Flexible Supply Chain Simulation

J. Manuel Feliz-Teixeira Flexible Supply Chain Simulation

Thesis

01 MARCH 2006

Text submitted in partial fulfilment for the degree of Doctor in Sciences of Engineering Supervisor António E. S. Carvalho Brito Advisor Richard Saw

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Flexible Supply Chain Simulation

Thesis

J. Manuel Feliz-Teixeira*

01 March 2006

Text submitted in partial fulfilment for the degree of Doctor in Sciences of Engineering

Supervisor: António E. S. Carvalho Brito Advisor: Richard Saw

Research sponsored by the:

European Social Fund (III framework)

Through the:

* Complete name: José Manuel Feliz Dias Teixeira

i To my mother and my father, and my old professors of Physics.

J. Manuel Feliz-Teixeira

IMPORTANT NOTE: NOTA IMPORTANTE:

The contents of this text are registered with the Portuguese Society of Authors and protected by the law of intellectual rights, including the copyright. No reproductions or publications are allowed without the expressed permission of the author.

O conteúdo desta tese encontra-se registado na Sociedade Portuguesa de Autores e está protegido pela lei geral e específica dos direitos de autor, morais e patrimoniais (copyright). Não é permitida qualquer reprodução ou publicação sem o expresso consentimento do autor.

Here I declare, however, that I authorize the Faculty of Engineering of the University of Porto (FEUP) to reproduce, within the scope and exercise of its statutes of public educational institution, and as far as such reproduction is done without any lucrative objectives, the number of copies considered reasonable for the good divulgation and utilization of this text, without this meaning that any property rights have been transferred. An electronic version of this text in PDF format has been made available to FEUP for such purpose.

Aqui declaro, no entanto, autorizar a Faculdade de Engenharia da Universidade do Porto (FEUP) a reproduzir, no âmbito do seu estatuto de instituição de ensino público e sem quaisquer fins lucrativos, o número de cópias que considere razoável à boa divulgação e utilização da obra, não sendo com isso transferidos quaisquer direitos sobre a propriedade intelectual. Uma versão da obra em formato electrónico PDF foi disponibilizada à FEUP para esse efeito.

English revision: Deidré Matthee

Ó J. Manuel Feliz-Teixeira

Abstract

We live in a time where many different approaches for simulating systems are already being used and explored worldwide, mainly due to the last decade’s explosion in high level programming languages and the vertiginous level of information conceded by the Internet. The simulation of Supply Chain systems is no exception. However, it is still very difficult to obtain information about the details behind most of the Supply Chain simulators announced out there, for they easily turn into products of high value to those who develop them, as well as to the consultancy agencies that make them highly rentable. But the world of simulation has also changed, from the trustful and innocent academic old-fashioned reliability to the present suspicious and voracious days of modern commerce. In reality, there are no available books or articles or any other texts on how to project a Supply Chain simulator obviously, therefore my option has been to build up my own personal approach from the ground. This thesis, based on some hypotheses of flexibility, intends to expose the ideas behind such an approach, as well as to document the relevant steps that have been taken to reach this challenge, from the bibliographic review and the study of conceptual issues on Supply Chain systems and trends of simulation, to the conception and development of a practical simulator written in C++ with which some results and conclusions have been obtained.

iii Flexible Supply Chain Simulation

Sumário Résumé

Vivemos num tempo em que uma grande Nous vivons dans un moment où plusieurs variedade de métodos de simulação são approches de simulation sont employées et utilizados e explorados em todo o mundo, déjà explorées dans le monde entier, principalmente devido à explosão das surtout grâce à l'explosion des langages de linguagens de programação de alto nível programmation de haut niveau vérifiée à la verificada na última década, e à vertiginosa dernière décennie et au niveau vertigineux capacidade de comunicar introduzida pela de la communication assurée par l'Internet. generalização da Internet. A simulação de La simulation des chaînes de distribution Cadeias de Distribuição não é excepção. n'est aucune exception. Cependant, il est Contudo, continua a ser francamente difícil toujours très difficile d'obtenir des obter-se informação sobre os detalhes de informations sur les détails derrière la construção dos simuladores de Cadeias de plupart des simulateurs de chaînes de Distribuição anunciados, uma vez que estas distribution annoncés, parce qu’ils se aplicações facilmente se convertem em transforment facilement en produits de produtos de elevado valor, para quem os valeur élevée pour ceux qui les desenvolve e para as agências de consultoria développent, aussi bien que pour les que os tornam altamente rentáveis. Mas o agences de consultation qui les rendent mundo da simulação também mudou, da fortement rentables. Mais le monde de la confiança académica atribuída aos “velhos” simulation a également changé, dès la tempos, ao actual ambiente de suspeita e crédible et démodée fiabilité académique voracidade de moderno comércio. De facto, aux soupçonneux et voraces jours actuels du não há livros ou artigos ou quaisquer textos commerce moderne. En fait, il n'y a aucun onde se ensine a projectar um simulador de livre ou article disponible ou aucun autre Cadeias de Distribuição, pelo que a minha texte sur la façon de projeter un simulateur opção foi desenvolver a minha própria de chaînes de distribution, donc mon option versão desde raiz. a été celle de développer mon approche A presente tese, apoiada em hipóteses de personnelle dès le début. flexibilidade, tenciona expor as ideias por Cette thèse, basée sur quelques detrás desse desafio, desde a revisão hypothèses de flexibilité, prévoit d’exposer bibliográfica e o estudo das Cadeias de les idées derrière une telle approche, dès la Distribuição e das tendências da simulação, révision bibliographique, l'étude des issues à até à concepção e desenvolvimento de um propos des chaînes de distribution et les simulador prático escrito em C++ com o qual tendances de la simulation jusqu’à la foram obtidos alguns resultados. conception et au développement d'un simulateur pratique écrit dans C++ avec lequel quelques résultats et conclusions ont été obtenus. iv Ó J. Manuel Feliz-Teixeira

Acknowledgements

António E. S. Carvalho Brito, GEIN, FEUP, Universidade do Porto, Portugal J. Barros Basto, GEIN, FEUP, Universidade do Porto, Portugal J. António Sarsfield Cabral, GEIN, FEUP, Universidade do Porto, Portugal Alcibíades Paulo Guedes, Escola de Gestão do Porto (EGP), Portugal Jorge Freire Sousa, GEIN, FEUP, Universidade do Porto, Portugal Francisco Vasques and others, GEIN, FEUP, Universidade do Porto, Portugal Richard Saw, University of Cranfield, UK Christine Rutherford, University of Cranfield, UK Peter Klaus, Friederich-Alexander University, Erlangen-Nürnberg, Germany Gunter Prockl, Friederich-Alexander University, Erlangen-Nürnberg, Germany Achmed Nasa, Barkawi & Partner GmbH, München, Germany Andreas Baader, Barkawi & Partner GmbH, München, Germany Joern W. Ewaldt, Barkawi & Partner GmbH, München, Germany Lutz Arnold, Barkawi & Partner GmbH, München, Germany Juan Florencio Martin Arnanz, BMW headquarters, München, Germany Cláudio Fernandes, INFENION, München, Germany Fátima Rateb, Universidad de Granada, Spain José Carlos Fernandes, GalpEnergia, Porto, Portugal

And others:

Noreen, Hilary and others, University of Cranfield, UK Ricardo, George and others, University of Cranfield, UK Soledade, Isabel, Carmo and others, GEIN, FEUP, Universidade do Porto, Portugal José Ramiro Teixeira Santos, Frankfurt, Germany Franciska Nasa and Cherima Nasa, Nürnberg, Germany Cláudio Fernandes & Paula, München, Germany Fátima Rateb, Anne Rateb, Paris, France J. Paulo Feliz Teixeira, Viseu, Portugal Pedro & Deidré, Porto, Portugal

Specially, I would like to emphasize my gratitude to my supervisor António E. S. Carvalho Brito, for his patience, sympathy and excellence, which always pushed me to finish this project, and to Richard Saw who kindly guided me in the visits to the University of Cranfield, for his advice, pragmatism, sympathy and sense of humour.

v Flexible Supply Chain Simulation

Sources of copyright figures:

Fig. 1.1 - Courtesy of Antique Automobile Club of America, http://www.aaca.org/ Figs. 1.2; 1.3; 1.4; 1.5; 1.6; 1.7 - Courtesy of Computer History Museum, http://www.computerhistory.org/ Fig. 1.14; - Courtesy of TECNOMATIX, www.tecnomatix.de/ Fig. 1.17 – Courtesy of Fujitsu-Siemens, http://www.fujitsu-siemens.com/ Fig. 1.20 – Courtesy of eM-Plant, http://www.emplant.de/simulation.html Fig. 1.21 – Courtesy of CPN Group, University of Aarhus, Denmark, http://www.daimi.au.dk/CPNtools/ Fig. 1.22 - Courtesy of Los Alamos National Laboratory, USA, http://cnls.lanl.gov/avalon/ Fig. 3.9; 3.28 – Based on a lost reference, thank you anyway Fig. 5.4 – Courtesy of GalpEnergia, http://www.galpenergia.com/

vi Ó J. Manuel Feliz-Teixeira

Table of Contents

FLEXIBLE SUPPLY CHAIN SIMULATION 1 INTRODUCTION 1 RELEVANCE 2 HYPOTHESES 4 METHODOLOGY 4 THESIS STRUCTURE 6 REFERENCES: 8

CHAPTER 1 11 LOGISTICS, LIFE AND SIMULATION 11 1.1 INTRODUCTION 11 1.2 THE PAST 13 1.2.1 The days of 1950s 13 1.2.2 The decade of 1960 16 1.2.3 The 1970s 19 1.2.4 The 1980s 23 1.2.5 The 1990s 27 1.3 THE PRESENT 33 1.3.1 Life and SC management 34 1.3.2 IT and Simulation 38 1.4 THE FUTURE 44 REFERENCES: 46

vii Flexible Supply Chain Simulation

2.3 SUPPLY CHAIN MODELLING 74 2.3.1 System Dynamics 75 2.3.2 Petri nets 75 2.3.3 Sequential objects 76 2.3.4 Distributed objects 78 2.3.5 Agents 80 2.3.6 Web-based 81 2.3.7 Parallel 83 2.4 WHICH APPROACH TO CHOOSE 84 2.4.1 Level of detail 84 2.4.2 Where to stop 85 2.4.3 Simulation intents 87 2.5 MODEL CONSISTENCY, ACCURACY 89 2.6 GETTING RESULTS WITH THE MODEL 92 2.7 FINAL COMMENTS 94 REFERENCES: 97

3.7.1.3 Technology 120 3.7.2 The fleet 121 3.7.3 The production section 121 3.7.3.1 Manufacturing policy 122 3.7.4 The management section 123 3.8 MODELLING THE CSU ACTIVITY 124 3.8.1 Process of purchase (or ordering) 125 3.8.2 Process of stocking 125 3.8.3 Process of delivery 127 3.8.4 Process of manufacturing 128 3.9 PRODUCTS, ORDERS AND VEHICLES 128 3.9.1 About the system products 129 3.9.2 The material-order structure 129 3.9.3 Modelling the vehicles 130 3.10 ABOUT THE FLEXIBILITY (MATRIX) 131 3.10.1 The rigidity concept 131 3.10.2 Matrix representation 133 REFERENCES: 135

ix Flexible Supply Chain Simulation

4.10.2 The graph window (GraphDlg) 152 4.10.3 Visual modelling (Area) 153 4.11 SUPPLY CHAIN ENTITIES 154 4.11.1 Paths 155 4.11.2 Nodes 156 4.11.3 Vehicles 159 4.11.4 Material-order (Encomenda) 161 4.11.5 The product 162 4.11.6 Product-box 163 4.11.7 Product-plan 165 4.11.8 The CSU entity 166 4.11.8.1 Configuring the facility 167 4.11.8.2 Icon 167 4.11.8.3 States (activities) 167 4.11.8.4 Event handlers 168 4.11.8.5 Other methods 169 4.11.8.6 Main resources 169 4.11.8.7 Suppliers 169 4.11.8.8 Fleet 170 4.11.8.9 Graphs 170 4.11.8.10 Final metrics 170 4.12 THE SIMULATOR APPLICATION 171 REFERENCES: 175

CHAPTER 5 177 CASE STUDIES AND COMMENTS 177 5.1 INTRODUCTION 177 5.2 VERIFICATION STRATEGY 177 5.3 ABOUT VALIDATING THE SIMULATOR 179 5.4 CASE 1: PROCUREMENT OF ADDITIVES IN THE OIL REFINERY OF PORTO, PORTUGAL 179 5.4.1 About the system 179 5.4.2 Building the model 180 5.4.3 Validation, corrective factors 183 5.4.4 The simulation strategy 185 5.4.5 Final results and comments 187 5.4.6 Conclusions 189 5.5 CASE 2: COMPARING STANDARD AND NAIVE ORDERING POLICIES IN AN INLINE SUPPLY CHAIN 190 5.5.1 Introduction 191 5.5.2 Overview on the KANBAN 191 5.5.3 The in-line Supply Chain 192 5.5.4 Simulating the case 193 5.5.5 Results with the KANBAN 194 x Ó J. Manuel Feliz-Teixeira

5.5.6 Results with the naive BanKan 195 5.5.7 KANBAN versus BanKan 198 5.5.8 Conclusions 200 5.6 CASE 3: DISTRIBUTED SIMULATION GAME FOR SUPPLY CHAIN MANAGEMENT TRAINING 201 5.6.1 Introduction 201 5.6.2 The “Cranfield Blocks Game” 202 5.6.3 The client-server application 203 5.6.4 Results and comments 206 5.6.5 Conclusion 208 5.7 CASE 4: COMPUTING THE RIGIDITY MATRIX OF A DIDACTIC SUPPLY CHAIN 209 5.7.1 Inventory-rigidity-to-demand 209 5.7.2 Interpreting the matrix 210 5.7.3 Conclusions 211 5.8 LAST COMMENTS 212 REFERENCES: 213

CHAPTER 6 215 CONCLUSIONS AND FUTURE WORK 215 6.1 THE FINAL CHAPTER 215 6.2 SPECIFIC ADVANCES 216 6.2.1 The CSU concept 216 6.2.2 New delivery elements 216 6.2.3 New reordering policies 217 6.2.4 The Gk geometric factor 217 6.2.5 The ultimate customer 217 6.2.6 Ordering by Internet 217 6.2.7 Three levels of results 217 6.2.8 Demand steps 218 6.2.9 The theory of flexibility 218 6.2.10 Events receiving pointers 218 6.3 VERIFICATION OF THE HYPOTHESES 219 6.4 GENERAL CONCLUSIONS 221 6.5 FUTURE WORK 221 6.5.1 Rigidity (flexibility) studies 222 6.5.2 Reverse logistics 222 6.5.3 Holistic metrics 222 6.6 CLOSING 224 REFERENCES: 225

BIBLIOGRAPHIC REFERENCES 227

APPENDIX 1 235

xi Flexible Supply Chain Simulation

C++ SIMULATOR CLASSES 235 CLASS : 235 CLASS : 235 CLASS : 240 CLASS : 240 CLASS : 241 CLASS : 242 CLASS : 243 CLASS : 243 CLASS : 245 CLASS : 246 CLASS : 247 CLASS : 247 CLASS : 247 CLASS : 247 CLASS : 248 CLASS : 249 CLASS : 249 CLASS : 250 CLASS : 251

APPENDIX 2 255 WEB LINKS 255 SOME WEB LINKS ABOUT SIMULATION: 255 USEFUL WEB LINKS FOR SUPPLY CHAIN MANAGEMENT: 256 CONFERENCES, MAGAZINES, JOURNALS, DATABASES: 257 INSTITUTIONS RELATED TO SUPPLY CHAIN MANAGEMENT: 258 OTHER WEB LINKS: 259

INDEX 261

xii Ó J. Manuel Feliz-Teixeira

xiii Flexible Supply Chain Simulation

FLEXIBLE SUPPLY CHAIN SIMULATION 1 INTRODUCTION 1 RELEVANCE 2 HYPOTHESES 4 METHODOLOGY 4 THESIS STRUCTURE 6 REFERENCES: 8

xiv

Flexible Supply Chain Simulation

Doctoral thesis

ã J. Manuel Feliz-Teixeira

01 March 2006

flexible enough to represent their variety Introduction and, in addition to that, analyse them in “Pause on the esplanade of La Sabika and terms of what is currently referred to as gaze upon your surroundings. The city is a “flexibility”. In this place, one is definitely lady whose husband is the hill. She is able to identify from the old times human clasped by the belt of the river, And paper chain linking warehouses, retailers flowers smile like jewels at her throat… La and consumers, to the most modern Sabika is a crown upon the brow of Information Technology (IT) and advertising Granada, In which the stars yearn to be Supply Chains of nowadays. It sounds studded. And Alhambra — God watch over somehow ironic, however, to realise that in it! — Is a ruby at the crest of that crown.” many cases consumers still prefer the wrote the poet Ibn Zamrak in the 14th former approach. century about the place where I decided to start this thesis. In reality, it seems that Concerning the simulation of the Supply there could not be a better place to start it Chain, it is important to notice that we live than in this town of Granada, where people in a time where many different approaches live the modern times of distribution of are already being used and explored products and services and the speed worldwide, mainly due to the last decade’s induced by such processes, and, at the same explosion in the number of people using not time, the inspiration and the tranquillity of only high level language programming but the Arabic quarters, where palm trees and also the advantage of the information little vendors still coexist in an almost exchange made possible by the Internet. We unreal harmony. live, in effect, in the times where people There is, in truth, some justification for invade Internet cafés to constantly this fact, for this work have been strongly communicate with one another by email or motivated by the idea of conceiving and by chat; in the last few years the universe developing an approach to simulate Supply of programmers have exploded, and the Chain systems in a way that it could be usage of simulation tools obviously started

1 Flexible Supply Chain Simulation to exhibit the same trend. Nowadays, if not document in which one can expect to find yet simulating everything, humankind is at much about the structure of the internal least already trying to simulate everything. processes involved in such tools, or how Nevertheless, and in a certain sense as a they have been conceived, but at least paradox, it is still very difficult to obtain some information can be captured from it detailed information about the concepts indirectly. This, and the difficulty of finding behind most of the Supply Chain simulators new interesting aspects in the literature announced out there, for they easily turn more than the usual discrete approaches into products of high value to those who based on events, processes, Petri-nets, etc., develop them, as well as to the consultancy as well as on some new trends using agencies who make them highly rentable. It “intelligent” entities named Agents (Nwana, is a fact that everybody wants to handle a 1996), which I do not consider an attractive model of his/her problem, from the United approach to simulate the dynamics of the States Department of Defence (DoD), to the Supply Chain, in the end the choice have most recondite and anonymous flower been to build up my own personal approach producer of Beira Alta1. Such a wide spread from the ground. interest explains the “boom” in advertising This text intends to expose the ideas these precious tools. On the other hand, the behind the development of such an world of simulation has also changed approach, as well as to document the dramatically during the last years, from the relevant steps that have been taken to trustful and somehow innocent academic reach that challenge, from the bibliographic old-fashioned reliability to the present review and the study of conceptual issues suspicious and voracious days of modern about Supply Chain systems, their structure commerce. The pressure of advertising is so and their management, as well as the high that it frequently hides the quality of current trends of simulation, to the the product, and this is a general conception and the development of a phenomenon also observed in simulation practical simulator written in C++ with tools and certain other complex software which some results and conclusions have tools. In reality, there is not any available been obtained. books or articles or any other texts on how to project a Supply Chain simulator obviously, and, apart from a few extremist Relevance researchers that decide to free the source The simulation of Supply Chain Systems is of their simulators on the Internet, the most presently widely recognized as of great reliable information about the structure of value, as these kinds of systems frequently the products is only achieved in their exhibit complex behaviours, too difficult to Operation Manuals, which means, after be represented by analytical equations or buying them, or, with some luck, borrowing other continuous mathematical techniques. them from a friend. Of course the The modelling approaches in use at the Operations Manual is not the appropriate moment are diverse, as diverse still are the schools of modelling and the objectives for

which simulation is used. In general, as we 1 A Province of Portugal.

2 Ó J. Manuel Feliz-Teixeira will see soon in the chapter reserved for the modelling process to the handling of “real” literature review and the state of the art, elements such as transport paths or even events, Petri-nets, Bond graphs, Agents, complete facilities. Each element is also etc., are concurrent paradigms with which stochastic, and a set of graphical output modellers continue to try to represent their data attached to it will be available. Thus, systems. There is not, however, any special in a certain sense, from the point of view of reason for electing any of them as the the modeller, the present approach can be primus-inter-pares, as their usage strongly considered not only a paradigm of “real depends on the school of modelling or on objects” but also, and in a certain way, of the sympathy the modeller has for a “real reasoning”. particular paradigm or tool. It was not so surprising to meet a senior researcher at a A last issue that must be mentioned is the conference in Naples, three years ago, who universe of applicability of this proposal. kept on using an old version of SIMULA, This simulator have been developed based since this language has continued to fulfil on the next-event dynamics, and thus it is his needs and was already the “mother” of prepared to handle quite well not only the Object Oriented Programming (OOP) events separated by some seconds, minutes paradigm. or hours, but also those separated by weeks, In that sense, the approach presented in months or years. That will depend, of this work is still based on the classical next- course, on how the modeller configures event paradigm, although it will be seen each element in the simulator, on how he that the conceptual model of the Supply defines the appropriate transportation, or Chain already includes some novel features, on how he considers the patterns of as it tries to follow issues like just-in-time demand. Therefore, and also since every (JIT), KANBAN, flexibility and even what element is configured by “default”, at least some people call demand pipelines, for theoretically it will be possible to simulate example. These are seen as recent the systems at the operational perspective, tendencies for research in this field of as well as for tactical or strategic purposes. knowledge, and practically ignored in the In reality, it will not be difficult to obtain common Supply Chain simulators. results with this approach from a simulation of “some days” to a simulation of “some Another important note is the fact that years”. The treatment of the output data most of the present simulators seem to and the process of its analyses will be the persist in using the common methods to last term that will establish the point of create the model, mainly based on some view in fact considered. It is, however, sort of queue-activity diagrams from which important to keep in mind that usually the the dynamics naturally emerge. To the uncertainty of the results will grow as the contrary, the present approach has been period of simulation increases. conceived in order to encapsulate such “low-level” diagrams and enable the user to faster and easily employ a logic similar to that of the real system, thus reducing the

3 Flexible Supply Chain Simulation

Hypotheses only related with each facility but as well “Flexible Supply Chain Simulation” has with the entire Supply Chain structure. been chosen as the title of this research for Different scenarios are expected to be two main reasons. Firstly, the adjective possibly compared based on these sorts of “flexible” must be applied to the simulation measures. itself, since its structure has been conceived Methodology with the intent to allow a wide range of scenarios to be modelled without losing the To achieve the objectives expressed in simplicity of the modelling process, not only these hypotheses, a first plan for the in terms of different Supply Chain network program of research was established, structures, but also regarding the usage of inspired by the believe that “one must first different stock and production policies, understand the system so that later one is from the classical approaches of producing- able to model it”. This implied the to-stock, to the modern trends of JIT. In orientation of the research in two main this first sense, the hypotheses can be directions: described by the following two statements: a) Simulation: study of the literature and H1) the simulator will be able to investigation on the “state of the art” by represent diverse kinds of Supply Chains in reading articles published in conferences, terms of different network structures, and journals, magazines and on the Internet, as allow the analyst to draw conclusions about well as about its basis in relevant books, by their performances at least based on some means of which it was finally possible to standard Supply Chain measures. classify the various recent tendencies of simulation applied (or applicable) to Supply H2) the simulator will also be able to Chain, and to recognize the merits of each represent different stocking and producing of them. policies at each facility, as well as to test different kinds of vehicles for delivery, in b) Logistics and Supply Chain: study of order for these to be compared by means of the basis of Logistics and Supply Chain some standard measures. management in some relevant books on the field, followed by an extensive literature Secondly, the adjective “flexibility” must review focused on scientific articles be applied to the Supply Chain itself, and obtained from magazines, conferences, that means that the present approach is journals, Internet, etc., with the intent of expected to give the user some quantitative learning as much as possible (given the time indications about the degree of “flexibility” constraints) about the kinds of systems that of a certain scenario. The third point of the were to be simulated. This study has been Hypothesis is, therefore: extended to most matters concerning Supply Chain management, but in the end it has H3) by means of this simulator it will be been mostly focused on production and possible to obtain a quantitative measure of inventory policies, on different delivering the “flexibility” to demand variations, not vehicles and on the understanding of the

4 Ó J. Manuel Feliz-Teixeira various Supply Chain structures, as well as strategy in the development and the the actual trends and philosophies involving implementation. Although only after the the field. A historical overview on the learning process reaches the bottom level Supply Chain management has also been one can start to implement, this method is accomplished. probably the most interesting and reliable method to use in projects of this kind, as, Once the knowledge concerning these without doubt, it ensures that the results matters had reached a satisfactory level, a will be derived from a good background of new process was started in parallel, leading knowledge. This scheme also provides the to real contacts with people, centres of researcher with enough information about knowledge, universities and enterprises, the relevant matters, giving him or her the with the intent to definitely take a real ability to better handle the real problems measure of the problems and ideas and even to formulate new suggestions. occurring in the field, often quite distant That was the case of the theory of Supply from the usually optimistic literature. Chain flexibility proposed in this work, and meanwhile published in Feliz-Teixeira & These contacts included meetings and Brito (2004), even if this matter was not discussions with people strongly related to specifically related to simulation; and also the management of Supply Chains, most the case of a small study comparing an notably with the Management School of inline classical Supply Chain with an Porto - EGP (Portugal), the University of imaginary demand pipeline chain (Feliz- Erlangen Nuremberg (Germany), the Teixeira & Brito, 2005) since the last is enterprises BARKAWI & Partners and BMW indicated by some authors as the Supply from Munich (Germany), the Centre for Chain of the future (Hewitt, 2001). Logistics and Supply Chain Management of the University of Cranfield (UK), and, In order to enable the exploration of any finally, some interviews with the interesting concepts that could meanwhile GalpEnergia company (Portugal). emerge from the research, efforts have been made to ensure the methodology was Parallel to this, the process of kept flexible enough, instead of a priori implementing and day-by-day improving the reducing it to a predefined set of C++ Supply Chain simulation application constraints. This fact has also made possible started. This would later lead to the very the development of a distributed Supply first version of the Supply Chain simulator, Chain Game (Feliz-Teixeira et al., 2004), for which basis had then been presented at the example, with which a class of students “European Simulation and Modelling seated at different computers could Conference”, Naples (Italy), in 2003 (Feliz- exercise inventory management in a Teixeira & Brito, 2003) . didactical Supply Chain.

This method of framing the research Apart from this, several procedures of reveals a top-down strategy in the process verification have been carried out in the of learning, followed by a bottom-up proposed simulation approach, in which the

5 Flexible Supply Chain Simulation simulator internal processes were approved, is expected that people specifically from and a first validation of its outputs have these fields do not necessary dominate both been achieved by means of simulating some matters to an extent adequate for practical cases, as far as possible. The most understanding the present work, it was significant of these was a real case study considered justifiable and even convenient based on data provided by the company that each field would be firstly presented in GalpEnergia, which resulted in the some detail before moving to the confirmation of the practical value of the presentation of the work. In a way, this text approach, and contributed to testing and may, therefore, also be considered didactic. confirming the hypotheses. The thesis is mainly about Simulation, but it is applied to a specific type of system, The hypotheses were finally tested and which is known as Supply Chain. It is, inferred as true by means of analysing the therefore, essential to ensure the data obtained in the GalpEnergia case, as convergence of knowledge of the two fields well as in the demand pipeline case and to a point that Supply Chain managers and some other appropriate Supply Chain “small Simulation experts can both understand and paradigms”; and, finally, by the success in use this text. Chapter 2 is, for this reason, the calculation of a quantitative measure of dedicated to an overview of the relevant flexibility for a didactic Supply Chain, issues concerning both the Supply Chain and known as the “Cranfield Blocks Game” the various modelling and simulation (Saw, 2002). technologies used to model these kinds of systems.

Thesis structure In chapter 3, the proposed approach for The thesis begins with an introduction to Supply Chain modelling and simulation is Logistics and Supply Chain Management conceptually presented, with each of its (SCM) as an overview of how the concepts elements explained and analysed in detail, related to these matters have evolved since with emphasis on its central element, the the end of the Second World War, as well as general Supply Chain facility that we have of how the computer Simulation, scientific named Customer Supplier Unit (CSU). development and even certain aspects of Modelling the fixed and the variable costs of common life have progressed concurrently. the Supply Chain, the products, the facility This opening results from a general resources, as well as the facility dynamics literature review focused on those issues and the vehicles dynamics, and finally, with the intent to establish a general view presenting the theory of flexibility, are the on the evolution of the times and the state type of subjects presented in this chapter. of the arts (chapter1). Chapter 4 is dedicated to the C++ Since the present work involves a implementation of the current simulation significant specific knowledge in two approach, and includes an overview of the different fields, the field of Logistics and structure of the simulator, as well as the SCM and the field of Simulation, and since it code and comments on the most important

6 Ó J. Manuel Feliz-Teixeira object classes in the application. Although there is a good extent of C++ code in this chapter, each important object or structure has also been described by means of accessible diagrams, so that non-C++ users may also benefit from its information. A short overview of the final application for modelling and simulation concludes this chapter.

Following a brief discussion concerning the verification and the validation of the simulator application, the rest of chapter 5 is reserved for the presentation of some experimental results achieved by simulating a few practical cases. Relevant comments and discussions will be dispersed throughout this chapter.

Finally, chapter 6 is entirely dedicated to the final conclusions and the perspectives for future research. It is here that the verification of the hypotheses is also done, as well as some ideas related with holistic measures presented.

NOTE: Bibliographic references are presented at the end of each chapter and also at the end of the thesis.

7 Flexible Supply Chain Simulation

References:

Feliz-Teixeira, J. M., & Brito, A. E. S. C. (2003). An Approach for Dynamic Supply Chain Modelling. Paper presented at the 2003 European Simulation and Modelling Conference, Naples, Italy. Feliz-Teixeira, J. M., & Brito, A. E. S. C. (2004). On Measuring the Supply Chain Flexibility. Paper presented at the 2004 European Simulation and Modelling Conference, UNESCO, Paris, France. Feliz-Teixeira, J. M., & Brito, A. E. S. C. (2005). Comparing a Standard and a Naïve Stock Refill Policies by Means of Simulation. Paper presented at the 2005 European Simulation and Modelling Conference, University of Porto, Porto, Portugal. Feliz-Teixeira, J. M., Brito, A. E. S. C., et al. (2004). Distributed Application for Supply Chain Management Training. Paper presented at the Industrial Simulation Conference 2004, Malaga, Spain. Hewitt, F. (2001, May 2001). After Supply Chains, Think Demand Pipelines. Supply Chain Management Review, 5, 28. Nwana, H. S. (1996). Software Agents: An Overview. Knowledge Engineering Review, 11, 1-40. Saw, R. (2002). Cranfield Blocks Game: Centre for Logistics and Supply Chain Management (CSCM), University of Cranfield, UK.

