Designing and Evaluating Value Added Services in Manufacturing E-Market Places

DESIGNING AND EVALUATING VALUE ADDED SERVICES IN MANUFACTURING E-MARKET PLACES

Designing and Evaluating Value Added Services in Manufacturing E-Market Places Edited by

G. PERRONE Università degli Studi di Palermo, Italy

M. BRUCCOLERI Università degli Studi di Palermo, Italy and

P. RENNA University of Basilicata, Potenza, Italy

A C.I.P. Catalogue record for this book is available from the Library of Congress.

ISBN 1-4020-3151-3 (HB) ISBN 1-4020-3152-1 (e-book)

Published by Springer, P.O. Box 17, 3300 AA Dordrecht, The Netherlands. Sold and distributed in North, Central and South America by Springer, 101 Philip Drive, Norwell, MA 02061, U.S.A. In all other countries, sold and distributed by Springer, P.O. Box 322, 3300 AH Dordrecht, The Netherlands.

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TABLE OF CONTENTS PRESENTATION

IX XIII

PREFACE

CHAPTER I: MANUFACTURING E-MARKETPLACES: INNOVATIVE TOOLS FOR THE EXTENDED ENTERPRISE 1

Giovanni Perrone 1. 2.

THE EXTENDED ENTERPRISE: IN SEARCH FOR SUPPORTING TOOLS

1

E-BUSINESS: A SUPPORTING TECHNOLOGY FOR THE EXTENDED

ENTERPRISE

3.

ENTERPRISE E-BUSINESS MODELS: THE BUSINESS-TO-BUSINESS

3.1 3.2 4. 5. 6.

B2B solutions for the extended enterprise E-marketplace: a tool for the extended enterprise? AIMS AND MOTIVATIONS OF THE RESEARCH

BOOK CONTENTS AND ORGANIZATION CONCLUSIONS AND FURTHER PATHS OF RESEARCH

REFERENCES

CHAPTER II: AN AGENT BASED ARCHITECTURE FOR MANUFACTURING E-MARKETPLACES

6 8 10 11 16 18 19 19 23

Giovanni Perrone and Giovanni Montana 1. 2.

THE AGENT-BASED FRAMEWORK

SYSTEM ARCHITECTURE

2.1

The system context

2.1.1 2.1.2 2.1.3

3.

The overall system Customer system The Supplier system

27 28 30

SYSTEM DYNAMICS

3.1

Customer and Supplier Systems interaction

3.1.1 3.1.2

3.2 3.3

3.4

Customer system activities Supplier system activities

Order Data Input Technological requirements inputting and processing

3.3.1 3.3.2

CAD on-line activities Process Planning Agent activities

MPA – PrPA interaction

3.4.1

23 25 25

35 35 36 37

38 39 39 40

41

The MPA activities

41

v

vi 3.4.2

4.

The PrA activities

CONCLUSIONS

REFERENCES

CHAPTER III: PROCESS PLANNING IN MANUFACTURING EMARKETPLACES

42

43 43 45

Giovanni Celano, Antonio Costa, Sergio Fichera 1. 2.

INTRODUCTION THE CAD ON-LINE

2.1

The structure and the activities of the CAD on-line

2.1.1 2.1.2 2.1.3 2.1.4 2.1.5

3.

Geometry Definition Additional features definition Material characteristics definition Part Design & Data file The user interface

THE PROCESS PLANNER AGENT (PPA)

3.1

The structure and the activities of the Process Planner Agent

3.1.1 3.1.2 3.1.3 3.1.4

The feature recognition The operation attribution The Petri Net construction The Data Base construction

REFERENCES

CHAPTER IV: MANUFACTURABILITY MODELS FOR MANUFACTURING E-MARKETPLACES

46 47 48 49 51 52 54 54

58 59 59 61 63 64

66 67

Lanfranco Imberti and Tullio Tolio 1. 2.

INTRODUCTION INFORMATION DEFINITION

2.1 2.2 2.3 2.4 3.

Type of information Information formalization Non Linear Process Plan (NLPP) Information in the Manufacturing Planner Agent DEFINITION OF PALLET LAYOUT

3.1 3.2 4. 4.1 4.2 4.3 Agent

Pallet configuration Pallet configuration in the Manufacturing Planner Agent MAPPING THE PROCESS PLAN ON THE PRODUCTION SYSTEM

Splitting: points in favour Splitting: drawbacks The Process Plan mapping activity of the Manufacturing Planner

67 69 69 69 72 76 78 78 82 86 87 88 89

vii 5.

CONCLUSION

REFERENCES

CHAPTER V: NEGOTIATION MODELS IN MANUFACTURING EMARKETPLACES

93 94 97

Giovanna Lo Nigro, Manfredi Bruccoleri, Umberto La Commare 1. 2.

INTRODUCTION

THE NEGOTIATION PROCESS

2.1 2.2

Negotiation as a coordination mechanism A Negotiation Taxonomy

2.2.1 2.2.2 2.2.3

2.3 3. 4.

Negotiation static dimension Negotiation dynamic dimension Negotiation protocol

Negotiation Performance NEGOTIATION IN MANUFACTURING E-MARKETPLACES THE PROPOSED MODEL

4.1 4.2 4.3 4.4 5.

Agent Architecture Negotiation dimensions and protocol The negotiation decision process Negotiation policies C ONCLUSIONS

REFERENCES

97 99 99 100 101 103 105

107 107 109 109 110 111 115 116 116

CHAPTER VI: PRODUCTION PLANNING IN E-MARKETPLACES 119

Marco Cantamessa and Matteo Gualano 1. 2.

INTRODUCTION INTEGRATED PRODUCTION PLANNING AND ORDER NEGOTIATION

2.1 2.2 2.3 3. 4. 5. 6.

A taxonomy Revenue management Demand forecasting SUPPORTING MAKE-TO-STOCK PRODUCTION SUPPORTING MTO / ATO PRODUCTION THE PRODUCTION PLANNER AGENT CONCLUSIONS

REFERENCES

119 121 121 124 125 128 134 138 141 142

viii CHAPTER VII: IMPLEMENTATION, NUMERICAL EXAMPLES AND TESTS 143

Giovanni Perrone and Paolo Renna 1. 2.

THE AGENT BASED ARCHITECTURE IMPLEMENTATION THE FUNCTIONALITY TEST

2.1 2.2 2.3

Inputting Technological Data Inputting Commercial data Agent activities

2.3.1 2.3.2 2.3.3 2.3.4

3.

First round of negotiation Second round of negotiation Third round of negotiation Fourth round of negotiation

NUMERICAL TESTS

3.1 3.2 4.

Numerical test data Numerical test results CONCLUSIONS

REFERENCES

143 148 148 153 154 161 161 162 162

163 163 167 169 169

CHAPTER VIII: BENCHMARKING VALUE ADDED SERVICES IN MANUFACTURING E-MARKETPLACES 171

Antonio Grieco and Emanuela Guerriero 1. 2. 3.

INTRODUCTION AND MOTIVATION PROBLEM AND GOAL STATEMENT THE UTILITY FUNCTION DEFINITION

3.1 3.2 3.3 3.4 4.

THE BENCHMARK MODELS FORMULATION

4.1 4.2 4.3 5.

Input Data and decision variables Mathematical model formulation – First step Mathematical model formulation – Second step THE TEST CASE

5.1 5.2 6.

Due-date Utility Function Cost utility Function Demand Utility Function Supplier profit utility function

Input data The benchmark results CONCLUSIONS

REFERENCES

171 174 178 178 180 181 182 184 184 185 188 189 189 190 197 198

PRESENTATION The rapid development of Information and Communication Technology and the market globalization are leading manufacturing enterprises to new business models and management strategies. In order to be competitive in the global market, characterized by the need for mass customization and by distributed and different social and economic environments, manufacturing enterprises need to adopt new business models for achieving flexibility, rapid time to market, and reduction of investment risk. This is why, nowadays, big and multinational enterprises are moving from the traditional hierarchical and centralized organization models towards more distributed and networked business models. In such new models, the enterprise focuses all of its efforts on its core competences and businesses and outsources the other value chain activities. Strategic relationships are constituted with suppliers, which involve themselves at the enterprise risk. The production capacity is delocalized in different and geographical dispersed plants in order to achieve higher flexibility, lower logistic costs, privileged contact and proximity with customers, and better integration with the specific social-economic environment. While until few years ago the enterprise network was typically created and managed by the original equipment manufacturer, which constituted stable relationships with the other supply chain actors, in the last years such a business model is spreading versus small and medium enterprises, which by being part of a collaborative and goal-oriented network can gain manufacturing capacity and financial assets suitable to compete in the global market. The increasing diffusion of the networked enterprise business model has been supported by the new web-based digital technologies that reduce communication costs among enterprises, which, even though localized in different geographical areas, can cooperate and collaborate as being an “extended enterprise”. Within such a collaborative context, e-marketplaces are assuming a crucial role. The electronic marketplaces are virtual locations where buyers and sellers are just one “button click” distant from making economic transactions. E-marketplaces can be classified according to stakeholderfocus, i.e. according to who control the e-marketsite. E-marketplaces are buyer-centric, when they are established by a big buyer, seller-centric, when they are established and managed by a big seller, or neutral when they are established by third party, usually referred as an exchange owner, who setsup the e-marketplace and gets a fee from EM transactions and services offered to EM participants. This last solution is largely indicated as the more ix

x adequate for small and medium enterprises because it increases the enterprise visibility and enhances a symmetric knowledge sharing for both the actors of the transaction. Although e-marketplaces represent a very important solution oriented to transaction cost reduction, they do not solve all of the difficult and complex tasks and activities of managing the extended enterprise. New innovative tools are needed for managing the collaboration among actors more than their competition. The research work done by the authors of this book is mainly focused on this idea. According to their studies, an e-marketplace can represent a valid support tool for the extended enterprise only if it allows, besides transaction cost reduction, information sharing and collaborative planning among partners. The project addresses an industrial context in the manufacturing mechanical components industry consisting of an extended enterprise of extremely fragmented small and medium enterprises which adopt a neutral e-marketplace where a set of registered customers and suppliers operate in make to order environment. A proper agent based architecture has been designed in order to support value added services in the e-marketplace like, for example, information sharing among agents in the different phases of order fulfillment. The business models and the agent-based architecture which represent the final result of the projects titled ““Distributed process and production planning in manufacturing enterprise networks” have been developed by different Italian research units and represent innovative solutions also extendible in other business contexts. The obtained results, tested by numerical examples, demonstrate that a cooperative research, made by different units with different scientific core competences, can achieve high levels of efficiency and effectiveness when a valid coordination activity is conducted. The reader of this book will surely appreciate the scientific competence and ability of the project coordinator, who wrote the first chapter of this book giving a clear overview of the book context and making easier the reading of the following chapters.

Prof. Ing. Sergio Noto La Diega Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale Università degli Studi di Palermo

"It is with great pleasure that I am writing this note for the book of Professor Giovanni Perrone entitled "Designing and evaluating Value Added Services in manufacturing e-marketplace". I have known Prof. Giovanni Perrone over the years, and I am well aware of his professional qualities, which undoubtedly reflect to this book. Prof. G. Perrone, from the time he was a graduate student of mine at MIT until today, has always shown great capabilities, in terms of research and teaching activities. I am confident that the reader of this book will not only enjoy it but he/she will also see it as a fine intellectual challenge."

Professor George Chryssolouris Chairman Department of Mechanical Engineering and Aeronautics University of Patras Greece

PREFACE The “extended enterprise ” is a new emerging paradigm in the manufacturing arena. Indeed, global competition is pushing manufacturing enterprises in several industries either to split geographically the production capacity or to work together in supply chain organizations involving several independent entities. This dynamic is involving both big companies, whose organisation is always more and more decentralised and geographically distributed, and Small and Medium Enterprises (SMEs) that are embracing new organisation forms such as the Virtual Enterprise (VE) one. The “extended enterprise” allows gaining agility, reactive ness, even proactiveness, and, of course, efficiency in the highly dynamic markets of the mass customisation and knowledge based economy era. However, the “extended enterprise” paradigm scales management complexity both at the strategic and operational level up. This requires new tools for managing the complexity of the extended enterprise. The Information and Communication Technology (ICT) enables the possibility to create new and innovative “tools for managing the extended enterprise”. This book addresses the above introduced issue of the tools for the extended enterprise. More specifically, it presents the results of a research developed under a two years program titled ““Distributed process and production planning in manufacturing enterprise networks” and funded by the Italian Ministry of Education, University and Research (MIUR) under the program PRIN2001. This research program, involving six Research Units from six Italian Universities, aimed at developing an innovative tool for manufacturing enterprise networks organized into an e-marketplace. However, the results reached by the research project are of general interest for any kind of extended enterprise and the research program represents itself a significant advancement in the tool for the extended enterprise research context. The book is organised in 8 chapters. Chapter 1 introduces the concept of the “extended enterprise” highlighting the necessity of specific and proper tools for managing it; it discusses how the electronic business (E-business) provides business models and technology that can support the extended enterprise. Specifically the possibility to use electronic marketplace (E-marketplace) business models and technology as support for the manufacturing enterprise networks is addressed in Chapter 1. Through an extensive analysis of the available literature it can be concluded that neutral e-marketplace is a suitable business models for supporting manufacturing enterprise network when proper added xiii

xiv value services able to support transactional, information sharing and exchange, and even collaborative relationships are provided in. This is the central aim of the research program presented in this book: to develop a proper IT based solution able to support Make To Order (MTO) operations among SMEs operating in the mechanical components manufacturing industry within a neutral e-marketplace business model. Chapter 2 presents an overview of the agent-based architecture proposed in the research program. The chapter illustrates the proposed solution by presenting its general structure, the main components and the way how it allows supporting MTO operations through automated workflows. Chapters 3 to 6 discuss in more details the methodologies adopted to support technological information exchange and processing, production planning and negotiation during MTO operations in a manufacturing emarketplace. Specifically, Chapters 3 and 4 discuss the methodologies adopted within the agent-based architecture to exchange technological information and to breakdown it in order to obtain technological planning. Chapter 5 presents the negotiation mechanism and policies that have been adopted during the negotiation phase. Finally, Chapter 6 discusses the methodologies adopted for making production planning and re-planning trying to meet customer commercial requirements. It also shows how production planning allows building the necessary information to negotiate with the customer. Chapter 7 reports how the agent-based architecture has been implemented in an open source simulation environment and how its functionality has been tested and proved by running realistic examples. Furthermore, it reports some numerical example demonstrating how the proposed architecture is able to provide true added value both to the customer and the supplier in the e-marketplace. Finally Chapter 8 presents the result of the agent-based architecture benchmarking. Indeed, in order to understand how good is the value the proposed architecture is able to bring to the participants to a neutral linear emarketplace, its results have been compared with a benchmark consisting of a “central planner” able to make optimal decisions for all the buyers and sellers within the e-marketplace. The benchmarking results, demonstrates, once again, how good is the performance of the proposed agent-based architecture in providing value within e-marketplace contexts. The editors would like to thanks the Italian Minister of the Education, University and Research (MIUR) for the support given to the research project, and all the Research Units that have been involved in the project and specifically:

Preface

xv

– prof. S. Fichera, Dr. G. Celano and Dr. A. Costa of the Research Units at University of Catania who carried out the research presented in Chapter 3; – Prof. T. Tolio and Dr. L. Imberti of the Research Unit at the Politecnico of Milano who carried out the research presented in Chapter 4; – Prof. U. La Commare, Dr. G. Lo Nigro and Dr. M. Bruccoleri of the Research Unit at the University of Palermo who carried out the research presented in Chapter 5; – Prof. M. Cantamessa and Dr. M. Gualano of the Research Unit at the Politecnico of Torino who carried out the research presented in Chapter 6; – Prof. G. Perrone and Dr. P. Renna of the Research Unit at the University of Basilicata who carried out the research presented in Chapter 7; – Prof. A. Grieco and Dr. E. Guerriero of the Research Unit at the University of Lecce who carried out the research presented in Chapter 8. Thanks go also to Eng. G. Montana who helped prof. Perrone in the coordination of the research project and co-authored Chapter 2.

The Editors Prof. Giovanni Perrone Dr. Manfredi Bruccoleri Dr. Paolo Renna

Chapter 1 MANUFACTURING E-MARKETPLACES: INNOVATIVE TOOLS FOR THE EXTENDED ENTERPRISE Supporting manufacturing enterprise networks Giovanni Perrone Dipartimento di Ingegneria e Fisica dell’Ambiente, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano, 10, 85100, Potenza - Italy

Abstract:

This Chapter introduces the research carried out under the research program titled “Distributed process and production planning in manufacturing enterprise networks” and funded by the Italian Ministry of Education, University and Research (MIUR) under the program PRIN2001. The research program aimed at developing an innovative concept of neutral linear emarketplace able to support manufacturing enterprises networks in transactional, information sharing and exchange and collaborative relationship. The Chapter discusses the needs of innovative tools for supporting the new paradigm of the “extended enterprise” showing how the e-marketplace concept, when specific added value services are designed and provided, offers a suitable business model to sustain production networks of SMEs.

Key words:

Extended enterprise, E-business, Supply chain management, E-marketplace

1.

THE EXTENDED ENTERPRISE: IN SEARCH FOR SUPPORTING TOOLS

The increasing market globalization is pushing manufacturing enterprises towards the adoption of new business models. Indeed, the need for high and global competitive strategies, for rapid response to market changes, for costs and time to market reductions, and for highly customized products, leads the enterprise value chains to be more and more distributed. 1 G. Perrone et al., (eds.), Designing and Evaluating Value Added Services in Manufacturing E-Market Places, 1–21. © 2005 Springer. Printed in the Netherlands.

Chapter 1

2

This brings manufacturing enterprises in several industries either to split geographically the production capacity or to work together in supply chain organizations involving several independent entities. Figure 1-1 shows the path of the enterprise form towards a more and more distributed pattern [Perrone, 2003]. As the reader can notice, starting from the early stages of the mass customization era, industries have modified their structure to improve their agility, effectiveness and pro-activeness from a structured and vertically integrated shape, typical of the mass production era, to a Virtual-Distributed Enterprise structure. At the beginning of the mass customization era, supply chains were very structured and vertically organized and managed, characterized by a strong leadership, by very strong control and contractual power from the leader, by very stable relations and few collaboration and integration among the partners. This structure allows improving productivity, product quality and flexibility in mass customization. However, organizations were still not enough agile especially in those markets characterized by high dynamicity.

Agility, pro-activeness, Effectiveness, Complexity

Modular ular Horizontal onta Supply pp Chain n Vertical er cal S Sup Supply pl Chain C hain a

Structured tructured and an

40s-late 60s

uc od Pr

tw Ne n o ti

ks or

customisation era

Figure 1-1. Enterprise form evolution

In many industries such as electronic, semiconductor, automotive, indeed, increasing globalization, rapidly changing customer requirements, high technology pushes and social-economic environment transformations are forcing multinational companies to become, if possible, even more

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

3

complex, distributed and globally spread by assuming a modular horizontal supply chain structure [Bruccoleri et al., 2003]. This kind of organizations is focused on agility by improving partner autonomy and improving collaboration among partners in strategic issues such as product design and production planning activity; the organization is stable and an integration and co-ordination role is performed by the most representative partner in the supply chain (generally the retailer in consumer goods supply chains or the most technological partner in high-tech supply chains). However, such transformations are pushing leading companies in such industries towards an articulated network architecture where operation management problems are scaling up exponentially. Finally, in other industries such as machine tools, mechanical components, industry equipment and so forth, Small and Medium Enterprises (SME) are adopting business models consisting on coalitions of manufacturing enterprises, which allow gathering together the advantages coming from a typical multinational holding business model (as, for instance, the ability to compete in the global market) and the advantages coming from a typical SME business model (as, for instance, the ability to rapidly react and adapt to market changing requirements). These enterprise networks are, indeed, able to rapidly react to market changes, to rapidly modify the product according to the customer needs, to efficiently share information and knowledge, and, finally, to reduce the risk associated to a big multinational holding company. Therefore, new business paradigms, such those referred to as Virtual Enterprises (VE) or Organizations (VO) are emerging in the manufacturing competition arena [ Bultje et al. 1998]. These organizations are characterized by high level of heterogeneity, where each partner is focused on core competences (knowledge and skill); VE may have a stable or temporary structure, but they are always goal driven and no hierarchies generally exist in the organization; however, a coordination and representation role has to be identified within the organization. These organizations are very agile and pro-active, but the management complexity, both at strategic and operational level, reaches the maximum level. In both the cases, big distributed organizations or SME networks, companies need to be able to design, organize and manage distributed production networks where the actions of any entity affect the behavior and the available alternatives of any other entity in the network [Wiendahl et al., 2002].

Chapter 1

4

This situation calls for a new generation of tools able to support production networks in effective and efficient strategic and operations planning and management. The rapid development of the Information and Communication Technology (ICT) and the growing of the INTERNET diffusion offer the possibility to support enterprise networks. In particular, this objective can be achieved by the implementation of a distributed and integrated computer network that allows enterprises coordination and collaboration with each other throughout all of the product life-cycle. When an enterprise network adopts ICT as tool for performance and coordination improvement during its operation, the network is commonly referred to as “Virtual Manufacturing Network” (VMN) and the ICT tools that are used for its coordination activities are referred as “tools for the extended enterprise”. This new generation of tools aims at extending the concept of Enterprise Resource Planning (ERP) at the supply chain level in order to improve co-ordination and even collaboration among the several partners in the value chain. Therefore these tools are generally referred as “tools for the extended enterprise” and their basic concept is summarized in Figure 1-2.

