governments adopting a similar approach? Or is this a unique case of political economy where the interests of an exceptionally


powerful firm happen to coincide with the interests of a particular state government? The next section shows that the clustering strategy is anything but unique. In fact, new entrants such as Ford, Hyundai and Daewoo are trying to emulate Maruti. Ford and Hyundai are creating their own clusters and fueling the growth of an auto district in Chennai, 2500 km from Delhi, with assistance from the Tamil Nadu state government. Daewoo Motors and Honda have located in an industrial area on the border of Delhi state and, by deciding to rely largely on local suppliers, are helping consolidate the supply base in the Delhi auto district. In addition, Daewoo is copying Maruti’s strategy by encouraging its non-local suppliers to move to its vicinity and is working with the UP state industrial development agency to develop a 300-acre supplier park in Surajpur. According to a Daewoo official, Daewoo Motors has 217 vendors [or suppliers] spread across the country. We would like to get them together and closer to Daewoo’s facilities in Surajpur. This would not only help the company in better inventory and quality management but also improve the logistics. Daewoo already has 11 vendors near its premises now. (Economic Times, March 16 1998)

Ford, Hyundai, and the growth of the Chennai auto district Ford’s location decision Ford began operations in India in 1996 as a joint-venture partner with Mahindra & Mahindra, the nation’s leading utility vehicle manufacturer. To ensure a quick entry into the market, the partners decided to extend by 20 000 vehicles the capacity of a Mahindra factory at Nashik, in Maharashtra, and started assembly operations using imported knocked-down kits. Ford subsequently increased its equity stake in the joint venture to 90 percent, and its longer term plans for India and Asia required that the firm set up an integrated manufacturing facility with an initial capacity of 25 000–50 000 vehicles per year that could be expanded to 100 000 vehicles. It started shopping for a plant site and was looking for an attractive incentive package to help seal its location decision. The competition quickly whittled down to two locations – Pune in Maharashtra and Chennai in Tamil Nadu. Ford eventually selected Chennai, even though its first assembly plant was operating in Maharashtra. Discussions with senior managers at Ford in Chennai


suggest that the following factors affected the location decision.118 First, and most important, Chennai’s supplier base for auto components is reputed to be one of the best outside of Delhi.119 Second, both skilled labor and good engineering graduates are easily available, labor costs are relatively low, and the state has a better industrial relations record than Maharashtra. Third, Chennai has one of the largest international ports in the country. Fourth, Tamil Nadu’s incentive package was attractive particularly because it included “freehold” land for the manufacturing facility and a 14-year sales tax holiday. (Maharashtra offered only “leasehold” land and was unwilling to consider sales tax concessions.) Fifth, the Tamil Nadu government assured adequate infrastructure for the plant – it committed to invest in a water supply system and a sewage treatment plant and to connect the assembly plant to two separate power stations to provide more reliable electricity. Ford estimated that these infrastructure investments were likely to the cost the government about Rs 200–300 million (or US$5.7–8.6 million). The assembler appears to have negotiated highly favorable terms on land and infrastructure. Ford paid a total of Rs 300 million (US$8.6 million) for its 300-acre site and, per its own estimates, infrastructure development alone may cost the government that much. The unit cost works out to US$28 600 per acre. By comparison, the 250 acres of land for the Maruti supplier park cost HSIDC – which relied on eminent domain and below-market compensation rates – about US$20 000 per acre in payments to the land owners, plus an estimated US$56 200 per acre for infrastructure and land development.120 The low price that Ford paid for its site does not, then, appear to be entirely due to low land prices in Chennai. Rather, Ford appears to have gotten the land almost free and the price that it paid to the government is, at best, likely to cover only the infrastructure and land development costs. The result is that land costs represent a mere 2.3 percent of Ford’s initial investment of US$370 million in this assembly plant project. With the selection of Chennai as the site of its integrated manufacturing facility, Ford decided to phase out assembly operations in Nashik. Hyundai and Mitsubishi follow Ford to Chennai Within months of Ford opting for Chennai, Hyundai – Korea’s largest car maker – followed suit. Hyundai received the same incentives package as Ford, and decided to build an assembly plant for 120 000 vehicles about 60 km from the city center. Mitsubishi Motors of Japan also decided to locate in Chennai. With three major car assemblers located