8 Ó J. Manuel Feliz-Teixeira

9 Flexible Supply Chain Simulation

Chapter 1

Logistics, Life and Simulation

An overview on the times and on the state of the arts

glossary maintained by LogisticsWorld.com3. 1.1 Introduction Although such diversity of opinions fails The expression “Logistics” has begun to to free us from the initial confusion on the be used far after mankind have begun to matter, it is somehow true that the term is handle logistic processes, and it mainly in general accepted to reference an art, a results from the need man of 20th century process, a discipline or a science, other felt to classify those processes in order to than a system or a network, what at least start treating them in a more scientific way. can be considered a positive aspect pointing Taxonomy is many times the beginning of to a certain agreement. such courses. It is a fact that each one adopts the Anyhow, as often happens with many definition which seems more adapted to other designations, the term “Logistics” is his/her purposes or convictions, similarly to still on the mind of many authors as a kind what happens in matters of subjectivity, of diffused idea about its meaning, even if like love or friendship. Nevertheless, there some insist on searching for its ultimate was one of those definitions that touched definition, as it can be noticed on reading me most due to an almost naïve simplicity, common literature on the subject. Also in stated by a little Australian enterprise as the Internet it is easy to realise there is a follows: “In an industrial context, logistics wide range of such definitions, stated by a means the art and science of obtaining, wide number of institutions, like the Council producing, and distributing material and of Logistics Management1, the United States products in the proper place and in proper Department of Defence (DoD), the Logistix quantities. In a military sense, its meaning Partners Oy2, from Helsinki, the Websters can also include the movement of 4 Dictionary, the American Heritage personnel.” Dictionary, or the Canadian Association of Although this characterization already Logistics Management, etc., only to name tries to declare a slight innocent distinction some of the dozens forming part of a between civil and military “Logistics” (in reality most of the technology have been

1 Founded in 1963, is a preeminent association for military driven, till now), the truth is that in individuals involved in Logistics and Supply Chain Management (http://www.clm1.org). 3 http://www.logisticsworld.com/logistics.htm 2 A consultancy agency, based in Finland. 4 http://www.homercomputer.com.au

11 Flexible Supply Chain Simulation general most people still correlate delivery, forecast, sales, etc., as well as in “Logistics” to the movements of troops, some other financial topics. facilities and materials in the war. That is In a certain way, one could say it would the origin of the term and perhaps still is be impossible to simulate Logistics for the the best way to visualise the idea behind it. same reasons it is impossible to simulate In that sense, one can easily imagine Optics, for example, since both are in fact “Logistics” as the overall process which is fields of knowledge, disciplines or arts, and responsible for fulfilling the needs of the not real systems. troops on their movements and their stay Anyhow, simulation techniques have far from “home”, where goods and already been applied with success to some materials could be easily accessible. Thus, areas of Logistics, for instance, to “Logistics” is the operations that “support” warehousing, or to production, where the cause. normally the systems are already too Of course processes of this kind are complex to predict and characterize. And already known at least since the time of the lately, simulation has also been used on Romans, where huge amounts of materials studying the “Physical Distribution” across and men had to be moved along huge the Logistic network, a complex process distances, and for that reason thousands of including the transport of materials that kilometres of roads have been built across since the 1960s (Metz, 1998) has evolved to the Empire, to mention just a case, even if what is nowadays referred to as the “Supply those processes could not yet be managed Chain”. with the perfection of nowadays. Logistics, therefore, deals with supplying This chapter will introduce the fields of the needs of people, and thus of producing, Logistics and Simulation in terms of their of storing and handling the products, of historical courses and relevancy, and at the their transportation and delivery, as well as same time in relation to the advances in of all the sub-processes related with each of computers, electronics, operations, political this processes, including information. Even and social events, as well as to everyday if it has never been (and will never be) life, in an interrelated holistic phenomena detached from the military, who still are of development. This is expected to help the masters on this art, with the time the reader on better following the close Logistics have also naturally spread to civil relations existing between the two main organizations and systems, turning them fields, similar to what happens between more powerful, efficient and reliable. science and engineering, for instance. In between, also the “state of the art” in More than a system, Logistics is thus a each matter will be presented, resulting huge field of knowledge including different from a literature review on Simulation and kinds of systems, different issues, and even on Logistics and operations management, diverse levels of reasoning, acting in a which slowly will be focused on the most network of dependencies. That explains why relevant issues for the present work, that is, it usually is studied in separated subjects, those related to the Supply Chain. as procurement, production, storage,

12 Chapter 1 - Logistics, Life and Simulation

Finally, some considerations will be made and many other aspects that became marks about what probably will be the future of the modern American society. tendencies in those fields. Also in terms of social life, the country was living one of its most promising times, inspired by an “optimistic vision of a semi- 1.2 The past Utopian technological future, including such Half a century ago, in the decade of devices as the flying car” (Wikipedia, 1950, the big acceleration that would result 2004a). A clad black-and-white SuperMan in the current world of “all kind of was frequently seen on the TV, while the achievable commodities” was beginning. cars in the streets exhibited a certain Some years before, America and its allies coloured flying machine style lent by the had won World War II and that fact was “Flash Gordon” movies of 1930-40, almost reflecting on the people a wave of as in a dream. Henry Ford, for instance, confidence and enthusiasm which clearly already with a long practice in mass would lead to the prosperity of the production, was in 1955 producing models following decades. The world was preparing like the popular Thunderbird as well as the itself for the craziest race of the 20th Fairlane (shown in figure 1.1), while for the century, already in good use of the early first time young girls could play with Barbie Scientific Management concepts due to dolls. Frederick W. Taylor (1903), as well as dominating and practising notions like the Statistical Sampling for quality control (Shewhart, 1931), the Economic Order Quantity (EOQ) from Harris (1913), and many other tools from the field of Operations Research, as, for instance, the Simplex Method for Linear Programming developed some years earlier. Fig. 1.1 Ford Fairlane, from 1955

1.2.1 The days of 1950s In the streets, the ordinary family man After the war, together with thousands of was a relaxed man driving one of these nice ordinary Jewish people, many engineers and and styled heavy cars, while the movement scientists immigrated or were even invited in the towns was growing and the new to join the “American Dream” in order to standards of living changing the day-by-day develop and implement the new trends that life. On the minds of America, and in the would lead mankind to the big change. world in general, there was a unique fervour People moved in enormous ships from to believe in a glorious destiny for mankind, Europe to America, many of them carrying obviously inspired by science, while figures their entire families, and this mass flow of like Humphrey Bogart, Kirk Douglas, Lana manpower made possible the fast progress Turner, Paul Newman and Elizabeth Taylor of science, technology, medicine, politics were breaking the hearts of lovers around

13 Flexible Supply Chain Simulation the planet, and Hollywood produced “Cat on words and an input/output technology a Hot Tin Roof”, and “The Bad and the based on magnetic tapes, unityper and Beautiful”. printer, and was costing the equivalent of At the same time, Wernher Von Braun, around 1,000,000€, including the “high the former top expert in ballistics who had speed” printer (C.H.M., 2004). achieved the bombing of London from Germany with his V2, was now in charge of the ambitious American space program, as director of the NASA's Marshall Space Flight Centre. The aggressive German regime had been defeated. Now the objective was the Moon.

Driven by the prodigious mind of John von Neumann, a Hungarian-born scientist living in America who in 1945 outlined the basis of Fig. 1.2 The UNIVAC-I computer, from 1951 the architecture of modern computer, as well as by the invention of the transistor in But also the number of computers was 1947 by John Bardeen, Walter Brattain and very small, compared to nowadays, and William Shockley, the fifties have also been some of them could need the space of a times of strong computer development and building floor. During 3 years, IBM would technological incubation as well as the only sell 19 units of its first electronic beginning of the trend of reducing the size computer (the 701) to some research of electronic devices, till then dominated by laboratories, aircraft companies and federal the vacuum tube technology. governments, in the times of 1953 (C.H.M., At the end of the 1940s, computers were 2004). But meanwhile transistorized incredible machines with which only some programmable computers appeared in 1956, experts could communicate by means of with the TX-0 built at MIT’s Lincoln writing special codes in a paper tape or Laboratory (C.H.M., 2004), and from then a using switches, codes that only later could great reduction in size would start. be directly loaded from a magnetic tape. The output, in characters code, was first The top of Logistics in that time was the presented in a kind of printer and only many Korean War (1950-53), a particularly savage years after on a vacuum tube screen. A good conflict that succeeded in repelling the computer memory size could be 2,000 North Korean communist invasion of the binary words (˜0.002 MByte), and an South Korea, and where America could expectable speed around 2,000 operations already practise Logistics as a routine, as documents from the time confirm (N.H.C., per second (˜0.002 MHz). The potent 5 UNIVAC-I (in figure 1.2), for instance, which 2000). Also during that time, French occupied an entire big room, could work with a speed of 1,905 operations per 5 The French Vietnam War, during the 1950s, was in reality second, a memory size of 1,000 12-digit the seed of a later American war that would stay to the history as the Vietnam War (1964-75).

14 Chapter 1 - Logistics, Life and Simulation military was already practicing heavy (R. Müller, 2000), the models of the 1950s Logistics, while battling Ho Chi Min in where largely dominated by scale sculpts of Vietnam (TheHistoryPlace, 1999), precisely the systems, continuous mathematical when Inventory Management was first models, as well as mechanical or introduced by Thomson Whitin (1953). electromechanical representations6. However, modern enterprises were still There were, anyhow, already some trials distant from integrated Logistics worries, on the development of simulation languages since they were much more focused on and to introduce mathematical, statistical scientific development and production, as and stochastic concepts into the computer, was the case of NEC, Texas Instruments and what in 1958 lead to the first symposium on Bell Labs, for example, all of them related "System Simulation" held in Baltimore at the with the digital computer. And even if it American Institute of Industrial Engineering was true that the industry of transports was (AIIE). growing worldwide, the term “Logistics” The field of System Dynamics7 was also was still a property of the military, and undergoing changes from the hand- “Supply Chain” literally meant nothing yet. simulation stage to the computer modelling Instead, the term in use was “Physical stage (Forrester, 2004), and Richard Bennett Distribution”, concerning the distribution of was creating the first system dynamics materials between the elements of the computer modelling language, called SIMPLE supply network, which were still being (Simulation of Industrial Management managed on their own, without any special Problems with Lots of Equations), while, a concepts of partnership, data interchange year later, in 1959, Phyllis Fox and or network optimization. In a certain way, it Alexander Pugh wrote the first version of is understandable that things were like that, DYNAMO (DYNAmic MOdels), an improved since the most important focus in those version of SIMPLE that became the industry times was not on delivery, but on new standard for over thirty years, as Forrester inventions as well as production lines. Only (2004) observes. when the rate of products flow got higher Also important contributions to the field more attention was turned to the of Simulation were coming from the RAND distribution process. Nevertheless, “Physical Corporation, with important advances on Distribution” was already dealing with "Systems Analysis", while general efforts freight management, warehousing, material were running to find mechanical models for handling, inventory, plant and warehouse economic, biological and psychological site selection, etc., matters which would processes (N. F. Morehouse et al. 1950; O. stay in its domain till today. 6 In fact these models were a kind of analogical automation About simulation, which actually has of a continuous mathematical model, where blocks of differentiation and integration were used to process the been used by scientists since the beginning output of a differential equation system. of science, there is some evidence that ship 7 System Dynamics is a continuous mathematical modeling models were already built for hydraulic methodology developed by Forrester during the 1950s, for th experimenting in the 17 century, first in studying and managing complex feedback systems. Only the England, then in Holland, France and Russia study of the whole system as a feedback system will lead to correct results (Forrester, 2004).

15 Flexible Supply Chain Simulation

J. Smith, J. M. Erdley 1951; Arnold Tustin In those times, a powerful technology was 1953; Donald E. Broadbent 1957), as cited already available, but still it was especially by Müller (2000). heavy, space and power demanding and very expensive for the civil society. And it One can think of the fifties as dominated was mainly concentrated in places where by transistorized computers, more compact the ordinary man would never expect than those of the previous decade, but still access. For the common man of that time, a accessible only by military and space computer was probably a kind of mystic research institutions, or by the most machine of electronic intelligence with powerful civil companies, as it was the case which some “crazy” scientists could project of the telephone company (Bell Labs). On space ships and Atomic Bombs, and would the other hand, programming languages never ever cross his mind that each one of were not yet available and during most of us would nowadays carry its own. FORTRAN the decade the code introduced in those was already available, but there would be machines was linear, time consuming and no general propose operating systems until dedicated to resolving specific problems. the end of the decade, when in 1969 The introduction of FORTRAN was only in Kenneth Thompson from Bell Labs 19578. With this language, for the fist time developed the first UNIX operating system was possible to make use of “DO” (Bell-Labs, 2002). Anyhow, the computer programme loops, which extremely have languages flourish, as one of the most improved computational processes. interesting challenges of the time was to Only in the end of the decade, in 1958, transform the computer in a multi-propose Jack Kilby from Texas Instruments was machine. COBOL, LISP, BASIC and LOGO, presenting the primitive prototype of the were born in the sixties. integrated circuit, the component which would evolve to the technology used in the Following this tendency, the simulation computers of today. The digital computer, programming was also passing from its in use since 1944, would be extremely Period of Advent to what some future improved by means of this integration researchers as Richard E. Nance (1993) technique. would identify as the Formative Period (1966-1970), with the computer simulation mainly being driven by simulation language 1.2.2 The decade of 1960 developments. SIMSCRIPT, the general- In one year the Berlin Wall would purpose simulation language created by separate East and West Germanys. In two Harry Markowitz et al. at RAND Corporation years the tensions with USSR would reach in 1963 could now be applied to large the red limit with the Cuban Missile Crises, discrete simulations (Kinnersley, 1995). bringing the world to the doors of a nuclear During the next year, SIMULA I (SIMUlation confrontation. LAnguage) was developed in Norway by Kristen Nygaard & Ole-Johan Dahl, and launches the basis for the future Object 8 In this year the European Economic Community (EEC) Oriented Paradigm, while IBM produces the was also created , by the Treaties of Rome.

16 Chapter 1 - Logistics, Life and Simulation first computer based on silicon chips ordinary people, which in a certain way (IBM360), and researchers recognize the could be seen as the awakening of the need for a general theory of systems and masses. The memories of the horrors of the models separated from the simulation Atomic Bombs, which have astonished languages (Lackner, 1964). people worldwide with the events of In the middle of the decade, some Hiroshima and Nagasaki, slowly were analysts could already make use of facilities transforming into a wave of movements for like the Computer Aided Design (CAD), since peace and search for different ways of in 1964 IBM have developed the first CAD living, mainly among the youth, which a Computer, the "System 2250”. As Müller decade later would lead to the affirmation (2000) also notices, CAD was advanced from of POP culture as an icon committed to US-military research on space travel, and destroy such concentration of powers and later made accessible for the public. It turn the political issues more public, more would then spread to the civil industries of “popular”, as well as the technologic ships, automobiles, electrical circuits and advances meanwhile achieved by science. injection moulds. A higher optimization of The Beatles were already playing since the industrial processes and product distribution beginning of the decade, they used electric was taking place, and the National Council guitars and electronic sound systems, the of Physical Distribution was created in the beatnik generation was also on the move, United States. But such pressure imposed by while common people in America were the mass production race was also starting starting to blame the high rate Mass to be reflected on many aspects of social Production for everything from the crisis in life. higher education to the demise of the With the industry openly running in the family (Stephens, 2003). Not even the Mass Production Era, as Heizer & Render launch of Von Braun rockets towards space, (2001) define the period of 1910-1980, the which could usually be seen by everyone on 1960s were marked by an extraordinary the TV, was able to calm down the tensions scientific and technological advance mainly provoked in the civilian society by the imposed by the automotive industry, the Vietnam War. There was somehow a armament race of “Cold War” and the “revolution” approaching. “ideological” race of “Space Conquest” (the Although such “revolution” had first regimens had to sustain their ideologies by made itself noticed in the artistic field constantly exhibiting their powers and their (groups like “The Beatles” or “The Stones” abilities). In the centre of all this, anyhow, could easily show the tremendous power was the digital computer. contained in the masses), the fact is those ideas slowly have expanded to other With the intents of preventing the spread population concerns and started to pervade of communist ideology, previous tensions and sway the behaviours of the following between America and North Vietnam would decades. The conservative anti-communist in 1964 give rise to a new American war. society that ten years earlier had watched Anyhow, opposing such a war mindset, an unexpected trend was also emerging from

17 Flexible Supply Chain Simulation

“The Day the Earth Stood Still”9 (1951) was Gyroscope Company, UK, and later on the now to face a force in another direction, digital computer. He had been chairman of whose tendency was to oppose the war and the UKSIM, a society related to the to suggest the release of individual tensions, American Society for Computer Simulation that tensions had to be released, not (SCS) and the EUROSIM. allowed to rise constantly seemed to be the doctrine. Thus, the new proposal implicitly In 1965 Man was giving the first steps in would include the usage of technology for the era of minicomputers, with the Digital purposes other than war, as entertainment, Equipment Corp. introducing the PDP-8 (Fig. artistic expression, day-by-day life or even 1.3), which was faster, much smaller and pleasure. The society of consumption was one-fifth the price of a small IBM mainframe fast developing. May 1968 took place in of that time. The demand of such machines Paris, once again confirming the anti-war increased, as Microelectronics advance with trend and the potential hidden in the chips technology, and the components get masses, which were not anymore the huge more compact, less power consuming and human crowd without any voice or faster, leading to more memory and more aspirations as they seemed to be. And all complex operations. this would slowly lead the future to the inevitable liberalization of technology10 observed in the last decades of the 20th century. It seems masses were slowly thinking on products, and such tendency would later set in motion an even bigger acceleration in society change11. A compassionate description about the research during these times can be found in Zobel (2001), who in 1964 was working on the analogue computer at the Sperry Fig. 1.3 The DEC PDP-8, from 1965

9 Movie where an extraterrestrial comes to Earth to warn of the dangers of Nuclear Bombs, also related to the fears of Computer is slowly becoming affordable Communism. to society, mainly to people at the

10 This is the point of view of the author, probably shared by universities. Thus, more and more people some and rejected by others. feel attracted by software development, and general-purpose languages naturally 11 The Information Society of nowadays was in fact the result of the popularization of the electronic and digital evolve, as well as discrete simulation technologies, including videos, TVs, computers, musical languages and other computer techniques. instruments, mobile-phones, networks, computer In 1967 the first “Computer World” applications, games, etc., what in our times make the customer the most precious element in the economical magazine is published. system. In fact, due to this popularization of products, devices and services, nowadays customers are measured in millions, an extremely attractive number for those out there Simulation is obviously moving towards thinking of selling… the discrete field, and the main paradigms

18 Chapter 1 - Logistics, Life and Simulation of discrete simulation take place, most of Obviously, all this improvement in the them resulting from different views on the computer field was spreading progress into state transition diagram used for system’s the different aspects of society, from the representation. The most relevant of theses space industry and military to the ordinary paradigms were the process-interaction devices for domestic use. In the end of the approach used by Gordon (1961) in his decade, the Formative Period of discrete GPSS12 simulation language, focused on how simulation was over (Nance, 1993), but the entities flow through the system and field of Operations Management continues providing a process for each of these to evolve with the computer, already facing entities; the activity-scanning approach matters like Decision Analysis, Theory of proposed by Buxton & Laski (1962), which Scheduling, and even Advertising. The first represent the model dynamics based on the ARPANET/INTERNET site was also set in 1969 execution of certain activities every time in the University of California, Los Angeles certain conditions are satisfied; the event- (UCLA). Nevertheless, the people’s greatest scheduling approach developed at Rand fascination stayed the Moon, and in 1969 Corporation, describing the model as a Von Braun could finally land the first human sequence of events that cause the changes astronauts there. Figure 1.4 shows the in the state of the system; and later the computer which was responsible for steering three-phase approach published by Tocher Apollo 11 to the lunar surface. (1963), an interesting compromise between events and activities. Based on these approaches, languages like CSL, SIMSCRIPT and SIMULA, emerge and become standards. Moreover, it is important to point out that also Petri nets, first introduced by Petri (1962), became since that time an important formalism directed at distributed systems. This approach was inspired on the activity-scanning of automata programming, and turned into a formal and graphical Fig. 1.4 The Apollo Guidance Computer (1968) language already using the notions of concurrency, non-determinism and synchronization, and was considered the 1.2.3 The 1970s first general theory for discrete parallel In the book “Distribution Management: systems to be formulated (P.N.W., 2004). mathematical modelling and practical Actually, in contemporary simulation analysis” from Eilon, Wantson-Gandy & conferences Petri nets appear to dominate Christofides (1971) it seems evident that the the taste of a substantial number of dominating issues in the beginning of 1970s researchers. on the “Physical Distribution” (which slowly was being called just “Distribution”), were still the problem of depot location, linked to

12 General Purpose System Simulator strategic planning, which first contribution

19 Flexible Supply Chain Simulation have probably been from Weber (1909), as paradigm. Visual and Interactive Simulation the authors notice, and the problem of fleet (VIS) was also giving the first steps with management, more focused on tactics and Hurrion (1976), and with the first VIS on the travelling salesman problem, a simulator SEE-WHY in 1979 (Fiddy et al., classic of Operations Research (Flood, 1981). 1956). Numerical analysis was normally chosen to board these matters with the help One could say that what was happening in of the computer, and by considering various simulation was, in a certain way, happening static cost models as the base to calculate in other fields of knowledge. The pioneering the “best solution”. spirit of the previous two golden decades was entering a kind of relaxation, and Simulation was entering the period that seemed to be replaced by the main idea of Nance (1993) classifies as the Expansional upgrading and refining the technology Period (1971-1978), and the basic paradigms meanwhile achieved. Hopp & Spearman meanwhile developed started to spread in (2001) argue that this could also be related several different ways, naturally leading to to the view of the Professional Manager in an explosion of simulation languages and America, that since the sixties had engaged simulation tools, like in a tree. From now first in a Marketing outlook and then in a on, it starts to be less easy to follow the Finance outlook, while devoting less timeline of all the descendents from the attention to the operational levels of basic paradigms, as they fast reproduce to a industry. Unlike Henry Ford and his wide range of simulation applications. In the contemporaries, who knew everything about middle of the decade, for instance, in his the processes running in their factories, book “Systems Simulation, the art and the modern managers started to get apart from science”, Shannon (1975) was already those processes and have entered too deep indicating as the most important simulation the abstraction. One of the results would be languages the following: CSL, ESP, FORSIM- a natural relaxation in the industry’s ability IV, using activities; SIMSCRIPT, GASP II, to face competition, for example. SIMCOM, on the event approach; and SIMULA, OPS, SOL, GPSS and BOSS for those And at the same time, more and more who preferred to work with processes. Of people were rallying in the streets against course, he forgot the SLAM, another event the Vietnam War, confronting the politicians approach language of the time directed at with the “Peace & Love” trend which was analogue modelling, but from such a list of spreading and acquiring an unexpected languages it is not difficult to induce that authority as a new political intervenient. there were not any special novel trends in POP was giving cards. And two years after the field during those times. Instead, the the beginning of this decade, the Vietnam tendency seemed to be the upgrading and War was finally finished, mainly due to the refinement of the old concepts in order to power of American and European public adapt them to a wider range of subjects and opinions. situations, thus leading to more perfection People would now engage in a series of and an increase in the potentials of each new expressions against war and mostly

20 Chapter 1 - Logistics, Life and Simulation against nuclear energy. They seemed much designing its “XEROX Alto”13 (Fig. 1.5). This more interested in enjoying peace and was the first workstation using a built-in comfort and diversion than ever. They mouse for input, and already storing several would buy washing-machines, hair-dryers, files in windows with menus and icons, and TVs, and any kind of electronic devices that also being able to link to a local area technology was already able to produce, as network (C.H.M., 2004). well as flowered dresses and psychedelic cloths, books and music. Progressive Rock, for instance, a new intellectual expression in electronic music aligned with these ideals, fast conquering numerous adepts worldwide, with PINK FLOYD, VANDERGRAAF and GENESIS, among others, was pushing millions of people to buy their works recorded in vinyl discs. But to listen to these discs an electronic sound system was needed, thus also home sound systems were selling at a rise. Computers were still in the domain of universities and some powerful institutions, but Management could now Fig. 1.5 XEROX Alto, the workstation with mouse (1974) start to think on focusing better the attention on the “Physical Distribution” of What in the minicomputer was occupying these products. a normal plaque of electronic components could now be reduced to the dimensions of a single chip, and being faster and less In the year of 1973, Dennis M. Ritchie had power consuming. written the C programming language for the UNIX operating system, and with the But in 1974 the sudden Arab Oil Embargo improvement of precision in manufacturing started to shake the industry, and the techniques Microelectronics was getting economy of America entered in recession. able to compact electronic components to The barrel of crud was jumping from $3 to the order of including in a single package $12 (Williams, 2003). On the other hand, (chip) all the fundamental elements of Von Germany and Japan had already completely Neumann’s architecture: the Microprocessor recovered from WORLD WAR II, and their was born, and with it the microcomputer. industries gave signals of more competition Actually, the young Internet (ARPANET) than what was expected by analysts. In soon would be in expansion for connecting particular Japan, implementing the ideas of scientific groups and universities for free statistician William Edwards Deming (1900- sharing of email and files, while XEROX was

13 Although XEROX never sold the Alto commercially, it gave a number of them to universities. Engineers later included its features into other computers (C.H.M., 2004).

21 Flexible Supply Chain Simulation

1993), previously ignored in his own approaches, and knowledge and technology country, was showing an ability to manage would start to spread to other parts of the production and distribution with more globe, as it had to be. In the car industry, superior performances than ever. The for instance, France, Britain, Germany and ability of the Japanese to fast absorb Japan were again important players, with concepts and quickly turn them into high- Japan side by side with America (A.A.C.A., quality new products was shifting the 2004). At the same time, China, in the end production of stereo-radios, TVs, microwave of 1975, was successfully launching its first ovens and other appliances to Japan, as recoverable satellite, becoming the third Goldratt & Fox (1986) seriously remind in country with the ability to launch this kind their book “The Race”. of satellites, after America and the former Maybe already previewing a decline in USSR (C.I.I.C., 2004). Bill Gates & Paul Allen the American industry, and thus the need found Microsoft precisely in this year, and for better control manufacturing processes, started to try spreading BASIC programming Orlicky (1975) developed the first closed language. The magazine “Computers and loop Material Requirements Planning Operations Research” was established. (MRP)14. This is a technique for dependent Researchers gave attention to “Queueing demand which decomposes any multipart Systems Theory” (Kleinrock, 1975), and in product in the tree of its elemental parts the field of simulation Zeigler (1976) and, from that, schedules production and proposed a formalism based on the event- establishes the new orders to the suppliers scheduling approach for the design of (Heizer & Render, 2001). It is a push system, hierarchical and modular models, named since elemental parts are produced based Discrete Event System Specification (DEVS). on their independent demand and pushed in Meanwhile, the flexible disk (diskette) the direction of the assembling line. appeared, and, in the year of 1977, starting Energy got more expensive, processes realize the dreams of many researchers started to get leaner, with quality being the worldwide, several personal computers are major issue since “everything” could be released in the market. The COMMODORE compared with the Japanese products. The PET, shown in figure 1.6, was the first of 10% level of defects accepted till the end of them. 1960s no longer would be a measure of quality. Now, the target had to be much less than that, as Goldratt & Fox (1986) observe.

Nevertheless, Mankind was at the doors of a new burst of growth on its way to maturity, and all these “problems” would transform into something positive. Since then, we would enter a world of diverse Fig. 1.6 COMMODORE, first personal computer (1977)

14 It seems a first MRP approach has been tested in the sixties, but with no success, and was abandoned (Goldratt & Fox, 1986).