Supplier

(Procurement side) e-Procurement

“Enabling” Application E-registration, e-Payment E-business support tools e-delivery, e-HR

Customer

e-CRM

Supply Chain Management

Decision lever source

Supplier ERP

(Sales side) e-commerce

e-Planning make deliver return

Transaction level

Customer ERP

Figure 1-2. Tools for the extended enterprise

As the reader can notice, such tools constitute a bridge between the supplier’s and customer’s ERP systems in a generic supply chain. Extended enterprise tools may support, with specific solutions, procurement (eProcurement side) or retail transactions (E-commerce side), customer’s assistance and relationship (e-CRM); they can offer enabling services such as on line payment, delivery tracking, registration management and so forth. Furthermore, the most advanced extended tools claims for supporting supply

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

5

chain management and operations through the management of the whole information flow between supplier and customer. From the analysis of the available literature, it becomes obvious that the impact of effective information technology (IT) will be a vital survival need as well as a potential competitive weapon in many industries. On the research side, this is testified from the significant numbers of major research projects that have been undertaken in the attempt to extend or to adapt CIM architectures and tools to distributed network enterprises (see for instance XCITTIC project [Zhou et al., 1998] and the EUROFRAME framework [De Ridder et al., 1997]). On the industrial side, this process is evidenced by the number of Collaborative Planning and Scheduling (CPS) tools that the main ERP players have developed and released in the last years. APS (Advanced Planning and Scheduling) tools, indeed, have been specifically designed to manage complex operations in distributed manufacturing environments. Such tools are spreading very fast in manufacturing industries; in particular, a recent review from the ARC Advisory Group has showed that CPS tools have experienced a remarkable growth throughout the 1990s and the 2000s, and they have forecasted that CPS will grow at a Cumulative Average Growth Rate of about 23% over the next five years. This growth will be driven by strong sales of collaborative planning solutions in Demand Management, Supply Chain–Centric APS, and Collaborative Infrastructures [see http://www.arcweb.com]. This book addresses the issue above introduced of the tools for the extended enterprise. It presents a research developed under a two year research program titled ““Distributed process and production planning in manufacturing enterprise networks” and funded by the Italian Ministry of Education, University and Research (MIUR) under the program PRIN2001. The research aimed at developing an innovative tool for manufacturing enterprise networks organized into an e-marketplace. However, the results reached by the research project are of general interest for any kind of tool for the extended enterprise. This chapter introduces the research presented in the book. In particular, section 2 introduces the e-business concept as an Internet based technology able to support tools for the extended enterprise. Section 3 explores the business-to-business solutions providing characteristics and limitation of the actual tools for the extended enterprise. Paragraph 4 discusses motivation and goals of the research presented in this book, while sections 5 presents the book organization. Finally, paragraph 6 addresses possible future paths of the research here presented.

6

2.

Chapter 1

E-BUSINESS: A SUPPORTING TECHNOLOGY FOR THE EXTENDED ENTERPRISE

Digital Economy has been defined as an economy based on Information and Communication Technologies (ICT), and this in both the cases ICT is used as “core” technology or as an “enabling” one. Especially, the Internet based economy is taking off. Indeed, those that until the year 2000 seemed crazy or at least too optimistic forecasts are indeed coming true [The Economist, 2004]. As an example, back to 1999, Forrester, a company specialized in e-business forecasts, predicted an online retail sales turnover for 2002 in America of $100 billion; consumptive turnover data for online spending in America are in 2003 $120 billion. Furthermore, according to America’s Department of Commerce on line retails rose by the 26% in 2003 reaching the 1.6% of the total retail sales in America [The Economist, 2004] and almost 4.5% if online travel and auctions sales are included (see Figure 1-3). This path is quite generalized also in Europe as depicted in Figure 1-3, where the percentage of on line retail sales is reported for 2003 and forecasted for 2004, for several European countries.

Figure 1-3. % of on line retail sales in European countries (font The Economist)

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

7

As previously mentioned, ICT are, basically, enabling technologies for several kind of businesses. Indeed, ICT are changing the way to do business in several industries. According to OECD, the term E-business is meant as the electronic exchange of information that support and govern commercial activities including organizational management, commercial management, commercial negotiations and contracts, legal and regulatory frameworks, financial settlement arrangements and taxation. The E-business indeed impacts several issues in doing business: from the communication point of view, the e-business refers to the possibility to exchange and use information among business partners; from the business process point of view, e-business allows speeding and improving several business processes by using automated workflows; finally, from the economic point of view, e-business allows improving business effectiveness and efficiency by reducing transactional costs, improving customer relationship and product quality, and more in general, supply chain efficiency. This is perhaps the reason why several business models have been conceived within the e-business area. In particular, Figure 1-4 reports the ebusiness map, where the following business models can be located:

A B2

C B2 C2A C2A A C2C

A2A 2A A

B2 2B 2 B

A B2 Figure 1-4. E-business map

C B2

Chapter 1

8

– Business to consumers (B2C); it is a business model, generally referred to also as E-commerce, where retail companies use the Internet and the WWW technology to reach and assist their customers; the E-commerce is changing the way to do business in several retail industries such as books, music, leisure industry, video, computer and so forth all over the world. – Business to business (B2B); it is a business model involving companies, who use the Internet and the WWW technology to make business transactions such as procurement, supply chain management, payment, delivery planning and control and so forth. – Business to administration (B2A); it is a business model where companies use the Internet and the WWW technology to make transactions with any kind of administration, such as obtaining authorizations, submitting proposals, and so forth. – Consumer to administration (C2A); it is a business model where consumers use the Internet and the WWW technology to make transactions with any kind of administration, such as submitting tax documentation, obtaining documentation and authorizations, checking personal positions, and so forth. – Administration to administration (A2A); it is a business model where administrations at different levels, local, regional, federal, use the Internet and the WWW technology to exchange data and make administrative transactions. – Consumer to consumer (C2C); it is a business model where final consumers reach other consumers by using the Internet and the WWW technology to exchange music, DVD, and other electronic data. Several e-business models can provide technological and organizational support for enterprise networks. Among all, the B2B is surely the most important for manufacturing companies, since it offers solution and tools for managing the extended enterprise. Therefore, the next section will introduce business-to-business solutions for the extended enterprise in order to highlight characteristics and limitations of the available technological solutions.

3.

ENTERPRISE E-BUSINESS BUSINESS-TO-BUSINESS

MODELS:

THE

Business-to-business has always played a strategic role within the ebusiness arena. This is basically due to the enthusiastic approach to B2B both from entrepreneurs and market research forecasts.

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

9

Indeed, already at the beginning of the year 2000 The Economist accounted for more than 750 B2B market places in existence [The Economist, 2000] and Business Week esteemed a turnover for the B2B in 2003 of about 1.3 trillion of US$, that is six times larger than the ecommerce value [Business Week, 2000]. Furthermore a research analysis carried out by Jupter Research and Gartner Group in 1999 [Barratt et al. 2002] predicted a transactional volume for B2B e-marketsites respectively of 4,137 and 7,300 billion of US$ for the year 2004. Several industries were actually interested on the B2B phenomenon such as automotive (Convisint), steel manufacturing (e-Steel), chemical (ChemConnect, Chemdex), plastic manufacturing (Plastic Net), pharmaceutical (Neoforma), telecommunications (Deutsche Telekom’s MarketPlace), Maintenance Repair and Operations (Grainger), paper industry (Papersite), retailer industry (Retail Exchange, GlobalNetXchange, Transora) and many other. However, despite promises, many of the B2B exchanges have failed in the last few years. For example, in Italy at the end of 2000, more than 120 operators were active, while three years later this number fell to approximately 40, and less than half of these survivors are able to reach the break-even point [Ordanini et al., 2004]. The same trend seems to occur in other European countries [Brunn et al. 2002], and also in the US [Day, et al. 2002]. Again, as in the case of E-business, recent numbers seems to confirm initial projections also for B2B. Indeed, a recent survey from eMarketer [eMarketer, 2002] forecasted a global revenue for European B2B of about 0.8 trillion of US$ for the year 2004, that is not far from predictions made in the boom years. In any case, market numbers confirm that B2B certainly represents an interesting and profitable business, able to speed up and increase profitability in many industries sectors. However, as the recent failures have demonstrated, not all of the B2B business models are able to provide value and guarantee profitability. Indeed, as also stressed by Ordanini et al. [Ordanini et al., 2004] it is very important to understand what kind of business model makes the success of B2B solutions and what kind of features make the difference between successful initiatives and failures. With this purpose, B2B business models will be analyzed in the following in order to understand what kind of solutions and features are able to provide success for manufacturing networked enterprises.

Chapter 1

10

3.1

B2B solutions for the extended enterprise

Several classifications of B2B solutions have been proposed in literature. Most of them will be analyzed in what follows. However, according to Rangone et al. [Rangone et al., 2004] a basic classification of B2B solutions can concern their business model focus; indeed is possible to classify B2B solutions in those that are procurement oriented, d and those that are supply chain oriented. d Procurement oriented solutions are those that support the company in procurement transactions. These solutions are essentially referred as emarketplace or electronic marketplace. Thanks to their diffusion and success, most of the market data available in the B2B sector are related to emarketplaces. Supply chain solutions support the company both in the execution of the entire procurement cycle (ordering, delivering and payment) and in supply chain collaboration, meaning with that collaborative planning, forecasting, replenishment and integrated inventory management. Supply chain solutions are often offered by ERP vendors who provide extended solutions for supply chain integration, also called e-ERP [Ash et al., 2003]. However, e-ERP seems to have several drawbacks basically related with the difficulty of integrating IT solutions from different vendors and with implementation times and cost of backbone ERP platforms, especially for Small and Medium Enterprises. These drawbacks seem to slow down the diffusion of B2B supply chain solutions. On the other hand, e-marketplaces propose business models that are of easier technical and organisational implementation (and this is the reason of their diffusion), but they seem focused on procurement transactions. Recent researches have investigated the extent to which B2B emarketplaces can support B2B supply chain management and therefore extended enterprises [Eng et al., 2004]. In this case the question concerns what kind of e-marketplace business model should be adopted, what kind of features should be provided within e-marketplaces to support the extended enterprise, what kind of technology should be used to support those features. When these questions are referred to manufacturing enterprise networks, the previous questions represent the central issue of the research presented in this book. Therefore, e-marketplaces will be deeply analysed in the following sections, trying to understand how they can support the extended enterprise.

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

3.2

11

E-marketplace: a tool for the extended enterprise?

Several definitions of e-marketplace (EM) have been provided in the literature from 1988. However, a basic definition has been proposed by Grieger [Grieger, 2003] that is “an EM brings multiple buyers and sellers together (in a ‘‘virtual’’ sense) in one central market space. If it also enables them to buy and sell from each other at a dynamic price which is determined in accordance with the rules of the exchange, it is called an electronic exchange; otherwise it is called a portal”. l This definition expresses the basic idea of EM as a procurement solution that is the possibility to use ICT to create an ideal neoclassical market, able to reduce transaction costs to a negligible minimum [Schmid, 1993; Bakos, 1991]. From this point of view, EMs can be classified in several business models according to the following characteristics [Barrat et al., 2002]: – Buyer behavior; – Centricity; – Accessibility; From a buyer behavior point of view, Kaplan et al. [Kaplan, 2000] have proposed to categorize EMs into four overall groups according to ‘‘how’’ and ‘‘what’’ the companies buy.

Sistematic buying

MRO Hubs

Catalogue Hubs

Yield Mangers

Exchanges

Howfirms firmsbuy buy How

Spot buying

Accessory material

Core material

Whatfirms firmsbuy buy What Figure 1-5. EM classification based on “how” and “what” firms buy (Kaplan et al., 2000)

Chapter 1

12

The “how” can be a systematic or a spot buying, while the “what” can be the buying of core or accessory materials. As the reader can notice from Figure 1-5, four typologies of EMs result from this classification: – MRO (Maintenance, Repair and Operations) Hubs are mainly established to improve the efficiency of procurement of regularly purchased non-core items; – Yield Managers focus on spot buying of non-core products; their goal is to reduce the price volatility in the procurement transactions; – Exchanges are generally focused on spot sourcing of manufacturing items; these markets are mainly created for commodities such as steel, metals, chemicals and plastic, and therefore, they are specialized on product typology (vertical EMs); – Catalogue Marketsites are planned to improve systematic sourcing of highly specialized items with a high degree of product specification, by automating the entire process. Another way of categorizing market sites upon the buyer behavior is by determining who the buyers are [Blodget et al., 2000, Detourn, 2000]. If the buyers belong to the same industry, buying the same kind of products, the M Examples of these EMs are e-Steel (Steel EM is referred to as Vertical EM. industry), ChemConnect (Chemicals industry), PlasticNet (Plastic industry) and so forth.

Buyer Buyer

Seller Seller Seller Seller

Buyer centric marketplace

Buyer Buyer

Seller Seller

Buyer Buyer

Seller centric marketplace

Seller Seller

Buyer Buyer

Buyer Buyer Buyer Buyer

Buyer Buyer

Seller Seller Neutral linear marketsites

B/S B/S

Seller Seller

Seller Seller

Neutral exponential EM B/S B/S

Figure 1-6. Centricity in EMs

B/S

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

13

On the other hand, if buyers belong to several industries EMs are referred as Horizontal EMs such as MRO.com, Ariba, Travelocity and so forth. Centricity deals with stakeholder-focus, that is with those who set up, own and control the e-marketsite. From “centricity” point of view, see Figure 1-6, EMs can be classified as: – Buyer centric e-marketplace; these EMs are established by larger buyers, usually up 1000 Fortune companies, who set up these EMs by inviting their usual suppliers within an own managed EM. An example of buyer centric EM is ‘‘Covisint’’ the automobile exchange set up by Ford, GM, and Daimler–Chrysler. – Seller centric e-markeplace; these EMs are established by larger sellers, usually up 1000 Fortune companies, who set up these EMs by inviting their usual customers within an own managed EM. Examples of these EMs are those settled by Dell and Cisco corporations. – Neutral Marketsites are designed for improving efficiency in highly fragmented industries, by offering increased visibility and a neutral knowledge base for both buyers and sellers. Generally these EMs are established by third party, usually referred as an exchange owner, who sets-up the e-marketplace and gets a fee from EM transactions and services offered to EM participants. These EMs are linearr when each actor behaves as a seller or has a buyer; on the other hand, they are called exponentiall when actors can behave as seller or as buyer depending on the specific transaction. Neutral EMs are also referred to as Virtual Districts and they are specifically designed for SMEs. Finally, from “accessibility” point of view, EMs can be classified in open EM M or closed EM M depending on whether the e-marketplace participation is public or restricted to a determined set of suppliers and buyers. Advantages of procurement-oriented e-marketplaces have been always associated with procurement transaction costs reduction for buyers, and with the possibility to expand the market for sellers [Favier et al., 2000]. However, as also pointed out by Ordanini et al. [Ordanini et al., 2004], not all of the business models previously introduced seem to have success in term of growth and profit capabilities. Especially neutral e-marketplaces have encountered problems in reaching a critical mass able to guarantee EM survival. This is because such business models were built on the idea of a progressive decrease in transactions cost (efficiency) — that is, an increasing value of online exchanges through the marketplace [Sawhney et al., 2003]. Indeed, according to Wise and Morrison [Wise et al., 2000], these business models may fail in bringing value to EM participants for the following reasons:

14

Chapter 1

– Buyer value; in order to get transactions costs reduction these EMs put sellers in competition each other through competitive bidding; this provides lower prices for the buyer. However, prices are not the only font of value for the buyers, especially in markets where transactions do not concern commodities; issues such as product quality, delivering times and production quantities are indeed as important as price in several markets; – Seller value; it is true that suppliers have access to more buyers with little marketing costs, but this advantage is balanced by the profit reduction due to the price competition among suppliers; furthermore, when competition is only price-based, suppliers cannot account, as a value for the buyer, for issues such as technological knowledge, product quality, reliability and so forth; – EM value; finally, most of the exchange owners believe that putting together buyers and sellers in an electronic market is just enough for bringing them value; on the contrary, in order to bring value to the participants it is necessary to understand buyers and sellers needs and provide a set of added value service able to guarantee a sort of “stay together economy” for all of the EM participants. From the above considerations it appears to be possible to argue with Greiger [Greiger, 2003] that, theoretically, the relationship between EMs and supply chain management appears problematic. Indeed, co-operative supply chains aim to reduce the number of suppliers and form long-term strategic alliances that “lock in” suppliers and “lock out” competition, while EMs promote competition and allow buyers to search for suitable suppliers and support ‘‘transaction-based’’ partnering. Are then EMs, and particularly neutral EMs, not suitable to support the extended enterprise? This conclusion seems to be in contrast with a definition of EM given in a whitepaper published by IBM, i2, and Ariba [IBM, i2, & Ariba, 2000], where an e-marketplace is defined as a many-to-many, web-based trading and collaboration solution that enables companies to more efficiently buy, sell, and collaborate on a global scale. Indeed, as also pointed out in a document from the American Manufacturing Research Inc. [AMR, 1998], three broad categories have been identified in a relationship among trading partners: transactional, information-sharing, and collaborative relationships. Transactional relationships involve the activities carried out to execute the buyer’s purchase of a commodity. Historically, EMs have mostly dealt with the transactional aspects of a relationship, by simple automating the cycle ordering-invoicing-delivering. Information sharing relationship consists either on sharing information or exchanging information among the partners [Noekkenved, 2000]. For

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

15

example a Catalogue based e-marketplace, allows the buyer seeing the product by sharing the information with the seller; besides, an e-marketplace allowing negotiation during the trading, permits information exchange among the partners in order to reach a good agreement for both. Information sharing and exchange allow improving trust among the partners, making better and informed decisions, and, finally, providing value for both. Finally, collaborative relationships consist on ‘‘working jointly with others, especially in an intellectual endeavour’’ [Noekkenved, 2000]. In a collaborative relationship, information is not just exchanged and transmitted, but it is also jointly developed by the buyer together with the seller. Collaborative efforts can concern joint product development, demand forecasting, planning activities and negotiation phases. Collaboration allows partner to improve the effectiveness and efficiency of the relationship. These issues are also stressed by Eng [Eng, 2004] who identifies two kind of services in order to make EMs a real support for the extended enterprise: transactional services such as order and payment processing, auctions, reverse auctions, catalogues, buyer and seller search; strategic services such as collaborative project management, demand and inventory management, forecasting and replenishment, technical information exchange. The same issues are also stressed by a document of IBM [IBM et al., 2000] where it is affirmed that e-marketplaces built upon a shared Internet based infrastructure could provide firms with a platform for: – Core commerce transactions that automate and streamline the entire requisition-to-payment process online, including procurement, customer management, and selling; – A collaborative network for product design, supply chain planning, optimization, and fulfillment processes; – Industry-wide product information that is aggregated into a common classification and catalogue structure; – An environment where sourcing, negotiations, and other trading processes such as auctions can take place online and in real time; – An online community for publishing and exchanging industry news, information, and events. From the above considerations is possible to conclude that: – most of the existing EMs are essentially transactional-oriented; – the supply chain dimension of EMs is for the most part mistreated and handled insufficiently [Greiger, 2003]; – pure transactional-oriented neutral e-marketplaces have shown problems in gaining critical mass [Ordanini et al., 2004];

16

Chapter 1

– however, EMs represents a suitable supporting tool for the extended enterprise when proper added value services able to support transactional, information-sharing, and collaborative relationships are provided. d

4.

AIMS AND MOTIVATIONS OF THE RESEARCH

The aim of the research titled ““Distributed process and production planning in manufacturing enterprise networks” is framed in the above described context. In particular the project addresses an industrial context consisting of a set of Small and Medium Enterprises in the manufacturing mechanical components industry. This is an industrial context highly relevant to the Italian industrial structure. In Italy, most of the value chains in mechanical components are, indeed, extremely fragmented both horizontally and vertically. Therefore, a full exploitation of the potential inherent to B2B applications in this industry might represent a significant contribution to the development of such Industry that contributes to a significant value of the Italian GNP. In particular, the research project aims at understanding what kind of advantages this Industry can gain by the implementation of ICT tools for the extended enterprise based on the e-marketplace concept. The EM business model adopted in the project is the “neutral linear emarketplace”. Indeed, as acknowledged from a large part of the specific literature, this business model is the most suitable for SMEs. Therefore, the operative context of the research project is a neutral linear e-marketplace where a set of registered customers and suppliers operate in Make to Order (MTO) modality. The MTO modality has been chosen because it is consistent with the industrial context under investigation. Therefore, the research program aimed at conceptualizing, designing, implementing and testing an ICT tool able to support transactions, information-sharing and even collaboration within a neutral linear emarketplace of MTO manufacturing SMEs. In order to do that, specific needs of MTO manufacturing enterprise networks have been analyzed. One of the crucial problems in mechanical components Industry is the lack of coordination and integration between the commercial and the production functions during order negotiation phases. The presence of ineffectiveness during this phase often leads to poor customer satisfaction and enterprise inefficiencies, such as delivery delay, not completed orders, rush orders, low profit margins for the supplier.

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

17

MTO transactions in mechanical components industry are particularly complex. Figure 1-7, shows a simplified transaction workflow. First of all, the customer needs to input order requirements both from commercial (required volume, due-date, price and so forth) and technological point of view (part design, working features). This is an information sharing and exchange phase, since customer and supplier have to share and exchange information in order to clarify transaction issues. Afterwards, the supplier needs to breakdown technological and commercial info in order to plan technological activities and production; indeed, only through technological and production planning, the supplier can provide reliable information about costs, delivery times and offered production volume to the customer. This is a phase of information processing. Then customer and supplier can negotiate the order agreement. The negotiation phase is surely an information sharing and exchange phase, since they need to exchange proposal and counter proposal information to negotiate, but it can also be a collaboration phase, if negotiation is designed in order to encourage an agreement among the parts. Finally, if an agreement is reached the order is settled and the transaction ends.

Customer

Supplier

Input order with technical and commercial requirements

Technical and Commercial info processing Technological and commercial info breakdown

Information sharing and exchange

Information processing

Technological planning Production planning Order Ne egotiation

Order Se ettlement

Information sharing and exchange Collaboration Transaction

Figure 1-7. Order transaction workflow in a mechanical components industry

Chapter 1

18

As the reader can notice, the workflow is very complex and articulated to be executed in automated way and in real time fashion. The research project aimed at designing a set of “added value services” within a neutral linear e-marketplace able to support the above workflow. In order to do that a proper agent based architecture has been designed. Indeed, agent-based technology has been often addressed as a suitable technology to support e-marketplaces with complex added value services [Kang et al., 2002]. For what has been discussed in this chapter, this architecture may represent a tool for supporting manufacturing extended enterprise within EM business models.

5.