in and around the same city and only a short distance from each other, the foundations for a Chennai auto district are in place. And these assemblers’ localization or clustering strategies are triggering additional development. Ford’s transportation solutions: logistics planning and localization Well before it started production in mid-1999, Ford developed a radical logistics plan to improve the efficiency and delivery performance of its non-local supply chain (discussed in Chapter 3). Specifically, Ford invited bids from internationally reputed logistics companies to serve as its total logistics partner. It created collection hubs in Delhi, Pune, Bangalore, and Chennai, where shipments from different suppliers in the area are consolidated into full-truck loads and dispatched to the assembly plant. Instead of leaving the responsibility for transportation and just-in-time delivery to the suppliers, as is the common practice in India and other countries, Ford and the logistics partner have taken over that responsibility and bear directly the costs of freight and of carrying inventories in-transit. Ford’s primary transport solution, however, is not the logistics plan but its localization strategy. Like Maruti, Ford has been encouraging its suppliers to locate in close proximity to its plant. Ford considers distance to suppliers to be such a significant problem that it has altered its standard supplier selection criteria, telling potential component suppliers that they need to be located in Chennai if they are serious about working with the company. According to Ford’s vice president for supply, … in selecting our suppliers, their (agreeing) to locate here (in Chennai) is top-priority. Our objective is to have as many suppliers as possible close by. There is no substitute for this simple logic – “minimize transport and logistics costs.” Even if there were no sales tax incentives, we would have done this (localization) anyway. (Personal interview, August 1997) To enhance localization of its supply chain, Ford has taken the following steps.121 It has developed a supplier-park within its own site (about 1 km from the assembly building) and hosts six firms producing items that are hard to ship, such as seats, wheel assembly, glass, and fuel tanks. Each of these suppliers accounts for a significant proportion of Ford’s total domestic purchases by value. Further, Ford has ensured that an additional 25 of its 75 suppliers will deliver from a manufacturing or


assembly facility located in or around Chennai. Of these 31 suppliers, only 16 were originally from the area. Ford negotiated with the 15 nonlocal suppliers and convinced them either to relocate or establish a new facility in Chennai. The result is that supplier firms located in the Chennai auto district will account for an estimated 70 percent of Ford’s total domestic purchases once production has stabilized. Only in cases where creating an additional or new facility would be clearly unjustifiable did Ford agree to source from non-local suppliers. Specifically, for certain components either the volumes that Ford wanted were too low or the capital costs of a new facility were too high, or both. For these 45 or so non-local suppliers that account for an estimated 30 percent of its total domestic purchases, Ford devised its elaborate, experimental logistics arrangement. There can hardly be stronger evidence that Ford is serious about implementing lean production and that, because of the poor transportation systems in India, it considers distance to be a serious obstacle in achieving that goal. Hyundai’s localization strategy Hyundai’s localization strategy is surprisingly similar to that of Ford, and perhaps more aggressive. The Chennai plant, Hyundai’s only fully integrated manufacturing facility outside South Korea, started production in November–December 1998. According to a manager in the purchase department, Hyundai’s starting goal was a domestic content of 70 percent, with a steady increase to 100 percent. To achieve this goal, the purchase department changed the supplier-selection criteria from “quality, price, and delivery” to “quality, price, and location.” In South Korea, daily or just-in-time delivery does not depend on location because distances are small and infrastructure is relatively better (it takes a maximum of five hours for Hyundai’s shipment to travel from Seoul to Pusan, a distance of 420 km). In India, by contrast, the location of the supplier is an important criterion because it can take from seven to ten days for a shipment from Delhi to reach Chennai. Suppliers based in Delhi cannot deliver on a daily basis, and inventories tend to stay high. Based on supplier negotiations completed by mid-1997, Hyundai estimated that about 80 percent of its domestic purchases would come from Chennai. Many of the supplier firms based in Delhi and Pune agreed to establish, at least, an assembly plant in Chennai to help ensure just-in-time deliveries and allow Hyundai to avail itself of sales tax concessions. To encourage suppliers to locate in Chennai, Hyundai presented a three-pronged argument. First, it emphasized that its volumes would


be significant – unlike, for example, General Motors which had an installed plant capacity of 20 000 vehicles. Second, it was adopting a single-source strategy – for a given item it would rely on a single supplier, and intended to maintain long-term and good business relationships with its supplier firms. Third, it was developing India as a supply base for export markets, specifically, for plants in South Korea and some of the other 14 countries in which Hyundai has CKD assembly plants. Overall, both Ford and Hyundai have been relatively successful in encouraging their suppliers to relocate to Chennai. Ford expects about 70 percent of its domestic purchases to be sourced from Chennai, and Hyundai about 80 percent. Arguably, the localization strategies of Ford and Hyundai, individually, might have had little impact on suppliers’ decisions to move. Many suppliers would have found it too risky or uneconomical to invest in a new facility for one assembler. With Chennai boasting three major assemblers – Ford, Hyundai and Mitsubishi – the idea of making fresh investments in the area became reasonable, in particular, because none of the assemblers is expecting these firms to serve as “captive” suppliers. In other words, the decision of major assemblers to cluster together is facilitating the growth of the supply base in Chennai and creating an auto district.