22 Chapter 1 - Logistics, Life and Simulation

This machine was already sold fully field would be driven by three important assembled and straightforward to operate, factors: (1) the graphics microprocessor with either 4 or 8 KByte of memory, two developed at the end of last decade, (2) the built-in cassette drives and a membrane release of IBM Personal Computer (PC) in keyboard (C.H.M., 2004). 1981, and (3) the development of Visual and Apple Computer would also introduce the Interactive Software (VIS). The first made APPLE II, and Tandy Radio Shack its TRS-80 available a powerful technology; the second desktop computer. Very conveniently, David would make the computer accessible to any Bunnell publishes “Personal Computing”, company, since the price of such machines and a year later around half a million would get near the 4,000€ (of nowadays computers exist in the USA, even if there is money); and the third would make much not yet a unifying operating system, a easier than before the human interface with problem that soon would be solved by Mr this machine. The evolution to follow would Bill Gates. Microprocessors gain power by naturally result from this. Programmers got increasing operating speed and memory focused on developing applications of every available, while using minicomputers some sort, mostly to run on the PC’s operating devote to research on distributed system, the MS-DOS created by Microsoft. simulation (Chandy & Misra, 1979). Minds Spreadsheet LOTUS-123 emerges, as well as seem to open worldwide, the last fascist word processors like WordStar, in the regimens of Portugal and Spain disappear attempt to turn the computer even more from Europe; yet, the Cold War continues, popular. Integrated office software would as well as the Space Race, in a permanent be soon available, somehow imitating what measure of forces between capitalist big companies were doing with their UNIX America and communist USSR. and VMS mainframe systems, like DIGITAL, for instance. And when in 1984 Apple Computer launches the “Macintosh”, whose 1.2.4 The 1980s screen is represented in figure 1.7, people If in 1974 the barrel of crud was jumping were definitely fascinated with its graphic from $3 to $12, in the beginning of this user interface. decade an even more dramatic increase in prices was observed due to the Iranian Revolution followed by the Iraq-Iran war. World crud prices would reach more than $50 per barrel and would stay above $20 during all the period of 1979-1986 (Williams, 2003). Coincidentally, this is the phase described by Nance (1993) as the Period of Consolidation and Regeneration concerning simulation. Robinson (2004), on the other hand, prefers to call this decade the period of Revolution in simulation. Nevertheless, Fig. 1.7 The screen of first “Macintosh”, from (1984) the fact is that the next advances in the

23 Flexible Supply Chain Simulation

It is important to notice that in this automotive industry), managers realized decade the microcomputer is passing from that manufacturing processes had to be the level of expert to the level of ordinary more efficient and leaner17. They answered user, hence the importance not only of with upgrading the old MRP system to the developing programs of general interest but new Manufacturing Resource Planning (MRP also of searching for an easier and more II). intuitive interface. The simulation tools Unlike the first MRP, which basically were also following this trend. ensured sufficient material was available Besides, in parallel with microcomputer when needed in the next workplace, the popularization, hard-line computers running new MRP II would include, in the same powerful operating systems like the UNIX or computer system, information from other the VMS would stay, and a lot of work on departments concerning demand, supply, simulation would be done on them, mainly statistical analysis, forecasting, and so on, when related to big systems from the fields what in fact was turning it more powerful of aeronautics, military or space research. than its predecessor. But also parallel supercomputers15 were The other answer was the change to the being used. For instance, a CRAY16 X-MP Flexible Manufacturing Philosophy, which in running CX-OS, the first UNIX-like operating 1986 was defined by the Institution of system for supercomputers was delivered to Production Engineers, England, as: “The NASA Ames in 1984 (Wilson, 1994). As we proven response of a total facility which will see later on, the United States can serve a volatile market with minimum Department of Defence (DoD), where response time from order input to saleable supercomputers run complex war games, is product using the minimum of working still nowadays one of the most involved capital” (Carrie, 1988). In practical terms, institutions on parallel and distributed however, as noticed by Carrie (1988), many simulation. Most of the attention, however, experts believe that any system which seeks was in that time turned to the popular to achieve certain objectives by a variety of microcomputer, which day-by-day conquers means is a Flexible Manufacturing System more fans. (FMS). At the same time, managers also began With industry facing so many challenges, talking of Just-In-Time (JIT), the Japanese mainly demand increase and variability, pull philosophy practiced by Toyota at least quality (less than 1% of defects was now since the previous decade (Ohno, 1978). acceptable) and the Japanese competition Thus, zero defects started to be the new (meanwhile Japan is number one in the minimum acceptable level for quality, since the Japanese were meanwhile measuring

15 the quality of their products in parts per Supercomputers are usually based on multi-processor parallel digital machines, while common computers use the million scrap, as Goldratt & Fox (1986) single processor architecture. notice in “The Race”, a book published two

16 The fastest computer of that time; its speed was in part due to its “C” shape, that was reducing the time of 17 Actually, Heizer & Render (2001) define the period of propagation of the signals. 1980-1995 as the Lean Production Era.

24 Chapter 1 - Logistics, Life and Simulation years before Taiichi Ohno made available in As other consequences from this decade, English the first secrets of his “Toyota TCP/IP protocol for network communication Production System” (Ohno, 1988), where was inducing countries worldwide to join the Kanban18 plays the major role. the Internet, as well as some companies like Computer-Aided Design (CAD), a kind of DIGITAL and Hewlett-Packard. Object graphical interaction with the computer by oriented C++ programming language was which one could draw the parts on the born at Bell Labs due to Bjarne Stroustrup, computer’s screen and then send that in his attempt to write discrete simulations information to numerical machines, was also like in SIMULA while taking advantage from being used to try improving manufacturing the superior speed of C language. A formal processes; as well as other new concepts, method for representing the dynamics of like Electronic Data Interchange (EDI), an discrete systems was introduced by exchange of electronic documents among Schruben (1983), based on event diagrams partners using message standards between that became known as event graphs. The computers to speed bureaucratic processes; CDROM was born. The 32 Bit microprocessor or even Total Quality Management (TQM), a appeared, at the end of the decade. strategy of management seeking to achieve Managers talk of Empowerment, an the quality in all organizational processes in approach to stimulate the self-help efforts order to improve production and reduce of the poor, instead of providing them with waste, supported by statistical methods. social benefits, or, from the point of view of Japanese JIT, however, in its innocence, management, to give workers greater ruling was the main responsible for all this and resources for better serving customers “occidental” panic, once it seemed in itself and the interests of the business (Wikipedia, a miracle for achieving total quality. Thus, 2004b). America had lost the leadership of one could ask, why did the “occident” not microchips. Civil logistics got practiced, apply these Japanese techniques? But apart mostly focused on fleet management and from the fact this seem an intelligent warehousing, as fleets get larger and their question, an effective answer has never action spread, many times developing into been given, probably because it lives in the international companies, like the Haeger & domain of the proud and absurd. The most Schmidt GmbH19, the Mercantile20 and one can find in literature about this is the others. The speed of materials flow was argument of different cultures… increasing, while lead-times and product Two good books from these times are life-cycles were being dramatically reduced. “The Race” (Goldratt & Fox, 1986), focused The term “Logistics” in published literature on manufacturing, and where an interesting have surpassed the term “Distribution”, see resentment seems directed to the Japanese list of references in (SOLE, 1999) and success of that time, and “Computer (Guedes, 1994), since the tendency was now Simulation in Management Science” (Pidd, the integration of the entire Logistics 1984), a more elegant and neutral text network (Gattorna, 1983), counting with about discrete simulation.

18 The Kanban is a signal to authorize the production of next 19 Former ABX-LOGISTICS, (ABX-Logistics, 2004). bin of material, and it is often associated with a card. 20 Former Maersk-Logistics, (Maersk-Logistics, 2004).

25 Flexible Supply Chain Simulation supply, production, warehousing and As a curiosity, in figure 1.9 is represented delivery, as represented in figure 1.8. the amount of different simulation tools directly referenced in the Ph.D. thesis of Guedes (1994), plus the book “Simulation of Manufacturing Systems” (Carrie, 1988), and the online bibliography of The International Society of Logistics (SOLE, 1999). Obviously, it shows an explosion of tools for simulation during the 1980s.

Fig. 1.8 The idea of “Logistics” as an integration function

In the field of simulation, packages like WITNESS, HOCUS, GENETIK, SIMAN/CINEMA and ProModel were already available by the end of the decade to those who wanted to work with Visual and Interactive Software (VIS), most of them data-driven21. Simulation was already perceived as an interesting decision-aiding tool, principally in manufacturing, even if its adoption was Fig. 1.9 Simulation tools cited in articles, 1960-90 not yet widespread, as Robinson (2004) points out. But also the well known SIMFACTORY (CACI, 1986) was available, as Regarding the rest of the world, the price well as other simulation tools like ECSL, for of crud have finally returned to near $20 the activities, EXPRESS, SIMON, GASP, SLAM II, barrel, Benetton challenging marketing SIMAN, SIMSCRIPT II.5, for events, and campaigns was becoming known worldwide, GPSS/H, SIMULA-P and again SIMAN, for the first ZARA store outside Spain opens in processes, as referred by Carrie (1988). It Porto (Portugal) (Dutta, 2002), and the was, in fact, an explosion of simulation relations between Occidental and Eastern applications, as it can be noticed by blocks were getting less tense, thanks to the inspecting figure 1.9, from the pre-written fatigue of 45 years of Cold War and to the simulation libraries like GASP or SIMON, to moderate approach followed by Mikhail the most fascinating VIS packages of Gorbachev in the USSR. People were bored WITNESS or SIMAN/CINEMA. of constantly living under the fear of a huge nuclear confrontation, with 20,000 nuclear missiles at each side pointing towards the

21 Data-driven tools are those who freed the user from the other side, but soon two new events would need of programming to build the model. change such an atmosphere: the fall of the

26 Chapter 1 - Logistics, Life and Simulation

Berlin Wall (8-Nov-1989), which finally South Korea, demolishing the hegemony of signals the end of Cold War, and the IBM in the field. invention of the Worldwide Web (WWW), in With enterprises naturally enlarging their CERN22, Switzerland, by the physicist Tim domains towards global levels, frequently Berners-Lee (Berners-Lee, 1989), finally with joint-ventures or acquisitions of other signalling the beginning of the Information companies in order to remain competitive, Society. the complexity of the network connecting all these organizations increased, and soon researchers understood it was necessary to 1.2.5 The 1990s focus the attention on those systems as a The nineties begun with all the whole, thus introducing for the first time condiments pointing towards a volatile near the subject of Supply Chain Management future. On one hand, the tensions from Cold (SCM), foreseen as an integrated Logistics War suddenly vanished, leaving people with management, see Ellram (1991), Curran the feeling of confidence about the days to (1991), Towill (1992) and Christopher come again, but on the other a political (1992). As Martin Christopher at that time instability in the East that would soon lead stated, “The Supply Chain is the network of to the Soviet Union collapse and the organizations that are involved… in the emergence of several new countries. The different processes and activities that social and economical recovery of reunited produce value in the form of products and Germany began, while too many things were services in the hands of ultimate changing at the same time. customers…”, and adding, “Supply Chain Although America was still leading the Management is not the same as vertical ration of exports23 in the aircraft industry integration. Vertical integration normally (3x), Japan was already leading in computer implies ownership of upstream suppliers and business machines (7x), communications and downstream customers” (Christopher, equipment (8x), scientific instruments (6x), 1992). Already with a good usage of EDI, the and electricity transmission equipment (4x), recent developments in communications while Germany was leading in the industry were in fact opening the doors of the of drugs (2x), as noticed by (Chryssolouris, organizations to the idea of SCM. 1992). And people were now much more comfortable to sample the products and services that meanwhile invaded the markets, mainly electronic news. The pressure of a global business concurrence was increasing, and soon PC compatible computers were coming into sight from Asiatic competitors based in Taiwan and

22 European Laboratory for Particle Physics. 23 (Exports/Imports). Notice the values inside brackets are approximated. Fig. 1.10 An illustration of a Supply Chain (network)

27 Flexible Supply Chain Simulation

Expert enterprises on Logistics, known as millions seated at home in front of their Third Party Logistics (3PL), would now have TVs. to face the evolving of SCM. Nevertheless, it One year later, the European Union (EU) seems the term SCM was not promptly was birthed by the Treaty of Maastricht adopted by managers and logisticians, and, (1992), succeeding the European Economical for example, Sussams (1992), in his book Community (EEC), and people and goods “Logistics Modelling”, does not yet mention started to move around in Europe without such a term, even recognizing the need of the need of passport or customs checks at integrated Logistics. On the other hand, most of the internal borders (E.U., 2004). Guedes (1994), in its Ph.D. thesis on This also accelerated the establishment of Logistics simulation, at Cranfield University Pan-European enterprises, and again the (U.K.), the single reference to SCM was need for improving the Logistics approach. linked to Christopher (1992). Anyhow, the tendency was about to invert. Simulation, once again, was following the trends of the systems, and taking the most On the other side, WINDOWS 3.0, the first possible advantage from the evolution of graphical Microsoft multi-windows system, computers and networks. In these times of released in 1990, was turning the PC more the beginning of the decade, Law & Kelton user-friendly and attractive than before, (1991) were presenting as basic models the pushing millions of minds to think on single-server queuing system and also the acquiring one. Prices of microcomputers inventory system, which they modelled with were falling, and of course the number of general-purpose languages like FORTRAN, programmers was increasing at the same PASCAL and C; but as well they were rate. The Internet was also entering the already mentioning the interesting advent domain of ordinary enterprises, and the of distributed simulation, an active area of Worldwide Web (WWW) was step by step research with pioneers like Chandrasekaran connecting more institutions and computers & Sheppard (1987) and Chandy & Misra worldwide, for the first time giving the (1979), in which several computers were chance of sharing information in a very linked in a network to run parallel individual attractive and graphical quality, which soon tasks, therefore reducing the overall time of would be used for anything from advertising the simulation process. Actually, even and email to file-transfer or online instant Artificial Intelligence was slowly entering messaging (chat). The world was getting the field of Simulation, by introducing the denser, and seriously keen on information. A concept of Distributed Simulation Agents24 short war (the UN-Iraq conflict) became an (Graham & Wavish, 1991). impressive televised show, not only of the Using a completely different approach, military power of United States forces but Towill (1992) is studying certain effects on also of its ability on Logistics, by which thousands of soldiers, guns, boats, airplanes 24 An Agent is a component designed for distributed systems which include not only attributes and methods, as the and land vehicles could be send to the classical class, but also a certain ability to learn and make Persian Golf in a very short time, in front of decisions on its own. It is able to communicate with other Agents asynchronously. Nwana (1996) is considered the “Bible” for the Agents.

28 Chapter 1 - Logistics, Life and Simulation the Supply Chain behaviour using System Dynamics Simulation, an approach directed to continuous mathematical modelling based on difference equations and causal loop diagrams (Forrester, 1960). Brito (1992), on the other hand, using event- scheduling, explores CAD techniques for configuring VIS warehouse dynamic models, a new approach for warehouse design, while Sussams (1992) makes use of static cost functions for modelling processes associated with transportation, warehousing and other

Logistics issues. Later, Guedes (1994) would also use static functions to model facilities Fig. 1.11 Classification of simulation tools, based on and transport costs in the Supply Chain, Pidd’s (1992) proposal, and irrespective versatility and developing a VIS simulator for Logistics simplicity on usage Strategy Planning (LSP) centred in the facility location problem. Obviously, VIM is more recent than VIS, and naturally follows the trend of software In the field of Petri nets, Jensen (1992) applications designed to run on modern was publishing “Coloured Petri nets. Basic multi-windows operating systems. Anyhow, Concepts, Analysis Methods and Practical an example of a VIS system is shown in Use”, and Silva (1993) his “Practice of Petri figure 1.12, from a simple model made with nets in Manufacturing”, between others. SIMPROCESS, the process-oriented support Nevertheless, it was with Michael Pidd of SIMFACTORY. that an attractive classification of tools for discrete simulation had been proposed, with the seven different classes presented in figure 1.11. In this figure, these classes have been organized in the form of a graph, trying ordering them by the versatility and the simplicity of usage. It is important to notice that Pidd makes a clear distinction between VIS systems and Visual Interactive Modelling (VIM) systems, since the first run the simulation as an operational game, watching and interacting, while the second uses a Graphics User Interface to build the model as well as for visual interaction (Pidd, 1992).

Fig. 1.12 Modelling a simple manufacturing process with SIMFACTORY II.5, a VIS system

29 Flexible Supply Chain Simulation

In the middle of the decade, America had encyclopaedias, stock markets, flight and recovered from the crises and, although not train timetables, and much more. Virtual dominant, it was again seen as the stronger realities like the Worlds Away, a graphics and the most competitive economy of the virtual world created by Fujitsu where “global” world. Nevertheless, the invasion people assume a figure built by their own of global markets by cheap PC compatible and take for granted certain unrealistic computers produced in Asia and the release powers, capture the interests of many in of Microsoft WINDOWS 95 were the two the community, as do virtual games. main facts that would provoke another burst Information Society was expanding as a in all fields of knowledge. Everybody would boom, due to the recent advances in the benefit from this. In Science, Engineering, field of Information Technology (IT). Medicine, Business, Sociology, Architecture, Arts, etc., the boom comes naturally from Regarding Management, the concept of the dramatic fall of the computer into the Enterprise Resource Planning (ERP) is hands of common institutions and citizens. starting to replace MRP II, as the vision Such a powerful machine could now be concerning manufacturing processes also useful for almost anything, from the gets wider and extends to the entire integrated-office software commodities to enterprise process. The new ERP will the design of new products using CAD, as append the MRP II with a sophisticated set well as to access, share and present high of software tools specially designed for each graphics information when connected to the enterprise department, where simulation as “global world” of Internet25. For the first well as optimization can be incorporated, time, each user had the world at his and finally integrated by the main system’s fingertips. Future benefits or ruins would application. SAP26 is one of the most popular depend on how users would use their distributed systems of this kind (see also fingers. It was not as much a technologic EPICOR27, PeopleSoft28 and others29). The advance than a kind of social distribution of enterprise maintains the original push technology. Coming from the top, the philosophy of MRP II, contrasting with JIT, computer had touched the ground and but it is now supported by a sophisticated reached the public domain. software environment for decision and The human power of computation is thus planning. spreading to a larger extent of brains, But not only the manufactory face was to precisely at the same time communications change thanks to the new advances, also are getting global. The world turns more the relations with last customers, which compact, and time compresses. Internet could now be measured in millions, cafés open in Tokyo, London, Berlin, Paris, connected to each other by the Internet New York, etc., and online providers like and asking more and more products with AOL, CompuServe and MSN, give the user their own specific tastes. The Mass the possibility of accessing a wide range of

“virtual” services, like news, forums, 26 http://www.sap.com:80/index.aspx 27 http://www.epicor.com/www/ 25 Apart from the fact Internet is just the support for the 28 http://www.peoplesoft.com WWW, the two concepts have merged in usual language. 29 http://www.2020software.com/

30 Chapter 1 - Logistics, Life and Simulation

Production Era was giving the place to the By the end of the decade, and apart from Mass Customization Era (Heizer & Render, some memories of a fast NATO intervention 2001), and customers become the “precious in the Balkans in 1995, with the United thing”. At the same time, e-Commerce start States leading the mission and bombing emerging, with some of the important Belgrade without the permission of the companies already presenting their products Security Council of the United Nations, the by Internet to the public, in a kind of a world seemed relatively calm on its growing digital catalogue from which customers can consumption of products. For some months, directly order their products, and in certain China had frozen WTO talks due to the cases even customize them in accordance to mistakenly bombing of the Chinese embassy their tastes. in Belgrade (CNN, 2001), but globalization Besides, with the international tariffs was spreading and the global network of reduction due to the endorsement of World international enterprises increasing in Trade Organization (WTO), the signatory complexity, and even China was giving countries borders become more permeable obvious signs of the intention to open its to foreign companies, and once again the gigantic market. Common people in many global expansion is stimulated. Managers occidental countries could already afford talk of Learning Organization as the idea of not only colour TVs, high-fidelity sound easing and promoting the flow of relevant systems, mobile phones, and all kinds of information between partners about their appliances, but also personal computers particular experiences. Enterprises try to equipped with digital sound blasters and meet the International Quality Standard ISO prepared to run high graphics video games, 900030, while the idea of agility reaches the as well as with telephonic connections to domains of Manufacturing and the Supply the Internet, and many other “interesting” Chain. Agile-Manufacturing, as explained by devices seen by everyone as authentic Hopp & Spearman (2001), is the practice of pleasures. Mainly due to the exponential using equipment and layouts in a way to increase of concurrence, new products, or enable accommodation of new products, at least new versions of the products, are change in volume and shifts on products released into the markets in cycles of just a mix. Agility in the Supply Chain refers to few months, while demand figures get the ability of the Supply Chain to thrive in extremely volatile. Such a high level of turbulent market conditions, as stated by consumption, on the other hand, seemed to Harrison et al. (1999); see also Goldman et make people more and more anxious for al. (1995), who give special attention to consuming, and if in the late 1960s a customer value and cooperation between growing number of persons was complaining partners in the Supply Chain31. about the demise of family, now a growing number of persons started to complain 30 A standard created in the later 1980s by the International about the general demise of “values”, being Standards Organization (ISO), accepted by many countries and criticized by some managers for being little practical. by partners. At the same time, the concept of cooperation 31 As the Supply Chain is a network usually belonging to results naturally from the composite “geometry” of the more than one company, ultimate customer’s satisfaction problem. Cooperation also brought to light the problem of naturally becomes a measure of performance easily accepted trust, in terms of SCM.

31 Flexible Supply Chain Simulation personal or cultural, since everything menu-based user interface, and some even seemed to start being reduced to products, including a three dimensional (3D) view on economics, and ambiguous politics, as well the model and on the simulation. As as pointing out the dangers of “globalizing” mentioned by Robinson (2004), commercial such a simplistic theory of world32. VIM tools like ProModel, ARENA, QUEST, Taylor II, AutoMod, AweSim, Micro Saint In what concerns simulation, the usage of and Enterprise Dynamics had been graphic compilers by most programming presented along this decade at the “Winter languages and the popularization of Object Simulation Conference, USA”, one of the Oriented Programming (OOP), provoked a major international events on simulation general trend of adapting simulation tools advances. to these kinds of interface and paradigm, as Anyway, also simulation languages and it is the case of the VIM system shown in other simulation related tools have been figure 1.13. developed, like MODSIM, for example, an Object Oriented simulation language in use since the beginning of the decade (Herring, 1990), or C++SIM, a simulation library with SIMULA-like routines, including random number generators, queuing algorithms and thread package interfaces (Little & McCue, 1994); or even SimJava, a simulation library written in Java, allowing graphic animations (Howell & McNab, 1998), and much more. A very useful online source of simulation tools can be visited at Rizzoli (2004).

Of course most of these tools can be used to build models of Logistics systems and

Fig. 1.13 VIM warehouse simulator, from Feliz-Teixeira Supply Chains, as most of them are general & Brito (1999) propose simulation applications, unlike the one shown in figure 1.13, which is strictly This trend led to the proliferation of a directed to warehouse modelling. Analysts new brand of high graphics VIM systems, at IBM Research, for instance, were using most of them offering a very comfortable MODSIM language to model and analyze the European PC business due to the decline of 32 The Anti-Globalization Movement has a wide support IBM sales in Europe (OR/MS, 1996). The around the world, mainly in central Europe and Canada, as Supply Chain was represented by blocks well as in South Korea, Japan, Brazil, and recently in Africa and even in America. It is more against what is being based on objects and later made to run in globalized than against the natural process of globalization, SIMPROCESS. Although the alleged saving of and defends the respect for diversity of geographic regions and cultures, and the planning of the development in terms $40 million per year does not contribute of their own natural resources and believes, instead of very positively to the simulation believers, simply imposing the neo-liberal paradigm by installing there since later IBM lost the PC market, the fact companies from rich countries, see (Wikipedia, 2004c).

32 Chapter 1 - Logistics, Life and Simulation is that this was a very interesting approach any induced changes on materials flow to a practical problem, and deserves to be (Tecnomatix, 1999). studied, see Feigin et al. (1996). On the other hand, also the technology of Agents was being used by Fox et al. (1993) in the development of an Integrated Supply Chain Management System, an architecture based on interactions between Agents to reengineer operations in the Supply Chain in order to make it react quickly to changes. Much of this belongs to the trend of what was starting to be called the “Virtual

Enterprise”, or the “Virtual Supply Chain” Fig. 1.14 eM-Workplace simulator, from Tecnomatixâ (Chandrashekar & Schary, 1999). Agents were in this case used for simulating “cooperation” and “negotiation” between certain activities in the Supply Chain. 1.3 The present Despite the reflections of Günter Müller et The arrival at the 21st century is getting al. (1999) about the risks of these kind of so complex in terms of diversity of trends, Multi-Agent Systems for Supply Chain systems and believes that the use of coordination, many researchers were at the classification becomes complicated. It is time focused on this approach, as in the time, perhaps, to substitute the classical case of Eymann et al. (1998) or Chen et al. deterministic approach for a more holistic (1999) or Bruzzone & Mosca (1999), and perspective on systems, as it happened in others. the 1930s in the field of Physics with the Using other kind of approaches, Zeigler et introduction of Quantum Mechanics33. al. (1999) were at the time proposing to What in terms of technology is driving adapt the previous IBM Supply Chain this generalised complexity are the high- Analyzer to the more recent DEVS/CORBA speed communications, using fast optical- distributed simulation environment, while fibres and satellites; the powerful digital Kemers & Merkuryev (1999) were using microprocessors (in the early days of 2000 a ARENA to simulate a case of Logistics microcomputer could already use a clock Strategy Planning (LSP) for some IT speed of around 500 MHz, 128 MByte of RAM distributors in the Baltic countries. Besides, and 2 GByte of hard-disk-storage); the ATS Automation-France was choosing globalization of information, with the Tecnomatixâ eM-Workplace software to massive spread of WWW and Internet (8.2 help design its systems. In figure 1.14 is billion emails were sent worldwide in 2000, shown a typical 3D view by this tool, which provides detailed layout design capabilities 33 Quantum Mechanics is not interested in a deterministic knowledge of a certain process, but on the “modes” in which and materials-flow simulation in a single such process can be found, and in the probabilities of environment, enabling users to evaluate finding it in each of those “modes”. The father of this performance and logistics and the effects of approach is considered to be the German physicist Werner Karl Heisenberg, 1925.

33 Flexible Supply Chain Simulation and the WWW was estimated to surpass 1 on the medium. And this seems to be a billion indexable webpages); the advances serious issue that affects most systems, in nanotechnology (IBM makes experiments mainly when the regimen of their utilization with a microprocessor based on a molecule is made to approach limits. with five fluorine atoms); the advances in Although for many people the need for genetics (the human genome have been facing this feedback problem represents a completely decoded); as well as the manned serious obstacle to development, already a travel to Mars, somehow of controversial substantial number of modern enterprises interest, at the moment. In addition to this, and institutions are treating the issue as there is the general decrease of transport part of their Total Quality Management and communication costs. (TQM) policy, moving towards the modern “green” state of mind. Somehow following this same sort of 1.3.1 Life and SC management logic, Supply Chain Management (SCM), for Defying such wish of expansion, however, instance, struggling to handle so many kinds resources of oil, steel, and some other of products, resources, times and interests crucial raw materials happen to be too few on the current turbulent markets, instead of to satisfy the ever increasing demand, with the “old” concurrence between partners, is the steel, for instance, not being enough in already recognizing the value of cooperation Earth to produce a car for us all and for and trust, thus understanding that there each Chinese citizen. Besides, too much will be a future reflex in its universe pollution in industrial districts and towns depending on the actions taken at the and an excessive level of carbon dioxide in present. Concurrence, as it is argued, is now the atmosphere are pointed out by present between Supply Chains, instead of scientists as responsible for the climate between partners. But the problem is not so change and the rise of sea waters, as well as simple anymore, since partners can also for certain diseases and the increasing choose to belong to more than a single power of storms, naturally leading to the Supply Chain… Perhaps SCM is becoming spread of the idea of Sustainable more an art than a science, as Richard Saw34 Development, soon defined by the United declared during one of my visits to Nations (UN) as the “Development that Cranfield, obviously enlightening the high meets the needs of the present without complexity of the processes involved in such compromising the ability of future discipline, but it also means we are reaching generations to meet their own needs” (UN, a wider view on systems behaviour. 2004). Unlike the 20th century, in which most A very simple strategy on facing such processes were seen as being linear and complex behaviour can be to decline without any substantial feedback on their understanding it and trust in systems to surrounds, the 21st century promises to be subject to recognizing that processes are 34 Richard Saw is at the moment the director of executive many times nonlinear and in certain cases development of the Centre for Logistics and Supply Chain with a subtle but strong feedback influence Management (CLSCM) at University of Cranfield, UK. email: [email protected]

34 Chapter 1 - Logistics, Life and Simulation sooner or later regulate themselves through To-Stock (MTS), where parts are produced each other, and so our job might only be to based on demand forecasts and the ensure the best flow of what flows between customer is served from the stock, or the systems. In that sense, the present idea of expensive Engineering-To-Order (ETO), with visibility is seen as a means for achieving emphasis on the design of a solution such transparency, thus leading to the best specifically tailored to a customer order, possible performances in practice. This, of the preference is obviously directed to the course, is just an approach, a hypothesis approaches of Make-To-Order (MTO), which which considers all systems intended for is very close to the JIT concept, as well as good, and therefore neglects the human to the Assemble-To-Order (ATO), which is tendency to pervert, as well as the amount the most important representative of our of time wasted due to it, for example. Mass Customization Era. In reality, these Nevertheless, in SCM this is believed to approaches tend to locate the Supply Chain result also in a natural improvement on decoupling-point35 at different places, as decisions and follows the trends of EDI, ERP, figure 1.15 suggests. cooperation, virtual enterprise, etc., as well as the idea of pushing the demand information upstream on the supply network to allow reducing inventories and minimize the amplification known as Bullwhip Effect (Forrester, 1960). Of course, this is in the direction of JIT, and can well be seen as a good support for e-Commerce. These trends are at the present defended at most schools of management, anyhow, in practice all these “theories” constantly face a serious challenge related precisely to perversion. Such challenge is called trust. Fig. 1.15 Different positions of the demand decoupling- It seems the complexity of our days has point, inspired on Dong’s (2001) Ph.D. thesis brought us to a very interesting situation: either we become trustable to each other or As Dong (2001) notices, the MTS locates we will have no chance to manage anything. this point at the finished-products seller In that sense, perhaps the answer must also (retailer), the ETO locates it at the raw- pass by what Hopp & Spearman (2001, pp. materials level, while both ATO and MTO 43) call the “need of managers to embrace locate it somewhere between those detailed technical knowledge and not only extremes. ATO is usually at the level of maintain a love affair with marketing and semi-finished products, while MTO is at the finance”. level of components. The same author

35 “The decoupling-point is a standard term given to the Focusing a little more on manufacturing, position in the materials pipeline where the product flow today, even if the value of other approaches changes from PUSH to PULL” (Mason-Jones & Towill, is still recognized, like the traditional Make- 1999).