BOOK CONTENTS AND ORGANIZATION

The architecture has been conceptualized, designed implemented and tested within the project and this book reports the results of this research. In particular, Chapter 2 presents a general description of the agent-based architecture and its functional and dynamic view. Chapters 3 and 4 discuss the methodologies adopted within the agentbased architecture to exchange technological information and to breakdown it in order to obtain technological planning. Chapter 5 presents the negotiation mechanism and policies that have been adopted during the negotiation phase in the workflow of Figure 7. The proposed approach is able to support multi dimensional negotiation, since several issues such as price, due-date and production volumes may be the object of bargain during the order negotiation. Chapter 6 discusses the methodologies adopted for making production planning and re-planning trying to meet customer commercial requirements. It also shows how production planning allows building the necessary information to negotiate with the customer. Chapter 7 reports how the agent-based architecture has been implemented in an open source simulation environment and how its functionality has been tested and proved by running realistic examples. Furthermore, it reports some numerical example demonstrating how the proposed architecture is able to provide true added value both to the customer and the supplier in the e-marketplace. Finally, Chapter 8 presents the result of the agent-based architecture benchmarking. Indeed, in order to understand how good is the value the proposed architecture is able to bring to the participants in a neutral linear emarketplace, its results have been compared with a benchmark consisting of

1. Manufacturing e-marketplaces: innovative tools for the extended enterprise

19

a “central planner” able to make optimal decisions for all the buyers and sellers within the e-marketplace. The benchmarking results, demonstrates, once again, how good is the performance of the proposed agent-based architecture in providing value within the EM.

6.

CONCLUSIONS AND FURTHER PATHS OF RESEARCH

This book presents the results of a research project titled “Process and Production Planning in manufacturing Enterprise Networks” funded under the program PRIN2001 by the Italian Ministry of Education, University and Research between years 2001 and 2003. The aim of the research program was to design, implement and test an agent-based architecture able to support transaction, information sharing and exchange and even collaboration in a manufacturing enterprise network organized through a neutral linear e-marketplace business model. The results reached by the research program, which have been reported in this book, testified how the proposed architecture is able to provide true value to the EM participants; therefore, the proposed architecture, when opportunely optimized, can represent a valid support tool for the extended enterprise. Present and future research paths concern the improvement of the collaboration part of the proposed approach in order to make even more interesting the proposed architecture for EMs. In particular, the possibility to build coalitions among the suppliers within the e-marketplace has been taken into consideration and a coalition model has been already developed and tested [Argoneto et al., 2004]. The results seem to confirm that coalitions may improve the interest of staying into EM both for suppliers and customers.

REFERENCES AMR, Are We Moving From Buyer and Seller to Collaborators? SCM Report, American Manufacturing Research Inc, 1998. Argoneto P., Bruccoleri M., Lo Nigro G., Noto La Diega S., Perrone G., Renna P., Evaluating multi-lateral negotiation policies in manufacturing e-marketplace, Proceedings of the 37th CIRP-International Seminar on Manufacturing Systems, May 2004, Budapest, Hungary.

20

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Ash C.G., Burn J.M., A strategic framework for the management of ERP enabled e-business change, 2003, European Journal of Operational Research, 146, pp. 374–387. Bakos, J. Y., A strategic analysis of EM. MIS Quarterly, 1991, 15 (4), pp. 295–310. Barratt M., Rosdahl K., Exploring business-to-business marketsites, European Journal of Purchasing & Supply Management, 2002, 8, pp. 111–122. Blodget, H., McCabe, E., The B2B market maker book, Merrill Lynch & Co., In-depth Report February 3, 2000, from http://www.netmarketmakers.com. Bruccoleri M., Lo Nigro G., Federico F., Noto La Diega S., Perrone G., , Negotiation mechanisms for capacity allocation in distributed enterprises, CIRP Annals, 2003, Vol. 52/1, pp. 397-402. Brunn, P., Jensen, P. and Skovgaard, J., E-marketplaces: crafting a winning strategy, European Management Journal, 2002, 20 (3), pp. 286–298. Bultje, R.M., Wijk, J., Taxonomy of Virtual Organisation, based on definitions, characteristics and typology”, VoNet: The Netwsletter, 1998, @ http://www.virtualorganization.net, 2 (3), pp. 7-20. Business Week, B2B: the hottest net bet yet?, January 17th, 2000, pp. 36–37. Day, Q. and Kauffmann, R.J., Business models for internet- based B-to-B electronic markets. International Journal of Electronic Commerce, 2002, 6 (4), pp. 41–72. De Ridder, L., Rodriguez, B., Basset. T., The EUROFRAME Initiative: Building a CIM Framework for Semiconductor Manufacturing. IiM’97, 1997, The European Conf. on Integration in Manufacturing. Detourn, N., B2B E-CommerceFthe dawning of a trillion dollar industry, Motley Fool Research, March 14, 2000, http://www.FoolMart.com. eMarketer, Europe eCommerce: B2B & B2C - Executive Summary, 2002, www.emarketer.com. Eng T.-Y., The role of e-marketplaces in supply chain management, Industrial Marketing Management, 2004, 33, pp. 97– 105. Favier, J., Condon, C., Aghina, W., Rehkopf, F., Euro eMarketplaces top hype. Forrester Research, Inc., May 2000. Grieger M., , Electronic marketplaces: A literature review and a call for supply chain management research, European Journal of Operational Research, 2003, 144, pp. 280–294. http://www.arcweb.com/Research/ent/cps.asp. IBM, i2, & Ariba, A. M. (2000). E-marketplaces changing the way we do business. Available at: www.ibm-i2-ariba.com, accessed May 2000. Kaplan, S., Sawhney, M., E-Hubs: the new B2B marketplaces, 2000, Harvard Business Review, 78 (3), pp. 97–103. Kang N., Han S., Agent-based e-marketplace system for more fair and efficient transaction, Decision Support Systems 34, 2002, 157– 165. Noekkenved, C., Collaborative Processes in e-Supply Networks––Towards Collaborative Community B2b Marketplaces, Research Report, PricewaterhouseCoopers, 2000. Ordanini A., Micelli S., Di Maria E., Failure and Success of B-to-B Exchange Business Models: A Contingent Analysis of Their Performance, European Management Journal, 2004, Vol. 22, No. 3, pp. 281–289. Perrone G., Virtual Enterprises: Concept, Design, Management and Applications, presentation at the CIRP STC-O meeting, Paris, 2003. Rangone A., Bertelè U., Il B2B in Italia: Finalmente parlano i dati, Rapporto dell’osservatorio B2B, Marzo 2004. Sawhney, M. and Di Maria, E. The real value of B-to-B: from commerce towards interaction and knowledge sharing, Proceedings of the CIBER/CMIE Conference ‘Managing in the

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Global Information Economy’, 2003, September 12–13, 2003, The Anderson School at UCLA (USA). Schmid, B., Elektronische Markte. Wirtschaftsinformatik, 1993, 35 (5), pp. 465–480. The Economist, A perfect market: A Survey of e-commerce, May 15th 2004; The Economist, E-commerce takes off, May 15th 2004. The Economist, Seller beware, March 2000, pp. 61– 62. Wiendahl, H.-P., Lutz, S., Production Networks, Annals of CIRP, 2002, Vol. 51, 2. Wise, R., Morrison, D., Beyond the exchange –– the future of B2B, 2000, Harvard Business Review, pp. 86–96. Zhou, Q., Souben, P., Besant, C.-B., An information system for production planning in virtual enterprises. Computers Ind. Engng, 1998, Vol. 35, N. 1-2, pp. 153-156.

Chapter 2 AN AGENT BASED ARCHITECTURE FOR MANUFACTURING E-MARKETPLACES The Agent Based Architecture Giovanni Perrone and Giovanni Montana Dipartimento di Ingegneria e Fisica dell’Ambiente, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano, 10, 85100, Potenza - Italy

Abstract:

This chapter presents the Agent Based Architecture developed within the research project, titled "Process and Production Planning in manufacturing Enterprise Networks". As mentioned in Chapter 1, the architecture has been developed to support “added value services” in neutral linear e-marketplaces, i.e. in virtual districts. In this chapter the architecture will be described from a functional and dynamic point of view by using the formalisms used in the project. In particular, from a functional perspective, the architecture is described by using the IDEF0 formalism, while its dynamics are specified by UML activity diagrams.

Key words:

E-marketplace, Agent Based Systems, Process Engineering

1.

THE AGENT-BASED FRAMEWORK

The framework is depicted in Figure 2-1. It consists of a neutral manufacturing e-marketplace (a virtual district) where a set of manufacturers ( i), constituting the reference industry, and a set of customers (SSi) can be (P located. Manufactures and customers are connected through an electronic network, i.e. an e-marketplace managed by a third party, the exchange owner. As the reader can notice, each buyer and supplier consists of a set of agents constituting respectively the Customer System and the Supplier System [Cantamessa et al., 2002]. Finally, the exchange owner is equipped with a Scheduler Agent, who is in charge for managing synchronization in Customer and Supplier system communication. 23 G. Perrone et al., (eds.), Designing and Evaluating Value Added Services in Manufacturing E-Market Places, 23–43. © 2005 Springer. Printed in the Netherlands.

24

Chapter 2

The Customer system consists of two autonomous agents: – the Customer Negotiation Agentt (CNA), that puts the order (characterized by a set of technological and commercial information such as volumes and due dates) on the net and negotiates it with the manufacturer; – the Controller agentt that disciplines the access to the network. Similarly, the Supplier System performs the negotiation through the following agents: – the Supplier Negotiation Agentt (SNA), who is in charge with order processing and counter-proposal formulation; – the Controller agent, that disciplines the access to the network; – the Virtual Manufacturing System. The Virtual Manufacturing System (VMS) aims at providing the necessary information to the manufacturer negotiation agent. The VMS consists of four independent and autonomous agents: – the Process Planner Agentt (PPA) is in charge with the manufacturability analysis of the order; it examines the order and defines all of the technological operations which are necessary for manufacturing the parts. It also locates possible manufacturing alternatives, and, eventually, the necessity of buying manufacturing capability outside the firm; – the Manufacturing Planner Agentt (MPA) receives information on the technological operations from the PPA and produces a set of alternative process plans and possible schedules over the manufacturer’s available resources. The MPA is able to determine manufacturing times and costs for each process plan; – the Production Planner Agentt (PrPA) receives information on the process plans from the MPA and it plans the production activities over the resources in order to determine the order feasibility in term of production volumes and due dates required by the customer. The PrPA is also able to find possible alternatives in term of commercial characteristics of the order; – the VM controllerr disciplines the communication among the above described agents and the Negotiation Agent. To sum up, in the proposed system, apart from the technical purpose of the controller agents, the main agents are: the Customer Negotiation Agent (CNA) and the Supplier Negotiation Agentt (SNA), which represent the commercial function, while the Process Planner Agentt (PPA), the Manufacturing Planner Agentt (MPA), and the Production Planner Agent (PrPA) (the three agents representing the VMS) embody the production function. By the interaction of these agents, the manufacturers are enabled to negotiate the order with the customers.

2. An Agent Based Architecture for Manufacturing E-Marketplaces

Network Network Controller Controller

Virtual district

Seller Seller

Buyer Buyer

Buyer Buyer

Seller Seller

Customer CustomerNegotiation Negotiation Agent Agent(CNA) (CNA)

Buyer Buyer

Seller Seller

Network Network Controller Controller

Scheduler Scheduler Agent Agent

25

SupplierNegotiation Negotiation Supplier Agent(SNA) (SNA) Agent

VMcontroller controller VM ProcessPlanner Planner Manufacturing ManufacturingPlanner Planner Process Agent(PPA) (PPA) Agent(MPA) (MPA) Agent Agent

Manufacturing Data Base

ProductionPlanner Planner Production Agent(PrPA) (PrPA) Agent

Virtual Manufacturing System

Production Data Base

Figure 2-1. The Agent-based architecture

2.

SYSTEM ARCHITECTURE

In the present section, the architecture that constitutes the described agent-based framework is formalized through the use of the IDEF0 formalism [Feldmann, 1998]. Every diagram (or Node) IDEF0 is defined through some boxes which represent functions or activities performed by the system: beginning from the diagram of context A-0, every node corresponds to a specific level of detail of the system, and it can subsequently be specified through a “child” diagram.

2.1 The system context The context in which the system operates has been defined through the diagram A-0 of Figure 2-2 that distinguishes the system from the external environment.

Chapter 2

26 Technological Constraints Network Negotiation Constraints Customer and Supplier: Commercial and Competitive Strategies

Capacity Constraints Production system workload

ORDER DATA INPUT

PROCESS AND PRODUCTION PLANNING

PRODUCTION ORDER

0 A0 Negotiation Models Planning Models

Purpose: Agent-Based System modelling for "Process and Production Planning in manufacturing Enterprice Networks" Viewpoint: System Development Team NODE:

A-0

TITLE:

AGENT BASED PROCESS & PRODUCTION PLANNING SYSTEM

NO.:

Figure 2-2. Node A-0: Context Diagram

The global input of the system is given by the Order Data Input: it deals with the activity of the Customer operator who manually inserts the commercial data (work piece code, volume, price and date of delivery, that di, pi)) and technological will be passed in the form of the array (i, Vi, dd features (CAD sketch of the piece and related features) of the order. The Production Orderr constitutes the final output of the System; if the negotiation process achieves the success, the order is launched in the physical system of the Supplier. The system is subject to the following constraints: "Customer and Supplier: Commercial and Competitive Strategies", representing the initial settings for the parameters of customer and supplier negotiation agents; ““Network Negotiation Constraints”: that is the rules that the net manager imposes for information sharing and exchange between the customer and the supplier; “Technological Constraints”: the order technological feasibility is defined by the set of technological operations that the manufacturer is able to perform; “Capacity Constraints”: they represent the maximum manufacturing capacity of the supplier; the “Production System Workload” d is the set of scheduled orders of the supplier’s firm: it will be used to compute the available manufacturing capacity. The system operates through the following mechanisms: Negotiation Models and the Planning Models. They provide the operational scheme respectively for the bargaining among the negotiation agents (both the

2. An Agent Based Architecture for Manufacturing E-Marketplaces

27

customer and the supplier’s one – see forward), and for the planning activity through which the VMS produces the set of production alternatives supporting the negotiation. 2.1.1 The overall system The overall structure consists of two parts: Customer System and Supplier System (box 1 and box 2 respectively, in Figure 2-3).

Customer: Commercial and Competitive Strategies (Cl_C0.1)

Customer: Network Negotiation Constraints (Cl_C0.2)

Customer System

Customer: Commercial Requirements eq (Cl_O0.2) p s (Cl_O0.3) 1 Customer: Counter-proposal A1

Customer: Negotiation Models (Cl_M0.1)

Customer: Offer proposal (Cl_I0.1)

Supplier: Production system workload (Fo_C0.3)

Customer: Technological Requirements (Cl_O0.1)

ORDER DATA INPUT

Supplier: Capacity Constraints (Fo_C0.2)

Supplier: Commercial and Competitive Strategies (Fo_C0.4)

Supplier: Technological Constraints (Fo_C0.1)

Supplier: Technological Requirements (Fo_I0.1) Supplier: Commercial Requirements (Fo_I0.2)

Supplier: Network Negotiation Constraints (Fo_C0.5)

PRODUCTION ORDER

Supplier System

Supplier: Counter-proposal (Fo_I0.3)

2

Supplier: Offer proposal (Fo_O0.1)

A2 Supplier: Negotiation Models (Fo_M0.1)

Supplier: VMS Models (Fo_M0.2)

NODE:

A0

TITLE:

LEVEL 0 - OVERALL SYSTEM

NO.:

Figure 2 -3. Node A0: Level 0 – Overall System

The inputs of the Customer System are the following: the global input of the System (“Order Data Input”) and the “Offer Proposal” originated by the manufacturer. The Customer outputs are the “Customer Technological Requirements” of the order (i.e. the CAD sketch of the part, completed by additional features as step, slot, plane etc.), the “Customer Commercial Requirements” (i.e. Volume, Price, Due-Date), and the “Counter-proposal”, i.e. the possible query for a new proposal to the Supplier. The three outputs of the Customer system constitute the inputs of the Supplier System that processes these data to produce its "Offer Proposal" (output of the box 2 together with the possible "Production Order"); this proposal closes the loop of the negotiation being the feedback for the customer, whose negotiation agent had started the bargaining.

Chapter 2

28

The Supplier System creates its offer-proposal on the basis of the process/manufacturing/production planning information performed by the Virtual Manufacturing System. 2.1.2 Customer system The Customer System (see Figure 2-4) accomplishes two fundamental activities. The first one is the Formulation of commercial proposal, which involves the customer operator interfacing with the system for the definition of the Technological and Commercial Requirements of the order. The technological information is addressed to the Supplier System, particularly to the Virtual Manufacturing System (VMS), which will use it during the planning activity.

Customer: Commercial and Competitive Strategies (Cl_C0.1)

ORDER DATA INPUT

Customer: Tech. Requirements (Cl_O0.1)

Formulation of commercial proposal

Customer: Network Negotiation Constraints (Cl_C0.2)

Customer: Commercial Requir. (Cl_O0.2)

1 A11 (AgNCl_I1.1)

Customer Negotiation Agent

Customer: Commercial Order formulation Models (Cl_M1.1)

Customer: Counterproposal (Cl_O0.3)

2

Customer: Negotiation Models (Cl_M0.1)

Customer: Offer proposal (Cl_I0.1)

NODE: A1 TITLE:

LEVEL 1 - CUSTOMER SYSTEM

NO.:

Figure 2-4. Node A1: Level 1 – Customer System

The second function of the Customer System starts when the Customer Negotiation Agentt (CNA) receives the commercial requirements; the same information is addressed to the Supplier Negotiation System (SNA). The CNA triggers the negotiation, and during the transaction it can close the negotiation either by accepting supplier offer or by rejecting it, or, alternatively, it can keep on negotiating by asking for a new counterproposal.

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2.1.2.1 Formulation of Commercial proposal As previously seen, the order consists both of commercial and technological information processed respectively by the following applications: the Application Form and CAD on-line respectively (see Figure 2-5: the Node A11 is the “child” of A1, so it is the second level of detail of the Customer System). The Customer identification process and its access to the system, as well as the specification of the order (part selected, volume, due-date and price required), are disciplined by the Application form; this activity is performed through the Commercial order formulation Models and it is subject to the Commerciall and Competitive Strategies of the customer. Afterward, in the CAD on-line application, the customer is called to specify the geometric shape of the part and it is allowed to customize the product by adding additional features such as steps, holes, slots, etc…

Customer: Commercial and Competitive Strategies (Cl_C0.1)

ORDER DATA INPUT

(AgNCl_I1.1)

Customer: Commercial Requir. (Cl_O0.2)

Application Form 1

CAD on-line: Part Design CAD on-line starting-up

CAD on-line 2

Customer: Commercial Order formulation Models (Cl_M1.1)

CAD on-line: Data File Customer: Technological Requirements (Cl_O0.1)

CAD on-line: Graphic Interface (PP_M2.1) CAD on-line: Java Routine (PP_M2.2)

NODE: A11 TITLE:

LEVEL 2 - FORMULATION OF COMMERCIAL PROPOSAL

NO.:

Figure 2-5. Node A11: Level 2 - Formulation of Commercial Proposal

The software installed in the customer Host is provided with information about technical operations that the manufacturer can perform, so that technological feasibility of the order is guaranteed. The output of the CAD on-line is the Part Design and the Data File; this last file contains a proper description of the additional features, the kind of material and the surface’s roughness of the part. These two outputs together

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represent the Technological Requirements (constraint CI_O0.1). The CAD on-line activity is triggered by the following models: Cad on-line Graphic Interface, that is a Web Application allowing the part drafting, and the Cad on-line Java Routine that supports the drafting procedure in a WWW environment. 2.1.3 The Supplier system As already mentioned, the Supplier System at “level 1” detail, see Figure 2-6, consists of the Supplier Negotiation Agentt (SNA) and the Virtual Manufacturing System (VMS). The SNA has three inputs: the order Commercial Requirements, the query for a Counter-proposall and the Commercial Feasibility Info (the last one is the output of VMS). The SNA is in charge with the elaboration of the Offer Proposal: after Vi, dd di, pi) from the CNA, the Supplier Negotiation receiving the array (V Agentt estimates the customer importance basing on the historical data base; then it passes the Customer Commercial Requirements to the VMS, and it asks for production alternative plans. Finally, the SNA elaborates and transmits an order proposal to the CNA. If the customer accepts the offer proposal, the SNA launches the production order.

Supplier: Commercial and Competitive Strategies (Fo_C0.4)

Supplier: Network Negotiation Constraints (Fo_C0.5) Supplier: Commercial Requirements (Fo_I0.2)

Negotiation Agent: Customer Commercial Requirement (AgNFo_O1.1)

Supplier Negotiation Agent

Supplier: Offer proposal (Fo_O0.1) PRODUCTION T N ORDER

1

Supplier: Production system workload (Fo_C0.3)

Supplier: Counterproposal (Fo_I0.3) Supplier: Negotiation Models (Fo_M0.1)

Supplier: Capacity Constraints (Fo_C0.2) Supplier: Technological Constraints (Fo_C0.1) VMS: Customer Commercial Requirement (VMS_I1.1)

Supplier: VMS

Supplier: Technological Requirements (Fo_I0.1) Negotiation Agent: Commercial feasibility info (AgNFo_I1.1)

NODE: A2 TITLE:

2

Commercial feasibility info (VMS_O1.1)

A22 Supplier: VMS Models (Fo_M0.2)

LEVEL 1 - SUPPLIER SYSTEM

Figure 2-6. Node A2: Level 1 – Supplier System

NO.:

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31

The VMS, box 2 in Figure 2-6, has the following functions: the generation of the technological sequences, the elaboration of a set of alternative process plans, the setting of possible alternative production plans and finally, the production of the Commercial feasibility info that will be used for negotiating with the customer; such activities are described in diagrams A22, A221, A222, A223. 2.1.3.1 The Virtual Manufacturing System - VMS The Virtual Manufacturing System, shown in Figure 2-7, through the child node "A22" (detail of level 2 of the Supplier System), consists of the following three planning agents: the Process Planner Agent (PPA), the Manufacturing Planner Agentt (MPA) and the Production Planner Agent (PrPA). These agents are constrained by the Technological Constraints of the supplier production system, by the manufacturing Capacity Constraints, by the Production system workloadd and, finally, by the Commercial and Competitive Strategies of the Supplier; the VMS works through the VMS models, that will be deeply described in the following sections.