The emerging geography of production In this chapter it is argued that transportation-related problems are forcing auto firms in India to cluster. If this argument is valid more generally, we would expect to see similar clusters in other countries where distances are large and transportation infrastructure is poor. Conversely, we would expect to see far less geographic concentration in countries, such as the United States, where the transportation infrastructure is good. This section compares the geography of production in the Indian and US auto industries,122 and shows sharp contrasts between the two. The section below paraphrases Rubenstein’s (1992) analysis of some of the major geographical trends in the US auto industry. It, then, contrasts these trends with those in the Indian auto industry and suggests some of the reasons why the trends tend to diverge in the two countries. The geography of the US auto industry Recent upheavals in the [US] auto industry have resulted in increased concentration of production in the interior [along the I-65


and I-75 corridors]. For geographers, the most distinctive element … is the extent to which strategic decisions are being based on minimizing freight cost, consistent with neoclassical location theory. However, local labor climate is the most critical factor influencing the distribution of … production within the I-65 and I-75 corridors … . Producers are more likely to select individual communities within the I-65 and I-75 corridors … because of a desire to avoid concentrations of militant and unionized workers. Rubenstein (1992) Rubenstein contends that in the 1980s the long-term locational pattern within the US auto industry suddenly shifted. Automobile production had been centered in southeastern Michigan, especially the area near Detroit, since about 1903. Under the influence of Ford and later General Motors, most auto components were manufactured in southeastern Michigan as well, but most final assembly plants were located elsewhere, especially near population concentrations on the northeast, south, and west coasts. In the 1980s, US producers closed nearly all of their assembly plants in coastal locations and retrenched in the interior. The new Japanese assembly plants – usually referred to as “transplants” – also chose locations in the interior and in communities that were not traditionally associated with the auto industry. The result is that most auto assembly plants in the US are now located within 100 km of Interstates 65 and 75. Interstate 65 runs 1500 km from Indiana to Alabama, that is, from an area near Lake Michigan to an area near the Gulf of Mexico. Roughly 200 km to the east, Interstate 75 runs nearly 3000 km between Michigan and Florida. Rubenstein argues that this macro or regional level change in location – the concentration of assembly plants in the I-65 and I-75 corridor – can be explained almost entirely as an attempt by auto assemblers to minimize freight costs, particularly, the cost of distributing vehicles to a national market. This outcome is in accordance with neoclassical or, more precisely, Weberian industrial location theory. Freight costs do not, however, explain the emerging micro-level geography – why a specific community may be selected for a plant location over another one – within the I-65 and I-75 corridor. At the micro-level, the new investments are dispersed among communities and states. In fact, Ohio is the only state that has been able to attract two Japanese transplants. Rubenstein contends that the local labor climate is the most critical factor influencing the distribution of production. Auto makers have selected communities that do not have a


tradition of unionization and, usually, those without an auto plant. This micro-level outcome is more consistent with the structuralist argument that the decision to open or close a plant must be understood as one of several social impacts stemming from global changes in the organization of industrial production. The impact of just-in-time (JIT) on supplier location Because assemblers have located several plants in the I-65 and I-75 corridors, suppliers also have been attracted to the area. To meet JIT delivery requirements, some suppliers have located production facilities near final assembly plants but usually not in the same community. For example, Japanese transplant suppliers have located in the same states as the transplant assembly plants they serve, but are widely dispersed within those states. Other suppliers have located outside the I-65 and I-75 corridors – often outside an “ideal” JIT distance of 150 km – to take advantage of lower labor costs. That is, labor cost rather than JIT delivery is the key factor influencing the location decisions of these suppliers. The just-in-time logic has, however, helped stem the loss of automotive suppliers from southeastern Michigan. In the late 1970s and the early 1980s, when the restructuring of the industry began, the predominant spatial tendency was to relocate some production from the north to the south in search of a lower cost and non-unionized labor force. This change in the location of suppliers appeared consistent with structuralist theories concerning the spatial division of labor. The diffusion of JIT helped check the trend toward decentralization. Since the early 1980s, the number of component producers in southeastern Michigan and Ohio has increased. Meanwhile, except in the I-65 and I-75 corridors, fewer firms have opened new facilities in the south since 1980. Some suppliers find that with JIT delivery requirements the savings in labor costs achieved through a southern location are now outweighed by freight penalties, that is, the additional cost and time involved in shipping to customers in the north. In sum, the concern with minimizing aggregate freight costs helps explain the macro or regional-level geography of the auto industry and the decision to locate production along the I-65 and I-75 corridors. Concern with labor issues and the firms’ tendency to avoid concentrations of militant and unionized workers help explain the micro- or community-level location decisions of assemblers. The spread of justin-time production systems has helped stem the outflow of auto component suppliers from southeastern Michigan and has also attracted suppliers to the I-65 and I-75 corridor. But suppliers often are not