35 Flexible Supply Chain Simulation associates with each of these approaches mobile-phone communications to trace the following primary objectives: to MTS the products on delivery, code-bar references, responsiveness; to ATO the customization; as well as the almost instantaneous online and to MTO and ETO the lean production. information shared by partners through An extensive work about the importance of WWW sites, place the present information the position of the decoupling-point in the potentials much above the simple EDI serial Supply Chain has been made by Mason-Jones transmissions from the 1990s, thus allowing & Towill (1999), who also have studied the a better synchronization of all these new Bullwhip Effect on inventories by applying processes. System Dynamics simulation to the Beer Cases deserving a special reference in the Game36, see Mason-Jones et al. (1997). literature for adopting this JIT “style” are, But with the ever increasing demand of among others, DELL Computers, Harley- products in the global market, also other Davidson, Matsushita, Volkswagen, and, as less common approaches are being used in the primus inter pares, ZARA37, which is the manufacturing/distribution processes, almost considered in itself a paradigm. like the merge-in-transit, the cross-docking, Assuming mostly a philosophy of reacting or even other schemes nowadays practiced instead of predicting, these companies are by DELL or UPS, for instance, in a kind of made to operate with low inventories and MTO in which the last manufacturing steps high rotation of stocks, while generating are completed while in movement to the high Return On Investments (ROI)38. customer. All this is in the trend of a flexible, synchronized and “demand driven” By studying the literature on the matter, response to customer orders, and some one easily realizes that nowadays operations researchers claim it as the seeds for the tend generally to be driven by the following future “moving factories”, of which the management concerns: “factory ship” would be a particular case (Hewitt, 2001). The new concept of Demand Lean React/Predict Pipeline Management (DPM) is even being suggested by Hewitt (2001) to replace SCM, THE PRESENT due to the continuous-like flow of products (as in a pipeline) observed in many Feedback Synchronization distribution processes of our days, and more recently also defended by firms like the MarketBridge Corp. (Furey, 2003). A higher Fig. 1.16 Current concerns in Operations Management competence in manufacturing and in assembling techniques, the usage of digital Or, in summary, companies are thinking lean to minimize the waste on materials,

36 The Beer Game was developed at the Massachusetts Institute of Technology (MIT) in the 1960s in the lessons of 37 Fashion clothes ZARA is a company from INDITEX Sterman (1992), and it is a lineal chain including Retailer, Group, which has headquarters in the town of La Coruña, Wholesaler, Distributor and Factory. The game is played by Spain. Apart from its success, ZARA is known for rejecting hand and between groups. For more information please see: any kind of advertisement: http://www.zara.es http://web.mit.edu/jsterman/www/SDG/beergame.html 38 ROI = (Revenue – Costs)/Assets

36 Chapter 1 - Logistics, Life and Simulation processes and organizational; choosing from openly opposed by the world’s public react or predict to optimize inventories and opinion as well as by states like France, operations costs, without affecting service Germany, Russia and China. levels; thinking synchronization to reduce Thus, what in terms of economy, politics, the waste of time and resources while social or general development will result interfacing with each other and with the from this in the years to come, one has to customer; and thinking feedback in order to wait to see. It would be unwise to search for minimize future impact on the medium and a trend while the system is still oscillating. on the enterprise itself. This starts to seem Nevertheless, such instability was also a more in harmony with Leonardo Da Vinci’s confirmation that the mechanics of a thought “everything connects to everything “globalized” world are much more complex else”. than expected, since actions will more and more reflect on the entire globe. A problem It was, however, in this kind of global in Asia can produce a storm in Beijing or business atmosphere that in 11SET2001 New New York, and the opposite is also true. York lost its most emblematic symbols of globalization, the “Twin Towers” of the But life continued. Meanwhile, China was World Trade Centre. The deadliest, the finally accepted as member of the World most implausible and destructive terror Trade Organization (WTO) at the end of attack of history has brought down those 2001, and the new currency of European giant skyscrapers by crashing into them two Union (EU), the EURO (€), was adopted in commercial flights. On the entire globe January 2002. Even under the uncertainties minds paused, confused, terrified and resulting from the Iraq war, climate apprehensive while following the scenes on change39, as well as the new crisis of oil that TV. Many analysts agreed that since that brought the crud barrel higher than $65, day the world would never be the same global markets continue developing, mainly again. with the consolidation of the economies of At the present time, people and business European Union (EU), which meanwhile was around the world are still adapting to the enlarged to 25 members, and the fast generalized instability provoked by such economic growth of China (around 9% a event, which in the first approach have year). At the same time, the mobility of leaded to a significant disorder in many people, merchandise and business inside the business areas, as well as to a global EU greatly increased, from the outset giving political insecurity due to war. First, it was the effective idea of a “unified” and opened the war of Afghanistan, to remove the continent. In these times of the Information Taliban regime from power, and about one Society, easier contacts, more sources of year later the military invasion of Iraq by American and British forces, also to 39 In reality, some scientists use to claim nothing was overthrow the defying regime of Saddam happening deserving to be called “climate change”, but the truth is that in many places the atmosphere have reached Hussein. This last action, however, was insupportable levels of degradation, and even the Pentagon since the beginning classified as illegal by is at the moment seriously concerned with the issue, see the United Nations (UN), and has been (Townsend & Harris, 2004) or (Stipp, 2005).

37 Flexible Supply Chain Simulation information, more variety of solutions and Flexibility, Agility and Pipeline Management points of view, easier international are concepts trying to respond to such a travelling and less bureaucracy are leading complexity as well as to the turbulence the societies to a transparency which also resulting from it. However, there is still a enables students, teachers, managers and lack in metrics to effectively describe the scientists to often take part in international Supply Chain performance, since different projects for common interest. companies are usually involved in the same process of supply (Lambert & Pohlen, 2001). But a worldwide sourcing of materials has also turned the procurement processes more 1.3.2 IT and Simulation complex, and managers many times face the With the popularization of powerful high- need for simpler approaches to effectively memory/high-speed microcomputers, which act. Vossebeld (2002), for instance, notices meanwhile also got extremely compact and that the worldwide concurrence of suppliers started to incorporate direct interfaces to greatly increased the need for managing the Internet; as well as due to the wireless inbound Logistics, and, adding the difficulty communications boom and the explosion of of calculating total Supply Chain costs when WWW oriented services and tools, transportation is frequently outsourced to Information Technologies (IT) have fast 3PL groups, he argues it is important to dominated our lives. A light-and-portable simplify the SCM by using software based on machine like, for instance, the one shown in a more holistic perspective. To this, one figure 1.17, with 700 MHz of CPU speed, 256 must add the reverse Logistics concerns, MByte of RAM, and 20 GByte of hard- related to the recycling of unused stock and storage-disk, and access to Internet via LAN the traceability of products (Ritchie et al., or telephone, is nowadays familiar even 2000), as well as to the need of handling an among students. increasing rate of product obsolescence (Blumberg, 1999) which can have serious environmental and economic implications, as it is the case of the end-of-life of computers (Knemeyer et al., 2002). A good text linking reverse Logistics with products life-cycle40 can also be found in Tibben- Lembke (2002).

On concluding, it seems that the current Fig. 1.17 Fujitsu-Siemens LifeBook BSeries (from 2002) trend of transparency reduces the friction between the elements participating in the In truth, not only business is searching for processes, but at the same time reveals a agility. Also people do. And IT seems to much more complex world to be managed. deliver this “chance”. Documents from the most important libraries in the world, public

institutions, magazines, journals, etc., are 40 A Life-Cycle Diagram is commonly used to represent the potential sales in the product phases: Introduction, Growth, now available “online”, as well as a huge Maturity and Decline.

38 Chapter 1 - Logistics, Life and Simulation sea of “virtual” companies dedicated to methods like Bond Graphs (Paynter, 1961) every sort of business. In this “virtual” or Neural Networks (McCulloch & Pitts, world, where the homologous of book 1943). The modelling approach is usually became the e-Book, the commerce became chosen in accordance to the school of the e-Commerce, and the business the e- modelling, the ability of the analyst, and Business, people are interfacing with one the type of system in study. In figure 1.18, another and with institutions through email the major topics at the EUROSIS41 European and online messaging, plus using WWW sites Simulation and Modelling Conference, as outlets from where other more direct Naples 2003, are presented, which gives a information can be obtained, including the reasonable idea of the diversity of article download of files of all kinds, from plain subjects offered in a single conference of text to high fidelity sound and digital video. simulation of nowadays. Today, anyone can work on an isolated mountain and keep contact with the entire world, by means of a computer connected to a telephone line. Companies and institutions are also exploring all these opportunities.

In this atmosphere, simulation is once again following the trends of the epoch, but playing an increasingly significant role in it each time. In truth, people nowadays are building models of almost everything (yet not always with accuracy, as frequently they are built by the inexpert on modelling) as well as facing direct contact with them Fig. 1.18 Major topics at the EUROSIS 2003 European Simulation and Modelling Conference, Naples, Italy on the Internet, where Agents are specially busy trying to capture users profiles, as well Visual and Interactive Modelling (VIM) is as at bank cash machines (ATM), computer already an interface commonly used in software or even cybernetic toys. many simulation tools, specially when they Also ERP, for instance, is supported by a are directed to industry. Also, the Object collection of simulation packages tied to Oriented Paradigm (OOP) became the most algorithms or heuristics of optimization attractive programming approach, due to its which then output to other packages for modular structure and advantages of enterprise management. But models are hierarchy and inheritance. At the same also used in projects and studies of bridges, time, visual effects have also been brought vehicle tests, urban traffic flow, disease to simulation in its most perfect technology, contamination, human behaviour and human leading to the high quality and fast 3D performance, and several other matters, in a diversity of approaches that extends from 41 the simple numerical analysis to complex The European Simulation Society, a division from the European Technology Institute. http://www.eurosis.org

39 Flexible Supply Chain Simulation graphics currently seen in virtual reality Blocks and add-ins for optimization and games and interfaces. This trend, however, decision automation are also sometimes even being very attractive and surprising to appended to these integrated commercial most people, can as well be considered an simulation packages (in ERP systems, for element with which often the real example), simplifying obtaining filtered consistency of the model behind such results from the simulation, instead of raw excellent graphics is obscured. At the same outputs many times difficult to interpret. time, since only in the end of a simulation But as those facilities already act like process any serious quantitative information “filtering” the real results, they also impose can be retrieved from the model, the on the analyst a certain mode of looking at present tendency is to look at 3D graphics the output data, that in certain cases can simulations as excellent tools for marketing, be damaging for a critical spirit of analysis easy communication between the modeller and, at the same time, induce easier but and its “clients” (Waller & Ladbrook, 2002), erroneous conclusions. Hopp & Spearman and, being the model consistent, to teach (2001), for instance, upon referring to ERP and train personnel and, probably, to gain software packages, defend that “the rise of some qualitative knowledge. Professional such monolithic software packages which flight simulators, for instance, are perhaps purport to encapsulate “best practices” the most notable machines based on virtual may prove to be a giant step backwards in reality, at the moment. terms of managers better understanding Another bet of commercial simulators is their practices”. interfacing with other applications as a way Many different kinds of algorithms and of better integrating the simulator in its heuristics can be applied for this purpose, environment of “work”. This means offering but, in general, as Robinson (2004) explains interfaces with data-bases, CAD systems, (please see figure 1.19), “the problem is packages for analysis or graphing as similar to that for any mathematical spreadsheets (usually the MS-EXCEL), etc., optimization problem. Given an objective or even the ability of exchanging function (expressed in terms of the information with distributed applications, simulation outputs), the aim is to find the which can be spread through the Web42. XML optimum value of some decision variables data format is one of the most interesting (simulation model inputs), subject to a set contributions to this kind of interfacing. of constraints (allowable range for the However, the consequence of all this is not simulation model inputs). The difference necessarily more reliable simulations, but from mathematical optimization is that no mainly more comfortable processes of algorithms exist that guarantee an optimal simulating and, in principal, less time solution will be found”. consuming on configuration and analysis. Yet, the simulation process lives in the core of the system, and strongly depends on the talent of the modeller.

42 Nick name commonly given to the WWW.

40 Chapter 1 - Logistics, Life and Simulation

out(k) MicroSaint, SIMUL8, or even CPN/Tools, out(3) among others. out(2) out(1) All these tools are user-friendly OOP Objective standing alone applications designed mainly Function to run in single processor computers. They

MODEL usually communicate with other peripheral “Optimum” applications by means of data files, Dynamic in(1) Data Exchange (DDE), or even Object in(2) in(n) Linking Embedding (OLE), but the process of simulation is, in itself, sequential, not Fig. 1.19 A generalised simulation model followed by an distributed. CPN/Tools, however, can also optimization module, inspired by the comments of Robinson easily be used in a distributed environment, (2004) as Coloured Petri nets by essence provide a

framework for the construction and analysis Apart from this, powerful simulation of distributed and concurrent systems packages are currently used in industry with (Kristensen et al., 1998). very good performances, as is the case of eM-Plant, directed to manufacturing and business processes, which is used by BMW, Boeing or Philips, for instance (Fig. 1.20).

Fig. 1.21 A general view of CPN/Tools, a multipurpose simulation application (free software, can be licensed and downloaded from: http://www.daimi.au.dk/CPNtools/)

Anyhow, as we will see later, the Supply Chain simulator developed in the aim of this Fig. 1.20 A general view of eM-Plant simulator, from work was based on the OOP sequential Tecnomatixâ (http://www.emplant.de/simulation.html) technology, which at the moment is the

most popular modelling technology in the In this same level of integrated OOP industry, probably for being powerful, technology, one can also refer to the cheap and highly stable. Excellent OOP WITNESS, used by Virgin Atlantic or compilers are also spread to the community Volkswagen; the ShowFlow, developed from of programmers. Taylor II system; as well as AutoMod,

41 Flexible Supply Chain Simulation

But the same cannot yet be said about Web-based modelling, anyhow, can also some other interesting approaches like the be seen as a collaborative network from Agents, or the Web-based simulation, or where users would download basic blocks of even about the Distributed or Parallel simulation, and then build more complex simulations. Agents, as a trial of merging models. Yet, little interest seems to exist in Artificial Intelligence (AI) into distributed the industry regarding this idea, probably objects, see Nwana (1996), are still under due to the difficulty of finding reliable discussion concerning the advantages of and models and to the instability in security even the need for control. Since these while handling such resources in an open elements can be considered more free for network like the Web. Integrated ambiences action than the simple objects, and are said for modelling and simulation appear to be to exhibit the ability to learn, Agents the preferable approaches for most naturally tend to impose a higher degree of industries of today. uncertainty in the simulation process, what often complicates the interpretation of In what concerns Distributed and Parallel results and even the validation of the simulations, the central value is generally model. Perhaps due to this aspect, this the computational power that emerges from technology is mainly used in research and to these solutions. At least while the Quantum simulate systems where the dynamics Computer is not available — you can follow emerge from the interaction between it at Wikipedia (2005). Therefore, these entities of the same kind, as, for instance, approaches are mainly used in projects in artificial life studies, self-organizing involving large scale computing or high structures, fractal mathematics, complex performance simulations, like the build and systems, etc. Their advantage seems to test of cosmological models, simulation of reside in the aspect of interaction. See also molecular motions and interactions, the PhD thesis of Travers (1996), presented atmospheric and climate behaviour, as well at MIT. as in military and space projects. A fast manipulation of high level mathematics and Web-based simulation, on the other huge amounts of data are essential to these hand, basically focuses its interests on using cases, rendering the power of processing common WWW interfaces to access remote fundamental. simulators, in order to configure, run or Since long ago these architectures are even acquire results from them. These being used by the scientific community, but interactions are frequently done by means lately they got more visible due to the of a Web browser (Lau, 1997), the same interest revealed by the United States kind of technology used in the virtual Department of Defence (DoD) on proposing enterprise. Distributed users can therefore a standard architecture to develop and run interact with the same simulation model. distributed simulations with high level of The online MIT Beer Game (MIT-FSCI, 2002) interoperation and maximum component is a simple but good example of this type of reusability. The High Level Architecture approach. (HLA) has resulted from this, see (DoD/US, 1997). More recently, as Robinson (2004)

42 Chapter 1 - Logistics, Life and Simulation notices, an international standards group known as Beowulf Clusters43, seemed to based at Brunel University (HLA–CSPIF), is start to turn the attention to these powerful also looking to export the same idea for solutions, starting at research groups and commercial simulation software. Names of universities. The recent interest of DELL relevancy on this kind of simulation are, Computer in these machines, who in 2002 between others, Fujimoto (1998), related was already testing clusters as an option for with the distributed time management in high-performance computing, see Shankland the HLA/RTI framework; and, on promoting (2002), as well as the selection of these kind the spread of distributed simulation to the of machines to perform advanced weather commercial sector, Zeigler et al. (1999), modelling at the Universal Weather and who explored the distributed DEVS/CORBA Aviation, Inc. of Huston (linuxHPC, 2003), environment and even have presented the seem to suggest that supercomputing is basis for a distributed Supply Chain probably preparing for soon approaching the simulator; and Sudra et al. (2000) as well as industry. Besides, the release of Microsoft Taylor et al. (2002), who preferred the dot-NET framework can also be seen as Generic Runtime Infrastructure for another indication of the spreading trend of Distributed Simulation (GRIDS), which they distributed computing… also have proposed for distributed Supply Chain simulations. Between GRIDS and CORBA, however, the difference is mainly in the method used for time synchronization between components. CORBA uses a master- slave relation, while GRIDS uses what they call ThinAgents. On the other hand, Jensen (1992) and his research group at University of Aarhus, Denmark, are the central references on Fig. 1.22 The AVALON, a Beowulf Cluster developed at parallel and distributed simulation with Los Alamos National Laboratory, USA Petri nets, who recently were also adding CPN/Tools to the simulation tools used by NOKIA (DAIMI, 2003). This technology is also In terms of modelling languages, the very popular in several German universities, more relevant reference appears to be the and meanwhile has spread to Italy, Spain, Unified Modelling Language (UML), which USA and even Australia. proposes building the models using a standard graphical representation (OMG, Although most of these distributed and 1999) very much inspired by the well known parallel simulations traditionally required activity diagrams, and similar to what is very expensive workstations or distributed computer systems, since the end of the last 43 Group of computers connected in network to perform parallel and distributed tasks. Linux running in common decade the attraction felt by people for commercial computer machines is nowadays used to build building cheap supercomputers made of these supercomputers at extremely low prices. Take a look several ordinary computers, which become at AVALON, from Los Alamos National Laboratory (USA): http://cnls.lanl.gov/avalon/.

43 Flexible Supply Chain Simulation already done since long ago in Petri nets, for instance. In that sense, UML is essentially a proposal to try to unify the practice of representing and developing models in a certain structured way, which can perhaps be an interesting cause to force analysts to express their ideas in the same form. But UML is mostly for model description, which comes before model implementation. For some interesting texts concerning this matter please see Fowler & Scott (1999), Arief & Speirs (2000), and, for instance, Barjis & Shishkov (2001) and DAIMI

(2003). Fig. 1.23 A simplified tree of the simulation of our times On concluding, the field of simulation is nowadays a complex tree of approaches, proposals, theories and tools with which people are studying systems and optimizing 1.4 The future processes, and some are even trying to face The future is already embedded in the one of the most fascinating subjects: the present, of course, but no one knows how it prediction of the future. Till now, however, will be. Starting from the present, an little more than good characterizations of infinite number of futures can emerge. Even certain systems have been achieved, as well if we could look at all them, the problem as the creation of exciting and excellent would be which one to choose. Thus, virtual realities fast diverging from the true coming perhaps mostly from a Bergsonian course of Time. Perhaps the future cannot inspiration, I would instead indicate what I be predicted by computational power, expect future to bring us, from the current applying the ideals of Kant, but probably by point of observation. Tomorrow, perhaps I simpler processes of intuition, more in the will have a different position… 44 direction of what Bergson suspected. In terms of Supply Chain, it probably The tree shown in figure 1.23 represents would be very interesting if the world would the simulation approaches and some tools move more to the react (instead of predict) currently in use by the worldwide simulation approach, getting closer to JIT philosophy. community. It was build by the author based Even without the need for renaming it. This on data collected from proceedings of would imply a wider view on the systems conferences, articles from journals and and a mind-formation better focused on the magazines, and the Web. reduction of waste and more effective and fair consumption and management of resources. To achieve this, anyhow, it would 44 Henri-Louis Bergson, a French philosopher from the beginning of 20th century. be crucial that traditional managers and

44 Chapter 1 - Logistics, Life and Simulation researchers become “open minded” enough to truly understand such a paradigm, instead of wasting some more decades insisting on refusing it. “The Earth is a finite system”, as Lehoucq45 (2005) argues in a wise article regarding present energetic resources versus energy demand figures. It terms of computer technology, my expectations would be that the Quantum Computer would soon become available, at least as a tribute to the exceptional mind of Heisenberg (1925). Finally, in the social: better health, and the move to the Society of Knowledge, not to the poorer Society of Fantasy.

45 Roland Lehoucq is an Astrophysician from the Commissariat à L'énergie Atomique (Saclay, Paris).

45 Flexible Supply Chain Simulation

Organization. Journal of Logistics Management, 10 (2), 27-37. Chandy, K. M., & Misra, J. (1979). Distibuted Simulation: A References: Case Study in Design and Verification of Distributed Programs. IEEE Trans. Software Eng., SE-5, 440-452. A.A.C.A. (2004). Automotive History - Since 1960. Antique Chen, Y., Peng, Y., et al. (1999). A Negotiation-based Multi- Automobile Club of America. Retrieved Nov 2004, agent System for Supply Chain Management. from http://www.aaca.org/history/cars_90.htm Paper presented at the ACM Autonomous Agents ABX-Logistics. (2004). ABX LOGISTICS (Deutschland) Workshop on Specifying and Implementing GmbH. ABX LOGISTICS. Retrieved Nov 2004, Conversation Policies, Seattle. from Christopher, M. (1992). Logistics and Supply Chain http://www.abxlogistics.com/DE/ENGLISH/history/i Management: Strategies for Reducing Costs and ndex.aspx Improving Services (second ed.). London: Pitman Arief, L. B., & Speirs, N. A. (2000). A UML tool for an Publishing. automatic generation of simulation programs. Chryssolouris, G. (1992). Manufactoring Systems, theory Paper presented at the the Second International and practice: Springer-Verlag. Workshop on Software and Performance CNN. (2001, December 10, 2001). China's long march to (WOSP2000), New York. WTO entry. 2004, from Barjis, J., & Shishkov, B. (2001, June 26-29). UML Based http://archives.cnn.com/2001/WORLD/asiapcf/east/ Business Systems Modeling and Simulation. Paper 09/18/china.wto.timeline/ presented at the The 4th International EUROSIM Curran, C. (1991). Integrated Supply Chain Information Congress, Delft, The Netherlands. Systems: The Next Phase after EDI? Logistics Bell-Labs. (2002). Bell Labs Early Contributions to Information Management, 4 (1). Computer Science. Lucent Technologies. DAIMI. (2003). UML + CPN @ Nokia. DAIMI - University of Retrieved March 2004, from http://www.bell- Aarhus. 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49 Flexible Supply Chain Simulation

CHAPTER 2 51 ON MODELLING THE SUPPLY CHAIN 51 2.1 SYSTEMS AND MODELS 51 2.2 SUPPLY CHAIN OVERVIEW 52 2.2.1 Elements of the chain 52 2.2.2 Flows and resources 56 2.2.3 Management levels and policies 58 2.2.3.1 Inventory policies 60 2.2.3.2 Production policies 62 2.2.3.3 Distribution policies 65 2.2.4 Traditional metrics and flexibility 67 2.2.4.1 Facility related metrics 68 2.2.4.2 Fleet related metrics 71 2.2.4.3 The emphasis on flexibility 71 2.2.5 A step up to holistic metrics? 72 2.3 SUPPLY CHAIN MODELLING 74 2.3.1 System Dynamics 75 2.3.2 Petri nets 75 2.3.3 Sequential objects 76 2.3.4 Distributed objects 78 2.3.5 Agents 80 2.3.6 Web-based 81 2.3.7 Parallel 83 2.4 WHICH APPROACH TO CHOOSE 84 2.4.1 Level of detail 84 2.4.2 Where to stop 85 2.4.3 Simulation intents 87 2.5 MODEL CONSISTENCY, ACCURACY 89 2.6 GETTING RESULTS WITH THE MODEL 92 2.7 FINAL COMMENTS 94 REFERENCES: 97

Chapter 2

On Modelling the Supply Chain

Relevant issues, modelling approaches and technologies

Anyhow, to be aware of this is essential 2.1 Systems and models to a reliable process of simulation. Not to Words like “system” and “model” are lose the ground by confusing the model with sometimes used with little distinction in the system is a basic obligation. simulation literature, mostly in articles, and at this point it is crucial to establish what each of these terms signify in the aim of the System present text: a system will be considered the ultimate object of a study. A model, on the other hand, will be understood as any sort of representation of such system, in which experiments can probably be made Scale Conceptual models models and from which indirect conclusions can be obtained concerning the behaviour of the system.

As shown in figure 2.1, systems and Continuous Statistic Discrete models are in fact two different things that may not be confused during any moment in the simulation process. A prior, this remark Fig. 2.1 Models as representations of systems could well seem superfluous, but the fact is that some confusion often takes place since Somewhat inspired by the classification simulation is a process where frequently the proposed by Law & Kelton (1991), figure 2.1 system is absent, not in real touch with the shows that a system can in principle be analyst, and this could easily induce the loss represented as a model in two different of a certain reference to reality. Mostly ways: by means of a scale sculpture (scale when using the excellent graphics simulators model), or by a conceptual representation, of nowadays, this psychological effect can not physical, in which the model is built by easily become observable. Of course, also mathematical formulae and relations. Then, due to this particular feature simulation is based on Davies & O'Keefe (1989), one can many times used as a powerful instrument also classify the conceptual models as: (a) for marketing. Continuous, when the states of the system

51 Flexible Supply Chain Simulation are represented as a continuous set of connected with one another by means of values; (b) Statistical, when such three basic kinds of flows: materials, representation is static and the results are information and money. The first two flows achieved by statistical methods, like the give rise to what in the literature is called Monte Carlo method, for example; and (c) the materials pipeline and the information Discrete, when the states of the system are pipeline (Chandrashekar & Schary, 1999). represented as a discrete set of states, that However, perhaps due to an understandable usually leads to a discrete representation modesty, money is usually excluded from also along the time variable. this list, even though being the main engine As we will see soon, all these approaches for the other processes. Yet, in truth the have been explored for modelling Supply Supply Chain is basically a huge system in Chain systems, and their choice strongly which money flows from ultimate customers depends on the objectives of the analyst, to the entire list of enterprises involved in which can be strategic, tactical or even the business. Of course this flow is in operational. At the moment, however, let exchange of products (including services), us just notice that since the usual sequence and, in that sense, it is easier to recognize of actions in the process of simulation is (1) that ultimate customers are the true drivers understand the system; (2) create a model of the Supply Chain activities. If they would of the system; (3) implement the model; stop buying products, the entire system and, finally, (4) simulate the model; this would collapse. Figure 2.2 is an attempt to chapter is focused on boarding and making depict this idea. clear such issues, starting by an overview on the system.

2.2 Supply Chain overview Theoretically, any simulation technology or approach could be used for modelling Supply Chain systems. Like other systems, the Supply Chain can be simulated as long as a model of it is previously built. Anyhow, as it happens in general in simulation, prior to building the model one has to understand how the system really works. Only based on Fig. 2.2 The general structure of a Supply Chain, where such knowledge can a reliable “image” of ultimate customers are also represented (Customers) the system later be constructed. That is the purpose of this section. Typically, the companies forming part of these kinds of networks are distinguished by the major activities they perform in the 2.2.1 Elements of the chain business, which usually are classified as: From the physical point of view, the suppliers, when focused on the handling and Supply Chain is a network of companies delivering of raw materials or intermediate

52 Chapter 2 - On Modelling the Supply Chain products which later will be transformed of outsourcing. Instead of being owned by a into other products; factories, if the main single company, as in the old times of activity is the manufacturing of finished regional consumption, the supply network is products; warehouses, or depots, when the presently a space of self-ruling enterprises primary objective is to handle significant among which materials, information and inventories of finished products as interface money must be shared. And this makes the buffers for the last sellers located at the dynamics of these flows more voracious and points of consumption; retailers, or last complex than ever. As we have seen in the sellers, usually engaged in strategies of previous chapter, Supply Chain Management selling and handling small inventories; and, (SCM) is committed to embracing the new finally, the ultimate customers, habitually challenges presented by this business simply called customers, whose activity is to reality. get the products and pay for them. The difficulty of predicting the behaviour of this In simulation, however, despite the usage mass of customers is actually a headache for of different blocks of logic to create specific most Supply Chain managers, and, in trying models for the different elements in the to turn demand figures smoother, many Supply Chain, the entire Supply Chain companies frequently take advantage of system is rarely represented in practice, “spy” and “pressing” tactics, for instance, due to the increase in complexity a broader customer profile monitoring, advertisement, representation would add to the model and public exhibitions, as well as other to the results. In most cases, Supply Chain psychological techniques embedded in models are reduced to a certain number of marketing operations. As a curiosity, several warehouses connected to retailers, or, at a enterprises are seriously interested in maximum, including some factories. A few introducing Radio Frequency Identification central warehouses delivering to an (RFI) to track every item of its products extensive group of retailers, these in worldwide by using a chip the size of a grain numbers of 50, 100 or even 200, can be of sand that can be identified when it expected in a real case Supply Chain model, approaches a “reader” (Microsoft, 2003). as in Guedes (1994). On the other hand, The technology is from the times of World academic studies are commonly carried out War II, but, as the same source reveals, in models of networks having less than ten ”now many experts believe that it will be facilities, as in the famous “Beer Game” found in anything and everything by 2010, (Mason-Jones et al., 1997), or even in the and it will revolutionize supply chain, “Cranfield Blocks Game” (Feliz-Teixeira & manufacturing and retail efficiency”. Brito, 2005), for example. For the same motivation of simplicity, it It is a fact that the supply networks have is also common to ignore the primer turned more complex during the last suppliers and the ultimate customers, being decade, mainly due to the spread of global the retailer the driver for the demand in enterprises and worldwide procurement, most Supply Chain simulations. but also due to the trend of replacing the concept of vertical integration by the idea

53 Flexible Supply Chain Simulation

Apart from these tendencies, and since in as well, like central warehouses or certain the simulation approach presented in the factories, by using the classical method of next chapter all elements and facilities will ordering by catalogue or even the new e- be considered, it is at this point convenient Commerce services available by Internet, as to focus our attention on each element what happens in the “Amazon.com” online constituting the Supply Chain: bookstore or in the trendy “eBay.com” online market, for example. In practice, · The customer: after being “pressed” by customer demand is injected in the Supply every sort of advertisement, if possible, the Chain model as a collection of historical customer effectively establishes the direct values or by means of a distribution of demand to the Supply Chain. Thus, the probabilities, frequently in the form of a customer demand is the input to the standard Normal function. For simplicity, system. Historical demand figures are customers demand is almost always usually treated as time-series resulting from generated as an accumulated demand per the superposition of four basic kinds of day, per week, per month, etc. Besides, the components (see figure 2.3): trend, which customer is considered not to own any kind represents a gradual and constant variation of resources, like stock or fleet. over a long period of time; seasonality, as a pattern repeating itself in a certain period · The retailer: The retailer receives as of time; cycles, as patterns repeating after input the demand from customers (orders) long periods of time; and random variation, and, in reply to that, it is responsible for as the unpredictable component of “noise” delivering the respective materials. To in the demand signal. Forecasts are based maintain the appropriate level of stocks, on the analysis of these kinds of signals the retailer, in turn, will place orders for (Heizer & Render, 2001, pp. 83). stock replenishment whenever necessary to its “suppliers”, which can be warehouses,

Cycles factories, or even other retailers, depending on the case. However, in general, retailers

Demand Random place orders to warehouses. The retailer owns a physical stock, or inventory, and, of Trend course, an inventory replenishment policy. The amount of inventory is in general small,

Seasonal and usually only to cover the demand during the time needed for replenishment. The Time delivery process is normally done by some kind of transporters. Communication with Fig. 2.3 A generic demand signal and its components “suppliers” is frequently made by electronic

methods like Electronic Data Interchange But, how the demand is generated is also (EDI), email, or even using e-Commerce. important. Although traditionally customer Nowadays, Information Technologies (IT) orders are placed to retailers, they can be allow better synchronization between the placed to other facilities in the Supply Chain players in the Supply Chain. As the retailer