Supplier: Technological Constraints (Fo_C0.1) Supplier: Technological Requirements (Fo_I0.1)

VMS: Process Plan Agent

Supplier: Production system workload (Fo_C0.3)

PP Agent: Set of Technological alternative sequences (PP_O2.1)

Supplier: Capacity Constraints (Fo_C0.2)

1 A221

PP Agent: Models to identify technological sequences (PP_M2.1)

MP Agent: Set of Technological alternative sequences (MP_I2.1)

VMS: Manufacturing Planning Agent

MP Agent: Selected Plans (MP_O2.1)

2 A222 MP Agent: Models to select process plans (MP_M2.1)

Supplier: Commercial and Competitive Strategies (Fo_C0.4)

PrP Agent: Selected Plans (PrP_I2.1)

VMS: Production Planning Agent

VMS: Commercial feasibility info (VMS_O1.1)

3 VMS: Customer Commercial Requirement (VMS_I1.1)

A223

PrP Agent: Planning Models (PrP_M2.1) Supplier: VMS Models (Fo_M0.2)

NODE: A22 TITLE:

LEVEL 2 - VIRTUAL MANUFACTURING SYSTEM

NO.:

Figure 2-7. Node A22: Level 2 – Virtual Manufacturing System

The first agent, the PPA, receives, as input, the order Technological Requirements, which have been elaborated through the CAD on-line;

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afterwards, the sketch of the part and the additional features are translated into a Set of Technological alternative sequences. This set represents the output of the PPA, and it constitutes the input of the MPA; this last agent, by using process plan selection models, translates the sequences of technological operations into process plans, in which every operation is associated to one of the available machines of the manufacturer. The plans, which are selected on the basis of times and costs evaluation, constitute the output of the MPA agent. The PrPA receives as input the Selected Plans and the commercial information of the order: the agent objective is to evaluate the general profit deriving from every plan by examining the opportunity costs connected to the use of the machineries. From the set of the plans, ranked by decreasing profit, Commercial Feasibility Info about the order (global output of the VMS) is drawn and transmitted to the SNA. Let see in more details the agents constituting the VMS. Supplier: Technological Requirements (Fo_I0.1)

Supplier: Technological Constraints (Data-Base features) (Fo_C0.1)

CAD on-line: Part Design

CAD on-line: Data File

Features Recognition software 1

Operations Attribution 2

Petri Net Construction 3

Features recogniction Algorithm (PP_M3.1)

PP Agent: Set of Technological alternative sequences (PP_O2.1)

4 Operation selection Algorithm (PP_M3.2) Work-plans construction Algorithm (PP_M3.3)

NODE: A221 TITLE:

Database Construction

Database construction Algorithm (PP_M3.4)

LEVEL 3 - PROCESS PLANNER AGENT

NO.:

Figure 2-8. Node A221: Level 3 - Process Planner Agent

2.1.3.2 The Process Planner Agent The Process Planner Agentt (PPA) is the first agent of the Virtual Manufacturing System. Its mechanisms consist of models able to identify technological sequences, subject to the Technological constraints due to

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manufacturer equipments, from the customer Technological requirements, i.e. the part draw. The PPA has four main functions, corresponding to the four boxes in Figure 2-8, i.e. the third level of detail of the System: 1. Features Recognition software: by using proper algorithms, this application extracts additional features from the Part Design and from the Data File (that represent the external inputs to the child node); 2. Operations Attribution: this second activity recognizes technological operations which are necessary to manufacture the part; in particular, the agent identifies the operations (e.g. milling, drilling, etc.) able to manufacture the features recognized in the previous step; 3. Petri Net Construction: during this activity the agent uses a Petri Net model to build a work-plans containing all of the processes alternatives in form of a "graph"; 4. Database Construction: finally, the agent builds a database up containing the Set of Technological Alternative Sequences; this information constitutes the output of the PPA. The PPA will be deeply investigated in Chapter 3. 2.1.3.3 The Manufacturing Planner Agent The Manufacturing Planner Agentt (MPA) is in charge with two fundamental activities (see Figure 2-9):

Supplier: Machines Supplier: Tools Data- Supplier: Equipments Data-Base (MP_C3.1) Base (MP_C3.2) Data-Base (MP_C3.3)

Supplier: Times & Costs Data-Base (MP_C3.4)

MP Agent: Set of Technological alternative sequences (MP_I2.1)

Pallet Workplans Generation

MP Agent: Selected Plans (MP_O2.1)

Workplans

1

Times and Costs assessing 2 Times and costs assessing models (MP_M3.4)

Models for contraints Models for parts layout on among operations pallet (MP_M3.1) identification (MP_M3.2)

NODE: A222 TITLE:

Feasibility Models (MP_M3.3)

LEVEL 3 - MANUFACTURING PLANNER AGENT

Figure 2-9. Node A222: Level 3 – Manufacturing Planner Agent

NO.:

34

Chapter 2

1. Pallet Work-plans Generation; this activity is related to the generation of the part work-plan, by considering all of the technological constraints that the manufacturer resources put on the manufacturing process. In order to do that, the MPA uses information from the Machinery, Tools and Equipments Databases of the supplier. In particular, the agent uses the following mechanisms: algorithms for making parts layout on the pallet, algorithms for identifying constraints among operations and Feasibility models, that is algorithms able to define the positioning of the part in relation to the pallet, the direction of entry of this last one, and therefore the single operations mapping on the available machinery (considering the feasibility of such operations on machineries with 3, 4 or 5 controlled axes). 2. Times and Costs Assessing; this activity associates to each work plans generated during the previous step, manufacturing times and costs; in order to do that, the MPA uses cost and time algorithms. Finally, the MPA generates the Selected Plans, which is the complete set of valued and timed process plans able to manufacture the customer part; this information is transmitted to the downstream agent (PrPA). The MPA will be deeply investigated in Chapter 4. 2.1.3.4 The Production Planner Agent The Production Planner Agentt (PrPA) (see Figure 2-10) performs the following activities: 1. Opportunity Cost Computing; the agent receives manufacturing plans from the MPA and commercial requirements from the SNA: through proper Models for Opportunity Cost Computingg (subject to the Supplier Commercial and Competitive Strategies) the PrPA evaluates costs related to the resources utilization for each plan; afterwards it compares them to the best option not undertook, for example the most profitable job that the manufacturer cannot accept because of the production capacity’s saturation, and it associates opportunity cost to each process plan. 2. Production Planning; during this activity the agent finds out an optimal resource allocation plan for each alternative process plan; this allows the PPA to build a function mapping each production alternative over a profit level for the supplier; this function is referred to as the Commercial feasibility info, and it will be used by the Supplier Negotiation Agent, to build counterproposals for the customer during the negotiation. The PrPA will be deeply investigated in Chapter 6.

2. An Agent Based Architecture for Manufacturing E-Marketplaces

Supplier: Commercial and Competitive Strategies (Fo_C0.4)

Supplier: Capacity Constraints (Fo_C0.2)

PrP Agent: Selected Plans (PrP_I2.1)

Opportunity Costs Computing VMS: Customer Commercial Requirement (VMS_I1.1) Models for Opportunity Cost Computing (PrP_M3.1)

NODE: A223 TITLE:

Supplier: Production system workload (Fo_C0.3)

Opportunity Costs

1

35

Production Planning

VMS: Commercial feasibility info (VMS_O1.1)

2 Production Planning Models (PrP_M3.2)

LEVEL 3 - PRODUCTION PLANNER AGENT

NO.:

Figure 2-10. Node A223: Level 3 – Production Planner Agent

3.

SYSTEM DYNAMICS

3.1 Customer and Supplier Systems interaction In what follows, the dynamic behavior of the system presented in section 2 is described by using the UML Activity Diagram formalism [Marshall, 1999]. In particular, in here the information flow involving the Customer System and the Supplier System will be described, while the reader is invited to go further to the next chapters for a full description of the other agents’ interaction flow. As well know from the UML Activity Diagram notation, each diagram, describing a specific process, is divided into so many swim lanes how many the involved parts of the system are; in this way, each activity is placed in the swim lane correspondent to class/part of the system that plays that action. The UML activity diagram of Figure 2-11 shows the global activities of the entire system, considering the two main actors: the customer and the supplier. The diagram represents all of the processes performed by the Customer System and Supplier System at the highest level of abstraction (related to the

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level 0 of the IDEF0 diagrams - Node A0 - Overall System); the processes are the order data inputting, the information flow, the negotiation activity and the manufacturing planning. In what follows the activities and the respective descriptions are listed basing on the swim lane of affiliation. 3.1.1 Customer system activities The Customer system works out through the following activities: – Order Data Input: this first activity of the customer system consists in inputting technological and commercial requirements of the order; it is highlighted in boldfaced because it also involves an external actor, i.e. the employee of customer’s firm, who manually inserts the order’s requirements into the application menu. – Transmits Commercial & Technological Requirements: the information previously introduced is transmitted from the customer to the supplier system. – Waits for Supplier Counter-proposal: after the data transmission, the customer waits for the supplier’s counter-proposal (as previously seen, the action of waiting for another agent outputs is disciplined by proper controller agent).

Figure 2-11. Dynamic behavior: Customer System & Supplier System

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37

– Counter-proposal Assessing & Utility Threshold Updating: the customer can now evaluate the supplier’s counter-proposal on the basis of its commercial and competitive strategies and of the historical data about the previous businesses made with that manufacturer. Then the customer updates its utility thresholds for further (or future) bargaining with the supplier. – Sign Contract: in case of positive assessment of the proposal, the customer closes the negotiation admitting the terms of the contract. – Quit Negotiation: in case of overriding the maximum admitted number of negotiation rounds (rmax), the bargain is closed. This is the only way to quit negotiation without achieving any kind of agreement between the two parts. – Ask for another counter-proposal: if no agreement is reached within a negotiation step lower than the maximum number of allowed steps (rmax), the customer asks the supplier for another counter-proposal. – Show Negotiation Results: whatever the result of bargain is, it will be shown to the customer system. 3.1.2 Supplier system activities The Supplier system performs the following activities: – Waits for Order Proposal: the supplier is in the initial state of waiting for an offer proposal from the customer. – Order Data Processing: the system analyzes the format of data in input, and extracts both the commercial and technical information necessary to the bargain. In particular, the technical requirements are translated into data necessary to the manufacturing planning. – Counter-proposal Computing & Transmission: the supplier negotiation agent, after having evaluated the customer importance and according to its own commercial strategies, computes a counter-proposal on the basis of a set of production plans (generated by the Virtual Manufacturing System). Then the elaborated proposal is submitted to the customer. – Waits for Customer Answer: after the proposal transmission the supplier remains in a waiting state for a customer answer. – Keep on Negotiating (Utility Threshold Updating): if the customer rejectes the counter-proposal (i.e. it asks for a new counter-proposal), the supplier updates its own utility thresholds and restarts the negotiation process. – Production Order: in case of positive assessment of the proposal from the customer, the supplier launches the production order; as previously

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seen for the order data input (customer system), the production order is highlighted in boldfaced because it involves an actor external to the supplier system, i.e. the real factory of the manufacturer (for example the ERP system).

3.2 Order Data Input The UML activity diagram of Figure 2-12 describes in some detail the activity dealing with order data entry. The involved portions of the system are: the Application Form, the Customer Negotiation Agent (this two elements together represent the customer system) and the Supplier System. The diagram mainly describes the dynamics of the Application Form activities, besides underlining the call “CAD on-line” application that is better explained in the next section. For completeness, the CNA and the Supplier System are also shown in the diagram; this because the commercial and technological information is addressed to them; however, the negotiation activity of these two parts is better detailed in the following sections.

APPLICATION FORM

CNA

SUPPLIER SYSTEM

Waits for Order Data

Waits for Order Data

Waits for Order Data Input

System Access: Customer Identifying (Username/Password)

Commercial Requirement Entry

CAD on-line ne e

Technological Requirement Entry

Transmits Commercial Requiremnents q

Waits for Supplier Counter-Proposal

Counter-proposal Computing and Transmission

Negotiating

Negotiating

Transmits Technological Requiremnents q

Show Negotiation Results

Negotiation Loop

UML Activity Diagram - ORDER DATA INPUT (Application Form, CNA and Supplier System)

Figure 2-12. Order Data Input

The Application Form performs the following activities:

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– Waits for Order Data Input: this is the initial state of the input form, waiting for data entry. – System Access: Customer Identification (Username/Password): the access to the system is allowed only after customer identification through user name and password. – Commercial Requirements Entry: this is the insertion of the commercial info, such as order volume, due-date and price. – CAD on-line: this application triggers the applet that allows the part design, according with the technological capability of the supplier. – Technological Requirements Entry: after CAD on-line starting, the customer can design the part (geometry) and other technological requirements (additional features, material and surface roughness). – Transmits Commercial Requirements: commercial requirements of the order are transmitted both to the customer negotiation agent and to the supplier system. As seen in the IDEF0 diagram – Node A2 – Supplier system, the commercial info is addressed to the SNA, and from the last one to the VMS; in the Node A22 - Virtual Manufacturing System, this information flow is addressed to the PrPA. – Transmits Technological Requirements: the technological info inputted through the application form is transmitted to the Supplier system (in particular to the PPA of the VMS – see Node A22). Note that this information is not provided directly to the negotiation agents (CNA and SNA), but it is assigned to the planning activity. – Show Negotiation Results: as previously seen, whatever the result of bargain is, it will be shown to the customer system directly in the application form.

3.3 Technological requirements inputting and processing The activity diagram of Figure 2-13 details the definition of technological data through the CAD on-line applet and the consecutive transmission of the information to the PPA. 3.3.1 CAD on-line activities As previously discussed, after the entry of commercial data, the application form triggers the Java routine for the CAD on-line that is in charge with the design of the part and of the related features. In particular, the following activities are supported: – Waits for Starting up: the java routine is inactive until an activation signal will not arrive from the application form.

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– Geometry Definition: this activity deals with the definition of part geometry, starting from a prismatic raw work-piece. – Additional Features Definition: the part design is completed with the additional features (hole, slot, step etc.) – Material Characteristics Definition: part information is completed through the definition of material requirements and part surface roughness definition. – Writes Information: the information is collected into two files (part design and data file) and transmitted to the downstream agent. – Transmits Part Design & Data file (Technological Requirements): the technological requirements are transmitted to the PPA of the VMS. 3.3.2 Process Planning Agent activities

– – – –

–

The PPA interacts with the CAD on-line through the following activities: Waits for Technological Requirements: the PPA is in the initial state of waiting for technological requirements. Features Recognition: the agent, using proper algorithms, performs a scanning of the data aimed at features recognition. Operation attribution: the PPA generates all the technological sequences able to produce the features previously recognized. Petri Net Construction: this activity deals with the construction of a “graph” containing all the manufacturing sequences (or work-plans), named “Petri net”. Database Construction (Set of technological alternative sequences): all of the alternative sequences are collected in a suitable database for the successive transmission to the Manufacturing Planner Agent.

2. An Agent Based Architecture for Manufacturing E-Marketplaces

CAD on-line

41

PROCESS PLANNER AGENT Waits for Technological Requirements

Waits for Starting up

signal Features Recognition n=number of additional features

Geometry Definition

[n=0]

Operations Attribution

[n>0] [k=n+1] Petri Net Construction [k

@ ˜ aˆ ST (t,W )

(5)

where weights G are continuously updated by

­

x   a ST (t ,W ) º ½ »¾ ¬ a LT (t ,W )  a ST (t ,W ) ¼ ¿ ª

G (t ,W ) (1  H ) ˜ G (t  1,W )  H ˜ min ®1, max «0, ¯

(6) where H is used as a smoothing parameter. The mechanism works as follows: if the actual demand turns out to be very close to the long-term forecast, then the fraction in (6) will become close to 1 and parameter į will progressively be increased. This will give more weight to the long-term forecast. On the opposite, if the actual demand becomes close to the shortterm forecast, then the fraction will become close to 0 and parameter į will decrease, thus giving more weight to the short-term forecast. The smoothing parameter H can be adjusted according to experience. The following Figure 6-4 provides an example of an output from a combination forecasting procedure (input data is omitted because of obvious lack of space) which shows how demand (vertical axis) on a manufacturing resource is estimated over a time horizon and along a 10-day advance period.

3.

SUPPORTING MAKE-TO-STOCK PRODUCTION

The features of a Make-To-Stock environment suggest an approach in which the production planning and order negotiation process may be radically simplified. In fact, MTS operations cope with a large number of orders of small size in which negotiation must be performed quickly and with little computational burden, by taking into account both production capacity and available inventory. The decision-maker can therefore be viewed as a relatively unintelligent negotiator which enacts a two-step negotiation process. In the first step he proposes a menu of alternatives defined by a structure of prices and “classes” (e.g., “standard”, “express”, “premium”, etc.). In the second step negotiation is performed by having customers self-select product characteristics from the menu, possibly with some support from the system.

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Hourly resource revenue

350 300 250

(€)

200 150 100 50 0 10

10

8

5

6

now -5

4

-10

2

Advance (days)

-15 0

-20

Time Bucket (days)

Figure 6-4. Output from forecasting procedure in revenue management

This mode of operation is based on an inventory management system that defines, per each class, prices and availabilities as a function of forecast demand. Once the allocation of a resource to a given class is exhausted (e.g., the “express” machine hours available on machining center X on day 15), that class becomes unavailable. Resource allocations to classes (termed “booking limits”) can be optimized from the perspective of the supplier through a “Bid Price” revenue management method, which implements a particular inventory control policy using demand forecasts similar to the ones described in section 2. When applying this method to services such as airlines the decision-maker must only consider the inventory of available (and perishable) capacity. In the case of MTS manufacturing, one must jointly manage an inventory of capacity and of finished goods, and may use slack in the former to build up the latter in anticipation of higher future demand. The following mathematical programming model is an example of how booking limits may be set in the case of an MTS manufacturing environment. Sets j = 1…m resources

Chapter 6

130 k = 1…K classes t = 1…T T time buckets

Decision variables sjkt amount of resource j dedicated to class k in time bucket t, for goods to be stocked, qjktt demand of resource j dedicated to class k in time bucket t, for goods to be made “on order” ljkt demand for resource j and class k at time bucket t served from inventory blljkt booking limits for resource j dedicated to class k at time bucket t Ijkt inventory level of finished goods associated to resource j and class k at time bucket t Parameters rjkt expected unit revenue for resource j and class k at time bucket t (derived from forecasting models) Cj available capacity for resource j djkt expected demand for resource j and class k in time bucket t (from forecasting models) ijkk inventory cost of finished goods associated to resource j and class k

max ¦ jkt rjjkt q jkt  l jkt  ¦ijt i jk I jkt

(7)

subject to

I jkt  l jkt  s jkt j,k,t 

I jk ,t 1

(8)

q jkt  l jkt d d jkt j,k,t  t

(9)

q jkt  s jkt d bl jkt j,k,t

(10)

¦ bl

(11)

k

jkt

d C j j,t

l jkt , s jkt , q jkt , bl jkt , I jkt t 0 j,k,t

(12)

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The objective maximizes total relevant profit, given by revenue minus inventory costs. Constraints (8) define inventory dynamics, (9) bound supply to demand, (10) define booking limits, and (11) consider limited resources. Constraints (12) ensure non-negativity of decision variables. Once the model has been solved, it is possible to analyze the dual prices associated to each booking limit (10). These prices (termed bid prices) represent the marginal profit that one can lose by decreasing the size of the booking limit by one unit of production capacity. Bid prices provide the decision-maker with threshold values that tell what revenues should orders grant in order to be accepted. Figure 6-5 provides an example of booking limits calculations from the same case shown in Figure 6-4. The figure shows how the model varies the allocation of 20 units of capacity to the two classes along the time horizon, and generally prioritizes class 1, which is the one granting higher revenue per machine hour. booking limits resource 3 18

16

14

12

10

8

6

4

2

0 0

1

2

booking limits of class 1

3

4

5

booking limits of class 2

6

7

demand of class 1

8

9

10

demand of class 2

Figure 6-5. Example of booking limit calculations

In ordinary Revenue Management methods, and after a Bid Price model is run, customers “negotiate” with the system simply by examining whether there is any available capacity under the preferred class and time bucket. In case there isn’t, it is up to them to decide whether or not to “upgrade” to a more expensive class or to accept a different date. A good example of this mechanism can be experienced by trying to book a flight on the website of any low-cost airline, and by observing how often the system changes

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availability of classes. In MTS manufacturing, orders are slightly more complicated since they can involve more than one resource and can be split over multiple classes and time buckets. So, it is worthwhile to develop “Order Selection” models guiding customers’ choices within the constraints given by the booking limits, following a logic similar to Available To Promise modules in MRP systems [Plossl, 1994]. Figure 6-6 shows the interactions between the Forecasting, Bid Price and Order Selection models that altogether allow the integration of production and marketing decisions.

Demand forecast per class per time bucket

Resource allocated per class per order per time bucket

Database time series

Booking limit per class per time bucket

Accepted orders

Figure 6-6. Decision architecture for MTS production

Order selection models can be designed to minimize changes with respect to initial customer requirements (e.g., delays or upgrades to more expensive “classes”), as in the following example, derived from [Cantamessa et al., 2003]. For simplicity, we assume identity between items produced and resource usage (i.e., one item produced per time unit) and assume upgrades are not allowed, while delivery can be anticipated (with inventory cost accruing to the customer) or delayed (causing backlog costs). Sets t = 1…T time bucket k = 1…K K classes j = 1…m resources Decision variables qqikt amount of resource j class k, dedicated to the order at time t

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llljkt amount of finished goods inventory for resource j and class k made available to the order at time t Ijkt order-specific inventory at time t (due to goods being delivered ahead of time) Bjkt order-specific backlog at time t (due to delayed delivery of goods) Parameters rrikt ordered (desired) amount of resource j and class k at time bucket t qjktt resource j dedicated to class k in time bucket t and available “on order” (from bid price model, minus amount allocated to previously accepted orders) ljkt resource j and class k at time bucket t available from finished goods inventory (from bid price model, minus amount allocated to already accepted orders) hjk inventory cost per time bucket of goods coming from resource j and class k wjk backorder cost per time bucket of goods coming from resource j and class k

min

¦

ikt

h jk I jkt  ¦ jkt w jk B jkt

(13)

subject to

I jk ,t 1  B jk ,t 1

I jkt  B jkt  qq jkt  ll jkt  rrjkt j,k,t  t

(14)

qq jkt d q jkt j,k,t  t

(15)

ll jkt d l jkt j,k,t  t

(16)

¦ qq t

jkt

 ll jkt

¦ rr t

jjkt

 t j,k,t

I jkt , B jkt , qq jkt , ll jkt t 0 j,k,t

(17)

(18)

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The model minimizes inventory and backorder costs to the customer. Constraint (14) computes levels for order-specific inventory. Constraints (15-16) ensure compatibility between delivery to the customer and previously computed booking limits, while (17) aligns delivery with customer needs. Constraint (18) ensures non-negativity of decision variables.