located within the same community as the assembly plant, and many are located further than the ideal 150 km distance. Both the tendency to scatter within the I-65 and I-75 corridor and the decision by some suppliers to locate outside, represent their attempt to take advantage of lower labor cost. In other words, just-in-time production has affected the micro-geography of the US auto industry, but has been less important than labor considerations. How and why the geography of production differs in India Since the Government of India liberalized the auto industry in 1993, several auto firms have entered the Indian market. Their location decisions and supply-chain strategies are altering the geography of production in the industry. The emerging geography is strikingly different from that in the United States, despite the fact that some of the same firms are playing the game in both countries. First, several of the new entrants are locating their assembly plants in the vicinity of other new or existing plants, that is, they are clustering together. At least two locations, the auto districts in Delhi and Chennai, have three major assembly plants at a distance of 80 km or less from each other (Figure 4.3). The Delhi district – which includes industrial areas in the states of Delhi, Uttar Pradesh, and Haryana – was able attract Daewoo Motors and Honda, in addition to retaining the Maruti plant. Daewoo and Honda chose to locate not only in the same state (Uttar Pradesh) but also in the same industrial area (Greater Noida). The Chennai district, located in Tamil Nadu, about 2500 km from Delhi, was able to attract three new assembly plants – those of Ford, Hyundai and Mitsubishi Motors. Second, the assemblers in both districts are aggressively localizing their supply chains to ensure just-in-time deliveries. Their suppliers are responding by moving production facilities as close as possible to their major customers, usually 80 km or less. These specific divergences in the industrial geography point to a more important difference between the two countries, that is, the definition of “concentration” and “distance.” For Rubenstein, the location of plants within the I-65 and I-75 corridor – the length of which ranges from 1500 to 3000 km – constitutes “concentration.” In India, the 2500 km distance between Delhi and Chennai seems so large and insurmountable that assemblers are forcing suppliers to either relocate or invest in new facilities near their own plants. At the micro-level, supplier firms in the United States that have relocated to be close to their major customer are located, at best, in the same state but are


Delhi – Maruti–Suzuki – Daewoo – Honda

N

E

Halol – GM

I

Mumbai/Bombay – Premier Auto – Fiat

N

D

I

Pune – Telco – Mercedes-Benz

P

A

L

A

Chennai/Madras – Ford – Hyundai – Mitsubishi Bangalore – Toyota

I n

d

i

a n

O

c

e a n 0 0

240

Kilometers 480 600

186.5 3.73 Miles Approximately

One Centim etr e Equals 240 Kilometres One Inch Equals Approximately 373 Miles

Figure 4.3 The emerging geography of production in the Indian auto industry

widely scattered within the state. They are able to supply just-in-time from distances that are often significantly more than 150 km. In India, the assemblers seem to consider anything more than 80 km as “too far” for just-in-time deliveries. According to the argument developed over the last two chapters, the divergence between the economic geography of the two auto industries can be explained as follows. The difference lies, first, in the extent and


efficiency of the freight transportation system and, second, the relative costs associated with two basic inputs – infrastructure and labor. In the United States, Rubenstein (1992) notes, “deregulation and enhanced competition within the truck and rail industries have made a variety of locational alternatives more feasible (for suppliers that are required to deliver on a JIT basis).” The suppliers can, hence, choose their locations to take advantage of lower labor costs and still meet JIT delivery schedules. By contrast, in India, the auto assemblers’ concerns with inbound logistics costs and supply-chain unpredictability overwhelm concerns regarding labor and distribution costs. This is due, in part, to the fact that inventory carrying costs may end up being larger than either the distribution costs or the labor costs, as in the case of Maruti. Financial costs, however, are clearly not the only explanatory variable. For example, some of Ford’s supply-chain initiatives are likely to raise its financial costs. Rather, the more important explanation lies in the “hidden” and often intangible costs associated with supply-chain unpredictability, much of which is induced by infrastructure deficiencies. Thus, auto assemblers in India are clustering – not just to reduce freight expenditures and the total cost of carrying inventories, but to gain better control over their supply chain and to attain some of the quality and efficiency gains associated with lean production. In other words, the direct and indirect costs imposed by the poor infrastructure, combined with the logic of JIT and lean production – and not labor issues – are determining the geography of production in the Indian auto industry.

Appendix 4.1 Geography of purchases – data from first-tier suppliers located in Gurgaon

Total domestic purchases (%)

Maruti’s local suppliers also have local backward linkages First-tier suppliers – geography of purchases, by value,1996–97 (Region/location where inputs originated) 100 90 80

33%

33%

33%

31%

39%

32%

70 Origin unknown South India West India Rest of north

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