54 Chapter 2 - On Modelling the Supply Chain operates a stock and is responsible for a place orders to other warehouses, factories delivery, it has at least an inventory refilling or general suppliers. For the purpose of policy as well as a delivery policy. In some Supply Chain simulation, the warehouse is practical simulation cases, the retailer is usually modelled as a box including a stock modelled as a black box characterized by a resource being managed by some standard list of parameters like the lead-time, inventory control policy. As in the previous frequency of replenishment, fixed costs, case, a list of parameters allows the stock operation costs, delivery costs, etc., configuration of the element by the user, which can also be computed from standard like the lead-time, stock operation times, distribution functions. As mentioned costs, etc. already, the retailer replenishment orders are often considered the first input to the · The factory: Factories can be complex Supply Chain. elements. They receive raw materials and make products, or receive intermediate · The warehouse: warehouses, or depots, products and make other more complex are by excellence the elements centred on products, which in turn can later be parts handling large amounts of inventory, and for other products manufactured in other the facilities to which several retailers place factories. The assembling, as well as the their orders. Warehouse performance is, customization, from this perspective, may therefore, strongly dependent on the be seen as later manufacturing processes. inventory management policy adopted and As the most complex process of the Supply on the distribution policy, since the network Chain, manufacturing has been intensively of distribution significantly increases in studied for a long time. An excellent book complexity, in comparison with the about the matter is “Factory Physics”, from retailer’s case. A tied management on these Hopp & Spearman (2001). Nevertheless, policies is vital, and implies good inventory flows in the factories are formally synchronism between orders’ fulfilment, the very similar to those of warehouses, since scheduling of their transportation and the the main issue is to find the adequate moments of replenishment. Big automated quantity to produce (lot size) and the best warehouses, typical of the 1990s, could moment to do it; in fact, identical to the occupy the space of thousands of squared warehouse problem of finding the quantity metres, where tens of thousands of palettes to order to the supplier and the best of materials where kept in stock. Physically, moment to do it. Many authors indicate no they included arrays of bays for input and differences between the two cases, and, in output and truck alignment while loading some simulations, factories can simply be and unloading, as well as special conveyor found modelled as warehouses. In other networks and crane vehicles for handling situations, however, and mainly if higher the palettes inside the complex warehouse accuracy is expected, other specific structure. In general, EDI, email, and e- manufacturing factors must be taken into Business are nowadays technologies for account, like process times, maximum communicating with partners and with the production rates, number and organization “outside world”. Normally, warehouses of production lines, lot sizes, etc. In

55 Flexible Supply Chain Simulation general, factories place orders for stock Anyhow, if taken into account, this element replenishment to the suppliers of raw is very much considered a warehouse being materials, but in reality they can also order served by an infinite source of materials. to other facilities, especially when the processes of manufacturing are based on Apart from such definitions of the static assemblage, as, for instance, in the case of elements (nodes) of the Supply Chain, the Assemble-To-Order (ATO) practiced by sometimes it is not so simple to classify DELL Computer1. them in practice. In the previous chapter we have talked of certain emergent notions · The supplier: In the present context, like “pipeline management”, which “supplier” is to mean “primer supplier”, the transforms the concept of management in kind of facility which acts as the interface each Supply Chain facility, as well as about between the world of raw materials and the “moving factories”, for instance, where world of products. Suppliers typically handle some steps of the manufacturing process are unprocessed materials, but in terms of to be executed while in transit to the inventory can be seen as warehouses that client. With the constant evolving of provide such materials to the manufacturers manufacturing and delivering processes, of products. The classification of “supplier”, certain nodal elements of the Supply Chain anyhow, is not very linear in practice, as it can exhibit a kind of mixed “behaviour”, if really depends on how one looks at the expressed in terms of the traditional problem. For instance, a farmer producing classification. If one asks oneself what in vegetables for the food industry can truth is DELL Computer? Is it a factory? A simultaneously be considered a kind of retailer? A warehouse? The answer appears “factory” and a “supplier”. It depends on to become less easy than expected. Even whether the vegetables are being delivered recognizing the importance of the usual to the city market or to a factory of classification, the fact is every node of the vegetarian pies, respectively. Besides, the chain is in truth an element of business, raw material can already be a product, as in which will organize or arrange its business in the case of DELL, for example; or when a the form in which it will become more constructor of airplanes orders a wing from effective and advantageous. In reality, each a certain supplier which may be in itself a of these elements is simply a weighted factory receiving steel from other industrial equation of the classical archetypes. As we suppliers. For the purpose of simulation, will see in the next chapter, a proposal for a “primer suppliers” are frequently ignored generalization of the representation of and substituted by simple sources of these elements in the simulation will be materials. So, few simulations count with presented. the lead times and the delays caused by stock operations at the supplier level. 2.2.2 Flows and resources

1 DELL Computer is one of the most successful companies Between the elements of the Supply of our days using fast response logistics, derived from the Chain there is an interchange of materials, JIT philosophy. It assembles Personal Computers and information and money, as it was previously delivers them to the client’s door.

56 Chapter 2 - On Modelling the Supply Chain said. The physical structures and resources the cell-phone flexibility, the information that support these flows are, however, flow also became decisive in Supply Chain quite diverse. activity. By means of these resources, partners exchange data about inventories, Money usually flows throughout credits, production schedules, product specification, bank transfers, cheques, and is therefore transport coordinates, etc., with the intent centred in bank operations. Despite the fact to better synchronize their operations. Such that this flow is ignored in most simulation communicative ambience naturally shapes approaches, the effectiveness of certain itself in the form of a network of relations practical Supply Chain can especially rely on between companies, and, due to the weight it, or even extremely depend on it, if credit the “virtual world” of Internet carries in degrades. For example, suppliers can stop such ambience, by e-Commerce, e-Business, delivering to their clients due to the lack of e-Procurement, etc., soon was seen as a payment or excessive delays in the process. new business dimension named “Virtual This may seem a “third world” reality, but it Supply Chain” (Chandrashekar & Schary, is a serious factor to take into account while 1999). Since then, information flow was also modelling certain Supply Chains. Probably, considered an interesting matter for any well organized company from the simulation, but mostly focused on modelling “developed world” would not be able to the coordination and cooperation between survive random blockades in the flow of partners and/or departments. Agent-based money. Being less dramatic, one could at simulation (Kim et al., 2000) as well as Web- least contemplate how this flow affects based approaches (Lee et al., 2001) were decisions in the facilities and their technologies commonly used for this responses, or even their reorder policies. As purpose. The trend was also following the an example from the public domain show, development of software for Enterprise some factories installed in certain countries Resource Planning (ERP). These approaches, have later opted to leave those countries however, tend to represent the companies not only due to the less expensive labour of the Supply Chain by their organizational offered by recently joined eastern states of structure of departments, and not by their European Union, but also due to serious traditional physical processes. problems related to the flow of money. On the other hand, when the Supply Delays at this level can easily induce delays Chain is modelled based on its physical in the channels of information and materials processes (including cycles of production, and, from that, generate dramatic conduct warehousing, distribution), the concept of in the Supply Chain. information flow is normally absent and communications are considered with no Information flow, as learned from the delays. Yet, in reality the answer to an previous chapter, can be considered the email is many times delayed, or even not radical reality of our times. Supported by happening at all, thus able to induce the traditional telephone, post and FAX, and considerable effects on the performance of appended with modern technologies of EDI, the system. email and e-Business, and enhanced with

57 Flexible Supply Chain Simulation

Materials flow is the central flow in the mainly used for large batches of materials, Supply Chain. It holds the merchandise to be especially for high value raw materials like exchanged for money; it is the physical chemicals, medicines and dangerous cargo, generator of money. Therefore, the faster but also coal, food, machine spare parts, the merchandise will be delivered to the and even for large numbers of automobiles. client the better. Materials flowing in the The flexibility of the railway depends very Supply Chain are indeed an interesting much on its network type and on how it is phenomenon: everybody needs them to sell operated. On the contrary, large amounts of but no one wants them in hand. Materials low value materials, e.g. cement, are like hot potatoes, or cartoon time- sandstone, wood, iron, grain, crud, are bombs. often shipped through waterways like rivers, The flow of materials goes along the canals, the sea, etc. Being the slowest transport paths that compose the physical method of transport, the ship is nonetheless network of the Supply Chain. Trucks, trains, a very interesting option in terms of low airplanes, ships, pipelines, are transport costs for large batches. But pipelines are resources commonly used along these paths sometimes concurrent with ships and trains to handle the materials on its distribution to for continuous transport of chemical the clients. These transport resources can products, natural gas, oil, etc. And, finally, either be owned by the distributor or be airfreights are used to achieve fast response subcontracted as a service to a Logistics on handling high value low weight products, company dedicated to the transport of from the spare parts for machinery to the goods, usually known as 3PL operators. The transport of cloths and even flowers. This costs of these operations are therefore seen option is a trend nowadays, since some 3PL in different ways: in the first case, there is operators extended their activity to the normally a fixed cost related with the flight operation, like DHL, TPG/TNT, UPS, existence of the resource and variable costs Panalpina and others. Airfreight is also the associated with its operation, for instance transport option used for achieving long related to drivers, fuel, maintenance, distance JIT, and an essential service for roads, etc. In the latter, however, costs are world fashion suppliers like Benetton or normally of the variable type and depend on ZARA, for instance. the volume of goods transported. The cost per unit of product transported decreases with the increase of volume. 2.2.3 Management levels and policies Obviously, each transport system offers As next figure (Fig. 2.4) shows, the its own typical delivering speed and cargo management of a company is normally characteristics that make it more adapted founded on a structure sustained by the to certain situations. For instance, trucking following decision levels: strategic, tactical is the most representative form of transport and operational. These levels define a kind for finished products, since it is fast and of hierarchy of decision, with the strategic even flexible to adapt to the frequent being the highest and the operational the deliveries and small batches proposed by lowest level. modern JIT. On the other hand, railways are

58 Chapter 2 - On Modelling the Supply Chain

. Facility location agglomerated manner, in order to better Strategic . Long term analysis . Sourcing strategies adjust any decision to the long term objectives of the company. The horizon of . Forecasting interest can be of several months or even . Short term analysis Tactical . Scheduling years, and data for analysis is frequently sampled monthly. The establishment of . Inventory control contracts with important suppliers, the Operational . Detailed schedule . Order managing calculation of the location of the facilities

and their capacities, as well as the long- Fig. 2.4 The three management levels in a company standing business decisions like expansion, change of products or technologies, etc., The operational level deals with the art are the responsibility of this level. As in the of responding to the daily requests of the previous case, decisions at this level are business, that can involve order managing, founded on forecasts. scheduling of detailed tasks, for instance, assembling, packaging, loading and An interesting picture about the kind of unloading, as well as the calculation of reports produced for operational, tactical delivery, cargo space optimization, etc., and strategic purposes can be found in but, most of all, the management of the Schuelke (2001). inventory by means of some inventory control policy. One can say that this is the level directly interfacing with reality. Demand: Distribution: Manufacturing: Materials:

Strategic On the other hand, the tactical level Monthly Enterprise Enterprise Enterprise decides based on a more agglomerated view Forecasts Distribution Production Materials of the activity of the system, usually in the Planning Planning Planning horizon of weeks, depending on the case, and based on historical records of the Tactical business activity. Decisions at this level are, Distribution Master Weekly MRP Forecasts Requirements Production therefore, founded on forecasts taken from Planning Scheduling short term analysis of the performance of processes. From such knowledge, monitoring Operational of and quick adjustments in the scheduling Orders Inventory Process Materials of production can result, as well as in other Control Scheduling Release activities like transportation, supply of materials, delivery plans, customer ratings, Fig. 2.5 Management functions associated with the subcontracting, etc., within certain limits operational, tactical and strategic levels imposed by the upper level of strategic reasoning. In a healthy company, such levels are in good coordination with each other through At the strategic level, business activity is the exchange of data between sections and monitored and analysed in an even more departments, as the diagram of figure 2.5,

59 Flexible Supply Chain Simulation inspired on Fox et al. (1993), purports to model by means of adjusting operational show2. policies after analysing the results of For each management function, the previous simulations. In such an approach, activity at the operational level is restricted important replenishment policies will be by certain tactical constraints, and, at the derived from models known in the literature same time, tactical choices are confined (see Hopp & Spearman, 2001, pp. 48) like and directed in accordance to strategic the Economical Order Quantity (EOQ), the guidelines, in a manner that maintains the statistical inventory models (r, Q), (r, S), running of the business with the best etc., but also considering some new trends possible harmony. Tactical and strategic like the KANBAN, used for achieving JIT, and levels can therefore be seen as “regulators” the curious BanKan, an almost naïve method that create feedback in the operational proposed by the author which establishes a level. Anyhow, shaped by tactical variables kind of pipeline-like flow of materials. At and these variables constrained by strategic the moment, however, let us pay some factors, the operational level is the level attention to the most relevant operational where the real system runs. Operational policies: data is based on the “real”, while data in other levels is based on “forecasts”. 2.2.3.1 Inventory policies This allow us to expect that modelling Since the more important aspects of the the Supply Chain from the operational level supply policies usually belong to the will lead us to obtaining “raw” output data strategic level (with the establishment of which then can be “interpreted” not only the suppliers and their contracts), the operationally but also tactically or even replenishment policy is practically what is strategically, depending on the period of left of the relations with suppliers at the time simulated and on the type of analysis operational level. Several methods for made on such data. Besides, it is expected replenishment can be found described in that strategic and tactical constraints can any standard book of management, but in be indirectly imputed to the model on the aim of this work it seemed of more representing the configuration of each interest to expose such a problem in a facility, by means of the definition of the lighter way, which will be done with the policies of replenishment, production, help of the scheme represented in figure distribution, etc., and, therefore, that 2.6. in this figure, Q, representing the studying different scenarios as what-if cases inventory level, is usually expressed in will also let us conclude about different Stock Keeping Units (SKU), a term referring practical situations. to a specific product in inventory or in a The next chapter will expose in more catalogue (O.B., 2005); while the detail this bottom-up modelling design, in independent variable is the time (t), which which feedback will be injected in the is frequently expressed in days, weeks, months, etc. In practice, the SKU can also

2 be related to the palette or even to the As a curiosity, notice that there is not yet in this diagram a reference to Enterprise Resource Planning (ERP), but its container, depending on the case, since structure already points in that direction.

60 Chapter 2 - On Modelling the Supply Chain these are dimensions more appropriate to transport. 2AD Q = EOQ = (2.1) h

Q where A is the cost of one order, or setup Q2 Q3 cost, D is the total demand in the period of interest, and h the bank lending rate in the Q1 same period, or holding cost. On the other hand, the moment to place the order is when the inventory state will t1 t2 t3 t reach the Reorder Point (ROP) defined by:

Fig. 2.6 General behaviour of an inventory system r = ROP = DLT + ss (2.2)

From this figure, one can notice that at where DLT represents the demand during the instants t1, t2 and t3 the quantities Q1, the supplier’s lead-time and ss a corrective Q2 and Q3 previously ordered to the amount of stock used to protect the system supplier have arrived at the inventory, against stockouts (or negative stock levels). raising the stocks. However, at the same Figure 2.7 intents to represent this idea, time, the inventory is continuously being where the moments of placing the orders “consumed” by the “irregular” demand are signalled as well as the lead-time (LT) from the clients. The problem of inventory of the supplier. management is precisely to answer the following two questions: (1) which quantity Q (Q) to order; (2) when to place the order. A good answer must ensure a good balance EOQ EOQ between the costs of having the stock in EOQ hand and the costs of loosing clients if LT ROP LT LT stock is not available.

t1 t2 t3 t As can be found in the vast literature about the subject, the EOQ model for fixed lot sizes, the Wagner-Whitin procedure for Fig. 2.7 General behaviour of a (r, Q) system with r = ROP and Q = EOQ, showing a little stockout at t = t2 dynamic lot sizes, and the statistical (r, Q) and (r, S) models are perhaps the most This case can be considered a particular common mathematical bases to answer case of a more general (r, Q) model where those questions, for inventory systems of r and Q could possibly be computed by independent3 demand. In a fixed lot size, other means, in order to better adapt not for instance, the quantity to order (Q) can only to the lead-time variance but also to be computed as: the variance of demand.

3 Related to a product that is not a sub-product of any other product.

61 Flexible Supply Chain Simulation

Apart from these, also the Two-Bins Most production systems are designed in system and the KANBAN are methods accordance to one of the following four (4) frequently used to answer those same process strategies (see Heizer & Render, questions, as well as the fixed-period 2001, pp. 234): revision method4, which simply establishes the revision of the inventory at fixed- (1) Process-focus: in this approach, periods of time. For systems of dependent several basic tasks (or jobs) of fabrication demand, however, MRP is also widely used. are made available as a flexible option to Detailed information about this can be allow the making of diverse products. For found in Heizer & Render (2001), Hopp & instance, in a Job-Shop facility, different Spearman (2001) or, written in Portuguese, kinds of machines ensure that different Gonçalves (1999). processes of fabrication can run almost in parallel (Fig. 2.8). The challenge is the 2.2.3.2 Production policies scheduling of the tasks to optimize the Production policies are primarily strategic utilization of the resources, while ensuring issues, since they shape the production the intended variety of products output. processes running at the company, or Carpentry is a simple but didactic example factory, from the beginning. The definition pointed out by Heizer & Render (2001). of business itself is frequently based on how Although several different objects of the process of fabrication is organized, in furniture can result from such a facility, the terms of resources, layout and dynamics. A fact is that all of them have been made with different variety of products, volume of the same kind of tools or machines, and, production and level of utilization of probably, with several employees working resources will result from different on different processes. organizational alternatives. Of course, these kinds of issues must be faced and resolved before the installation of the company on 1 the ground, although agile organizations struggle to achieve the ability of changing 2 these strategies as required for reacting to the turbulence observed in markets. It is supposed, however, that the modeler of a 3 Supply Chain may also use this kind of 4 information to configure the facilities at the model. This is a simple way of inserting P(1) P(2) P(3) strategic feedback into an operational model. Once this information is known, the Fig. 2.8 Process-focus fabrication (Job-Shop) producing process of production can be established, as products P(1), P(2) and P(3) based on four different tasks well as the way the factory will react to the orders coming from its clients. These companies frequently try to work on a Make-to-Order (MTO) basis, even if 4 Also called News Vendor Model, since newspapers arrive at fixed periods of time (daily, weekly, etc.). often maintaining low stocks, just in case…

62 Chapter 2 - On Modelling the Supply Chain

This option is typical for low-volume of are now much more efficient than in those outputs, low-utilization of resources, and a times. high-variety of products. m1 m2 To model and simulate these sorts of m3 processes is usually more interesting to the m4 1 manufacturer than to the Supply Chain m5 manager, since it is a typical problem of production scheduling. Besides, it is also m6 2 usual that in simulating the Supply Chain products are considered independently m7 3 manufactured, therefore, to each of them m8 an independent process of production is considered as a model. Of course, that is an 4 equivalent model of the real process, which P(1) must be configured in accordance with the P(2) P(3) results obtained in practice.

Fig. 2.9 Repetitive-focus manufacturing (assembly line) (2) Repetitive-focus: in this approach, with output products P(1), P(2) and P(3) mounted up on four the fabrication basically consists of a different assembling cells sequence of operations of assemblage that, at the end of the process, gives rise to one Companies using this type of approach or more products. This is very much the frequently work on an Assemble-to-Order type like that of the assembly-line of Henry (ATO) basis, typical of an average/high- Ford. In each place of work, or work cell, volume, high-utilization, and low/average- one (or more) operation is completed before variety. Modelling this type of processes for sending the product to the next work cell Supply Chain simulation purposes may be (see Fig. 2.9). Usually, these operations achieved by attributing to each product a consist of the mounting of specific modules process-time function, probably retrieved (mj) on the skeleton of the product that is from a distribution of values. being filled, from place to place. The variety of products will result mainly (3) Product-focus: also named continuous from the number of combinations available production. In this kind of manufacturing, with the modules and, in certain cases, also the final products typically result from a by the combination of colours, for instance. sequence of transformations applied to raw This was the scheme responsible for the materials, usually by means of certain Mass Production Era (1910-1980), used to chemical or thermodynamic processes, that build automobiles, appliances, and almost frequently finish in a process of shaping. It all of the items produced for the public in is the case in the production of paper, industrial volumes, but nowadays it is also steel, oil, etc., which require processes used to achieve JIT manufacturing, since with long runs and high energy consumption, automation processes and modular design and therefore a continuous operation to

63 Flexible Supply Chain Simulation optimize costs. In terms of process diagram, been dramatically shortened. A high variety however, this sort of production line is of versions of the same base product allow similar to the assembly-line of figure 2.9, adjusting the product in a way to fit into a provided that the modules are replaced by wide spectrum of customer preferences. operations of transformation on the raw Customers can even sometimes use e- materials and, perhaps, adding a final Commerce to virtually assemble their own shaping. These systems are organized versions from modules presented in an around a specific product (or class of Internet catalogue, for instance, and then products), usually run on a Make-to-Stock place their orders directly to the company. (MTS) basis, and the production is adjusted Examples of this practice are VW, Fujitsu- to the forecasts by means of speeding up or Siemens, Motorola, among others, but with slowing down the production line. These are the current expansion of e-Commerce, more considered high-volume, high-utilization, and more companies seem tempted to low-variety manufacturing processes. follow such a trend.

(4) Mass-customization: this is the trend M1s for customer servicing of our time, once the option is possible. Products are made on a M2s 2 kind of a fast and flexible assembly-line in which assembling times are made the shortest possible, compared to other M3s 3 industrial processes, through the usage of a large variety of modules with flexible design 4 and a superior and flexible scheduling of tasks. Fast and synchronized operations in the delivery to the clients terminate the P(1) P(2) P(3) P(4) P(5) ... P(N) process (Fig. 2.10). This approach is predominantly used for fabricating computers5, appliances, pizzas, mobile-phones, etc., where assembling CLIENTS times are naturally short compared to the time the customer is prepared to wait for Fig. 2.10 Mass-customization of N products in three the product. Lately, however, even books different assembling cells, using a large variety of modules and automobiles are delivered in a JIT fashion using this manufacturing approach. This kind of production is normally for Clients must obviously be prepared to wait average/high-volume, high-utilization, high- for an automobile much longer than for a variety, and many times proposed for pizza, but compared to what was happening achieving JIT performances. in previous decades, total lead-times have On concluding this section, in the figure 5 DELL Computer was probably the first company using this 2.11 these four types of manufacturing are policy of production in order to deliver personalized (customized) computers to the door of its clients. compared in terms of volume, variety and

64 Chapter 2 - On Modelling the Supply Chain utilization of resources. Notice that the to that, factories (or certain kinds of utilization is represented by the area of the factories) are already delivering directly to circles. As it can be understood from the its customers, what also contributes to this figure, the choice of the appropriate type of effect of absorption in nomenclature. In manufacturing is highly dependent on the Heizer & Render (2001), for instance, the type of business run by the company. term delivery does not even appear listed in the general index. We may therefore consider that in Mass general the term distribution is related with Process customization focus the direct or indirect flow of materials from the facility to its customers. In that sense, a

system of distribution usually has to face Repetitive focus

ariety the two classical problems:

V Product focus (1) the establishment of the number and the location of facilities in the distribution network, which has to do with the design of Volume the network, constrained by some cost

analysis usually captured from long-period Fig. 2.11 Production policies in terms of volume, variety plans (of several years, for example): and utilization of resources (area of the circle) consequently a strategic issue; and (2) the management of the transportation through the network, which is related to the 2.2.3.3 Distribution policies handling of materials, vehicle routing and Distribution is in itself an art within scheduling, on the way to serving customers Logistics. Military and 3PL companies are and satisfy demand: a tactical or even masters of this art. In the 1970s, with the operational issue. eyes focused on the factory as the “centre of the universe”, the distribution was The first problem is normally addressed considered the out-bound logistics, which is by representing the distribution system as a associated to the flow of materials going out mathematical problem of a network of the factory (the inverse flow, from the nodes and arcs where the sum of a group of suppliers, was called in-bound logistics) plus weighted distances connecting the facilities the delivery, which was considered the last to its customers is to be minimized, as step of the distribution process, that is, the shown in the next figure (Fig. 2.12). This is transportation of goods at the end of the seen as being more or less equivalent to the chain to meet ultimate customers (see Eilon total costs minimization. The facilities are et al., 1971). considered as simple numbers (capacity for Today, with the eyes looking into every suppliers, demand for customers) while element of the Supply Chain with the same distances are weighted by the respective level of attention, the idea of distribution costs of transportation. frequently absorbs the concept of delivery as it is a more generalist term. In addition

65 Flexible Supply Chain Simulation

9€ S2 300 dependent) are rarely used for boarding C1 these kinds of problems, since the 100 3€ 500 11€ development of such models is much more 4€ 7€ S1 time consuming and demanding of 9€ C4 programming knowledge. These problems, 3€ 120 in themselves, being of the strategic type, 6€ already use previously “filtered” values

C3 frequently resulting from averages C2 90 computed from long periods of observed or 100 estimated operations, what extremely simplifies the cases and makes the dynamic Fig. 2.12 Example of a classical facility location problem, approach too complex to be useful. That with two suppliers serving four customers explains, to some extent, why dynamic

simulation is used more frequently by Linear programming methods, numeric consultancy experts, who in principle have analysis, analytic and heuristic approaches the time and the resources available for in- and even electrical analogue6 models form depth investigation of problems, or, if part of the heritage of techniques used to integrated in the company, by the people at solve this kind of problem. But also hybrid tactical or operational levels, where the techniques and statistic simulation started complexity of the problems is experienced to be used in the 1990s with the advance of in loco and the need for detailed simulation computer into the field of Logistics. approaches gets evident. Guedes (1994), for example, is the author of a remarkable work on this subject in About the second issue, that is, the which the facilities are modelled as “black management of the transportation through boxes” characterized by cost functions the network, one can say that in the independent of time (static), that he calls perspective of the tactical and operational “key drivers”, with the lead times assumed analysts, the location of facilities and the previously known. STRATOVISION, the topology of the network of transportation Windows application intended for Logistics are just a part of the initial conditions of Strategy Planning (LSP) was perhaps one of the problem. The problem in itself is now the most important results of his work, the management in time of the flow of where the analyst is also expected to make materials, and so, the management of use of “what-if” scenarios from which other inventories and of transport resources. At empirical conclusions can be obtained. these levels, the need for answering to new

questions emerges: (1) when to deliver; (2) To the contrary, dynamic simulation how much to deliver; (3) how to deliver7. approaches (where all processes are time Obviously, the answers to the first two questions strongly depend on the availability 6 Hitchings (1969), for example, considered the distance from the facility to the customer proportional to the length of transport resources (vehicles), type of of a wired resistor, in his electric analogue modelling of the problem. 7 From now on the term “delivery” will be used in its more general meaning of “taking to the client”.