4.

SUPPORTING MTO / ATO PRODUCTION

MTO and ATO operations, which characterize the manufacturing scenario common to all contributions in this volume, lead to more sophisticated negotiation processes, due to the uniqueness of each order. A more “intelligent” form of negotiation support should be developed for this production environment. Specifically, orders can be differentiated through a number of dimensions that can simultaneously influence the utilities of customers and suppliers within a multidimensional negotiation space. Moreover, negotiation may involve several parties at the same time, since a customer might ask a group of suppliers to bid for an order, while each supplier might simultaneously be negotiating other orders with different customers. In ATO and MTO operations one should also consider higher granularity of demand (i.e., the ratio between the average order size in terms of machine hours and the time bucket). It is therefore not possible to split time buckets in classes and then simply allocate orders to the latter, as proposed for MTS operations in section 3 of this chapter. The profitability of the decision to allocate production capacity to a customer order may however be evaluated through the concept of an opportunity cost, as discussed in section 2. The decision maker will therefore decide whether or not to fulfill an order depending on a forecast of machine occupation and on how the revenue associated to the order compares to the opportunity cost, which represents the expected value of future incoming orders per machine hour. On the side of the supplier, the main problem from the side of production planning consists in evaluating the profitability of an potential order (or Request For Quotation) that is being negotiated with a customer and provide the decision-maker with information on how such profitability may be improved and made acceptable (e.g., a price threshold that would make the order acceptable, or more desirable delivery dates). While the number of possible negotiation dimensions in MTO/ATO operations is fairly high, the two main aspects that may influence the production plan are price and due date. It should be stressed that, by tradition, production planning does not look at the side of revenues (and therefore prices) but simply aims to minimize total relevant costs. However, the main point being made in this

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chapter is the need to jointly optimize revenues and cost in order to maximize profits. In the context of a negotiation process, evaluation of order profitability must be performed rapidly, so that the agent performing negotiation may interact with the customer effectively with respect to time and quality of information used. This suggests developing rough-cut but computationallyefficient production planning models that may be run interactively, for instance testing the profitability of different combinations of prices and due dates. An example of this kind of models is presented in the following by using Mixed-Integer Linear Programming. The model, in which the number of integer variables is limited as much as possible in order to keep computational effort within bounds, maximizes profits of a firm to which a new job has been proposed. Production planning decides on acceptance of the order at the proposed price and due date, chooses among alternative process plans, and provides the negotiator with information on overall profit associated to the decision. Sets i = 1…m orders j = 1… n resources t = 1… T time buckets l = 1…L process plans Decision variables vill = 1 if job i chooses plan l, = 0 otherwise xill fraction of job i to be processed through plan l yijt amount of resource j allocated to job i at time bucket t amount of regular capacity used on resource j at time bucket t rjtt ojt amount of overtime work allocated to resource j at time bucket t Parameters pi order price di order due date FC Cill fixed (setup) cost of process plan l on order i rsijl amount of resource j needed to process job i under process plan l CR Rjtt opportunity cost of ordinary capacity on resource j and time bucket t (from forecasts) Fmi minimum fraction of job i to be processed (a priori decision on order being negotiated, or because order i has already been accepted in the past) COVj unit cost of overtime on resource j

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CAPRit ordinary capacity of resource j in time bucket t CAPO Ojt maximum overtime capacity of resource j in time bucket t

max ¦il xil ˜ pi  ¦il FCil ˜ vil  ¦ jt COV j ˜ o jt  ¦ jt CR jt rjt

(19)

subject to

¦

t ddi

¦v l

yijt t ¦l xil ˜ rsijl i,jj

(20)

d 1 i

(21)

t Fmi i

(22)

il

¦x

l il

xil d vil i,ll

(23)

¦y

(24)

i

ijt

d rjt  o jt j,t  t

rjjt d CAPR jt j,t 

(25)

o jt d CAPO jt j,t  t

(26)

yijt , xil , riit , oit t 0 i, j, l, t

(27)

vil  ^

(28)

` i,ll

The objective function computes profit as difference between revenue (proportional to the order fraction that is accepted), and relevant costs. These include fixed costs associated to using the alternative process plans, opportunity costs of resources in regular time and overtime costs.

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Constraints (20) compute order-specific workload on each resource, taking into account alternative process plans and due dates. Constraints (21) and (22) ensure the plan does not perform less or more work than the order requires. Constraints (23) ensure setup costs are paid when process plans are used. Constraints (24) and (25) compute resources usage. Constraints (26) put a bound on overtime, while constraints (27) and (28) define nonnegativity and integrality of decision variables. Within a negotiation process, it is possible to run the model on a grid of potential prices and due dates. Per each pair of price and due date, the negotiator could be provided with information on whether the order has been selected or not and the marginal profit accruing to the firm. An example is shown in Figure 6-7, which shows marginal profit that may be obtained at a number of price and due date alternatives. In the flat area in the lower left corner the order is not being selected because of unfavorable terms, and zero marginal profit is therefore shown. As the due date becomes moves further in the future and as price increases, the order is selected and provides a positive contribution. One can notice how the relationship between marginal profit and order price is linear, while the relationship with the due date is discontinuous. Without such information, it would therefore be quite difficult to the negotiator to correctly consider tradeoffs between order prices and due dates.

15

Figure 6-7. Exploration of a grid of price and due date alternatives in MTO operations

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5.

THE PRODUCTION PLANNER AGENT

The previous chapter clarifies the methodologies adopted for implementing the Production Planning Agent (PrPA) within the proposed agent architecture for manufacturing e-marketplaces. Indeed, the referred manufacturing e-marketplace works through a classical MTO approach, therefore the main aim of the PrPA is to support negotiation providing what in the IDEF0 diagram of Figure 2-10 in Chapter 2 have been referred as the Commercial feasibility info, that is that information to be used by the SNA to build counterproposals for the customer during the negotiation. For sake of clarity Figure 6-8 reports the UML activity diagram showing the workflow interaction among the PrPA and the SNA. CNA

SNA

PrPA

Waits for order proposal

Waits for production planning request

Transmits order Estimates Customer importance Estimates own importance

Computes Order Proposal Constraints

Computes utility thresholds

Runs PrP Algorithm

Computes production C alternatives

Provides Order Proposal Constraints Waits for SNA answer

Provides production alternatives

Wait for production data Updates utility thresholds Computes counter-proposal

Evalutes counter-proposal [Positive] Signs Contract

Transmits counter-proposal

[r>r max] Quits negotiation Waits for CNA answer

Asks for another counter-proposal

Updates customer database

Updates U d t profit fit limits Updates supplier database

Negotiation Process Detailed Activity Diagram

Figure 6-8. PrPA and SNA workflow Activity Diagram

As the reader can notice the PrPA, once activated by the SNA, performs the following processes: Runs PrP; the PrPA runs the production planning (PrP) algorithm; this is the MILP model presented in the previous section whose objective function is expression (19) and constraints are expressions from (20) to (28). The way the model is built and solved is deeply explained in what it follows;

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Computes Production Alternatives; as output of the PrP algorithm the PrPA computes a grid such as in Figure 6-7 for each volume offered to the customer Vj, that is:

Vj being

¦ x ˜ V il

l

i,o

(29)

¦ x the fraction of the volume required by the customer for l

il

product i and order o. As previously explained the grid of Figure 6-7 associates to each couple dj, and price, pj, a supplier profit ((Prrj). By collecting all the of due date, dd grids for each value of offered volume, each production planning alternative PA Aj (j = 1…n) associates a supplier profit (Prrj) to the triple consisting of an Vj) due date (dd j) and price ((ppj): that is PA Aj = (Prrj, Vj, dd d j, offered volume (V pj)  j. This is actually the Commercial feasibility info, provided to the SNA. Provides production alternatives; PA Aj is transmitted to the SNA. The activity diagram of Figure 6-9, describes in more detail the activity “Runs PrP algorithm”. It has been assumed that production plan activity proceeds by planning period of fixed length T T, i.e. all the orders received within the period T can be re-planned in the period; once the period is over, re-planning is not allowed for orders of the previous period. Orders re-planning will change production planning by maintaining the order characteristics already agreed with the customer, i.e. due date, price and offered volume. The algorithm works through the following steps: Initializes algorithm parameter: the PrPA set the orders counter i = 1. In Negotiation: this condition verifies whether the incoming order is a new one in the period (N.O.) or whether it is an already planned order that needs to be re-planned in order to find another optimal production plan; in this case the order is assumed to be “firm” (F.O.) meaning that, during the new planning, order due date, price and offered volume cannot change. If the incoming order is a new one go to Set N.O. Parameters activity, otherwise to Set F.O. Parameters. Set N.O. parameters; the PrPA sets the following Negotiation Order (N.O.) parameters: – i* = i, index of the N.O.; – Fmi* = 0, minimum production volume fraction of constraint (22); the maximum of the volume fraction is always 1. Sets F.O. Parameters: for each planned order j = 1,…,N_ord(t) the algorithm sets the following Firm Orders (F.O.) parameters:

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– Fm mj = Fp pj, i.e. the production volume fraction is fixed to the amount already planned ((Fp pj) that is also the maximum allowable; – ddinttj = ddenddj = ddp pj, i.e. the due date lower (ddint) and upper bounds (ddend) d are fixed to the agreed value (ddp pj);

Figure 6-9. Detail of the activity "Runs PrP Algorithm"

– pL Lj = pH Hj = pp pj, i.e. the lowest (pL ( ) and the highest ((pH H) price values are fixed to the amount agreed with the customer ((pp pj). i < N_ord(t): if the order index is lower than the numbers of orders already planned in the period t, it is a “firm order” and therefore there must be other others planned in t; in this case the algorithm goes to the activity Load Planned Orders; otherwise the algorithm goes to the Sets initial N.O. constraints activity.

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Load Planned Order: the algorithm loads all planned orders information for re-planning. Set initial N.O. constraints: the PrPA sets the following planning constraints for the new order: – pi* = pmaxi = pH Hi, pmini = pLi – dd di* = ddmini = ddinti, ddmaxi = ddenddi that is the actual price, pi* and the price higher bound, pmaxi, are fixed equal to the price highest value defined by the supplier, pH Hi; the lower bound of the price, pmini, is fixed equal to the lowest value of the price acceptable di* and the lower bound by the supplier, pLi. Similarly, the actual due-date, dd of the due-date, dmini, are fixed equal to the lowest value of the due-date defined by the supplier, ddinti; the due-date higher bound, ddmaxi, is fixed equal to the highest value of the due-date defined by the supplier, ddenddi. Updates N.O. parameters: the PrPA passes pi* and dd di* to the LINGO Solver. Runs optimization model: the PrPA activates the Lingo Solver that runs di* the optimization model presented in the previous section, where pi* and dd are respectively implied in expressions (19) and (20); after the LINGO Solver solve the model, optimal volume fraction and profit level associated to pi* and dd di* are found out, the PA array is built and passed to the PrPA. Reduces N.O. price: the PrPA reduces the previous price pi*’ according to the following expression: ( pi* = pi*’ – D· (pmax i - pmini), D  [0,1]

(30)

Increases N.O. due date: the PrPA increases the previous due date dd di*’ as in (31): dd di* = dd di*’ + D · (ddmaxi - ddmini), D  [0,1].

(31)

By iterating the last two activity and the model solving, the PrPA builds the production alternative matrix for each price in the interval [[pL Lj , pH Hj] and due-date in [ddinti, ddenddi].

6.

CONCLUSIONS

Literature has often supported the idea that order negotiation and production planning problems ought to be integrated on the basis that separation – as generally performed in industrial practice – is suboptimal [Eliashberg and Steinberg, 1993]. In the context of electronic marketplaces,

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in which negotiation processes occur very frequently, this plea for integration becomes a real necessity. This chapter has attempted to provide a broad overview on the integrated order negotiation – production planning problem and has drawn and adapted methods and tools from revenue management in order to discuss how the problem may be solved in both cases of Make-To-Stock and Make-To-Order operations. The research presented is by its own nature preliminary, and it has the objective of demonstrating the possibility of adapting and applying methods traditionally used in the service industries to manufacturing. However, in the specific case of MTO e-marketplaces, it has been showed how the proposed approaches can support negotiation in agents architecture. The methodologies adopted to implement the Production Planning Agent have been explained in the details. Future research ought to improve the models presented in the previous papers and tailor them towards specific applications. At the same time, another challenging line of research would consist in covering the case of Engineering-To-Order companies which, due to higher complexity of the negotiation problem, has for the time being been purposely been set aside.

REFERENCES Belobaba, P., Airline yield management. An overview of seat inventory control, Transportation Science, 1987, vol.21, n.2, pp. 63-73. Calosso T., Cantamessa M., Gualano M., Negotiation support for make-to-order operations in business-to-business electronic commerce, 2004, to appear in Robotics and ComputerIntegrated Manufacturing. Cantamessa M., Gualano M., Villa A., “A taxonomy of negotiation problems and solution approaches in manufacturing”, 2003, Proc. 6th AITEM, Gaeta, Sept. 8-10th, 639-651. CPFR, 2002, VICS-CPFR voluntary guidelines 2.0, available at http://www.cpfr.org. Eliashberg, J., Steinberg, R., Marketing-production joint decision making, Handbooks in OR & MS, 1993, vol. 5 - Marketing, pp. 827-880, Amsterdam: Elsevier. Franses, P.H., Time series models for business and economic forecasting, Cambridge: Cambridge University Press, 1998. Makridakis, S., Wheelwright, S.C., McGee V.E., Forecasting: methods and applications, New York: WILEY, 1983. Manugistics Inc., From Revenue Management to Enterprise Profit Optimization, 2001, available at http://www.manugistics.com McClain J.O., Thomas J.L., Mazzola J., Operations management: production of goods and services, 1992, Englewood Cliffs: Prentice Hall. Plossl G.W., “Materials Requirements Planning”, 1994, New York: MC GRAW HILL. Wise R., Morrison D., Beyond the exchange – the future of B2B, Harvard Business Review, 2000, nov.-dec. Yeoman, I., Ingold, A., Yield management – strategies for the service industries, 1999, London-New York: Cassel.

Chapter 7 IMPLEMENTATION, NUMERICAL EXAMPLES AND TESTS Implementing and evaluating real added services in manufacturing e-marketplace Giovanni Perrone and Paolo Renna Dipartimento di Ingegneria e Fisica dell’Ambiente, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza – Italy

Abstract:

In order to test the functionality of the proposed agent based distributed architecture for manufacturing e-marketplaces, a proper simulation environment has been developed. Such an environment has been used to test the functionality of all the features discussed in the previous chapters of this book, but also to understand the real value that some added value services, such as re-planning and negotiation, can bring to the participants in a virtual district. This chapter discusses the above issues; in particular, the first part is dedicated to show all of the implementation phases; in the second part of the chapter a numerical example able to show the full functionality of all the applications, described in the previous chapters, will be analyzed; finally, in the third part, a set of experimental results will be discussed in order to understand the economic value of real added services in manufacturing emarketplaces.

Key words:

Discrete event-simulation, added value services, numerical tests

1.

THE AGENT BASED ARCHITECTURE IMPLEMENTATION

The agent based architecture proposed in the research project [Perrone et al, 2003] has been implemented by developing a test environment consisting of a discrete event simulation environment able to execute all of the functionalities discussed in the previous chapters. The environment has been 143 G. Perrone et al., (eds.), Designing and Evaluating Value Added Services in Manufacturing E-Market Places, 143–169. © 2005 Springer. Printed in the Netherlands.

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developed by using open software technology entirely realized by using Java Development Kit. The implementation has been particularly complex since several IT problems had to be solved. All of the implementation issues related to the software development phases are not of interest for the purpose of this book and therefore they will be neglected in the following. In what follows, indeed, it will be clarified how the test environment (The System) interacts with external actors, how it has been formalized by using object-oriented methods, and how the simulation engine workflow works out. The UML use case diagram, reported in Figure 7-1, clarifies how The System interacts with external actors. System

provide technological capability

input the order

Network Supplier

Network Customer provide production capacity provide negotiation knoweledge

final deal approvement

provide network rules and protocols Network Manager

provide production planing Information ERP Supplier System

Figure 7-1. System Use Cases

As the reader can notice the following external actors interact with the system: – The Network Customer; it is a generic registered customer of the emarketplace, who interacts with the system by inputting the order (use case “input the order”), by providing the Customer System with the negotiation knowledge (use case “provide “ negotiation knowledge”) and by eventually approving the finally agreement reached after the negotiation (use case “final “ deal approvement”); – The Network Supplier; it is a generic registered supplier of the emarketplace who interacts with the system by providing the Supplier

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System with the negotiation knowledge (use case “provide “ negotiation knowledge”), the technological capability (use case ““provide technological capability”), and the production capacity (use case ““provide production capacity”); finally, even the Network Supplier is called to approve the negotiation agreement (use case ““final deal approvement”); – The ERP Supplier System; it is the supplier ERP system that provides the Supplier System with necessary information to plan the production activities (use case ““provide production planning information”); – The Network Manager; that is the exchange owner who manages the emarketplace by providing rules and protocols for the network (use case ““provide network rules and protocols”). Agent Class

Negotiation Agent

1

1

Controller Agent

provides altrnatives sequence q 1

PP Agent

askks network access

1 1 1

1

1

MP Agent

1 provides alternative PP 1 1

activates Customer Negotiation Agent

1

1..* negotiates 1

1

1

1

VMS

1

1

1 1

1 Customer System

activates

activates v

Supplier System 1

1

1 1

provides counter offer information transmits order information 1

Order

1

1

Supplier Negotiation Agent

elaborates r

1..*

PrP Agent

1

1 VMS controller

1

elaborates 1

Counter order

1

order inputting

Figure 7-2. Distributed agent architecture class diagram

Stepping down to implementation issues, Figure 7-2 reports the class diagram of the system where the following classes can be located:

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– Agent Class, that is the basic class type; – The classes Negotiation Agent Controller Agent, Production Planning Agent PPA), Manufacturing Planning Agent (MPA) and Production Planning Agent (PrPA) which are specialization of the Agent Class; – The class Customer Negotiation Agent and Supplier Negotiation Agent which are a specialization of the class Negotiation Agent; – The class VMS that is the aggregation class of the PP Agent, MP Agent and PrP Agent class; – The class VMS Controller that is a specialization of the VMS class; – The class Customer System that is a composition of the Customer Negotiation Agent and Controller Agent classes; – The class Supplier System that is a composition of the Supplier Negotiation Agent, VMS and Controller Agent classes; – The classes Order and Counter Order. Furthermore in Figure 7-2 the following associations have been highlighted: – The Customer Negotiation Agent class elaborates the order proposal; – The Supplier Negotiation Agent elaborates the counter proposal; – The Customer Negotiation Agent and the Supplier Negotiation Agent exchange order proposals and counterproposals during the negotiation; – The Supplier Negotiation Agent and the VMS class exchange information for counterproposal elaboration; – The Negotiation Agent asks the Controller Agent for network access; – The PP Agent provides the MP Agent with technological sequences, the MP Agent provides the PP Agent with alternative process plans, the PrP Agent provides, through the VMS, information for building the counterproposal; – The VMS controller allows order inputting; – The VMS controller activates PP Agent, MP Agent, and PrP Agent classes. The reader should notice how the Class Diagram of the System is perfectly responding to the agent architecture illustrated in Chapter 2 and to the workflows presented in Chapters 3 to 6. As previously mentioned, the described architecture has been developed through a simulation engine entirely realized in JAVA development package. The simulation environment has a discrete event engine, whose workflow is described in the UML activity diagram of Figure 7-3. As the reader can notice, the VMS controller agent manages the Supplier Web Applet, consisting of the Customer Web Page and the Cad on-line Application deeply described in Chapter 3, allowing the customer to introduce the technological and commercial data information (Order Inputting). g At this point, the VMS controller starts activating the VMS Agents; in particular, the PPA is the first agent which is activated (Run PPA); its aim is,

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as deeply explained in Chapter 3, at finding out alternative technological sequences ((Provide Alternative Technological Sequences); once the technological sequences are obtained, the VMS controller activates the MPA ((Run MPA), who finds out alternative process plans ((Provide Alternative Process Plans) as illustrated in Chapter 4. At this point the Supplier Negotiation Agent (SNA) is activated ((Run SNA); this, based on customer requests, builds up constraints for the Production Planning activity ((Provide bounds for Production Planning Alternatives) as explained in Chapter 5 and 6; the PrPA finds out the Production Planning Alternative and provides the SNA with Commercial Info ((Provide Production Planning Alternatives), that are used by the SNA to build the counterproposal and to start the negotiation with the Customer Negotiation Agent ((Negotiate with CNA, Negotiate with SNA). At the end of the process the VMS controller shows the negotiation process results in the Customer and Supplier Applet Web (Show Negotiation Result). VMS Controller

Order Inputting through the Supplier Web Applet

PPA

wait

MPA

SNA

PrPA

wait

wait

wait

CNA

wait

Run PPA

Pro ide P Provide id alternati alternatives lt ti es technological sequence wait

Provide bounds for Production Planning Alternatives

Negotiate with SNA Provide Production Planning Alternatives (Commercial Info)

Provide Alternative Process Plans

wait Run MPA

Wait Negotiate with the CNA

Run SNA

wait

Show negotiation outcome

Figure 7-3. Simulation engine workflow

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The described workflow is repeated each time a new order is obtained by the supplier and for each supplier involved in the order transactions, depending on the selected negotiation policy.

2.

THE FUNCTIONALITY TEST

The aim of this chapter is to show the functionality of the agent based architecture discussed in the previous chapters and implemented as discussed in the previous section. The functionality has been tested through a numerical example able to show the full operability of all of the agents involved in the distributed architecture.

2.1 Inputting Technological Data The customer enters the application by logging in a WEB page supported by the VMS controller of the Supplier System as depicted in Figure 7-4. This WEB page allows the customer inputting the part technology requirements, through the CAD-on line application deeply described in Chapter 3. Through this application, the customer can specify the geometric shape of the part and he/she can customize it by defining a set of additional features, (for example, slot, hole, step, etc.). The customer works directly on the CAD on-line application within the Supplier System and he/she provides information about the technical operations that the manufacturer can surely perform; in this way, the CAD-on line application assures that the technological feasibility of the order is always satisfied. The Figure 7-4 shows the CAD-on line WEB page. As the reader can notice, the customer is allowed to define a set of features, in particular through the buttons: – “disegna contorno”, the customer is able to define the geometry of the rough part; – “disegna foro”, it is possible to define holes features; – “disegna asola”, it is possible to define pocket features; – “vista assonometria”, it is possible to change the view of the part; – “avvia planning”, it is possible to start the Process Planner Agent. Furthermore, an help window provides the customer with the necessary information to properly run the CAD on-line application. Here it will be assumed that the customer will input an order for the part reported in Figure 7-5.