66 Chapter 2 - On Modelling the Supply Chain optimization of cargo space, due dates, dynamic simulation this technique also priorities, choice of integer or factionary started to be used by some enterprises. As delivery, etc., giving rise to a problem of we have seen in the last chapter, simulation volume handling and scheduling. The short- is even a strong component of most ERP term scheduling usually found in operative systems. Nevertheless, since dynamic ambiences can easily turn so intense that simulation models continue to be complex, one may consider that in those instances time consuming on development, exigent management really meets the art of concerning the simulation skills, and, on the improvisation. other hand, its usage rate is normally low, The answer to the last question is more these sort of approach continues to be more about the choice of transport modes (train, used either by powerful companies or by truck, etc., usually pre-selected by strategic private consultants. In its headquarters of directives), but also about the routing of Munich, BMW was recently using a dynamic the vehicle to the customer, the kind of simulation environment know as eM-Plant to delivery (multi-drop or single-drop), as well support and optimize car production, for as the type of fleet to use (own or hired), example. Many other strong companies use for example. this sort of approach, but most of the others frequently restrict the usage of simulation In operational ambiences of independent to strategic proposes, by means of static demand, the choice of the response to a modelling. specific order defines, often from a variety of options, the particular distribution policy adopted. However, in cases of dependent 2.2.4 Traditional metrics and flexibility demand, Distribution Resource Planning An issue that continuously remains on the (DRP) is commonly implemented. This is a agenda is the metrics by which managers kind of MRP adapted to distribution, which and researchers quantify the performance uses the demand forecasts at the retailer of Supply Chain systems. The natural level and further computes the dependent evolution has been viewed from below, demands upstream in the supply chain where the elements of a distribution facilities. From that, the transportation network were considered independently schedule is adjusted so that the materials (and using single company metrics), to an arrive precisely when needed. Some authors upper point of view, where those elements consider this a PULL system. start to be seen as a new kind of super- entity with the same global final goals, A large number of tools for managing giving rise to the concept of a network of distribution at the operational and tactical shared and interdependent motivations, levels is still based on spreadsheets, since presently known as Supply Chain. This these are extremely flexible and easy to means that the previous metrics of a “node” handle computer applications. In many were expected to evolve to metrics of a cases, they can be associated with Gantt collection of “nodes” linked to each other Charts as visual aids for scheduling and through a complex “network”. loading, but with the recent proliferation of

67 Flexible Supply Chain Simulation

In reality, however, till now this was not therefore propose to translate performance what happened. The concept of Supply into a financial measure related with the Chain is widely understood and practiced, added value of each Supply Chain branch but due to problems of complexity and trust based on profit/loss statements; as well as, such global metrics do not exist yet. Maybe among others, the author of the present one can conclude that markets, as we run text, in his trial to establish a simulation them, are, by nature, perverse, and that no based methodology to measure the Supply other law can superimpose the rudimental Chain flexibility (Feliz-Teixeira & Brito, law of the strongest and fittest. In a certain 2004). Obviously, the metrics considered in way, it seems that there is a wish to turn the simulation approach presented in the those systems more transparent and next chapter also carry in themselves such “clean”, but in fact there is also the danger anachronism. that such a new model would become too Despite the fact there is a large number fragile and give additional chances to some of enterprise measures that can be used in “partners” to take advantage by continuing practice, experts normally recommend that to pervert the system in the background. managers should choose only few of them to There is, for that reason, a generalised represent the performance of the company, caution about the cooperation and the for many times using a large number of transparency that a new level of metrics metrics introduces much more confusion would involve, if implemented. than benefits. The following ones have been Another problem is the increase in chosen and are automatically computed complexity that could be expected from along the period of simulation: such a new kind of metrics, since it would mean a jump from a single company metrics 2.2.4.1 Facility related metrics to an upper level of metrics of a complex These metrics are used specifically for network topology and even overlapping describing the performance of the facility. structures. Some of them are financial, and some are Instead of going that way, as Lambert & physical or operational. Financial measures Pohlen (2001) well emphasise, managers usually lead to more useful indexes because continue to apply old single enterprise most of the time they can easily be applied metrics and, through such measures, look at to multiple product systems. the Supply Chain as simple agglomerates of companies, where of course the law of the · Global costs: is considered the sum of the most powerful directly or indirectly overall components of the fixed and the dominates. variable costs, usually calculated at the end Trials to advance towards new directions of the period under study. This value gives are, nevertheless, being made by some the manager the idea of the global amount researchers, from which one can point out of money needed for maintaining and Lambert & Pohlen (2001), who argue that running the company. Costs will be inventory turnover and other common described later, in the next chapter. metrics are inadequate for the evaluation of these networks of multiple companies, and

68 Chapter 2 - On Modelling the Supply Chain

· Total variable costs: is considered the Average turnover rates of our times are sum of the various types of variable costs; around 3 to 6 (PRTM, 2000), but many therefore it is also a component of the companies work with around 2 while some global costs. It gives an idea of how much it others can even reach the two digits, when costs to operate the company. using JIT, as is the case of DELL Computer, for example, with turnover rates around 30- · Income: is the net amount of money 40 times/year (Vijayan, 2001). earned by the company in the exercise of its activity in a certain period of time. It is also · Service level: is a very common measure measured in the units of currency (€). that gives the idea of the extent in which the company is able to fully and quickly · Cash flow: is the difference between the satisfy its customers, in terms of answering incomes and the total variable costs. It is their orders. There are some variants of this normally calculated at the end of a certain measure, but in general one can compute period of activity. It can be seen as the the service level as the ratio between the financial compensation for the exercise. material directly served from stock and the total quantity served, that is: · Turnover: also known as inventory turns, OrdersServedFromStock is a measure of the frequency in which the ServiceLevel = (2.5) TotalOrdersServed materials in the inventory are renewed in one year. This is usually seen as an This is usually presented as a percentage. indication of the operational activity of the company. The higher the turnover, the less · Stockout ratio: is another measure of the the company is stagnated. This is basically same kind of service level, but based on a computed as: complementary criterion. It gives the idea

of how much the customers were not GoodsSoldInOneYear Turnover = (2.3) AverageInventoryInOneYear served. It is, therefore, a failure measure:

OrdersNotServed StockoutRatio = (2.6) Expected turnovers can vary significantly TotalOrdersServed in practice, since they are very much dependent on the kind of process and · Customer Satisfaction: this is a relatively business in question. But, as one can easily new measure (Christopher, 1994) which perceive, this metric gets inadequate when results from the fact that the customer is many products are considered in inventory. nowadays considered the most important For that reason, a more general financial element of the Supply Chain. Therefore, it version of it is usually applied in practice, is reasonable to consider a measure centred that is, in one year: on the customer. But this metric makes use of multi-dimensional criteria, and most of CostsOfGoodsSold Turnover = (2.4) those criteria are subjective, as well as InventoryValueOnHand dependent on the type of business in question (see Cacioppo, 2000), making it

69 Flexible Supply Chain Simulation difficult to use in quantitative simulation equilibrium lost due the sudden step on the systems. demand. During this imbalance period, the To surpass the problem, it was decided to inventory instability is compared with the consider only a one-dimensional perspective optimum response, in which the equilibrium of customer satisfaction which is based on would never be lost. This “excess” or the relative speed by which the customer “shortage” of SKUs due to imbalance is receives the materials ordered. Considering considered a measure of the rigidity of the DTo the customer expected lead time from system. This concept of rigidity made it its supplier, and DT the supplier actual lead possible to represent the overall rigidity of a time, this measure is computed as: Supply Chain as a matrix of the individual rigidities of the companies, as a true DT CustomerSatisfaction = 100 ´ min(1, o ) (2.7) network metric. Still, to get this measure it DT is necessary to simulate the system.

And used as a percentage. Some other quantitative metrics exist to represent flexibility, property seen as a Rigidity: this is an entirely new concept potential behaviour and not an operating applied to management. It was developed behaviour (Slack, 1983), but such metrics by the author during his Ph.D. studies and have been developed for manufacturing published for the first time in Paris in 2004 systems and not properly for the Supply (see Feliz-Teixeira & Brito, 2004). Briefly Chain. Even if some of them can be adapted described, it represents the energy spent on to this kind of systems, they will not be used imbalance while the company is reacting to in our simulation approach for the reasons a sudden step on the demand. This is explained shortly. considered the inverse of flexibility and is measured in SKU/day in imbalance. In figure Many other metrics for resources and 2.13 is represented the basic idea behind outputs could be mentioned, like the this measure. Return-On-Investment (ROI), Cash-to-Cash Cycle Time, Backorder Level, Supply Chain Cycle Time, Perfect Order Measurement, etc., but it was considered preferable for this work to reduce the analysis to only some of them. Nevertheless, it is also worth referring to the Balanced Scorecard (Kaplan & Norton, 1992) as an important metric used for Supply Chain, even if we will also not consider it since it deals with some aspects of the company that are not of our Fig. 2.13 Using demand steps to measure rigidity concern, like forecast errors, certification, hours of training personnel, etc. Basically, The system, usually the inventory, needs this is a measure in which a group of a certain time (Tq) to recover from the appropriate measures are chosen to align

70 Chapter 2 - On Modelling the Supply Chain the company to its strategic objectives. times from suppliers as well as the average time of delivery of the facility, that gives 2.2.4.2 Fleet related metrics the user some more interesting information It is also important to give some details about the system being simulated. concerning the metrics for transportation processes. As in the previous case, a 2.2.4.3 The emphasis on flexibility significant number of different measures Nowadays there is a great interest in can be applied in practice, but for our concepts like flexibility and agility, even if intents we will consider only three of them more recently people seem to talk slightly that already give a reasonable quantitative less about them. Despite such concepts idea on how the diverse vehicles of the fleet having been well absorbed even by Supply are being used. Notice that in our approach, Chain management, the same cannot be to each facility in the Supply Chain is said about their metrics, either due to a considered assigned a fleet of vehicles, lack of general acceptance (the case of which can be owned or not by the facility. flexibility) or simply because they do not Diverse kinds of vehicles may compose the exist, as in the case of agility. fleet. Each referred metric will be applied Flexibility, coming from manufacturing, is to each of those vehicles: usually understood as the ability to quickly adapt production to different product mixes · Usage: this represents the fraction of time or new kinds of products, and is frequently the vehicle has been operating in a certain associated with alternative ways of doing period of activity of the company. It is things. Once one way fails or is occupied, computed as a percentage, and gives an other ways are available. In fact, however, idea of the activity of that vehicle during it is more than that, and some quantitative that period. metrics have been proposed (see Beamon, 1999). Volume flexibility, for instance, · Occupancy: this metric represents the measures the ability to respond to demand average vehicle occupancy with products variations in volume; delivery flexibility, during its time of operation. It is expressed deals with the ability to manage delivery as a percentage of the full capacity of the due dates; mix flexibility is about the ability vehicle. This can be a useful measure of of producing multiple products; and new how the vehicle volume is being optimized. product flexibility the ability to handle production of short life cycle products. As · Total time of operation: measures, in Beamon (1999) notices, some of these types hours, the accumulated time of operation of could also be applied to Supply Chain, the vehicle. This can be useful to estimate specially volume and delivery flexibilities, maintenance costs or to help in the project which are similarly computed. of maintenance processes, for example. The problem we find in these metrics is that they are computed statically, without Apart from these transportation metrics, the need of any dynamic computational we also keep in our simulation approach a process. For instance, the volume flexibility continuous measure of the average lead is seen as the “range of volumes in which

71 Flexible Supply Chain Simulation the organization can run profitably” (Sethi adapting to a “turbulent” outside world, not & Sethi, 1990), and is proposed to be only concerning production, but as well calculated by representing the overall other enterprise functions like distribution, demand in the form of a Normal distribution procurement, etc., since also liaisons among function and, knowing the minimum and partners in the Supply Chain can suddenly maximum limits of profitable demand, be established or broken. The term agility is computing the ratio of two areas under such normally applied to Supply Chain. Anyhow, function. This can be done with any if one asks a top manager in a top school of spreadsheet. And, as such limits probably management what agility is, for example, result from enterprise analyses that are the answer stays consistently vague and beyond any Supply Chain simulation results, rather evasive. The impression one gets is we could not consider this metrics in our that there is a tendency for talking about approach. Figure 2.14 is intended to help in certain concepts in the same way sellers clarifying such measures. The curve talk about love: “love is… being this or represents the Normal distribution of the that”, and stamp it on a T-Shirt just with demand. the intent to sell the T-Shirt.

Profitable From the present discussion one can demand conclude that it is still hard, if not Overall impossible, to introduce practical concepts demand of flexibility and agility in a Supply Chain simulator. The development of the idea of rigidity was, in this sense, a trial to achieve such a goal and, at the same time, a modest -s m +s contribution for bringing management a min max little closer to science, in a tribute to Frederick Taylor, Henry Ford and Taiichi Fig. 2.14 How to compute volume flexibility, as proposed Ohno. by (Sethi & Sethi, 1990)

Of course, enough demand samples must 2.2.5 A step up to holistic metrics? be considered for that m can represent the As we have noticed, there are presently average demand during a certain period of very few notes on the kind of metrics that time as well as s the associated standard could be reliable and of practical interest deviation. Flexibility will then be simply when applied to complex systems. These given by the ratio of the two areas: systems are often based on intricate

structures where a high number of entities profitableDemand Flexibility = (2.8) overallDemand interact with each other. Metrics are there for appropriately characterizing the nodes or parts of such structures, or small amounts Agility, on the other hand, intends to be of them, but when the intent is a measure a wider concept, dealing with the ability of for the complete structure either they fail

72 Chapter 2 - On Modelling the Supply Chain or appear to be too simplistic. That is and is continuously improving, the overall certainly a good reason for modelling those movements tend to be more harmonious. cases using a strategic point of view, as in JIT already looks at systems in a certain doing so the complexity is reduced a priori. holistic way. But when more complex representations are needed, this issue seems sometimes also But, what concerning metrics? How can related to a conflict between different one measure such a high number of states scientific approaches: the classical western typically found in complex systems, in order reductionism, of anglo-saxonic inspiration, to effectively retrieve from them some sort which believes the best approach is to break of useful information? the system into small parts and understand, As a metric is a characterization, we model or control those parts separately and could think that maybe the modern Data then join them together, therefore looking Mining (DM) techniques could be extensively at the world in an individualist way; and a applied, for instance. These techniques use more holistic approach, a vision slowly decision trees and other algorithms to spreading and largely inspired by oriental discover hidden patterns in huge amounts of cultures, which considers that each part of data, and are nowadays applied to almost the system must be seen together with the any problem based on extensive data whole and not in isolation, and therefore records, for instance, in e-Commerce for locates the tone in how the interactions customer profile monitoring, in genetics between such parts contribute to the whole research, in fraud detection, credit risk behaviour. Hopp & Spearman (2001, pp.16), analysis, etc., and even for suspected for instance, comment about this saying “terrorist” detection (see Edelstein, 2001; that “too much emphasis on individual Edelstein, 2003). However, they often imply components can lead to a loss of the usage of high performance computers, perspective for the overall system”. sometimes with parallel processors, as well A significant number of authors defend as huge computational resources to analyse this opinion, pointing out the importance of GBytes or even TBytes of data. They are developing a more holistic point of view to useful when any single record of data can be interpret and study systems behaviour, in a precious for the future result, and thus way that analysis can maintain enough when all data must be analysed. fidelity to the system as a whole. As Tranouez et al. (2003), who apply On the other hand, in many practical simulation to ecosystems, would say: a simulations a significant amount of data is complex system is more than the simple not significant for the final conclusions, the collection of its elements. simulation process is in itself a filter, and In management science, for instance, the therefore such data may well be ignored in western approach frequently generates the outputs, even if it could have been difficulties at the interfaces of elements, essential to ensure the detailed simulation typically of inventory or communication process to run. In the perspective of the type. On the other hand, as JIT gives better author, maybe there is a way that could emphasis to the relations and interactions deserve some future attention: the idea is

73 Flexible Supply Chain Simulation to filter such data during the simulation From the old but always powerful System execution and, at the same time, to turn Dynamics to the modern tendencies of Web- the measures probabilistic by using an based, parallel and distributed simulation, approach somehow inspired by Wave Fourier passing by several other analytical methods Analysis or Quantum Mechanics. That is, to and proposals, almost all the simulation represent the overall system state (y) in approaches have aimed to enter this field. terms of certain base functions (yi), and As usual, each school faces the new then to measure the probabilities (ai) challenge by proposing to apply its own associated with each of these functions. The resources and approaches. The result is a interesting aspect of this is that each base wide spread number of possibilities, which state function (yi) could even be arbitrarily have principally been developed based on chosen by the analyst, and the probabilities the approaches shown in figure 2.15. And

(ai) easily computed during the simulation. this is only a part of the big simulation tree Final results would then be summarised in presented in the previous chapter (see some expression of the form: figure 1.23).

y = a1 y1 + a2 y2 + … aj yj + … an yn (2.9)

which could be interpreted as: there is a probability of a1 that the system will be found in the state y1, a probability of a2 that the system will be found in the state y2, etc. This would be the final measure of the system, in a sort of characterization of expectations under certain conditions. Although this matter could in itself give Fig. 2.15 Approaches and tools more commonly used rise to a new research project, which is not for Supply Chain modelling and simulation part of the aim of the present text, a little more about this will be said in the last From this, one can already have an idea chapter, when referring to future research. about what to expect in terms of Supply Chain modelling proposals. Except for the cases of System Dynamics and Monte Carlo, 2.3 Supply Chain modelling and the analytic methods not shown here, Since Supply Chain management became all the other approaches are primarily used one of the most vital management issues, for discrete modelling. Monte Carlo the various schools of simulation have (statistic simulation) is popular in strategic naturally showed a great interest in the Supply Chain models, while the other matter. A wide range of models for the approaches are also suitable for dynamic Supply Chain is therefore announced in representation and, therefore, they are also scientific journals, magazines and the Web, useful for tactical and operational built with almost all the technologies modelling. We will present a brief overview available to represent and simulate systems. of each of these tendencies.

74 Chapter 2 - On Modelling the Supply Chain

2.3.1 System Dynamics approach like this. Many of the symbols used System Dynamics, for instance, is known in this figure are reduced to the essential in to represent a system as a set of continuous practice and model diagrams may appear as equations interacting in loops of feedback almost a sequence of loops engaged with where variables act and are represented one another. through causal diagrams. The model is 2.3.2 Petri nets visualized as a casual diagram made of several feedback loops that define the Petri nets is another important formalism dynamics of the system. For instance, born around the same time as Systems Mason-Jones et al. (1997) use this modelling Dynamics, but in a different school. It can approach to study the Bullwhip Effect in the also be used for Supply Chain modelling. well known “Beer Game” linear Supply Davidrajuh (2000), for example, uses this Chain, Minegishi & Thiel (2000) use it to approach to address issues related to agile model a small poultry distribution system, Supply Chains, while Nikolic et al. (2004) and Bruniaux & Pierreval (1999) for testing have also studied the “Beer Game” and the various scenarios of a Supply Chain, among Bullwhip Effect. Good attention can also be other cases. A very simple causal diagram is given to the more formal contribution of represented in the next figure (Fig. 2.16), Landeghem & Bobeanu (2003) concerning which was inspired by a simple layoff this approach. analysis described by Soderquist & Harris Petri nets represent systems as sets of (2001). places (symbolized by circles) connected by arcs to transitions (symbolized by rectangles Revenue Expenses or bars) which via arcs send output to other rate rate places (Fig. 2.17).

cash place

loop

transition workers arc Adjusting rate

Fig. 2.16 Example of a causal diagram of a model for Fig. 2.17 A simple example of a Petri net, with some simple layoff analysis using System Dynamics tokens inside some places

As it is easy to perceive, the system is The places represent states, while the modelled in terms of “recipients” and transitions represent events. So, this is a in/out “flow rates”, as in a fluid like sort of state transitions diagram. The description of its dynamics. Variables are entities that will flow through this structure seen as “valves” that control those flows, are called tokens. A very interesting aspect thus controlling the behaviour of the model. of Petri nets is that it is not only a Notice that loops naturally emerge from an formalism to represent systems but also

75 Flexible Supply Chain Simulation with which systems can be graphically (ellipsis) linked together by arcs9. Since in modelled. This is simply achieved by discrete simulation live states frequently representing the tokens as dots inside the are referred to as activities, these diagrams places and moving them in accordance to can also be called activity diagrams or even the following basic execution rules: (1) a activity cycle diagrams. From time to time, transition is "active" when each of its input expressions like live activities and dead places contains a token; (2) each active activities are employed as well. transition in the diagram is "fired" by removing 1 token from each input place and Order from client generating one in each output place. The dynamics of the model emerge from Wait1 these rules, and therefore the analyst can Material OUTPUT even form an idea of how the model will to client from inventory behave while building it. This is usually LOAD truck pointed out as an interesting property for Wait2 Wait3 debugging of Petri nets. Actually, based on this, it is very simple to deduce that the Wait4 PACK 1 PACK 2 previous system will evolve to the state represented in the next figure (Fig. 2.18), as transitions can “fire”8 simultaneously: Fig. 2.19 State transitions diagram of a simple inventory output process with two packing lines and one output bay place The dynamics of the system emerge from the sequence of actions carried out by the transition arc active elements of the model, which are called entities. These actions will create the evolution of the system in time. Thus, a system is seen as a collection of Fig. 2.18 The next state of the previous example entities which are performing their activity

cycles and interacting with other entities by 2.3.3 Sequential objects means of common activities. The next figure (Fig. 2.20) better illustrates this idea, State transition diagrams is another based on the same inventory process. commonly adopt method for representing Notice that at least four entities could be discrete models (similar to the one shown in detected in the present case: the client figure 2.19, related with a simple inventory order, the inventory operator, the packing output process). operator, and the operator to load the In this kind of representation each active truck. There is, therefore, the same number element in the system is modelled as a set of live states (rectangles) and dead states 9 Live states represent states which duration can previously be known or computed, while dead states are those where this cannot be done, as waiting states and queues, for 8 The term “fire” is normally used to mean “action”. example.

76 Chapter 2 - On Modelling the Supply Chain of activity cycles which then intersect at Model application common live activities, as it is shown in the figure. Notice that we have chosen the symbol of a person to represent entities in order to suggest they carry some sort of “personality”. Activity diagrams can then be “translated” to computer code, usually by means of one of the classical modelling paradigms: process-interaction, activity- Fig. 2.21 Sequential objects application scanning, event-scheduling or three-phase. As in this case objects communicate from Order inside the application, there is no need for from client any communication protocol since they are Wait1 all confined to the same space of variables Material and functions (methods) that belongs to the to client Client order application.

Loading Inventory This is perhaps the most wide-spread operator operator approach for modelling in the industry of

Free3 LOAD truck OUTPUT Free1 our days. It runs in cheap single processor from inventory computers and supplies the most popular and general tools for simulation like

Wait2 Wait3 Wait4 SIMCSRIPT, ARENA, FlexSim, C++Sim, Packing operator Simul8, among many others. Small, didactic

PACK 1 Free2 PACK 2 or academic models, frequently of linear Supply Chains, can easily be found described in literature developed with such multi-

purpose tools, but concerning cases of real Fig. 2.20 The entire activity cycle diagram for the same inventory output process, showing the entities of the system Supply Chains the situation is slightly different. Much less interesting articles are This kind of representation is frequently available, and also few tools are specific for used to implement entities as classes of Supply Chain simulation. objects in Object Oriented Programming An exception is perhaps the IBM Supply (OOP) simulation tools. The entire model is Chain Simulator (see Bagchi et al., 1998), then a computer application that “contains” with which some real Supply Chains have those objects, as exemplified in figure 2.21. been modelled and studied in terms of site Such technology is sometimes referred to as location, replenishment policies, inventory sequential objects10. levels, lead times, customer services, etc., using a dynamic simulation perspective. This is a tool used by experts for consultancy. As the same authors noticed, usually clients do 10 In truth, this term is not frequently used and in practice not want their results revealed, in order to people refer to this approach simply as “objects”. Even so, it is used sometimes in order to distinguish the idea from keep business advantages, and therefore “distributed objects”.

77 Flexible Supply Chain Simulation few models of real Supply Chain cases related to the procurement of additives for appear published in articles. lubricants in the oil refinery of Porto, Another exception is the powerful eM- Portugal (Feliz-Teixeira & Brito, 2005). This Plant simulation tool, also for Supply Chain system included a factory and 10 suppliers Management (SCM), which for instance is working with around 200 different products. used by BMW in its international automotive The simulation of a one-year operation was manufacturing Supply Chains (Crooks, 2002), achieved in 15 seconds. or even in other real case studies (Byrne & Heavey, 2004). This is a commercial tool 2.3.4 Distributed objects allowing a good interface with databases Distributed objects is another approach through Open Database Connectivity Link presently entering the phase of maturity. As (ODBC) and the possibility of using the OOP the name suggests, this technology aims to language SimTalk to customise its objects distribute the objects, that is, to build the realistically. With a tool like this, one can overall model based on a collection of already achieve the simulation of Supply sequential objects interconnected by means Chains where products are in hundreds or of a protocol of communication (see Fig. even thousands and work centres in dozens, 2.22). Such objects, also commonly referred for example. Of course, that must be a job to as components, can be written using for a team of experts. interfacing technologies like COM, .NET, References to some other Supply Chain Win Sockets, etc. Of course now an object specific simulation tools based on this can be even more complex than those of the sequential objects technology can be found previous case, since it is seen as an in exploring the Web, such as the Supply individual application. Thus, an object can Chain Builder form Simulation Dynamics now be a detailed model of a warehouse, Inc., or GoldSim from GoldSim Technology factory or any other element of the Supply Group LLC, but also the user will find it Chain, for example, which then will be difficult to get enough detailed information connected to the others by some kind of about them. On one side, they usually protocol of communication. either belong to consultancy enterprises or are extremely expensive. On the other OBJ 1 hand, results obtained with them are also OBJ 2 often restricted, confidential, since the client managers understandably prefer them not to be published. OBJ 3

The Supply Chain simulator described in OBJ 4 the next chapter intends to be of the same nature as these dedicated simulators, even if it stills in development and some more improvement would be necessary to make it a commercial tool. Yet, a real Supply Chain case has already been analysed with it, Fig. 2.22 Distributed objects modelling approach

78 Chapter 2 - On Modelling the Supply Chain

Since each of these objects owns a of operation of a Supply Chain with 10 private space for its variables and methods suppliers and one manufacturer11 Ping et al. (distributed memory), all of them need to (2001) reported simulation times between communicate significantly more than in the 100 and 250 seconds, depending on the previous case (shared memory) in order to protocol of communication used. On the exchange the indispensable information. A other hand, with the same Supply Chain large amount of time is therefore spent on structure distributed between Singapore serial communications, what will always be and the UK, Turner (2001) reports slower than in a shared memory approach, simulation times of the order of 500 to 1000 especially when the communications traffic seconds, or even more when using a WAN increases, as is expected to happen in most network. This would be considered much complex systems. This often results in more than 10 times slower, compared with slower processes of simulation, especially if the performances obtained with the running in a single machine. But this previous paradigm. The model detail of approach also leads to difficulties in each facility and the number of products synchronizing causality among components, considered were not revealed, however. on facing dependence on the reliabilities of To better understand this tendency, we different objects, etc. These are certainly represent in figure 2.23 the scheme of a some of the reasons why the industry does generic environment of distributed objects. not seem interested enough in this proposal, Each dashed zone represents a processing despite the obvious advantages of model unit, which for simplicity can be thought as reuse and interoperability. But it is an a single computer. attractive approach for certain high performance simulations based on powerful computer systems, like clusters of PCs, networks of workstations, etc. Mainly, this approach is widespread in the military industry. Opinions of its major supporters can be found in Taylor et al. (2002). There were also some trials on promoting this trend for Supply Chain modelling (Gan OB et al., 2000; Taylor, Sudra et al., 2002; Zeigler et al., 1999), despite the fact that the inexpensive sequential objects appear more compact, more controllable and much less computer resource dependent. Despite the promises of fast simulation execution, interoperability, geographical distribution and scalability, which are very attractive Fig. 2.23 Distributed objects in different processing units characteristics for battle simulations, multi- player distributed games, air traffic control, 11 This structure is very similar to the one of the refinery etc., the fact is that in simulating 300 days simulated with our sequential objects approach, with which one year of operations could be simulated in 15 seconds.

79 Flexible Supply Chain Simulation

In the case of this figure there is a the models. That is, of course, the case of computer running one model, another military organizations and certain scientific computer running two models and a third research institutions. To the contrary, the one running four models. These computers industry of our times seems to maintain a can be connected by some sort of a preference for the robustness offered by network, or even the Internet. Of course, sequential objects applications. there are two obvious interesting aspects in this configuration: the full model is made of 2.3.5 Agents smaller independent models (objects, or Generically, Agents can be thought of as components), and these models are spread a kind of distributed objects with embedded by more than one computer, that in Artificial Intelligence (AI). They include principal speeds up the execution of the techniques for learning and for reasoning entire model. But, at the same time, this with which decisions are taken frequently scheme implies the need for a strong based on risk/benefits analysis. There is a synchronization between the times of the long list of different kinds of Agents — individual models and the simulation time, please refer to what is considered the if running a dynamic simulation12. Even if “Bible” of Agents (Nwana, 1996) — but, there are some methods of achieving such when applied to Supply Chain, Agents are synchronization (conservative, optimistic), largely seen as “intelligent” components for the fact is that this is a weak link in the automating actions of planning, optimizing, proposal, as the efficiency of such methods controlling, etc., playing in an ambience of is not so high (Porras & Ikonen, 1999). frequent exchange of data with other Actually, conservative methods need to Agents, that can include negotiation and block the time advance in the fastest collaboration. Agents communicate with models in order to follow the slowest, while each other by means of messages. optimistic methods need to unschedule Swaminathan et al. (1998), for example, events and to rollback the simulation in were proposing a multi-agent framework for those models that run too fast. Slowness or Supply Chain analysis that later have been instability is what results from this. used by IBM for modelling some real Supply Chains. In this framework they used a Another weak characteristic is that since combination of simulation modelling and the full model is not running in the same analytical modelling to provide the user machine, if any problem of communication with resources to analyse both static and exists, even in a single connection, the dynamic issues in the Supply Chain. Two whole simulation is affected. So, this seems kinds of elements were used as modelling an interesting approach for organizations primitives: structural, which comprised the that make use of high performance different Agents for assigning to retailers, computing and, at the same time, can have distributors, manufacturers, suppliers and physical access to all the machines running transportation; and control, in which other Agents were made responsible for the 12 This problem does not exist while running Monte Carlo policies of information, demand, supply and simulations (static simulations), so these systems are materials flow. extensively used for such sort of approaches.

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Recently, however, Agents’ technology interpreting results obtained with Agents in seems much more focused on dynamic dynamic simulations, as the uncertainties information systems to assist in on-moment rise with the self-ruled decisions taken by and online Supply Chain Management (SCM) such modelling components. As it is known, (Ahn & Lee, 2004; Benisch et al., 2004; uncertainties automatically propagate from Chandrashekar & Schary, 1999; Pardoe & the inputs to the outputs of the models, so, Stone, 2004). Ahn & Lee (2004), for naturally things are expected to get worse instance, present a dynamic information when sources of uncertainties exist within network based on multi-agents in which the the models, as easily happens in dynamic Agent behaviour is modelled with Petri simulations based on Agents. This is one of nets, and messages exchanged using the reasons not to include this technology in Internet protocols like TCP/IP, HTTP and our modelling approach. SMTP. In this case, each company in the Supply Chain is managed based on four basic 2.3.6 Web-based Agents: the Market Estimation Agent (MEA), What is habitually called Web-based which directly observes markets and simulation is a sort of simulation service estimates demands; the Order and provided within the Web. While the idea of Production Agent (OPA) which is focused on distributed objects intends to introduce the handling orders and production; the Supply advantages of a certain multiprocessing Chain Structure Agent (SSA), responsible for structure, the Web-based proposal aims constructing information networks; and the mainly to introduce the advantage of a Planning and Scheduling Agent (PSA) who multi-user system (Fig. 2.24). plans and schedules orders and production.

This is, of course, slightly different from user2 user1 the traditional simulation approaches where warm-up times must be considered and user3 several replications must be completed before retrieving any sort of quantitative information for characterizing the system. userj To the contrary, in these SCM proposals the goal is essentially the optimization of decisions, the help in on-moment SCM and e-Commerce, very appropriate to our times of expansion of Internet through the planet. Web-server userN

Anyhow, there, what is simulated is management decisions, based on some Fig. 2.24 The basic idea of Web-based simulation static functions constantly updated and inspected by “intelligent” algorithms, not Almost involuntarily, this makes us think the physical operations of the Supply Chain. of the 35 years old UNIX operating system, which included all these attributes in that In addition to this, it is common that time. How developed would operating researchers refer to the difficulties found in systems and networks be at present if we

81 Flexible Supply Chain Simulation would have explored better this direction? simulator, where these models were finally Metrics of evolution must certainly focus on interpreted and executed. what we have achieved, but probably they Commercial Web-based Supply Chain would even be of a superior kind if they simulation engines are not yet so frequent, would also focus on the excellent but one can mention the scSimulatorTM from opportunities we have lost, and measuring DecisionCraft13 company as it seems to be one against the others. one of those tools deserving reference (see also Lau et al., 2002). It already allows the Web-based simulation is very much representation and simulation of average- following the tendency of Internet service complex Supply Chains, at least with a level providing, as observed. Any special debate of detail that seems appropriate to the about this matter in order to understand or extended enterprise. justify the great advantages of such an This technology, however, seems not to approach is therefore dispensable, since be so attractive to the industry too, maybe they appear obvious from the kind of due to some important issues arising from network topology in question (see figure the fact that it is committed to run within a 2.24). public communications network. Industrial The simulation engine is made accessible espionage, low versatility in model on Internet, usually on an Internet site, and development and configuration, poor distributed users can use it from remotely simulation speed, when compared with as a resource of any ordinary Webpage. standard sequential objects, could be Notice that each user can also communicate pointed as some of these questions. The with other users by any of the means disadvantages are quite similar to those of provided by the Internet, as email, SMS, the distributed option previously discussed. chat, messaging, etc., making this approach Such approaches can still be very interesting especially interesting for distributed groups either for vertical institutions or in of decision, as in many cases is the Supply environments of trust, of course. Chain system. In reality, this technology has showed Frequently, the simulator engine is a more impact on academic and educational JAVA applet embedded in the HTML code of purposes, being applied in Web-tutorials, the site; the communication between the interactive teaching of complex dynamics, users and the model can be very simple, small-medium simulation studies, blend configuring the model by introducing values learning, etc., (Kuehn, 2005; Veith et al., into appropriate fields and receiving the 1998), and also in interactive multi-user simulation outputs in the form of tables of simulation games, as in the Web version of values or charts, or even by email, for the “Beer game”14, or in the more complex example. More recently, exchange of LINKS dedicated to SCM (Chapman, 2005)15. information is sometimes achieved by means There is sometimes the inclination to call of XML file transfers between the Web- Web-based simulation also to a certain browser and the simulation engine (Kuehn, 2005). Chatfield et al. (2004) also used XML 13 http://www.decisioncraft.com/scsimulator/ 14 to transfer Supply Chain models to a remote It can be played at: http://beergame.masystem.se:8000/ 15 Game LINKS at: http://www.links-simulations.com/

82 Chapter 2 - On Modelling the Supply Chain distributed simulation in which the support the time or share information, we say they network is the Web (Orsoni et al., 2003), run in a distributed manner. Of course, but that probably represents, at least in our some distributed component can also be opinion, a little inflation of nomenclature. broken down into smaller components which can then be made to run as parallel tasks in 2.3.7 Parallel a multiprocessor computer. This approach is often mentioned along This view on classification also explains with distributed simulation in the literature. why Monte Carlo simulations, being of a Distributed computer systems can in fact static type (there is no need for time-marks sustain distributed and parallel processing. or synchronization), are the simulations Although the nomenclature is somewhat often running in parallel machines. And it diffuse on this issue, one can always follow also helps explaining, indirectly, at least, the clear separation that exists between the why it is difficult to find dynamic parallel hardware processing structure, and the Supply Chain simulations: once they are software processing method. In the first dynamic, they need time synchronization, case, either there are some interconnected which automatically leads to the distributed machines each one with its own operating classification. system (distributed hardware), or there are several processing units running under the Apart from this, parallelism is being used same operating system (parallel hardware). as a way for improving speed in distributed This last option is considered the classical simulations, by breaking certain components parallel system, where diverse software jobs into smaller parts and making them run in run in different parallel processors, usually parallel machines (Ping et al., 2001), for in an environment of shared memory. example. But principally this approach is Nevertheless, in a distributed hardware being empowered by the modern tendencies structure one can also assign different jobs of Grid computing, a world wide distributed to different processing units in order that computing that makes use even of the they run independently of each other, and Internet, specially focused on parallel that is also considered parallel processing. processing of huge computational tasks. This Parallel therefore refers to time, while is seen as very promising for Monte Carlo distributed refers to space. As one can see, simulations (Hill, 2004), as on the Internet nomenclature can get considerably boring, thousands of machines can be made to run in its subtleties. Grid jobs simultaneously. As the same Thus, instead of talking of distributed- author notices, 50000 protein sequences distributed, distributed-parallel or parallel- could be compared with distributed Grids parallel, we prefer to look at the issue software where more than 75000 Internet based on how time and synchronization are users have participated. The results could handled among the distributed components be achieved in 2 months, while 1170 years of the model. If two components can run would be necessary to achieve the same in a independently of each other, we say they single computer. However, he also notices run in parallel. To the contrary, when they that many applications cannot be need to wait for each other to synchronize parallelized.

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Finally, also in this case one must be spots pointing at the nodes in the figure), to careful with nomenclature, since the term the consideration that each node is parallel simulation is sometimes used to described by a highly detailed model of the classify common discrete simulations made correspondent facility. to run side by side with the systems in order to help managers in optimizing on-moment decisions. Recent news about this can be node0 found in Bodenstab (2004).

2.4 Which approach to choose node2 With such a diversity of methods to create simulation models and to translate node1 them into computer applications, one could imagine that perhaps some difficulties would arise when deciding which approach Fig. 2.25 Elemental model of a 3 nodes Supply Chain to choose. In reality, however, such a problem almost does not exist in practice, Obviously, both the model complexity since usually people naturally follow the and the time needed for processing it approach of the simulation school they increase with the increase in detail. The belong to. Things seem to be slowly least detailed way of looking at the problem changing with the globalization of methods is to attribute to each node a single value and ideas, but in fact the first impulse is (as well as to each arc) and transform it into still given by the school, or the culture of a Linear Programming (LP) problem, a simulation that surrounds the analyst. method since long ago used for optimizing the location and capacity of the various 2.4.1 Level of detail facilities. At such level of detail, any Apart from that, it makes sense to discuss standard commercial computer has enough other motivations that can influence the processing power to fast resolve a network choice of the simulation approach. And the structure of a realistic Supply Chain, with most important is, probably, the level of the number of nodes varying in the order of detail required in the modelling. This is also a dozen to the order of some hundreds. related to the level of management in As the detail increases, the node will be question and the answers one expects from represented first by an analytic function the simulation. independent of time (static simulation); Concerning the Supply Chain, and using as then as a sequence of some time dependent support the three nodes example of figure internal processes (System Dynamics, if 2.25, the level of detail can go from the continuous; Petri nets, activity diagrams, consideration that each node is simply sequential objects, etc., if discrete); and described by a function fj(t) with a certain finally as a complex object with a level of number of input variables (the little black detail depending on the case. Obviously, the

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more complex the node gets the more the Activity0 slower the simulation becomes, since more and more events must be processed per unit of time. Roughly, if the node is 10 times T1 more detailed, the simulation speed will be Activity1 Activity2 Activity3 at least 10 times slower. This explains why approaches like distributed and parallel T2 processing can become extremely attractive to simulating systems where highly detailed nodes are considered. Higher detail in the Fig. 2.26 Illustrative example of increasing detail in the case of a simplistic stochastic model nodes also implies a higher number of input variables to configure the model, of course, The final duration T resulting from this and that contributes for the increase of the 2 “composed” activity will of course be time spent on preparing the model to run. affected by an uncertainty DT2 which 2.4.2 Where to stop roughly results from the summation of the individual uncertainties. Clearly, if this sum Apparently, there are no limits on the is inferior to the previous case, the detailed level of detail other than those imposed by model is contributing to the clearing of the the resources to create and to run the output signal (results); if not, the detailed model, in which time and money are of model is contributing to the increase of the course included. For many years a certain “noise” in the output signal (results). This idea that detail could be good ad infinitum simple case demonstrates that the detail has been established, but presently analysts itself can make the simulation lose start to be more prudent and moderate on “resolution”, due to an increase in the this issue (see for instance Clay, 2000; induced “noise”. Kulick & Sawyer, 2000). Here we will expose So, what in fact is relevant is to ensure a some of the most significant aspects that reasonable signal-to-noise ratio (to use the can justify limiting the model details. nomenclature of the signal processing field),

and that must be an important element of Uncertainties: let us consider an the analysis before deciding on the kind of illustrative case. Suppose that a certain live modelling approach to use. This largely activity named Activity0 has a stochastic depends on the quality of the input data duration T1 estimated from a probability available for the simulation. In practice, it distribution, which therefore imposes an does not make much sense to develop a uncertainty DT in the final measure, as 1 highly detailed model of a system when the represented in figure 2.26. Suppose now stochastic input parameters are only known that in a more detailed version of the model within a wide range of uncertainty. this same activity is substituted by a sequence of three new “smaller” activities Output complexity: another aspect is the (bottom part of the figure), each one with increase of complexity of the outputs when its own stochastic duration. detail is increased. Even supposing the

85 Flexible Supply Chain Simulation problem of uncertainty resolved, increasing Since complexity is not (yet) understood the model detail frequently results in as a whole, we need to break it down and making “visible” certain effects that classify it by means of simpler concepts, previously were filtered by a less detailed which then we can handle easier and follow version. Principally, this happens when its dynamics with less difficulty. In a modelling systems of intricate topology, as sentence, we need abstraction to think and is the case of most Supply Chains. If the to execute decision operations. system is very simple, with one, two or This means that when the detail of a three nodes, the possible combinations of model is too high it can turn too difficult to states are also small and such effects are classify its responses in all the diversity of not so significant. The problem becomes its states, and therefore to use abstraction evident when several nodes interact. A to understand the system. In such common case that can be used as example circumstances, the results obtained start to is the time variation of inventory in a contribute much more to confuse the mind factory connected to several retailers, for of the analyst than to clarify it. Of course instance. With a low level of detail in one can always filter such results again, and sampling inventories, say monthly, the retrieve from them the same sort of analyst still have the chance to “follow” and information that could be retrieved with a mentally “understand” the dynamics of the less detailed model. Obviously, the point is facility. But when the inventory is that frequently one trusts more in a represented daily the “signal” starts to be detailed model than in the opposite. so complex that few more information can be retrieved from it. Figure 2.27 represents Sampling rates: although this aspect is a simulation output obtained from a similar not found in literature about modelling and case using a didactic Supply Chain of a simulation, at least as much as we know, it single product. The inventory is computed can be mentioned as an additional help for daily. establishing the level of detail of the model. This is based on the old Nyquist sampling law (Nyquist, 1928) which since long ago has been intensively used in the diverse kinds of signal processing. This law basically states that a signal must be sampled at least with a frequency equal or superior to two times its bandwidth in order that it can be later recovered from the sampled version. Indirectly, this also means that if one is interested in analysing the system’s dynamics based on events separated in time

by DT0 one has at least to “sample” the

behaviour of such a system with a sampling Fig. 2.27 Example of an average complexity in the daily inventory of a factory in a relatively simple Supply Chain rate double that, which is f = 2 / DT0 . This means, that when we are interested in

86 Chapter 2 - On Modelling the Supply Chain analysing a system monthly, we must at of a repetition. Perhaps this is due to an least collect some data fortnightly. Of excess of optimism, but the fact is that it course, a weekly sampled data can help still causes some confusion in certain minds, even better in the reconstruction of the mainly when high quality graphics are used original behaviour, but the fact is that more to visualize simulation. Besides, probably than, lets say, 10 times the desired rate, is the level of such “predictions” is not much higher than that obtained by applying almost more than enough to build a simple good judgement, and with a little “representative” sample of such behaviour. more of wisdom perhaps it is even Therefore, after a certain level of detail surpassed. For instance, a large number of (say, 10 times the highest delay to be ordinary people have been able to perceived), the more the detail increases understand that the recent military the less it contributes to the definition of operations in Iraq would turn into something the output signals. very different from what the USA and its This can be translated to modelling in a allies were expecting, while it seems the very simple way. Suppose the minimum sophisticated simulations running in DoD delay we want in the simulation is 24 hours powerful super computers were not able to (1 day), like in some operational models of perceive it. Common sense, in this case, the Supply Chain. So, at least we have to showed to be more reliable than high-tech sample the system with events separated by extremely detailed simulations. As Schrage 1/2=0.5 day, if there is any change of state. (2000) observes, “truly effective models This means we have to choose details that aim not to predict the future but to envision possible futures that can be at least include live activities with durations managed successfully”. I believe as well of this order. In the maximum detail, that simulation can be a very useful tool for however, it makes sense to represent such analysing and to characterize systems, but activities with duration of 1/10 of a day, not to get any reliable conclusions based on but not much more than that. More than parallel realities running as a sort of that will probably become redundant. predictive game.

Future is not predictable: finally, this is 2.4.3 Simulation intents what I consider the first thought to have in The objectives behind a simulation study mind when building a stochastic model. The are perhaps what influence the choice of future is not predictable by manipulating the modelling approach the most. Such stochastic data from the past, that is, by objectives can of course be diverse, as they the usage of statistic methods. In fact, what is estimated with these methods is how frequently depend on a wide range of much the future will be similar to the past. issues, going from the simple purpose of In that sense, it is not a prediction of the promoting or advertising some system to the future, but a prediction of the past that will need for executing precise and complex probably repeat. This is important to be calculations while projecting an expensive referred since sometimes people increase structure, for example. In this section will model details as if they automatically would be pointed out the sort of objectives that enhance the ability of forecasting in terms have inspired the Supply Chain simulator of prediction and not of such an estimation described in detail in the next chapter.

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Together with the arguments discussed in With the current inclination towards JIT, the previous sections, this is expected to the ultimate customer is the one who contribute to justify the sort of approach triggers the actions, which explains the chosen. importance of these two basic principles. “Be aware“ gives the managers a sense of During my first visit to the Management vigilance, on monitoring how the system is School of Cranfield University, UK, the idea running and what kind of trends are there; was to be aware of and absorb the actual while “react fast“ gives the ability to tendencies and needs of the Supply Chain effectively answer in terms of actions, or manager. Concepts of flexibility and agility movement. This clearly results from applied to the Supply Chain seemed the managing what is called the information most interesting issues at the moment, due pipeline and the materials pipeline. In to the surge of turbulence in globalized order to test fast reactions the analysts markets and the need to frequently readjust must be able to simulate different kinds of both the structures and the policies. Some network structures, different policies, review of the literature, some discussions different process technologies, etc. On the on the various perspectives of simulation other hand, related to “be aware”, the and some brain-storm sessions with a highly analyst must also be capable of testing skilled team in Supply Chain Management different information delays, different (SCM), have finally led me to condense the visibilities of demand, etc. This led to the trends in the two major directives: (1) react choice of a simulation approach that could fast and (2) be aware. These were, if one at the same time allow trying different can say, the vectors which would establish system topologies and policies, as well as the design of the future Supply Chain different information delays and even some simulation tool (Figure 2.28 shows the processes of ordering material based on original sketch of the idea). Internet ordering and e-Commerce. Other aspects like cooperation and Test new locations visibility would not be fully implemented in

Optimize network Test new links the simulator due to some difficulties in React fast establishing mechanisms for synchronising Test new forecasts Optimize policies Test new inventory policies partners, in an approach that does not Test new production policies consider centralizing decisions. In reality, in Test new transport policies this version of the simulator there is not any Optimize process times Test new designs element in charge of the integrated Test new technologies management of the Supply Chain. Instead, Control partners the Supply Chain system is considered Be aware Test new information delays horizontally managed, with each facility See all Test synchronisation being independent from the others as in a

Test demand upstream data truly free-commerce structure. At the same time, as the race now stands between Supply Chains, which many times in practice Fig. 2.28 Sketch of the primer SC simulation objectives overlap their structures, it makes less sense

88 Chapter 2 - On Modelling the Supply Chain to contemplate a centralized management, as several elements could be captured in 1) the “crossed fire” of dissimilar policies of neighbour Supply Chains. Thus, if in reality the competition stands between Supply 5) Chains, a simulator using a horizontal 3) 2) management structure will probably be 4) more interesting for analysing real cases. Time

Finally, since the intention was to build a simulator founded on the dynamics of the Fig. 2.29 System and model evolutions with the time operational field, allowing fast modelling and execution of simulations, and since it Obviously, despite the discrepancies that was considered more relevant concentrating can be expected when comparing system the analyst in a single application where values and model values, this image shows a managers also could focus on examining the good consistency along time, since such Supply Chain, the approach chosen was the discrepancies seem to preserve the same sequential objects. Novel contributions for magnitude. The accuracy of the model is modelling and simulation of Supply Chain simply inversely proportional to these systems will consequently result much more discrepancies, as we know. from the modelling structure proposed than from any announcement of a new software The problem is that most of the time the or hardware technology. evolution of the model will not always “fit” so well to the evolution of the system, as 2.5 Model consistency, accuracy these discrepancies usually increase with the number of cycles simulated, resulting in A model must of course be consistent an inconsistent model evolution (Fig. 2.30). with the system it models. If not, the results obtained with it will diverge from the reality and no legitimate conclusions can be 1) retrieved from the process of simulation. In 3) games, this issue is not so critical, but when dealing with “serious” simulations this is of major importance. Figure 2.29 intends to 5) 2) represent five different moments in the 4) time evolution of a system (smoother violet line) and its model (lighter grey line). Time Notice that the model is represented never exactly fitting to the system. This is a general representation, thinking of the Fig. 2.30 Inconsistent model evolution system as a spatial component that evolves in the time.

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Two main reasons for this can be pointed. value itself already no conclusions can be On one side, there are some deviations that retrieved. in certain processes accumulate with the time, since discrete simulation processes 2) 5) are basically founded on procedures which 1) 3) have strong iterative or recursive logic. This happens, for example, in the extremely complex models used for “predicting” the weather. 4) In the Supply Chain, for example, it can Time mean that if one calculates the next value of the inventory based on its previous value and a mathematical function, for example, Fig. 2.31 Somehow a consistent model, but blurred it is expected that after some cycles of calculations the results will be diverging This problem is more common in complex from those expected in the system and that models using statistical input variables, as such deviation will depend on the deviation well as when the results are computed introduced by the function. This can give based on long processes of simulated rise to cumulative problems which probably activities. Since there is often little precise could be minimized by “reconfiguring” the information about the values of certain model with values regularly acquired from factors when modelling real systems, these the system, as in a kind of reset process. factors are often estimated also by means of Though, in most complex and extensive stochastic variables. Once again, this simulations this is evidently not possible, uncertainty can grow with time and, after a since the system is often either not certain point, make the outputs of the accessible or not available for retrieving model a confused space of blurred values. such measures. One must rely on the To minimize this, it is important that the accuracy of the mathematical functions and stochastic variables are as exact as possible, relations which make up the model. They that the model will not be used in an must be as error free as possible. And such excessive number of cycles of simulation, exactness has to be considered with regard and that the processes involved in the to both the spatial and the temporal calculation of the results are the shortest components of the model. possible, as uncertainty naturally increases while entities cross from state to state (or On the other side, the propagation of activity to activity). In some circumstances, uncertainties in stochastic models affects to as we have seen previously, it may be more a kind of blur the parameters related with advantageous to represent processes with this type of variables (Fig. 2.31). less detail than as a chain of detailed After a certain time of simulation, this activities. effect may turn certain variables useless for retrieving any reliable conclusion. When the These two problems are concurrent in uncertainty of a value is of the order of the practice, and the results of a simulation can

90 Chapter 2 - On Modelling the Supply Chain transform into a serious disorder if no in the model functionality. Once the model strategy is previously established to is implemented and verified, it can pass to minimize such effects, which, of course, are the phase of confrontation with reality, that much less observable when simulating is, to validation. simple systems or short periods of time. The strategy that normally confers some Validation is the procedure of comparing confidence to the model is based in the the results generated with the model with following sequence of procedures: those generated by the system. The model verification, validation and accreditation. is first configured with real data, and then Notice, however, that in reality only after made to run. Obviously, this implies some all the aspects of a model would be difficulties, as usually not all the output compared with the same aspects of the variables from the model can be confronted system one could ensure if the model is a with values generated by the system. The good representation of the system or not, available data collected from the system is but this is an impossible task to finish in usually less detailed and in much smaller practice, particularly for dynamic models amounts than that generated by the model. where the states vary with time. Thus, it is Thus, comparing these two sets of data can important to keep in mind that the decision require substantial manipulation, and in to consider the model suitable or not that case the validation becomes more suitable will frequently result from an indirect. A good idea is to base this process inference. Verification, validation and on the comparison of variables that accreditation will confer some legitimacy to represent accumulated values, if possible, the model, but do not ensure that all the as their deviations are more sensitive to any results will in fact be repeatable by the discrepancies between the model and the system. system. Point to point comparisons may also be used, and they are useful to enhance the Verification is the procedure of verifying validation process, but this needs detailed the logical consistency of the model so that data collected from the system. Another it can be used as a good representation of way is to run the simulator with historical the system. This is normally done during the data and compare the model results with phase of development and includes all the the respective chronological system results. reviews and adjustments considered as This is an excellent technique and it is “debug”. As there is no especial procedure recommended to be used whenever for doing this, the same methods are used possible. as for testing and debugging any ordinary In the validation process the ultimate computer programme. In the end, the logic idea is to calculate “an error rate based on of the model must appropriately mimic the data independent of that used to estimate logic of the system. Some tests with small the model. This exercise gives a statistically examples of models can be done and their valid estimate of the true error rate that results analysed and compared with those the modelling procedure produces. It does expected mathematically, for example, in not guarantee that the model is correct in order to gradually build up the confidence any way. It simply says that if the same

91 Flexible Supply Chain Simulation technique were used on a succession of models are intended to supply the same databases to build a model, the average field of objectives and to contribute to a error rate would be close to the one “convergent” goal. obtained this way” (Small & Edelstein, 1997). 2.6 Getting results with the model Accreditation is by some specialists, In general, one could say that the most among whom the Department of Defense of challenging aspects the analyst faces when USA (see DoD/US, 2002), considered the confronted with the outputs of a complex desirable last step to guarantee the simulation are: complex behaviour and accuracy of a model. It is a process that variability. Complex behaviour urges the requires an institution with recognised analyst to find ways to understand the authority to test and certify models. As output data, usually making him or her Balci & Saadi (2002) made noticed, the well known International Organization for react by classifying such behaviour in terms Standardization (ISO) prefer to distinguish of categories, as we often do with reality. accreditation and certification as follows: Seeking relations or patterns already accreditation is a “procedure by which an induces some abstraction and helps in authoritative body gives formal recognition getting some order out of the complexity. that a body or person is competent to carry There are several methods for assisting out specific tasks”; while certification is this process, as in the case of simply seen as a “procedure by which a third party graphing the data using suitable charts; gives written assurance that a product, searching for attractors using (xn, xn+1) process or service conforms to specified correlation graphs; comparing the data with characteristics”. Ant this is what is usually a threshold and extracting only some accepted worldwide. particular information from it; using the In practice, however, accreditation and Fast Fourier Transform (FFT) or Discrete certification are difficult tasks to achieve Fourier Transform (DFT) to examine the due to the complex issues converging on data in the frequency domain; counting them, ranging form the fact that each type events so as to build histograms; applying of modelling and simulation poses its own Data Mining (DM) techniques for searching technical challenges (see Balci, 2003), to for relations and reorganizing the data in the fact that such issues generate obvious certain spots, etc. controversy related to the power given to These are some examples of how people those who would dictate. At present, these face and try to react to the complexity of procedures are simply substituted by the certain simulations. Sensitivity analysis and credibility and skills of the model developer antithetic variates are other forms of or consultant who directs the simulation searching for possible relations among the project. Such “distributed” and “divergent” data by experimenting with the model in responsibility about model accuracy appears different simulation runs, for instance. A to be adequate for the diversity of civil unique solution, however, does not exist. projects, although justifiably it may be less Final results depend even on the criteria interesting for military purposes, where applied to the raw output data. For an

92 Chapter 2 - On Modelling the Supply Chain extensive text on the traditional methods of at the end of each simulation run the analysis used in simulation please refer to general uncertainty in Y will be given by: Law & Kelton (1991). ¶f ¶f ¶f dY = dx1 + dx2 + ... dx j + ... (2.11) Another difficulty about the results ¶x1 ¶x2 ¶x j generated by complex stochastic models is the variability due to the propagation of which shows that the final uncertainty is uncertainties during the simulation process, dependent not only on the initial responsible for the level of confidence of uncertainties of the input variables but on the output values. For the purpose of these multiplied by their derivative factors. illustration, the next figure (Fig. 2.32) Different outputs depending on the same shows the same simplistic 3 nodes Supply input variables can therefore exhibit Chain mentioned earlier, this time different uncertainties, as will be shown by presented with some stochastic input calculating the uncertainties of the Y01 and variables. Each stochastic variable xj is Y02 examples. In fact, these are: associated with an initial uncertainty dxj, represented in the figure as a little blur. dY01 = k(x5dx2 + x2dx5 ) (2.12) Also represented are two generic output variables Y and Y dependent on the same é x 1 ù 01 02 dY = kê 5 dx + dx ú (2.13) 02 2 2 x 5 input variables x2 and x5 and on a generic ëê(x2 ) 2 ûú constant k. And, with some manipulation, one gets: X6 X7 k Y01 = k(x5 + x2) dY = dY (2.14) 02 2 01 X5 node0 (x2 ) Y = k x / x 02 5 2 X8

This means that as x2 goes smaller than k X9 the uncertainty in Y02 can get extremely X3 high (please refer to Weisstein, 1999). node2 X11 X2 Using a similar type of graphic notation, node1 one can consequently say that after N X10 X4 independent runs, a generic output measure X1 Y will be represented as a group of N blurred spots from which a final value must

Fig. 2.32 A simple 3 nodes Supply Chain, with inputs xj be computed or inferred (see Fig. 2.33). There is obviously a cloud of uncertainty Since each generic output variable Y will associated with the measure of Y after the result from a function dependent on the replications. Different averages of Y result input variables xj, that is: from the different seeds with which the random number generator is initiated, in

Y = f (x1, x2 ,...x j ,...) (2.10) the beginning of simulation.

93 Flexible Supply Chain Simulation

Yi 2.7 Final comments Y Y2 3 Before entering the next chapter, it is YN important to mention that two other Y1 simulation approaches have been used in the aim of this work of research. The first Fig. 2.33 Results of output Y after N simulation runs was a test made on the distributed objects paradigm; the second, a distributed game Frequently, however, the propagation of somehow close to the idea of web-based uncertainties from the inputs to the outputs simulation. These “tools” were intended for in each replication is ignored and only the different purposes. average effects resulting from different replications are considered. That is, Distributed simulator: before deciding frequently each Yi is seen only as an average on the sequential objects, there was a value. The final result is presented, with its period of hesitation regarding the way to variance var(Y) and the confidence level go. Some distributed tests have, therefore, required G(q), as: been arranged based on the interconnection of two detailed warehouse simulators (Feliz- Y = Y ± var(Y ) ´ G(q) (2.15) Teixeira & Brito, 1999), using the TCP/IP protocol and the WinSockets as interface where: (Fig. 2.34).

1 N Y = Y (2.16) N å i i=1

N 1 2 var(Y ) = (Y - Y ) » s 2 (2.17) (N -1) å i i=1

G(q) is a value retrieved from a t-Student distribution when N<20 or from a Normal distribution for higher values of N. For a confidence level of 95% and N>20 we have G(q) around 2. Inserting now the contribution of the average variance observed at Yi, and considering the replications independent, a more accurate estimation of Y will finally be given by: Fig. 2.34 Two detailed warehouse models have been made to run in a distributed mode, using TCP/IP Y = Y ± var(Y ) + var(Yi) ´ G(q) (2.18)

94 Chapter 2 - On Modelling the Supply Chain

It was observed that this scheme (based various simulators, rather than on on a conservative time synchronization conceiving some kind of interesting method) worked, and even the visual structure to model and simulate Supply effects of each simulator could be Chain systems in a faster and easier way. In perceived, but, despite the speculative the end, this last aspect was seen as the interest of the approach, it was most relevant. substantially slower than the sequential objects. Supply Chain game: apart from this, also developed was a distributed simulation What was interesting about this approach application for SCM training, at least for the was that the time increment for operational and tactical levels. A manual synchronization may be chosen by the version of this game, known as “Cranfield operator (for instance, set to 1 day), so, Blocks Game”, was first introduced by each simulator could run independently till Richard Saw (Saw, 2002) to assist the that moment, when it would have to wait students at the Cranfield School of for an authorization to continue till the next Management. It is a multi-level Supply Chain time stamp. The time advance method was structure (shown in figure 2.35) in which therefore a kind of “time-slicing waiting for each group of students is in charge of one of the slowest”, if this term can be used. the seven facilities, from the Depot1 to the However, it was observed that the approach Factory. Certain demand patterns are was evidently slow even when simulating injected at the customers level (C1 to C4), two facilities, leading to suspecting that it and the students have to respond to this would be even less efficient when analysing demand in an appropriate way, serving the a real Supply Chain. It could of course be clients and ordering new materials to the improved, if each model would be less suppliers while adequately managing the detailed and the time increments longer, inventory. More detailed information about but in that case one could also use a single this game is presented in chapter 5, and in application and concentrate the goals more Feliz-Teixeira et al. (2004). on the aspects of Supply Chain Management (SCM) instead of on a simple technique of Supplier simulation. In effect, rough calculations after some cycles of experimentation led to Factory estimate a speed of simulation around 40 Warehouse1 Warehouse2 times slower than in the case of the sequential objects16. Depot4 Another disadvantage of this idea was Depot1 Depot2 Depot3 that the project to build the simulator would be mostly focused on developing methods for transferring and managing data C1 C2 C3 C4 and ensuring the stabilization between the Fig. 2.35 Structure of the Cranfield Blocks Game 16 It is interesting to verify that this estimation is not so far from the comparisons made in section 2.3.4.

95 Flexible Supply Chain Simulation

This simulation software is made up of a to the server since this has the ability of server application, which must be running in displaying the inventory records along the a computer accessible by TCP/IP, and a time. client application, which is the interface provided to each group of students. The screen projection server, of course, controls the game.