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Figure 7-4. CAD-on line WEB page

Vista Dall'alto

Figure 7-5. Example part

In this case, the first step of drawing the part in the CAD on-line application is the part geometry definition. The customer selects the menu “disegna contorno” and the window reported in Figure 7-6 is showed; then the customer inputs the rough part dimensions, through a proper tri-dimensional grid (x1,..x5, y1…,y5, z1,…,z5). By selecting the button “Elabora”, the application shows in the

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left windows the frontal and up views of the rough part, as depicted in Figure 7-7. Let us suppose now that the customer wish to design a pocket in the rough part; in this case he/she will select the menu “disegna asola”, as reported in Figure 7-8, and he/she will input the pocket position and geometric data. Afterwards, by pushing the button “Elabora”, the pocket will be shown in the left views of the part. The customer can always ask for a 3D view by pushing the button “Vista assonometrica”. Finally, the customer is called to input position and geometric features of the two holes through the menu “disegna fori”, as reported in Figure 7-9. After the additional features definition has been completed, the CAD on-line application requires the customer to input data regarding the material to be worked. Figure 7-10 reports an example of the available materials the customer can choose (Steel, Alluminium Alloy, Cuper Alloy, and so forth). Once the customer selects the row material, he/she pushes the “Scrivi File” button and all of the Technological Information is transferred to the Process Planning Agent, who will process it as explained in Chapter 3.

Figure 7-6. Rough part dimension definition

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Figure 7-7. Rough part views

Figure 7-8. Pocket menu

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Figure 7-9. Holes menu

Figure 7-10. Material pop-up menu

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2.2

153

Inputting Commercial data

Once technological data have been provided, the customer is called to input Commercial information; therefore, the application connects the customer with the WEB page depicted in Figure 7-11, where the following data are required: – Required volume for the order (“inserisci il volume”); – Required price for the order (“inserisci il prezzo”); – Order code (“inserisci codice ordine”); – Required due date (“inserisci la data di consegna”).

Commercial information WEB page

In the numerical example here considered, commercial information is the following: – Required volume for the order: 150 parts;

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– Required price for the order: 10.000 Money Units; – Order code: 1; – Required due date: 4 planning period after today. The customer transmits the commercial proposal by pushing the button “submit”.

2.3 Agent activities Technological data submission activates the Process Planner Agent (PPA). The PPA verifies the manufacturability of the order and determines the technological operations, which are necessary to manufacture the parts; furthermore it evaluates the possibility of alternative manufacturing sequences. The following figures report the output data of the PPA for the example part of Figure 7-5. For the meaning of the data the reader should refer to chapters 3 and 4; however, Figure 7-12 reports an example of the Geometry file, where the data dealing with the part geometry are collected.

Figure 7-12. A Geometry File example

On the other hand, Figure 7-13 reports an example of the Petri Net file, where the technological alternatives are stored in a matrix format and the precedence constraints between the manufacturing operations are reported. Figure 7-14 reports an example of the Feature file, containing the part and feature codes, that is the feature geometry, its position and orientation with respect to the part datum layers. Finally, Figure 7-15 reports an example of the Operation file, containing the part code, which is the reference to the corresponding feature, the required tool position and orientation with respect to the feature to be manufactured, the tool path, the cutting parameters and the required number of passes. The above information is transferred to the Manufacturing Planner Agent (MPA), who is activated; it utilizes these data to perform its own activities, which have been described in Chapter 4.

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Figure 7-13. A Petri Net file example

Figure 7-14. A Feature File example

Figure 7-15. An Operation File example

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The output of the MPA consists on a set of alternative process plans each one characterized by the set of machines involved in the machining operations, the machining time for each manufacturing operation, the machining cost, and finally, the set of pallet and tools involved in each technological operation. For sake of simplicity, Table 7-1 reports the machine and manufacturing time (expressed in seconds) for each alternative process plans located for the example part depicted in Figure 7-5. Table 7-1. Alternative process plans - machining times Machine Process Process index Plan 1 Plan 2 1 593132 0 2 0 605132 3 0 0

Process Plan 3 0 0 593132

Process Plan 4 0 0 0

A machining time different from zero in Table 7-1 indicates that the process plan is performed by the machine. In this example each process plan is performed just by one machine. Table 7-2 reports the tools utilized for each process plan, while Table 7-3 reports the pallet used for each process plan. Finally, Table 7-4 reports process plan costs in monetary units. Table 7-2. Alternative process plan - tools Tool Process Process index Plan 1 Plan 2 28 0 0 23 0 0 39 1 1 40 1 1 24 0 0 25 0 0 26 0 0 27 0 0 29 0 0 42 0 0 30 0 0 36 0 0 43 1 1 2 0 0 31 0 0 32 0 0 33 0 0 34 0 0 35 0 0 37 0 0

Process Plan 3 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

Process Plan 4 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

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Tool index 38 41 12 13 14 15 3 16 17 18 20

Process Plan 1 0 0 0 1 0 0 1 0 0 1 1

157

Process Plan 2 0 0 0 1 0 0 1 0 0 1 1

Process Plan 3 0 0 0 1 0 0 1 0 0 1 1

Process Plan 4 0 0 0 1 0 0 1 0 0 1 1

Process plan2 1 0 0

Process plan 3 1 0 0

Process plan 4 1 0 0

Table 7-3. Alternative process plan - pallet

Process plan 1 1 0 0

Pallet 1 2 3

Table 7-4. Alternative process plans – cost

Process plan 1 164759

Process plan2 193306

Process plan 3 197711

Process plan 4 210115

Figure 7-16 and 7-17 respectively report the machines and tools data base utilized in the numerical example.

index 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Machine 3_assi_1 3_assi_2 3_assi_3 3_assi_4 4_assi_1 4_assi_1 4_assi_3 4_assi_4 4_assi_5 4_assi_6 5_assi_1 5_assi_2 5_assi_3 5_assi_4 5_assi_5 5_assi_6 5_assi_7

axis x axis x axis z rapid rapid rapid travel in x velocity travel in y velocity travel in z velocity axis [mm] [mm/min] axis [mm] [mm/min] axis [mm] [mm/min] 700 40000 700 40000 700 40000 1500 45000 1500 45000 1500 45000 700 50000 700 50000 700 50000 1500 35000 1500 35000 1500 35000 700 40000 700 40000 800 40000 700 50000 600 47000 700 50000 700 50000 650 50000 700 50000 700 40000 700 40000 800 40000 700 50000 600 47000 700 50000 700 50000 650 50000 700 50000 650 70000 600 70000 670 70000 700 32000 700 32000 850 32000 800 32000 900 32000 850 32000 500 60000 500 52500 500 52500 1400 40000 1000 40000 1000 40000 2200 22000 1100 22000 1150 22000 2600 22000 1500 22000 1650 22000

change change tool time pallet [sec] time [sec] 2 40 3 50 2 40 3 50 2 40 2 50 2 60 3 40 3 50 3 60 2 60 2 50 2 40 3 60 3 50 3 50 2 40

Figure 7-16. Machine Data Base

power [Kw] 20 30 20 30 20 30 25 20 30 25 20 25 30 30 25 30 25

spindle speeds max [rpm] 12000 15000 12000 15000 15000 18000 18000 15000 18000 18000 15000 18000 20000 12000 15000 18000 15000

Cost per unit of time 100 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190

Chapter 7

158 work diameter tool code tool class tool [mm] T1 face_mil 125 drill 11 T10 face_mil 34 T11 T12 Drill 24 Face_mil 26 T13 T14 Drill 6,75 T15 Tap 14 T16 Boring_bar 33 T17 Side_mil 26 Face_mil 18 T18 T19 Boring_bar 52 T2 drill 10 T20 face_mil 32 T21 Drill 7 T22 Tap 8 T23 drill 8,25 T24 reamer 14 8 T25 reamer 6 T26 reamer 14 T27 reamer T28 drill 3 T29 reamer 5 T3 Face_mil 50 T30 tap 7 T31 tap 10 tap 10 T32 T33 tap 10 tap 10 T34 T35 tap 10 5 T36 tap T37 tap 10 T38 tap 10 T39 drill 4 T4 drill 6,75 T40 drill 6 T41 tap 10 6 T42 reamer T43 tap 6 T5 drill 17 T6 tap 8 T7 drill 5 T8 tap 6 T9 drill 9

function

drilling N

double N double Y double

Y

N double

Y

double

double double double

Figure 7-17. Tools Data Base

The information about the alternative process plans is transmitted to the Production Planning Agent (PrPA), which, by using production data of Table 7-5, computes the production alternatives.

7. Implementation, numerical examples and tests

Table 7-5. Supplier’s Production Data Process plan 1 Manpower ordinary 20 Cost (monetary units) Manpower overtime cost (monetary units) 40 Supplying cost (monetary units) 30 Ordinary capacity (hours per planning period) 102 Overtime capacity (hours per planning period) 78 Supplying Capacity (hours per planning period) 54

159

Process plan 2

Process plan 3

Process plan 4

20

20

20

40

40

40

30

30

30

102

102

102

78

78

78

54

54

54

Production alternatives for a production volume of 150 parts are reported in Table 7-6 depending on the due date (number of periods after the order has been placed) and price to offer to the customer (monetary units). As explained in Chapter 6, production alternatives provide the supplier’s profit as a function of each triple of offered volume, due-date and price; therefore, values in Table 7-6 represent the supplier’s profit for each production alternative. Table 7-6. Supplier production alternatives Price 14000 13600 13200 12800 12400 12000 11600 11200 10800 10400 10000

1 1370.63 1273.07 1175.51 1077.95 980.390 882.829 785.268 701.366 623.317 545.268 467.220

2 2905.27 2710.15 2515.02 2319.90 2124.78 1929.66 1734.54 1566.73 1410.63 1254.54 1098.44

3 4439.90 4147.22 3854.54 3561.85 3269.17 2976.49 2683.81 2432.10 2197.95 1963.81 1729.66

Due Date 4 5 5974.54 6956 5584.29 6556 5194.05 6156 4803.81 5756 4413.56 5356 4023.32 4956 3633.07 4556 3297.46 4162.83 2985.27 3772.59 2673.07 3382.34 2360.88 2992.09

6 7596 7196 6796 6396 5996 5596 5196 4796 4396 3996 3596

7 8196 7796 7396 6996 6596 6196 5796 5396 4996 4596 4196

8 8796 8396 7996 7596 7196 6796 6396 5996 5596 5196 4796

The production alternatives of Table 7-6, determines the supplier’s profit surface depicted in Figure 7-18.

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Supplier profit

9000 8000 7000 6000 5000 4000 3000 2000 1000

7

0-1000

1000-2000

2000-3000

6000-7000

7000-8000

8000-9000

3000-4000

10000

10400

10800

11200

11600

12400

Price

12000

4 12800

13200

14000

13600

0

4000-5000

Due date

1

5000-6000

Figure 7-18. Supplier's profit surface for an offered volume of 150 parts

The above information is captured by the Supplier Negotiation Agent (SNA) that builds the first proposal for the customer. In particular, the negotiation protocol is performed by setting the following data (please refer to Chapter 5 for notation): supplier negotiation protocol data: – dd dmin = dd* - 4; – dddmax = dd* + 4; – pmin = p*; · ; – pmax = 1.4·p* – Prrmin = 0.4·Prrmax; customer negotiation protocol data: – dd dmin = dd* - 4; – dddmax = dd* + 4; – pmax = 1.4·p* · ; – Thumax = 3; – Thumin = 1.5; – F = 2. In what follows, the negotiation rounds are reported.

7. Implementation, numerical examples and tests

161

2.3.1 First round of negotiation The customer request is: – volume = 150; – due date = 4; – price = 10.000; At the first round the SNA negotiates over the matrix of Table 7-6 and maximizing its own profit it builds the following first counterproposal: – offered volume = 150; – offered due date = 8; – offered price = 14.000; – associated profit = 8796. By using the approach described in Chapter 7-5, the Customer Negotiation Agent (CNA) computes the following utilities: – volume utility = 1; – due date utility = 0; – price utility = 0; – global utility = 1; – thresholds global utility = 3. Therefore, the CNA rejects the proposal. 2.3.2 Second round of negotiation The customer request remains unchanged. By adopting the profit reduction strategy discussed in Chapter 7-5, the SNA computes a new value for the minimum profit it can accept, that is 7388.64; Table 7-6, reports in light grey color, all of the possible counterproposals having a profit greater than the minimum acceptable; among all the following counterproposal is the one that better satisfy the customer request: – Offered volume = 150; – Offered due date = 6; – Offered price = 14.000; – Associated profit = 7596. Again the CNA evaluates the counterproposal computing the following utilities: – volume utility = 1; – due date utility = 0.5; – price utility = 0; – global utility = 1.5; – thresholds global utility = 2.53; and again it refuses the counterproposal.

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2.3.3 Third round of negotiation The customer request remains unchanged. Once again, by adopting the profit reduction strategy discussed in Chapter 5, the SNA computes a new value for the minimum profit it can accept, that is 6684.96; Table 7-6, reports in medium grey color all of the possible counterproposals having a profit greater than the minimum acceptable; among all the following counterproposal is the one that better satisfy the customer request: – volume = 150; – due date = 5; – price = 14.000; – profit = 6956. Again the CNA evaluates the counterproposal computing the following utilities: – volume utility = 1; – due date utility = 0.75; – price utility = 0; – global utility = 1.75; – thresholds global utility = 2.125, and again it refuses the counterproposal. 2.3.4 Fourth round of negotiation The customer request remains unchanged. The SNA computes a new value for the minimum profit it can accept, that is 5981.28; Table 7-6, reports in heavy grey color all of the possible counterproposals having a profit greater than the minimum acceptable; among all, the following counterproposal is the one that better satisfy the customer request: – volume = 150; – due date = 5; – price = 13.200; – profit = 6156. Again the CNA evaluates the counterproposal computing the following utilities: – volume utility = 1; – due date utility = 0.75; – price utility = 0.19; – global utility = 1.94; – thresholds global utility = 1.78; and, as the reader can notice, at this round of the negotiation the customer accepts the counterproposal and signs the contract with the supplier. Of course, the contract terms are those of the last round proposal.

7. Implementation, numerical examples and tests

3.

163

NUMERICAL TESTS

In this section a set of numerical examples conducted through the simulation environment described in section 1 have been reported. The aim of these numerical tests is to understand, what kind of real advantages emarketplace participants can achieve from added values services [Perrone et al., 2003] such as: – Planning and re-planning; that is the possibility to have a closed loop between the commercial function, specifically the order achievement, and the production function, that is the planning of the order; – Negotiation; that is the possibility to negotiate the order with the customer as fully explained in Chapter 5. For this reason an e-marketplace consisting of 9 customers and 9 suppliers has been considered. Each customer is characterized by the agent architecture referred as Customer System in Chapter 2, while each Supplier is characterized by the agent architecture referred as Supplier System in Chapter 2. Customer and supplier systems work as explained in the previous chapters. In order to focus the analysis on planning and negotiation features, technological information of the order has been neglected, and therefore Process Planner Agent and Manufacturing Planner Agent are not activated in these numerical tests. On the other hand, the reader should refer to chapters 5 and 6 respectively for the Negotiation Agents (customer and supplier), negotiation policies, and Production Planning Agent workflows.

3.1 Numerical test data The process starts with the order submission by the customer. The order consists of the array (i, V*, dd*, p*)0 being i, the supplier product index, o the order index, V* the required quantity, dd*, the requested delivery date, and p*, the asked price. The generic supplier computes the order proposal constraints; in particular, a feasible range of the required price ('pi) and due date ('dd di). Furthermore, it computes the minimum level of profit (Prrmin), respect the maximum profit achievable (Prrmax), that can be considered acceptable. Those values are reported in Table 7-7. Each customer can submit an order for 10 different part-types. Table 7-8 reports the 48 orders data that have been submitted for the test case. For each order index, o, the arrival time, toa, the due date, dd*, the required price, p*, expressed in monetary units, the required volume, V*, the

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customer identification number, C_id, d and the part type, are reported in Table 7-8. Table 7-7. Supplier constraint proposal

'dddi

'pi

Pr_min

[dd*-5,dd*+5]

[0.6·p · *,1.4·p* · ]

0.4·Pr_max

Table 7-8. Orders sequence and data dd* o ta 1 2 9 2 15 20 3 19 24 4 25 30 5 42 51 6 48 53 7 55 60 8 56 60 9 52 60 10 63 71 11 67 89 12 72 77 13 78 82 14 79 88 15 95 100 16 98 106 17 100 117 18 103 115 19 111 116 20 122 128 21 136 141 22 137 143 23 142 148 24 145 150 25 157 168 26 160 172 27 166 175 28 169 173 29 171 177 30 181 187 31 184 197 32 189 200 33 194 204 34 201 210 35 218 236 36 230 238 37 232 236 38 233 237

p* 556.690 391.249 100.678 250.819 641.576 50.816 82.577 141.847 123.921 409.207 759.415 262.234 81.574 735.504 228.229 90.266 540.165 699.524 394.020 507.832 256.757 263.635 402.521 115.069 397.363 750.230 383.990 120.418 274.699 209.061 533.574 464.609 520.106 80.650 1.267.693 288.852 183.398 376.985

V* 356 276 73 165 446 33 55 94 80 271 562 166 60 475 160 60 336 486 284 312 181 172 282 74 276 542 259 88 170 140 362 327 348 48 902 181 129 230

C_id 3 1 5 4 5 9 3 2 2 5 6 9 8 4 6 5 3 6 4 3 1 1 7 7 4 6 4 2 2 3 7 7 1 8 3 3 8 8

Part-type 2 7 8 9 8 4 5 10 1 8 10 3 10 2 1 6 2 7 1 3 1 7 9 7 2 1 3 9 9 9 1 6 7 5 2 10 8 9

7. Implementation, numerical examples and tests

39 40 41 42 43 44 45 46 47 48

235 244 246 263 265 271 273 274 279 296

240 251 253 268 270 276 283 295 300 298

177.906 316.362 323.239 337.758 273.187 149.202 660.202 1.119.717 1.176.105 170.216

108 206 206 224 172 94 411 728 728 116

165

2 7 8 6 4 6 1 3 8 4

6 7 7 10 1 9 3 4 10 3

Orders enter the Supplier System during a time horizon of 360 periods (days) divided in buckets of 30 periods where supplier is allowed to make re-planning. Each part type can be manufactured according to the routing whose manufacturing times for machines M1, M2 and M3 are reported in Table 7-9. For each part type only one plan is approved and provided as an input to the PrPA. Finally, supplier production data are reported in Table 10. Table 7-9. Part type routing and manufacturing times Part type M1 M2 1 1 4 2 3 4 3 4 4 4 5 3 5 5 10 6 5 9 7 3 12 8 8 20 9 10 18 10 12 16

M3 5 3 2 2 5 6 5 12 12 12

Table 7-10. Supplier resource cost and capacity

Process Plan fixed cost ( (PP_cost t) Ordinary cost (Ord_cost) per unit of time Overtime cost (Ov_cost) per unit of time Outsourcing cost (Out_cost) per unit of time Ordinary capacity (Ord_cap)

Suppliers 5 6

1

2

3

4

7

8

9

300

600

150

300

200

250

450

500

400

30

10

45

15

20

15

20

15

20

60

20

70

35

40

50

30

30

35

70

120

60

45

45

90

45

100

70

96

288

96

64

48

96

120

144

96

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166

Overtime capacity (Ov_cap) Outsourcing capacity (Out_cap)

24

12

36

12

12

32

12

48

24

96

32

192

24

24

96

48

24

32

Orders in Table 7-8 have been generated in the following way: – Order arrival time, tao, has been randomly generated in a way to have at least five orders within the same re-planning time bucket; – The order due date, dd*o, has been randomly generated for each order following a uniform distribution whose lower bound is tao+3, and upper bound is the end of the re-planning time bucket; – The volume of the order, V*o, has been randomly generated by using the following expression: V * o Unif Uniiiff >

@





ax O d _ cap j ( dd *o ta o 1 )

j S

(1)

being Ord_cap pj the ordinary production capacity of supplierr j as reported in Table 7-10. – The order price, p*o, has been computed according to the expression:

p*o







§ Max CU oo,, j Min CU oo, j ¨ j j mk up Min ¨ j S 2 ¨ ©

·¸

¸ V *o ¸ ¹

(2)

being: – CU Uo,j, the unit ordinary cost for manufacturing a single product of order o in the supplier j manufacturing system; – mk_up, the mark up applied for computing the price; the mark-up has been obtained by the following uniform distribution Unif [1.1, 1.4]. The price is computed by applying a mark-up strategy to the average ordinary cost for manufacturing the order over the supplier set. Table 7-11 reports data used by the customer in the counter proposal evaluation (please refer to Chapter 5), while the slope in the utility function Thu(r), F, is 2.7. Table 7-11. Data for customer counter-proposal evaluation Price Volume Utility Thumin=3, pmax =1.6·p* · Vmin = 0.3·V* Thumax=1.5

due date dd dmin = dd*–5, * dddmax = dd*+5

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167

3.2 Numerical test results The test case has been carried out under the following experimental conditions: – No_nego; in this case, no negotiation is allowed between customer and supplier; in particular, the CNA has a fixed utility threshold level equal to 1.5 and it accepts all the proposals whose global utility is higher than 1.5; the others are rejected and, afterwards, it quits the negotiation; – Nego, CMN; N in this case, a Contemporary Multi Negotiation policy is chosen; no coalitions are allowed among the suppliers; – Nego, SN; N in this case, a Sequential Negotiation policy is chosen; no coalitions are allowed among the suppliers; – Nego, RN; N in this case, a Random Negotiation policy is chosen; no coalitions are allowed among the suppliers; In all the cases the negotiation is performed as reported in Chapter 5. Simulation experiment results have been reported in Table 7-12, where, for each experimental condition, the following performance measures have been reported: – Customer utility; is the average utility the customer reports after having sequenced all the orders of Table 7-8; the utility is computed as it follows: Uc

p r

U v U dd

Up

(3)

being Uv, Udd, Up respectively the utilities of the volumes, the due date and the price, computed as:

Uv

U dd

Up

§§V V * V · · j j* min m i Min ¨ ;0 ¸ ¸ ¸ ¨ ¨ V * Vm min ¹ ¹ ©©

§ § dd jj* dd min dd max dd jj* · · Max ¨ Min ¨ ; ¸ ;0 ¸ ¨ dd max dd * ¹ ¸¹ i © dd * dd min ©

§ §§ Max ¨ Min ¨ ¨1 ¨ ¨ ©© ©

p jj* pmax

p* · · · p ¸ ;1¸ ; 0 ¸ p * ¹ ¸¹ ¸¹ p*

(4)

(5)

(6)

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168

– Supplier utility; the suppliers’ cumulative profit computed over all the orders has been assumed as a measure of the supplier’s utility; – Refused orders; this reports the number of customer orders refused by the suppliers; – Normalized customer utility; this is the normalized customer utility computed over all the customers’ utility reported in the first row of Table 7-12; – Normalized supplier utility; this is the normalized supplier utility computed over all the suppliers’ utility reported in the second row of Table 7-12; – Total normalized utility; this is sum of the normalized customer and supplier utilities. Table 7-12. Test case results Customer utility Supplier utility (Monetary units) Refused orders Normalized customer utility Normalized supplier utility Total normalized utility

No_Nego 2.12 9,831,401 23 0 0 0

Nego, CMN 2.48 12,852,848 5 1.0 1.0 2.0

Nego, SN 2.4 12,644,098 5 0.78 0.93 1.71

Nego, RN 2.37 12,657,262 5 0.70 0.93 1.63

From the analysis of the results reported in Table 7-12, the following conclusions can be drawn: – The negotiation brings a very significant advantage both for customers and suppliers; indeed, results without negotiation (No_Nego ( column in Table 7-12) are the worst in all the experiments performed; this confirms that negotiation leads a real value in manufacturing e-marketplace; – The Contemporary Multi Negotiation policy seems to be the best negotiation policy; this was quite expected at least in front of Sequential and Random negotiations. Therefore, even if CMN is quite more difficult to be implemented in e-marketplaces, its performance is higher than simpler negotiation policies, so that is possible to conclude that CMN provides a real value for both the participants in manufacturing emarketplaces; – As stressed in Chapter 6, planning and re-planning activities are an essential support for negotiation and therefore they, contribute in bringing the real value through negotiation services.

7. Implementation, numerical examples and tests

4.

169

CONCLUSIONS

This chapter presents some implementation and evaluation issues of the agent architecture for manufacturing e-marketplaces presented in the book. In particular, the first section addresses some implementation issues showing how the evaluation environment has been developed through an agent based simulation environment whose engine is based on a discrete event mechanism. The second section reports a functionality test that has been carried out in order to prove that all of the functionalities discussed in the previous chapters are effectively working properly. The results of the functionality test are quite interesting, since they demonstrate that the proposed agent architecture and workflows are effectively able to support order cycles from the order inputting, through the order breakdown both in technological and production planning terms, to the order negotiation. The functionality test shows how what has been proposed in this book is effectively able to support make to order procurement transactions in an extended enterprise environment. Finally, section 3 reports some simulation results which demonstrate that some of the value added tools proposed in this book, such as planning and negotiation, are able to bring a through value to both the participants in neutral e-marketplace. This conclusion should be considered in high consideration from exchange owner of neutral e-marketplaces. Indeed, by offering such value services, participants can improve their satisfaction and interest in staying in an e-marketplace.

REFERENCES Perrone, G., Montana, G., "PIANIFICAZIONE DISTRIBUITA DEL PROCESSO E DELLA PRODUZIONE IN AMBIENTE MANIFATTURIERO", Ricerca finanziata dal MIUR nell'ambito del programma PRIN 2001 - Programmi di ricerca scientifica di rilevante interesse nazionale http://web.dtpm.unipa.it/PRIN2001/risultati.htm; Perrone G., Renna P., Cantamessa M., Gualano M., Bruccoleri M., Lo Nigro G., “An Agent Based Architecture for production planning and negotiation in catalogue based emarketplace”, 2003, Proceedings of the 36th CIRP-International Seminar on Manufacturing Systems, 03-05 June 2003, Saarsbrucken, Germany: 47-54.

Chapter 8 BENCHMARKING VALUE ADDED SERVICES IN MANUFACTURING E-MARKETPLACES A mathematical programming approach to agent based models evaluation Antonio Grieco and Emanuela Guerriero Dipartimento di Ingegneria dell'Innovazione - Università degli Studi di Lecce - Complesso Ecotekne - Via Monteroni 73100 Lecce, Lecce - Italy

Abstract:

In this chapter the design and formulation of a general framework to test and evaluate the performance of the agent based architecture discussed in the previous chapters is presented. The framework is based on a mathematical programming formulation and it proposes a combination of different models specifically designed to evaluate all the different components of a distributed model as the one proposed in the book. In this way it will be possible to use the framework here proposed to benchmark and validate all of the added value services (planning, negotiation) that in the proposed agent based architecture are obviously offered in a distributed fashion. The framework has been validated through a test case and the obtained results are reported.

Key words:

goal programming, agent-based models.

1.

INTRODUCTION AND MOTIVATION

Market globalization requires companies to operate in a wide and complex international market by matching agility and efficiency. This can be achieved either by splitting geographically the production capacity or by working together in supply chain organization involving several independent entities. In both the cases companies need to be able to design, organize and manage distributed production networks where the actions of any entity

171 G. Perrone et al., (eds.), Designing and Evaluating Value Added Services in Manufacturing E-Market Places, 171–198. © 2005 Springer. Printed in the Netherlands.

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affect the behavior and the available alternatives of any other entity in the network [Wiendahl and Lutz, 2002]. Basically, two approaches are available for managing complex distributed production networks: a centralized approach, where a unique entity (the planner for instance) has got all the necessary information to make planning decisions for the entire network; this is the case, for instance, of the approach suggested by several ERP vendors with their Advanced Planning and Scheduling (APS) tools. On the other hand, a decentralized approach can be used; in this case, each entity in the network has the necessary information and knowledge to make autonomous planning decisions, while, the common goal is reached through cooperation among all the network actors. It has been quite acknowledged that, while centralized approaches are theoretically better in pursuing global system performance, they have several drawbacks concerning operational costs, reliability, reactiveness, maintenance costs, and so forth [Ertogral and Wu, 2000]. On the other hand, the main problem regarding distributed systems concerns their effectiveness and efficiency, especially when common goals have to be reached from the different actors involved in the distributed chain. This problem is particularly true when distributed systems are related to supply chain operation and management, such as in multi-plant coordination [Bhatnagar and Chandra, 1993], supply chain integration [Brugali et al., 1998], resources allocation [Tharumarajah, 2001], capacity allocation [Brandolese et al., 2000], production planning [Chandra and Fisher, 1994]. In all the above circumstances, as also stressed by Kraus [Kraus, 1997], the presence of proper coordination mechanisms, in order to guarantee goals achievement, should be supported in distributed system; in particular, not merely coordination should be supported, but actually co-operation among all the actors might significatively improve the overall behavior of the distributed system. This is the reason why in the supply chain operation and management literature several approaches have been proposed to assure coordination and even co-operation in distributed systems. For example, Bhatnagar and Chandra [1993] provide an extensive literature review of models for general and multi-plant coordination. The authors distinguish two broad levels of coordination, namely a general level (coordination of decisions of different functions) and a multi-plant level (dealing with decisions regarding the same function at different echelons in the organization). The multi-plant coordination can allow a company to plan and control the material flows within the whole production system. In this way, the production transfer among the plants can be pursued, in order to balance demand peaks generated by local markets. The value of this

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173

opportunity is strictly related to the uncertainty of the environment variables and to the system capacity (i.e. flexibility) of transferring the production load, which often requires complex organizational procedures and integrated management systems to be effective. The coordination also involves the choices of the technologies (product and process design), in that the similarity among plants and products increases the opportunity of the coordination itself [Flaherty, 1989]. Moreover, the success of a coordination policy is connected to the effectiveness of integration, at a central level, of the specific competencies of the single production units [Ferdows, 1997]. However, a stronger coordination, even if it could favor the performance optimization of a production system as a whole [Chandra et al., 1994], still remains complex to be achieved in the actual environment [Lootsma, 1994]. Indeed, the complexity and the variety of the real systems make the identification of a general coordination model able to find the optimal solution for every situation very hard [Federgruen, 1989, 1993]. This is also testified by the few studies in the literature concerning the integration within multinational companies referred, in particular, the analysis of performance such as lead time and the other operational performance [Vidal and Goetschalckx, 1997]. As confirmed in the above mentioned literature, the transfer process of the conceptual models into the real context leads to many difficulties, derived from the necessity of evaluating the decisional processes in terms of multiple criteria. For example, the creation and the improvement of a production system or solving management problems in the industrial production leads to a decisional process involving a lot of factors: the purchase and management costs, the flexibility, the production times, etc… [Browne et al., 1996] [Kim and Emery, 2000] [Brandimarte and Villa, 1995]. On the contrary if the transfer constraints are relaxed, a centralized model may be efficiently implemented in a simulated environment in order to design a framework to benchmark innovative solution to the multi-plant management problems. This attempt has been pursued in [Lo Nigro et al., 2004] where coordination and co-operation policies for distributed production planning have been benchmarked, in term of several performance, in front of a centralized planner able to perform global optimization. The study shows that, under a market demand not known a priori, the de-centralized system performance is very close, and for some performance measures even better, to the centralized one. If in supply chain operation and management co-ordination and even cooperation are required for decentralized systems, this is not always true in some e-business environment, such as e-marketplace, where several

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suppliers are in competition with each other for customer order achievement. This is the case discussed in this book. In such cases distributed approaches are the only possible ones in real terms; however it remains absolutely necessary to evaluate the performance of the distributed approaches provided in an e-marketplace, especially when such approaches offer added value to the participants. Indeed, in order to measure the ability of the proposed solutions to bring real value to actors, it becomes necessary to measure performance in comparison with a benchmark. This is the main idea inspiring the research presented in this chapter, which investigates the possibility to measure the efficiency of the agentbased system by comparing its performance with the one of a centralized system. This centralized system can be interpreted as a “planner” able to provide the e-marketplace participants with optimal solutions for their planning and negotiation problems. That is another kind of added value service, of course very hard to be actually implemented in an e-marketplace, the exchange owner could propose to the participants. Of course, if the distance in terms of performance between the distributed and the centralized approach is low, it will be not useful for the exchange owner to offer the “planner” as an added value service to the e-marketplace participants. Therefore the research presented in this chapter is very novel and interesting, since, very often distributed systems are not benchmarked in those cases where they are the only actual solution for implementation. The chapter is articulated as it follows: section 2 presents two different benchmark models which have been used to evaluate the distributed systems performances; in section 3 utility functions for each benchmark model and for each objective are defined; section 4 discusses the Mixed Integer Linear Programming model that have been conceived for each benchmark; finally in section 5 test cases and benchmark results are discussed.

2.

PROBLEM AND GOAL STATEMENT

The agent-based distributed model presented in this book, is made up by a set of competing suppliers, each one characterized by a production technologies set, by some long duration products and by a well-defined production capacities. The market is represented by a set of customers demanding some products, in some quantities, times and prices. The demand can be, also partially, satisfied by the suppliers, on the basis of the available technologies, production capacities and requested products. It has been also assumed that each supplier is an independent firm competing with the others in the same market consisting of the S customers.

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In this case the goal of each supplier agent P, is to obtain the order, avoiding that another P agent could get it. Therefore, P agent goals are in conflict each other and therefore they are incompatible. On the other hand, each agent S is characterized by a goal, which is the maximization of the customer requirements’ satisfaction. The agent based approach ensures the total independence between all the different units that belong to the considered environment. On the basis of the previously discussion, the agent-based model will be compared with a centralized model, a “planner”, where the objective function will be constituted by the goals of each element of the distributed model. Furthermore, it will be supposed that the “planner” has a complete and a prori knowledge of the main parameters of the customer’s bid (quantity required, range of acceptable due dates, maximum price acceptable per order) and of the supplier’s capabilities (maximum quantities producible per order in combination with the sale price). In other words, the “planner” has the aim at optimizing the performances of the overall system and it has the knowledge of all the parameters related to each agent. In particular, with the term “overall system” we mean the set of the Customers and Supplier systems, each of them having different targets. In order to reach this goal the multi-objective programming techniques will be used [Ramadan, 1997] [Rao, 1996]. The purpose of Multi-objective Programming is to solve optimization problems (multi-objective programs) characterized by multiple objective functions (also called criteria), in which the unknowns are subject to some constraints. To a multi-objective program, characterized by p>1 objective functions, it is possible to associate p single-objective programs: the i-th single-objective program is characterized by having just one objective function (i.e. the i-th objective function of the original program) and the same constraints as the original program. The optimal solutions of the p single-objective programs generally do not coincide, because the objective functions of the original program are often conflicting. This means that in most of the cases it is not possible to find an optimal solution with respect to all the criteria. For this reason the necessity of defining a new class of “optimal solutions” for the multi-objective programs, trying to find a compromise among all the objective functions, arises. The methods for solving a multi-objective program differ from each other for the computation time necessary to obtain one or more efficient solutions and for the way in which the information provided by the decision makers is used. In the goal programming models presented in the following, the decision makers fixes

176

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the desirable targets for each objective and successively he/she chooses the solutions that minimize the distances from these targets. In order to design a complete framework to benchmark the distributed approach, different mathematical programming models have been implemented on the basis of the different level of orders and suppliers knowledge aggregation. Two different levels of benchmark have been identified with the following properties. B1 – Benchmark of level one. In this case a complete and simultaneous knowledge of all the order parameters and of the suppliers’ characteristics has been supposed. Orders are assigned contemporary; in particular, any order may be potentially allocated to each one of the different suppliers with or without the constraint that at least one order is allocated per supplier at each execution of the mathematical assignment model; if at the end of each execution the set of non allocated order is not equal to the empty set, the model is cyclically executed after an appropriate model parameters redefinition. The operative schema is the following. The first step is to calculate the problem parameters. For any order each supplier will provide a set of alternative work plans feasible respect to the production order under consideration. For each work plan, the offered quantity, the unit price and the relative supplier profit level are assumed as known. The mathematical programming model illustrated in the following will give a solution to the assignment problem in order to maximize a combination of the customers and suppliers utility functions. If all the orders are assigned to one of the different supplier the procedure stops, otherwise the orders which have not been assigned are returned to the first step for systems parameters updating and, afterwards, the assignment model runs again. The procedure stops if all of the orders have been allocated or the model constraints violation makes infeasible the remaining orders allocation. B2 – Benchmark of level two. Also in this case a complete and simultaneous knowledge of all the order parameters and of suppliers’ parameters has been supposed. However, in this case orders are assigned one at time at each execution of the assignment model; the model is executed a number of times equal to the number of orders under consideration. The benchmark design is based on the definition of the utility function for the following variables: – the number of parts provided by each supplier out of the number of parts request for each order; – the due date assigned by each supplier out of the due date request for each order; – the order price requested by each supplier out of the maximum price acceptable by each customer;

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– the supplier profit level out of the maximum profit achievable on the basis of the orders under consideration. For each benchmark level, two different versions of the mathematical model have been proposed on the basis of a different formulation of the objective function, referred to as F.O.1 and F.O.2. The objective function F.O.1 is defined as the maximization of the weighted sum of the customer utilities over the different parameters, quantity, price and due date, and of the supplier utility. In the second version, referred to as F.O.2, the assignment problem is solved by a two-step procedure. In the first step, the objective function is formulated as the maximization of the weighted sum of the customer and supplier utilities, where the customer utility is equal to the minimum one among the single values of the three parameters defining the customer utility, again due-date, quantity and price. In the second step, the objective function is defined as in the formulation F.O.1 by introducing the output of the first step as a lower bound. The difference between the two formulations consists in the different weight assigned in the two cases to the set of customer and suppliers utilities taking into account the different number of parameters considered for the two classes. In particular, the benchmark models including F.O.1 are balanced respect to the objective to maximize both the customers and suppliers utilities whereas the ones including F.O.2 aims at maximizing separately all the components of the customers and supplier utilities. Moreover, for each version, different alternatives have been defined by considering the combination of two different parameters, i.e. P1 and P2. The parameter P1 introduces the feature to allow or not the possibility to split the production of each single order among different suppliers. The parameter P2 introduces the feature to allow or not the allocation of more than one order per supplier. By combining objective functions and parameters P1 and P2, 8 different alternatives, referred as idd B1(B2).1, …, 4/1, …, 4 for the benchmark level B1(B2), can be identified. Moreover, it would be possible to significatively increase the number of alternatives by considering different combination of the weights in the objective function. In the following, the weights in the objective function will be refereed as w1 for the customer side and w2 for the suppliers one. Therefore, the different models for the benchmark level B1 (B2) will be referred to as: B1(B2).(x)/(y)/w1/w2.

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178

The different alternatives are fully described in Table 1 for benchmarks B1 (B2). Table 8-1. The benchmark models Objective function F.O.1

F.O.2

3.

P1 No Yes No Yes No Yes No Yes

P2 No No Yes Yes No No Yes Yes

Id B1(B2).1/1 B1(B2).1/2 B1(B2).1/3 B1(B2).1/4 B1(B2).2/1 B1(B2).2/2 B1(B2).2/3 B1(B2).2/4

THE UTILITY FUNCTION DEFINITION

Customer utility requirements are represented by due-dates, product demands and the relative costs (i.e. price proposal). For each requirement a range of acceptable values, which is the support of a linear or piecewise linear utility function representing the related customer satisfaction has been defined. The lower and upper limit of the range is referred as Bmin and Bmax. The utility function quantifies the customer satisfaction respect to the supplier’s proposal. In particular, for each proposal the related supplier proposes to fulfill a percentage of the customer’s demand at a given due-date and at a unit production cost. The customer satisfaction is normalized between 0 (null satisfaction value) and 1 (full satisfaction value). In the following, the customer utility functions have been detailed.

3.1 Due-date Utility Function For each product demand, the support range of due-date utility function is the interval of acceptable due-dates [Bmin, Bmax]. As shown in Figure 8-1, the due-date utility function assumes value equal to one in correspondence of the most acceptable due-date value equal to: Bmax

Bm min 2

(1)

Given a constraint set X, the feasible due-date x maximizing customer satisfaction is the optimal solution to the non linear program (PD).

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The program (PD) is equivalent to the linear program P’D. Indeed, given the values Bmin e Bmax, the linear functions r1 and r2 are determined (i.e. r1: P1x + c1 and r2: d P2x + c2). (PD)

(2)

max f(x ( ),

(3)

subject to: Bmin d x d Bmax

(4)

xX (P’D)

(5)

max y subject to: y1 d P1x + c1

(6)

y2 d P2x + c2

(7)

y d y1

(8)

y dy2

(9)

0 d y1 d1

(10)

0 d y2 d1

(11)

yt0

(12)

Bmin d x d Bmax

(13)

xX

Chapter 8

180 f (x)

r2

r1

1

0

Bmin

Bma x

x

Figure 8-1. Due-date utility function

3.2 Cost utility Function The cost utility function is represented in Figure 8-2. In this case Bmin represents the highest threshold for full customer satisfaction. Between Bmin and Bmax the customer satisfaction linearly decreases, while costs higher than Bmax are unaccepted. F

1

0

Bmin

Bma x

x

Figure 8-2. Cost utility Function Given a constraint set X the feasible unit cost production x maximizing customer satisfaction is the optimal solution to the non linear program (PC.)

8. Benchmarking Value Added Services in Manufacturing EMarketplaces (PC) max F 

181

(14)



(15)

subject to: x d Bmax

(16)

xX The program (PC) is equivalent to the linear program (P’C). Indeed, given the values Bmin e Bmax, the linear function r is determined (i.e. r: Px + c ). (P’C)

(17)

Max r

(18)

subject to: rdPx+c

(19)

0 d r d1

(20)

x d Bmax

(21)

xX

3.3 Demand Utility Function The demand utility function is represented in Figure 8-3. The demand is expressed in terms of percentage of customer demand. In this case Bmin and Bmax represent the lowest and highest demand for a supplier proposal; the customer satisfaction linearly increases from Bmin and Bmax and proposals higher than Bmax are unaccepted. Given a constraint set X, the feasible supplier product demand x maximizing customer satisfaction is the optimal solution to the following non linear model. (PQ) ~ max F 

(22)



subject to:

(23)

Chapter 8

182 Bmin d x d Bmax

(24)

xX The program (PC) is equivalent to the linear program (P’Q). Indeed, given the values Bmin e Bmax, the linear function r is determined (i.e. r: Px + c ). (P’Q)

(25)

max r

(26)

subject to: rdPx+c

(27)

0 d r d1

(28)

Bmin d x d Bmax

(29)

xX

Figure 8-3. Demand utility Function

3.4 Supplier profit utility function The supplier characterizes each proposal in terms of profit satisfaction. The profit utility function is a piecewise linear function normalized between 0 (null satisfaction value) and 1 (full satisfaction value), see Figure 8-4.

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Given a supplier, a profit is assigned to each proposal. Therefore, given all the proposals made by a supplier, it is possible to quantify the maximum G max and minimum G min profit values. The supplier profit satisfaction linearly increases from G min to G max . Therefore, the profit utility function can be defined as it follows: (PP)

(30)

~ max F  



(31)

Gmin d x d Gmax

(32)

subject to:

xX The non linear program (PP) is equivalent to the linear program (P’P). Indeed, given the values Gmin e Gmax, the linear function r is determined (i.e. r: Px + c ).

Figure 8-4. Profit utility function (P’P)

(33)

max r

(34)

Chapter 8

184 subject to: rdPx+c

(36)

0 d r d1

(37)

Gmin d x d Gmax

(38)

xX

4.

THE BENCHMARK MODELS FORMULATION

The benchmarks have been formulated as MILP models in order to calculate an alternative solution to the negotiation algorithm in the agent based approach. The input data are the customer’s and supplier’s requirements, as illustrated in the previous paragraphs. In the following the main features of the benchmark models are illustrated. The main difference between the two formulations of the objective functions F.O.1 and F.O.2 is in the consideration of the customer parameters. The benchmark characterized by F.O.1 is limited to the application of the model whose objective function has the formulation of (41) for the customer utilities; the benchmark characterized by F.O.2 includes the two steps previously introduced and, in the first step, the objective function include the formulation (42) for the customer utilities.

4.1 Input Data and decision variables Symbols and notations used in the following are reported in Table 8-2. Table 8-2. Symbol and notation M N j i Lij k qijk dijk

Description number of suppliers; number of orders (there exists one order for each customer) ; suppliers index; order index; number of counter proposals proposed by the jth supplier to the ith customer; supplier counter proposal index (k=1,..,Lij); percentage of the ith order delivered by the jth supplier accordingly to the kth offer; due date proposed to the ith customer by jth supplier in the kth counte proposal;

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185

Description price proposed to the ith customer by jth supplier in the kth counter proposal; profit of the jth supplire related to the kth counter proposal proposed to the ith customer; the maximum and minimum acceptable percentage of the ith order; the most satisfactory due date for the ith customer Min/Max acceptable delivery date of the ith product demand; the minimum and the maximum acceptable unit price related to the ith order; the relative positive weight of the customer utility function; the relative positive weight of the supplier utility function; set of counter proposal proposed to customer ith by the jth supplier. binary variable equal to 1 if the ith order is assigned to the jth supplier accordingly to the kth counter proposal. Otherwise the value is 0. binary variable equal to 1 if the ith order has been assigned at least one supplier. The value is 0 otherwise;

pijk uijk

Qimin , Qimax Di Dimin Dimax Pimin Pimax PC PF Aij yijk zi

Zi

Customer satisfaction level respect to the due-date requirements.

4.2 Mathematical model formulation – First step In this step the objective function represents the maximization of the customer and supplier satisfaction level. In particular, the overall customer satisfaction level is modeled as the mean among the different parameters in (41) and the minimum satisfaction level respect to the considered requirements (i.e. price, delivered quantity and due date) in (42).

¦ Fc

Max w1

i

 w2

i

Fci

Fci

§ ¨ ¨ ©

¦ i

~ §¨ ¨ ©

¦ Ff

(40)

j

j

· q jki yijk ¸  ¸ kAij ¹

¦¦ j

¦ Zi  ¦ i

i

p§

¨ ¨ ©

·· pijk yijk ¸ ¸ ¸¸ kAij ¹¹

¦¦ j

3

§ min¨ ¨ ©

¦ i

~ §¨ ¨ ©

· q jki yijk ¸, ¸ kAij ¹

¦¦ j

¦ Zi , ¦ i

i

p§

¨ ¨ ©

·· pijk yijk ¸ ¸ ¸¸ kAij ¹¹

¦¦ j

(41)

(42)

Chapter 8

186

· ~ §¨ u ijk y ijk ¸ ¦ ¦ ¨ i kL ¸ ij © ¹

(43)

ijk

d 1,  j

(44)

ijk

d 1, 

(45)

¦

Ff j

j

subject to: Lij

¦¦ y i

k 1

Lij

¦¦ y j

k 1

¦¦y

ijk

j k Aij

t zi ,  i

Aij

¦¦y

ijk

(46)

t zi ,  i

(47)

j k Aij

Qimin zi d ¦ ¦ qijk Qimax yijk d Qimax z i ,  i

(48)

j k Aij

Dimin

¦y

k Aij

¦p

kAij

ijk

ijk

d

¦d

ijk

y ijk d Dimax

k Aij

qijk yijk d Pi max ,  ijj

¦y

ijk

,  ijj

(49)

k Aij

(50)

8. Benchmarking Value Added Services in Manufacturing EMarketplaces

¦¦p

ijk

qijk yijk d Pi max ,  ijj

187

(51)

j k Aij

§ § ·· Zi d f ¨ max id ¨ d ijk yijk ¸ ¸ , ijj ¨ ¨ k A ¸¸ © ij ¹¹ ©

¦

(52)

§ § ·· d ijk yijk ¸ ¸ ,  ijj Zi d f ¨ min id ¨ ¨ ¨ k A ¸¸ © ij ¹¹ ©

(53)

¦

Zi d

¦y

ijk

,i

(54)

kAij

zi  {0,1}  i

(55)

yijk  {0,1} se qijk t Qimin otherwise yijk = 0  i, j, k

(56)

yijk  {0,1}  i, j, k

(57)

Zi t 0  I

(58)

Constraints (44) state that per each supplier at most one order may be assigned. In the case in which it is feasible the hypothesis to assign more than one order per supplier (P2 set to Yes) the constraints are not taken into account in the model formulation. Constraints (45) state that each order i can be assigned to at most one supplier j. In the case in which one order may be split among more than one supplier (P1 set to Yes) the constraints are not taken into account in the model formulation. Constraints (46), (47) represent the relationship between the z’s and the y’s variables. Constraints (48) imposes that if the order i is assigned to a supplier (i.e. zi>0 ), then the

Chapter 8

188

delivered product quantity will be in the interval [ Qimin , Qimax ]. Constraint (49) guarantees, for each order ith, the fulfillment of the due date requirement. Constraints (50) and (51) guarantee for each order the fulfillment of the price requirement. Constraints (52), (53) and (54) define for each order i the due date satisfaction level respect to the required maximum acceptable lateness ( Dimax ) and earliness ( Dimin ). Constraints (55), (56), (57) state the upper and lower bounds on variables, as well as the integrality constraints.

4.3 Mathematical model formulation – Second step In the first and second step the set of the constraints is equivalent. The overall customer satisfaction level is modeled as the sum of satisfaction levels respect to due date, delivered quantity and unit price. Moreover, given the solution of the first step and the lower bounds determined for due-date, quantity and price utilities for the customers, and min for the profit for the suppliers (i.e. Fcimin 1 _ step , Ff j1 _ step ), the second step mathematical model seeks a solution improving all the customer requirements as it results from analysis of the constraints (61), (62), (63) and (64). Max

¦ Fc

(59)

i

i

§ ¨ ¨ ©

Fci

¦ i

~ §¨ ¨ ©

· q jki yijk ¸  ¸ kAij ¹

¦¦ j

¦ Zi  ¦ i

i

p§

¨ ¨ ©

·· pijk yijk ¸ ¸ ¸¸ kAij ¹¹

¦¦ j

(60)

subject to:

§ · q¨ f q y ¦i i ¨ ¦j k¦A jki ijk ¸¸ t Fcimin 1 _ step  i ij © ¹

(61)

¦Z

(62)

i

i

t Fcimin 1 _ step  i

8. Benchmarking Value Added Services in Manufacturing EMarketplaces p i

§

· min ¸ y ijk ijk t Fci1 _ step  i ¸ ¹

(63)

· min ¸ y ijk ijk t Ff j1 _ step  j ¸ ¹

(64)

¦ F ¨¨ ¦ ¦ p ©

i

j k Aij

~ §

¦ F ¨¨ ¦ ¦ u j

©

j

5.

i

k Lij

189

THE TEST CASE

The validation of the benchmark and the measure of the efficiency of the distributed agent based approach, in the solution of the order/supplier allocation problem, has been conducted on a test case composed by five customers and five suppliers. The benchmark level has been set to one (B1).

5.1 Input data The input data to the benchmark models and to the distributed one are reported in the following. In this section a detailed illustration of the experiment data are presented in order to provide a repeatable test case. The Production Planning Agent provides the information related to the quantity percentage of order producible by each supplier and of the related profit. An example (order number 3 and supplier number 5) of the input data is reported in tables 8-3 and 8-4. The number of rows and columns in the tables are defined on the basis of the user-defined due date and order price buckets as illustrated in Chapter 6. In Table 8-5, per each order, the minimum and maximum acceptable values for the parameters due date, quantity, price and the number of involved suppliers have been reported. Table 8-3. Order quantity accept level.

Order price

Delivery due date 0.17 0.17 0.17 0.17 0.17

0.20 0.20 0.20 0.20 0.20

0.22 0.22 0.22 0.22 0.22

0.25 0.25 0.25 0.25 0.25

0.28 0.28 0.28 0.28 0.28

0.30 0.30 0.30 0.30 0.30

0.33 0.33 0.33 0.33 0.33

0.35 0.35 0.35 0.35 0.35

0.38 0.38 0.38 0.38 0.38

Chapter 8

190 0.23 0.42 0.42 0.42 0.42 042

0.27 0.48 0.48 0.48 0.48 0.48

0.30 0.54 0.54 0.54 0.54 0.54

0.34 0.60 0.60 0.60 0.60 0.60

0.37 0.67 0.67 0.67 0.67 0.67

0.40 0.73 0.73 0.73 0.73 0.73

0.44 0.79 0.79 0.79 0.79 0.79

0.47 0.85 0.85 0.85 0.85 0.85

0.51 0.91 0.91 0.91 0.91 0.91

Table 8-4. Order Profit per time and price bucket.

Order price

Delivery due date 18779 20890 23001 25111 27222 29333 31444 33555 35665 37776 40306

21463 23876 26288 28700 31113 33525 35937 38350 40762 43174 46065

24148 26861 29575 32289 35003 37717 40431 43145 45858 48572 51825

26832 29847 32863 35878 38893 41909 44924 47940 50955 53970 57584

29516 32833 36150 39467 42784 46101 49418 52735 56051 59368 63343

32200 35819 39437 43056 46674 50293 53911 57530 61148 64766 69103

34885 38805 42725 46645 50565 54485 58405 62325 66245 70165 74862

37569 41790 46012 50233 54455 58676 62898 67120 71341 75563 80622

40253 44776 49299 53822 58345 62868 67391 71915 76438 80961 86381

Table 8-5. Minimum and maximum acceptable values Id Order C1 C2 C3 C4 C5

Min 6 13 20 25 37

Due date Max 14 21 28 33 45

Min 80 55 46 22 102

Quantity Max 268 185 155 75 343

Price Min Max 482400 675360 314500 440300 217000 303800 82500 115499 514599 720300

Num Supplier Min Max 1 5 1 5 1 5 1 5 1 5

5.2 The benchmark results In what follows the experiment results have been reported. In particular, Table 8-6 reports the frame of the experiments carried out, while Table 8-7 reports the synthesis of the experimental results. By the analysis of the obtained results, it is possible to note, as expected, that the performance of the agent based model are below the one obtained by the benchmark models, but the maximum percentage gap between the utilities functions, both for the customer and supplier side, of the assignment resulting by the best benchmark model and the distributed one is equal to 15%. The best benchmark model is the benchmark of level 1 in which the objective function is maximized by the application of the two steps procedure and in

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191

which it is admissible the splitting of the orders among the suppliers and the order allocation to more than one supplier. By taking into consideration this benchmark, the partial gap between the benchmark model and the distributed one on the customer utilities is equal to 17%, while on the supplier utility is equal to 3%. By considering separately the customer and the supplier component of the distributed model, the maximum gap between the benchmark models and the distributed one on the supplier utility is equal to 41% (benchmark B1.1/1/0.01/0.99), even if the overall utility, in this case, is lower in the benchmark case than in the distributed one. The maximum gap between the benchmark models and the distributed one, when measured on the sum of customer utilities, is equal to 17% (benchmark B1.2/4/0.50/0.50). Table 8-6. Benchmark definition Benchmark Num

Id

F.O

P1

P2

w1

w2

1 2 3 4 5 6 7 8 9 10

B1.1/1/0.01/0.99 B1.1/1/0.50/0.50 B1.1/1/0.99/0.01 B1.2/1/0.50/0.50 B1.1/2/0.50/0.50 B1.1/3/0.50/0.50 B1.1/4/0.50/0.50 B1.2/4/0.50/0.50 B1.2/3/0.50/0.50 B1.2/2/0.50/0.50

1 1 1 2 1 1 1 2 2 2

No No No No Yes No Yes Yes No Yes

No No No No No Yes Yes Yes Yes No

0.01 0.50 0.99 0.50 0.50 0.50 0.50 0.50 0.50 0.50

0.99 0.50 0.01 0.50 0.50 0.50 0.50 0.50 0.50 0.50

Table 8-7. Evaluation results. Benchmark

Distributed model

ID 1 2 3 4 5 6 7 8 9 10

B1.1/1/0.01/0.99 B1.1/1/0.50/0.50 B1.1/1/0.99/0.01 B1.2/1/0.50/0.50 B1.1/2/0.50/0.50 B1.1/3/0.50/0.50 B1.1/4/0.50/0.50 B1.2/4/0.50/0.50 B1.2/3/0.50/0.50 B1.2/2/0.50/0.50

U Tot

Uc

Uf

8.67 14.42 15.25 13.85 14.42 14.42 15.20 16.70 14.10 13.85

5.16 12.08 13.92 11.80 12.08 12.08 12.01 14.57 12.09 11.80

3.51 2.34 1.33 2.05 2.34 2.37 3.19 2.13 2.01 2.05

U Tot

Uc

Uf

14.13

12.06

2.07

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192

The detailed results for the distributed model and the benchmark are reported in Tables 8-8 - 8-18. In each table the following information have been reported: ( ) and due date (dd) d – the order number, with quantity (Q), price (P requested, ( ) and due date (dd) d assigned by the distributed – the quantity (Q), price (P model (table 8-8) and by the benchmark ones (from Table 8-9 to Table 8.18), – the number of the negotiation run (R) in which the order has been accepted; for the benchmark models R is equal to the maximum number of times which the mathematical model has been executed, – the single utilities (Ui) for the parameters quantity, price and due date assigned, – the sum of the utilities for the customer (Utot), – the number of the suppliers (Supplier Id) to which the order has been allocated, the profit and the corresponding utility. Table 8-8. Output of the agent based approach

1 2 3 4 5

Q 268 185 155 75 343

Requested P 482400 314500 217000 82500 514500

R

Ui

Utot

4 2 2 2 5

.78/.99/0 1/.20/1 1/1/1 1/1/1 .80/.29/1

1.77 2.20 3.00 3.00 2.09

Order

dd 10 17 24 29 41 Supplier Id 3 4 3 3 4

12.06

Q 227 185 155 75 296

Assigned P 409500 415140 217000 82500 570240

dd 14 17 24 29 41

Supplier Supplier profit 359765 236180 503101 573341 155090

Supplier Utility 0.51 0.64 0.77 0.89 0.41

1827477

2.07

Table 8-9. Benchmark B1.1/1/0.01/0.99 Order 1 2 3 4 5

Q 268 185 155 75 343

Requested P 482400 314500 217000 82500 514500

dd 10 17 24 29 41

Q 228 185 155 75 255

Assigned P 573300 440300 303800 115499 535500

dd 14 21 28 30 45

8. Benchmarking Value Added Services in Manufacturing EMarketplaces R

Ui

Utot

1 1 1 1 1

.78/0/0 1/0/0 1/0/0 1/0/.75 .63/0/0

0.78 1.00 1.00 1.75 0.63

Supplier Id 3 4 1 5 2

5.16

193

Supplier Supplier profit 523565 277840 283390 56720 494765

Supplier Utility 0.85 1.00 0.50 0.16 1.00

1636280

3.51

Table 8-10. Benchmark B1.1/1/0.50/0.50

1 2 3 4 5

Q 268 185 155 75 343

Requested P 482400 314500 217000 82500 514500

R

Ui

Utot

1 1 1 1 1

.41/1/1 1/0/1 1/1/1 1/1/1 .92/0/.75

2.41 2.00 3.00 3.00 1.67

Order

dd 10 17 24 29 41 Supplier Id 3 5 1 2 4

12.08

Q 158 185 155 75 324

Assigned P 283500 440300 217000 82500 680400

dd 10 17 24 29 42

Supplier Supplier profit 249065 257660 192515 72740 227510

Supplier Utility 0.33 0.91 0.28 0 0.81

999490

2.34

Table 8-11. Benchmark B1.1/1/0.99/0.01

1 2 3 4 5

Q 268 185 155 75 343

Requested P 482400 314500 217000 82500 514500

R

Ui

Utot

1 1 1

.41/1/1 1/1/1 1/1/1

2.41 3.00 3.00

Order

dd 10 17 24 29 41 Supplier Id 3 5 2

Q 158 185 155 75 297

Assigned P 283500 314500 217000 82500 498960

Supplier Supplier profit 249065 131860 192515

dd 10 17 24 29 41

Supplier Utility 0.33 0.44 0.28

Chapter 8

194 1 1

1/1/1 .81/.70/1

3.00 2.51

1 4

13.92

72740 83810

0.00 0.28

729990

1.33

Table 8-12. Benchmark B1.2/1/0.50/0.50 Order 1 2 3 4 5

Q 268 185 155 75 343

R 1 1 1 1 1

Ui .60/.50/.50 1/1/1 1/1/1 .91/.80/1 .49/.50/.50

Requested P 482400 314500 217000 82500 514500 Utot 1.60 3.00 3.00 2.71 1.49 11.80

dd 10 17 24 29 41 Supplier Id 3 4 1 5 2

Assigned P dd 415800 12 314500 17 217000 24 83160 29 397800 43 Supplier Supplier profit Supplier Utility 373715 0.56 135540 0.47 192515 0.28 27815 0.05 362495 0.69 1092080 2.05

Q 192,5 185 155 70 221

Table 8-13. Benchmark B1.1/2/0.50/0.50 Order 1 2 3 4 5

268 185 155 75 343 R

1 1 1 1 1

Q

Ui .41/1/1 1/0/1 1/1/1 1/1/1 .92/0/.75

Requested P 482400 314500 217000 82500 514500 Utot 2.41 2.00 3.00 3.00 1.67 12.08

Assigned P dd 10 158 283500 10 17 185 440300 17 24 155 217000 24 29 75 82500 29 41 324 680400 42 Supplier Supplier Id Supplier profit Supplier Utility 3 249065 0.33 5 257660 0.91 1 192515 0.28 2 72740 0 4 227510 0.81 999490 2.34 dd

Q

8. Benchmarking Value Added Services in Manufacturing EMarketplaces

195

Table 8-14. B1.1/3/0.50/0.50 Order 1 2 3 4 5

Q 268 185 155 75 343

R 2 2 2 2 2

Ui .41/1/1 1/0/.75 1/1/1 1/1/1 .92/0/.75

Requested P 482400 314500 217000 82500 514500 Utot 2.41 1.075 3.00 3.00 1.67 12.08

Assigned P dd 10 158 283500 10 17 185 440300 17 24 155 217000 24 29 75 82500 29 41 324 680400 42 Supplier Supplier Id Supplier profit Supplier Utility 3 249065 0.33 5 265570 0.94 2 192515 0.28 2 72740 0.02 4 227510 0.81 1007400 2.37 dd

Q

dd

Q

Table 8-15. B1.1/4/0.50/0.50 Order 1 1 1 2 3 4 5 5 R

Q

Requested P

268

482400

10

185 155 75

314500 217000 82500

17 24 29

343

514500

41

Ui

Utot

Supplier Id

3 3 3 3 3 3 3 3

92/ 40/1

2 32

4 5

1/.20/1 1/1/1 1/1/1

2.30 3.00 3.00

4 1 1

.99/0/.50

1.49 12.01

2

128 63 63 185 155 75 187 153

Assigned P 284580 127008 140616 415140 217000 82500 392700 321300

dd 10 10 10 17 24 29 41 39

Supplier Supplier profit Supplier Utility 259370 0.44 51218 77291 236180 192515 72740 362825 296855 1548994

0.16 0.24 0.85 0.28 0 0.69 0.53 3.19

Chapter 8

196 Table 8-16. Benchmark B1.2/4/0.50/0.50 Order 1 1 2 3 4 5 5

Q 268

482400

185 155 75

314500 217000 82500

343

514500

R 2 2 2 2 2 2 2

Requested P

Ui

Utot

.93/.90/1

2.83

1/.90/1 1/1/1 1/1/1

2.90 3.00 3.00

.93/.90/1

2.83

Assigned dd Q P dd 128 238680 10 10 238680 10 128 17 185 327080 17 24 155 217000 24 29 75 82500 29 231 360360 41 41 96 150150 41 Supplier Supplier Id Supplier profit Supplier Utility 1 213470 0.33 2 4 2 1 3 5

192515 135540 192515 72740 319705 66210 820245

14.57

0.28 0.47 0.28 0.0 0.46 0.19 2.13

Table 8-17. Benchmark B1.2/3/0.50/0.50 Order 1 2 3 4 5

Q 268 185 155 75 343

R 2 2 2 2 2

Ui .60/.50/.50 1/1/1 1/1/1 1/1/1 .49/.50/.50

Requested P 482400 314500 217000 82500 514500 Utot 1.60 3.00 3.00 3.00 1.49 12.09

Assigned P dd 10 193 415800 12 17 185 314500 17 24 155 217000 24 29 75 82500 29 41 221 397800 43 Supplier Supplier Id Supplier profit Supplier Utility 3 373715 0.56 4 135540 0.47 1 192515 0.28 1 72740 0 3 362495 0.69 944490 2.01 dd

Q

Table 8-18. Benchmark B1.2/2/0.50/0.50 Order 1 2

Q 268 185

Requested P dd 482400 10 314500 17

Q 192,5 185

Assigned P 415800 314500

dd 12 17

8. Benchmarking Value Added Services in Manufacturing EMarketplaces 3 4 5

155 75 343 R

1 1 1 1 1

6.

217000 82500 514500 Ui

.60/.50/.50 1/1/1 1/1/1 .91/.80/1 .49/.50/.50

Utot 1.60 3.00 3.00 2.71 1.49 11.80

24 29 41 Supplier Id 3 4 2 5 1

197

155 70 221

217000 24 83160 29 397800 43 Supplier Supplier profit Supplier Utility 373715 0.56 135540 0.47 192515 0.28 27815 0.05 362495 0.69 1092080 2.05

CONCLUSIONS

The research proposed in this chapter deals with the evaluation of distributed models even in those cases where such models are the only real solution, as in case of manufacturing neutral e-marketplace, where suppliers and customers are in competition with each other. The research highlights the importance of performance evaluation of distributed models by designing proper benchmark models. In the case discussed in this book, a proper benchmark model characterized by a full and optimal co-operation among the actors (customer and suppliers) and by a full and a priori knowledge of the market demand has been conceived, designed and implemented. By the overall analysis of the results the following conclusions can be drawn: – the agent based distributed architecture provides very good results if compared with an optimizing “planner” with perfect knowledge; indeed, in term of global utility the distributed approach provides a solution that is only the 15% worse that the best benchmark; – the distributed model reaches almost the optimal results in case the modeled agents have only one goal (i.e. the supplier agent), while improvements seem to be possible in the case of agents with multiple objectives (i.e. the customer agent). The results confirm the goodness of the proposed solution for manufacturing neutral e-marketplace in terms of value provided to customers and suppliers and, furthermore, they offer a path for possible improvements and researches in this field.

198

Chapter 8

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