SERVER Client

Factory Server Depot1 1 Wareh1 Depot2

Wareh2 Depot4 Depot3

Fig. 2.37 Cranfield Blocks Game playing structure

Turning the projector on or off can be Fig. 2.36 Server and client applications for the computer considered turning on or off the visibility to version of Cranfield Blocks Game all the Supply Chain partners. Experiments can be made based on these kinds of issues. Although the game can be configured to Some interesting tests have been made with play on its own, in the Auto-play mode, as a this game in Escola de Gestão do Porto sort of a distributed simulation tool, in (EGP) with a group of students attending a reality it was conceived for being operated Masters course on Management. The results by human players, therefore belonging to obtained with this experiment will be shown what Balci (2003) would classify as a human- in chapter 5, and have also been published on-loop type of simulation. Such simulators in Feliz-Teixeira et al. (2004). are strongly intended for training personnel. In this particular case, instead of playing the game by hand on a big table as it was the case of the manual version, the seven groups of players are distributed by several computers running the client application, as figure 2.37 suggests, and these computers are in turn connected to a SERVER where the server application is active. This scheme is frequently mounted in a single room, with a LCD-projector attached

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Davidrajuh, R. (2000). A Petri Net Approach for Performance Measurement of Supply Chain in Agile Virtual Enterprise: Narvik Institute of References: Technology, Narvik, Norway. Davies, R., & O'Keefe, R. (1989). Simulation Modelling With Pascal: Prentice Hall. Ahn, H. J., & Lee, H. (2004). An agent-based dynamic DoD/US. (2002). DoD Modeling and Simulation (M&S) information network for supply chain management. Verification, Validation and Accreditation (VV&A) BT Technology Journal, 22 (2). (Directive No. 5000.61): Department of Defense of Bagchi, S., Buckley, S. J., et al. (1998). Experience Using USA. the IBM Supply Chain Simulator. Paper presented Edelstein, H. (2001). Pan For Gold In The Clickstream. at the The 1998 Winter Simulation Conference. InformationWeek.com. Balci, O. (2003). Verification, Validation, and Certification of Edelstein, H. (2003). Using Data Mining to Find Terrorists. Modeling and Simulation Applications. 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Kim, K., Jr., B. C. P., et al. (2000). Agent-based Electronic presented at the AAMAS'04, July 19-23, New York, Markets for Project Supply Chain Coordination: USA. Department of CEE and Networking Research Ping, G. B., Turner, S. J., et al. (2001). Distributed Parallel Center, Stanford University. Simulation of Supply Chain Models (SIMTech Kuehn, W. (2005). Blended Learning Applying Interactive Technical Report No. MIT/01/029/MAPS): Discrete Event Simulation in a Web-based Singapore Institute of Manufacturing Technology. Learning Management System. Paper presented at Porras, J., & Ikonen, J. (1999). Approaches to the Analysis the Industrial Simulation Conference 2005, Berlin, of Distributed Simulation. Paper presented at the Germany. 11th European Simulation Symposium, Erlangen, Kulick, B. C., & Sawyer, J. T. (2000). The Use of Simulation Germany. Modeling for Intermodal Capacity Assessment. PRTM. (2000). 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Retrieved National Institute of Standards and Technology, July 2005, from Gaithersburg, USA, and Musashi University, http://www.pegasuscom.com/AAR/model2.html Nerima-ku, Tokyo, Japan. Swaminathan, J. M., Smith, S. F., et al. (1998). Modeling Mason-Jones, R., Naim, M. M., et al. (1997). The Impact of Supply Chain Dynamics: A Multiagent Approach. Pipeline Control on Supply Chain Dynamics. The Decision Sciences, 29 (3). International Journal of Logistics Management, 8 Taylor, S. J. E., Fujimoto, R., et al. (2002). Distributed (2), 47-61. Simulation and Industry: Potentials and Pitfalls. Microsoft. (2003). The Little Chip That Will Change Your Paper presented at the 2002 Winter Simulation Supply Chain Forever: Microsoft Business Conference. Solutions. Taylor, S. J. E., Sudra, R., et al. (2002). GRIDS-SCF: An Minegishi, S., & Thiel, D. (2000). System dynamics Infrastructure for Distributed Supply Chain modeling and simulation of a particular food supply Simulation. SIMULATION, 78 (5), 312-320. chain. Simulation Practice and Theory (8), 321- Tranouez, P., Lerebourg, S., et al. (2003). Changing the 339. Level of Description in Ecosystem Models: an Nikolic, D. M., Panic, B., et al. (2004). Bullwhip Effect and Overview. Paper presented at the The 2003 Supply Chain Modelling and Analysis using CPN European Simulation and Modelling Conference, Tools. Paper presented at the The Fifth Workshop Naples, Italy. and Tutorial on Practical Use of Coloured Petri Turner, S. J. (2001). A Generic Architecture for Large-Scale Nets and the CPN Tools, Department of Computer Distributed Simulations (PowerPoint presentation). Science, University of Aarhus. Singapore: UKSim. Nwana, H. S. (1996). Software Agents: An Overview. Veith, T. L., Kobza, J. E., et al. (1998). World Wide Web- Knowledge Engineering Review, 11, 1-40. based Simulation. Int. J. Engng Ed., 14 (5), 316- Nyquist, H. (1928). Certain topics in telegraph transmission 321. theory. Trans. AIEE, 47, 617-644. Vijayan, J. (2001, MAY 07, 2001). Inventory Turns. O.B. (2005). What does SKU mean?: OnlineBusiness.com. COMPUTERWORLD. 2005, from Retrieved March 2005, from http://www.computerworld.com/industrytopics/retail/ http://onlinebusiness.about.com/od/faqa/f/SKU.htm story/0,10801,60182,00.html Orsoni, A., Bruzzone, A. G., et al. (2003). Framwork Weisstein, E. W. (1999). ErrorPropagation. From Development for Web-based Simulation Applied to MathWorld - A Wolfram Web Resource. Retrieved Supply Chain Management. International Journal of 2004, from Simulation, 4 (1). http://mathworld.wolfram.com/ErrorPropagation.ht Pardoe, D., & Stone, P. (2004). Agent-Based Supply Chain ml Management: Bidding for Customer Orders. Paper Zeigler, B. P., Kim, D., et al. (1999). Distributed Supply Chain Simulation in a DEVS/CORBA Execution

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Environment. Paper presented at the Winter Simulation Conference, USA.

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CHAPTER 3 101 THE FLEXIBLE SUPPLY CHAIN APPROACH 101 3.1 INTRODUCTION 101 3.2 THE BASIC IDEA 102 3.3 A GENERIC NETWORK CONCEPT 103 3.3.1 The customer-supplier-unit 105 3.3.2 Connecting the CSUs 106 3.3.3 More about delivery 107 3.4 MODELLING THE CSU COSTS 108 3.4.1 Prices and costs 109 3.4.2 Purchasing costs 109 3.4.3 Stocking costs 110 3.4.3.1 Deeper into the holding costs 111 3.4.4 Manufacturing costs 112 3.4.5 Delivery costs 113 3.4.5.1 Another method for transport costs 113 3.4.6 Management costs 114 3.4.7 Total and global costs 114 3.4.8 The base price of the products 115 3.5 INFORMATION AND MONEY FLOWS 115 3.6 PRODUCTS AND PRODUCT-BOXES 116 3.7 MODELLING THE CSU RESOURCES 117 3.7.1 The stock 117 3.7.1.1 General information 117 3.7.1.2 Ordering policy 118 3.7.1.3 Technology 120 3.7.2 The fleet 121 3.7.3 The production section 121 3.7.3.1 Manufacturing policy 122 3.7.4 The management section 123 3.8 MODELLING THE CSU ACTIVITY 124 3.8.1 Process of purchase (or ordering) 125 3.8.2 Process of stocking 125 3.8.3 Process of delivery 127 3.8.4 Process of manufacturing 128 3.9 PRODUCTS, ORDERS AND VEHICLES 128 3.9.1 About the system products 129 3.9.2 The material-order structure 129 3.9.3 Modelling the vehicles 130 3.10 ABOUT THE FLEXIBILITY (MATRIX) 131 3.10.1 The rigidity concept 131 3.10.2 Matrix representation 133 REFERENCES: 135

100

Chapter 3

The Flexible Supply Chain Approach

An object oriented modelling design

range of issues can be addressed, in the 3.1 Introduction various areas of knowledge. As we have seen in the previous chapter, In the reality of industry, however, a developing a model commonly implies the substantial number of companies purchase built of a representation of the system with these kinds of generalist tools with the the help of general purpose primitives, like intent of addressing specific problems and the basic blocks of logic used in Petri nets, only later realise that it would be also System Dynamics, UML, etc., or even the necessary to contract a modeller specialist, wide spread state transitions diagrams, since the system to model reveals itself to which many commercial tools of simulation be considerably more complex than use. Building a model implies therefore the expected. The usual destiny for such appropriate ability to construct based on modelling systems is therefore the such blocks. Of course, today it is easy to wastebasket. Exceptions exist, of course, agglomerate a certain number of these among companies with a high capacity for blocks and assign them the “self” of an management and contracting, as is the case “entity”, using the properties of inheritance of BMW, for instance, which headquarters I of the modern object oriented languages, had the privilege to visit some years ago in but even so the modeller must have some Munich, and where a group of engineers is knowledge about the basis behind those specifically dedicated to model and approaches, since it is frequently necessary simulate the manufacturing processes. This to readjust such “entities” to the specific emphasis on the simulation as a matter for contours of a project. specialists seems an interesting policy, and To conceive building a model based on for sure the results emerge accordingly. basic multi-purpose blocks seems to be also Besides, the company was using eM-Plant, a interesting to the developers of commercial tool for modelling and simulation which simulation tools, as a higher number of already allows the usage of some pre- customers can be served and the idea of defined “real” elements with which the being flexible enough to answer to different engineer can construct the model; somehow types of modelling requests is promoted. similar to what is proposed in the approach The multi-purpose simulator had always described herein. been an attractive approach for selling, as it is a generalist solution with which a wide

101 Flexible Supply Chain Simulation

The approach presented in this chapter airport projecting, urban traffic design, also aims to dissolve the need for special nuclear central building, etc., our point of skills to build a Supply Chain model, while view is that it would be advantageous to at the same time maintaining the need for reproduce the same tendency in industrial reasonable skills in managing the simulation simulation tools, instead of systematically process. In this sense, the entities of the developing the models from the basis. This Supply Chain are represented as much as would provide the modelling community possible as they appear in the “reality”. with an extensive and interesting “data Thus, no special knowledge is needed to base” of specific area simulators available construct a model other than that of to any specialist planner. A huge data base creating a replica of the system based on of highly reliable specific area simulators is “scaled elements of the reality”. This can what we modestly intend to contribute to. probably turn out to be very interesting for those Supply Chain specialists who have few skills in modelling. 3.2 The basic idea In fact, each object is intended to be The concepts used in this approach seen by the user as an imitation of an appear not only from the object perspective object that exists in reality, with a certain taken from the Supply Chain system, but functionality encapsulated, and not exposed also from the intention to synthesise the to the modeller. Therefore, the level of behaviour of such systems in a more general abstraction required in the process of way, in particular in what concerns the modelling comes closer to that of a planner processes and the events that represent the than to that of a specialist on modelling. At business activity of suppliers, factories, this level, one does not need to define or warehouses, retailers, and, at the end of represent the dynamics of the internal the chain, last customers. Those events are processes of the Supply Chain elements, what trigger the flow of materials and since they are pre-defined in the respective information in the chain. objects of the application. The user only has Unlike some static approaches normally to configure or tailor these generalised used in simulation for strategic planning, objects in order to represent the particular where parameters like the lead time or the Supply Chain case with the accuracy average rate of material flow are held as needed. This approach can also be inputs to the system, the ideas described considered a “data-driven” approach, since here consider a representation of the Supply there is no need for programming in order Chain in which those parameters result from to model or to simulate the system. some dynamically and detailed simulation process. It allows, therefore, the ability for Finally, the present proposal also reflects modelling starting from the less abstract the tendency of providing the industry with processes till the more abstract point of simulators for specific areas of knowledge, view. The result is a more flexible method imitating the natural specialisation observed for building the model and a more realistic in the business world at the enterprise picture of the dynamics involved in the level. As there are companies specializing in system.

102 Chapter3 – The Flexible Supply Chain Approach

Due to this fact, this approach could independent of the time variable, and so, probably be seen as being more directed to what one could retrieve from them was also tactical managers than to strategists, but a kind of static expectation. Although these the intention is that the models constructed methods are still very important in the first with it can be used either to simulate short step of establishing the structure of the or long periods of time, therefore revealing Supply Chain on the ground (strategic), they also their value for strategic purposes. could not produce enough reliable results in order to help the tactical or the operational The basic idea behind this modelling manager. That is, they hardly survived the proposal is to consider the flow of products, need for describing the internal dynamics of of information and of money between any the network, or even the weekly need for elements in the chain as a general form of optimising the processes involved in it. To customer-supplier exchange activity, and address issues of these kinds the Supply also to treat each of these elements as Chain begun also to be modelled in a inheriting from a single element which dynamic way. The present approach must includes the basic behaviour (and resources) also be considered one more perspective to of a generalised node of the Supply Chain, enrich such trials. that is, simultaneously of a retailer, a warehouse, a factory, a supplier and even a F F last customer. A description of such a generic element will soon be made, showing w its structure as well as the costs associated with its various processes. As we will notice, w some parameters usually considered inputs by other modelling techniques will appear here as outputs, providing the analyst with R more interesting data for evaluating the R Supply Chain performance. Fig. 3.1 The common Supply Chain structure

3.3 A generic network concept As figure 3.1 suggests, the Supply Chain is During the last decades, most of the normally considered by most Supply Chain approaches used to model the Supply Chain simulators a kind of network connecting have been mainly devoted to study the factories (F), warehouses (W) and retailers problem of dimensioning and geographically (R), and each of these elements is treated positioning the components of the chain in a as exhibiting different internal structures way that the overall expected costs could and different behaviours. In the factory, for be calculated and, maybe, optimised. Such example, there is expected to run a process approaches were commonly dealing with of transformation which gives the raw average values computed over long periods materials the aspect of products; in a of time. In a certain way, the intent of warehouse there are only storage and those models was to make the results handling processes; and in the retailer the

103 Flexible Supply Chain Simulation main activity goes to the action of sales. the processes of its CSU and the kind of Then, products are what flow through the materials it handles as input and output. network transported by vehicles of some We can now think of each CSU as an sort. element living from a constant exchange with its neighbours, on one side receiving Here, however, we consider the elements from them materials and information and like factories, warehouses, retailers, etc., returning money to them, on the other inheriting from a single generic element delivering to them material and information that encapsulates a generic customer- and receiving money from them. This is, supplier exchange activity. That is, a basic obviously, the perspective of a market. And element that can be transformed into any each CSU already represents the model of a particular element of the Supply Chain by complex differential equation. simply configuring its internal structure and In order to be able to inspect how the parameters. We decided to name this economy develops among the elements of element customer-supplier-unit (CSU), to the Supply Chain, a reference to its own reflect this idea. Based on this CSU, it will economic account will be included in the even be possible to model the figures of the basic structure of the CSU. One must not primer supplier and the last customer, as forget that most management decisions are we will see later on, usually not considered taken not only based on technical in simulators dedicated to the Supply Chain. performances but also on how money The overall Supply Chain structure will spreads along the systems. This account, of therefore be transformed into a group of course, will include costs and incomes, in a CSU nodes connected together by a network way that by the results of the simulation of transports, where there can also be one can better predict how each of these exchange of information (Fig. 3.2). elements is contributing to the overall economical performance of the chain. F F Notice that in the primary point of view of this flexible1 proposal, the Supply Chain w is already established on the ground, that is,

w the network structure is predefined. The sort of problems to be studied or analysed are therefore related to the management of R R the physical processes actually running within that structure. These processes are

the dynamic processes of purchasing, Fig. 3.2 CSU equivalent of a generic Supply Chain stocking, manufacturing, delivering and

managing. Each of these processes is Now, what will distinguish a factory from a warehouse or from a retailer, or from any other nodal element, will only be how that 1 Here the classification “flexible” is related both to the ability for representing a high variety of Supply Chain cases particular element uses the resources and and the ability for measuring the Supply Chain flexibility in certain cases, as we will see later.

104 Chapter3 – The Flexible Supply Chain Approach treated in terms of measures of technical a principle that the fleet always belongs to performances but also in terms of the one who delivers, what seems a economical indexes, whenever possible. reasonable consideration for most practical This aspect appears very useful to help the cases, even when the fleet is hired, as we manager in making practical decisions, in will see later. particular when the Supply Chain under study contains elements belonging to S1 S2 different companies, thus expressing a . . . Sk network of concurrent interests. By simply Owns: representing the flow of money in the CSUi . Stock simulation, for instance, one can easily . economicAccount . inputQueue visualize the importance of each element in . outputQueue the network, or even compute the costs and . fleet C1 . product list the incomes for a particular group of . . . . suppliers list . customers list elements, an interesting feature for C2 Cj analysing complex horizontal multi-company examples. Fig. 3.3 Generalised concept of a CSU with k suppliers and j customers 3.3.1 The customer-supplier-unit The basic customer-supplier-unit (CSU) is Considering the sort of structure depicted then a conceptual element with which it is in figure 3.3, the CSU becomes a general possible to model any sort of Supply Chain element ready to be linked to other facility. This element “owns” a group of elements of its kind, thus making possible customers, to whom it delivers material and the overall Supply Chain representation. Of from whom it receives money, and a group course, different CSUs can be made to of suppliers, from whom it receives material handle different amounts of stock, different and to whom it returns money. In order to numbers of customers and (or) suppliers, be able to handle this basic functionality, different products, etc., depending on the the CSU must also include in its structure role they play in the Supply Chain. For some other general resources. Basically, instance, ultimate customers can be these resources are a stock, an economical thought as CSUs having null stocks, null account, an input queue where the supplier fleets and a null customer list, being vehicles will wait to deliver the materials, therefore reduced to generators of the and an output queue where other vehicles demand of products and the sources of will line up to receive the material that money for the Supply Chain. On the other must be delivered to the customers. hand, a supplier source can be seen as a In order to be able to manage these CSU having infinite stock and a null supplier resources, the CSU must as well include in list. Notice also that raw materials can be its structure a list of suppliers, a list of treated as simple products, even if later it customers, a list of products and, finally, a will probably be necessary to adapt the list of vehicles representing its own fleet. unity of transport to the proper case. Thus, Notice that in this approach we establish as this approach also opens the Supply Chain

105 Flexible Supply Chain Simulation simulator to the representation of ultimate predefined job. And this also means that customers and primer suppliers, which in each CSU must include the dimensions of a most simulators are simply treated as node. Of course, as the vehicle moves from statistical parameters. one node to the next node the delivery It is important to indicate, however, that costs are expected to rise, since they are the final CSU structure is substantially more calculated based on the previously known complex that the one described here, since costs of vehicle utilization per kilometre it includes some other features related with and driver cost per day, among others. production and many others variables and methods with which its internal dynamics CSUi are represented. Transport paths 3.3.2 Connecting the CSUs

In order to model the Supply Chain CSUj Node dynamics it is now necessary to ensure the connection of each CSU to its effective CSUk neighbours. This is ensured by a network of “physical” paths in which several kinds of vehicles can move. These paths were Fig. 3.4 Transport paths connecting supplier CSUi to customers CSUj and CSUk designed to represent normal roads, highways, railways, boat circuits, airlines, Notice also that, in principle, the time and even pipelines. Apart from these, an spent by the vehicles delivering on these information path was also considered and paths will not be easily predictable, also implemented in the simulator, with which because different vehicles from different materials can be ordered directly to any suppliers can reach the customer around the facility of the Supply Chain, somewhat like same time, and then it will be necessary to what happens in the order-by-catalogue and queue before the effective delivery will order-by-Internet. For the purpose of this take place. Situations like this will be, of section, however, we will assume that course, dependent on the layout of the general-purpose ground vehicles will be chain, on the quantity of suppliers serving responsible for moving the materials, as each customer and on the intensity of the suggested in figure 3.4. delivery flow in the network, as well as on Notice that each path can represent the delivery policies and on the size and different costs to the process of delivery. number of vehicles. For instance, vehicles can first run on a Another important notice is that each highway and then change to normal roads. vehicle is treated as an independent entity, This aspect, and the probable existence of therefore the movement of a vehicle road junctions in some practical cases, through these paths will be simply allow us to consider using also the concept dependent on the vehicle itself; that is, it of transport node (see Fig. 3.4). will result from the execution of a certain Vehicles will travel from node to node sequence of events that are the “property” with the objective of executing a certain

106 Chapter3 – The Flexible Supply Chain Approach of the vehicle object. Thus, from a higher- example. The figure 3.5, where 3 different level point of view, there are only two paths and 4 nodes connecting supplier CSUi commands (events) each CSU has to send to to customer CSUj are represented, helps in its vehicles: startDelivery and startReturn. imagining the complexity that can affect The first must be sent after attributing a lead times due to these issues, and mainly certain job to the vehicle, and the latter in understanding how one can expect the when a job must be finished. The vehicle results obtained with this process to be itself must then be capable of delivering the different from those obtained if lead times material without any other interference would simply be modelled by stochastic from its CSU, even if a job includes more variables. than one delivery point, as in the case of local delivery, for example. CSUi N0 CSUj 3.3.3 More about delivery N3 This concept of modelling also interferes normal road with the speeds of delivery observed in the normal road simulation. Unlike what is expected when N1 highway using stochastic variables for representing N2 lead times, here the delivery speeds will exhibit a complex behaviour due to the fact Fig. 3.5 Paths connecting supplier CSUi to customer that they are also dependent on the CSUj including normal roads and highway operations running in the CSU at a particular moment. In effect, after the material is In addition to the irregularity introduced ordered to a particular CSU, a sequence of in the delivery times by the CSU internal actions will begin in that CSU, leading to the operations, one must consider the source of processing of the order, then to the unpredictability related with the process of packaging of materials and finally to the transportation itself, mainly when the start of the transportation process. From delivery (or distribution) structure is to the perspective of the CSU customer, the include diverse types of transports or speed of the delivery will therefore depend diverse paths. In certain practical cases, the on the time spent on such sequence of materials must be transferred to different actions and even on the criteria used by the vehicles at a certain point on their way to CSU supplier to assign delivery jobs to its the customer. Deutche Bahan, to cite an vehicles. In reality, the effective lead time example, recently pondered the idea of will result from the summation of a series of using its own railway structure to help in delays that in principle are not computable distributing the spare-parts needed by the a priori. In addition to this, the time to manufacturers of locomotive engines, carry the materials along the paths to the creating points of interface with other kinds CSU customer must be considered, which of of vehicles in certain strategic locations. It course is dependent not only on the vehicle is also usual that intercontinental flights are speed but also on the limit speed of each used as links for connecting different and path along which the vehicle will travel, for distant Supply Chain structures, as ZARA,

107 Flexible Supply Chain Simulation

Benetton and other companies practise. the situation comparing each of those paths Situations like these have made us decide to with the paths associated with the actual include in this modelling approach the delivery job; (2) CSU node, if the node is primitives necessary for representing a more coincident with a CSU, that is, with a realistic transport model. facility of the Supply Chain. This node has precisely the same function as a normal We have assigned a limit speed to each node, except when the associated CSU is transport path, as well as a certain vehicle the customer of the present delivery job. In speed to each vehicle; so, the effective this case, the vehicle enters the queue of speed of transportation in that particular vehicles for input (inputQueue) of that CSU path can simply be calculated as: and waits to be unloaded. Only after this will the vehicle start the return to its CSU or transportSpeed = Min(pathSpeed, vehicleSpeed) (3.1) origin; (3) transfer node, if the node is used to transfer material from one vehicle to It is therefore considered that each time another. This sort of node can be used when the vehicle reaches a new node leading to a there is the need for interfacing two new path it will use this expression to different transport modes, for example, an recalculate its next speed of transportation. air-cargo or sea line with the land This new speed will be the speed to travel distributing vehicles (trucks, trains, etc.) along the new path. Notice also that, as it is serving a region. The transfer process easily understood, the limit speed assigned implies costs per unit of product to a path can serve not only to model the transferred, which are specified in the node legal limit speed of that transport properties; and (4) pause node, if the node connection but also to model the usual is to represent a certain delay that must be conditions of traffic flow in it. Highways introduced in the delivering process. Usually crossing the interior of big towns can be this node is used to model meal times, modelled with a very low maximum speed, sleeping times, etc., with which costs are if they are usually congested. automatically associated. In complex Supply Chains structures, In order to model such a variety of these issues can strongly contribute to the situations associated with the delivery of unpredictability of the speeds of delivery the materials, the following four types of observed in the simulation. nodes were considered as elements of intersection for transport paths: (1) normal node, if the node just represents a point 3.4 Modelling the CSU costs where vehicles will change to a new path. Before examining in more detail the This is what happens in a passage from a internal structure of the CSU and the highway to a normal road, for example. processes related to its dynamics, we Once the node is reached, the vehicle will decided to expose the ideas behind the enter the next path on the way to the representation of the costs associated with current customer. In the case of a node with this kind of elements. The reason for this is several exit paths, the vehicle will analyse to liberate the discussion as soon as possible

108 Chapter3 – The Flexible Supply Chain Approach from the well known economical issues, and delivery costs and management costs, each to move to other interesting aspects more of them being thought of as the result of directly concerned with the simulator other more specific costs, some assumed development. fixed and others considered of the variable Since the Supply Chain can now be kind. Yet, it is important to bear in mind represented as a dynamic system, the that the more the costs are detailed the associated costs must also result from higher is the number of events that must be certain dynamic processes, that is, costs added to the model. In this approach we must be calculated and afforded to the will be only interested in those costs that respective CSU by means of certain event usually are considered more relevant, and executions in time. It is, therefore, each of them will be handled as being an important not only to know the amount of a expression of a fixed and a variable particular cost but also the time when this component. The fixed component will cost must be afforded to the CSU. This will express the price to pay for the “existence” also allow, for example, a continuous of the process, while the variable will calculation of costs and incomes during the represent the price to pay for “operating” simulation process, offering the Supply it, or for maintaining its “activity”. Chain manager the important feedback of certain economic indicators. 3.4.1 Prices and costs Based on the idea that each process must There is sometimes some confusion about have its costs, and again considering that the terminology used to specify the value of the main processes running in the CSU are the product when it moves along the Supply purchasing, stocking, manufacturing, Chain. To establish the nomenclature from delivering and management (see figure 3.6), now on, it is assumed that the product cost we have decided to specify costs also is the value that must be paid by the CSU following this same taxonomy, as it fits customer to the CSU supplier for the perfectly with the nature of the operations product, while the product price is the running in the system. value by which the CSU supplier sells the product to the CSU customer. As we see, Purchasing the two values are in fact the same, but the

Management terms price and cost must not be confused. 3.4.2 Purchasing costs CSU These costs must be afforded to the CSU Delivering at the end of each purchasing cycle, that is, Stocking each time the products (or materials) are Manufacturing received from (and paid to) the supplier. The amount of these costs depends on the Fig. 3.6 Main processes for assigning costs to the CSU price the supplier asks for the products and on the administration involved in the We will consequently talk of purchasing purchasing process, which is mainly related costs, stocking costs, manufacturing costs, with the costs of ordering the materials.

109 Flexible Supply Chain Simulation

This aspect is modelled as a cost per expressed as: purchasing cycle, and as we can see it includes the idea of the “setup cost” StockingCost = [operations(t)+administration(t) ] + mentioned in the inventory management + occupancy + holding cost(t) literature. So, the purchasing costs will (3.3) occur at the end of each purchasing cycle and its amount will be given by: To simplify the representation of these costs in the simulation we will call stock PurchasingCost(t)=ProductsPrice(t)+ administration(t) processing costs the operations and the administration costs together, which are (3.2) variable costs computed per unit of material handled. Then, the occupancy costs will be Where administration is associated to affected continuously as the time increases, invoice preparation, papers, phone-calls, and will be based on a fixed cost per unit etc., and also to costs with personnel. time (a fixed amount per month). Finally, Notice that the purchasing costs are here the holding costs will be computed based on considered only as variable costs. Their the value of the money invested in the fixed component will be included in the products actually in hand, no matters fixed costs of maintaining the stock whether they are in house or in movement structure. This was viewed as a reasonable to the clients. This means that in-transit approach and even appropriate, since in inventories are considered in this approach. practice the existence of a purchasing In general, the holding cost for the ith process only makes sense in the case of the product in the inventory exhibits the existence of a stock structure. following dependence with the time:

3.4.3 Stocking costs holdCost(t)i = cpi hi Qi ( t – to ) (3.4) The process of stocking involves diverse aspects, and so, diverse kinds of sources for where: costs can be assigned to it. Some of these th sources commonly referred in the literature cpi = cost of a unit of the product i

(Sussams, 1992, pp. 55) are the operations, hi = bank lending rate per unit of time and product which include the receiving, the order Qi = quantity of products i in the inventory picking, the inventory replenishment, etc.; to = time when the product was purchased the administration, which includes the t = present time processing of the orders, the managing of accounts, personnel, etc.; the occupancy, It is important to notice that the holding related to rents, services, insurance, costs begin when the CSU pays the product depreciation, maintenance, etc.; and the to the supplier and will only end when the inventory (or holding) costs, which are due CSU receives the payment for delivering it to the existence of the products in stock, to the customer, hence the importance of usually 1% above the bank base lending rate. considering in-transit inventories. The overall stocking costs can then be Apart from these costs, it is also usual to

110 Chapter3 – The Flexible Supply Chain Approach consider the costs associated with the stock costs both in the receptor (CSUr) and in the breaking (Gonçalves, 1999) each time the sender (CSUs). In the first, the holding costs demand cannot be satisfied from the stock, are increased, while in the second the a situation known as stockout. Although holding costs are lowered. If the quantity of these variable costs are difficult to estimate product transferred is dQ, the holding costs due to their faulty characteristics, which per unit of time will increase by the amount can even interfere with the subsequent kr.dQ in the CSUr and will decrease of ks.dQ demand, here we will approximate them to in the CSUs, where kr and ks are the actual the quantity of material that have not been holding costs per unit of time and unit of served to the client. In addition to this, a product related with the product in cause, measure of the “satisfaction” of the client given by: is used, which represents the time spent to serve the client compared with the time the kr = cpr . hr (3.5) client accepts to wait for the materials to ks = cps . hs (3.6) arrive. This leads to the representation of the “customer satisfaction index” referred Notice that the actual costs (cp) of the to in the previous chapter (see equation product can obviously be different in the 2.7). diverse facilities, and in certain cases even be dependent on the time. Figure 3.7 shows 3.4.3.1 Deeper into the holding costs an example of holding costs calculation Since the holding costs are a special case along the time. of expenses, we will dedicate a little more time to them. In reality, these costs are obviously interrelating suppliers and customers, since everybody wants to get rid of the materials as soon as possible to minimise the risk of the investment. It is here that the materials can be compared to hot potatoes or cartoon time-bombs, as mentioned in the previous chapter. The holding costs are made to begin the moment the CSU issues the payment to its supplier for the materials received2, and will vanish the moment the materials delivered are paid back by the customer. Fig. 3.7 Dynamic calculation of the holding costs That is, only after the process of delivery is finished can the holding costs fall. Such an example helps in calculating The event of receiving material implies dynamically the holding costs of a product therefore the actualization of the holding in each event of receiving material, taking in account the time spent in the delivery 2 For simplicity, we consider that the moment of issuing the payment coincides with the moment of receiving the (in-transit inventory). From this figure one materials into the inventory. can conclude that the holding costs in the

111 Flexible Supply Chain Simulation instant tj can easily be calculated from the rearrangement is due to the manufacturing expression: process3, represented in the diagram of the next figure (Fig. 3.8).

holdCost(tj )= holdCostUnitTime(tj-1) x (tj-tj-1) (3.7) Manufacturing processes where Process(1)

holdCostUnitTime(tj-1)=cp x h x Qj-1 (3.8)

P5+ Process(2) R3 And, with a little more manipulation,

R2 P3

deduce that: P4+ P1 R1

holdCostUnitTime(t j ) = cp ´ h ´ (Q j-1±dQ j ) (3.9)

P+ Stock R&P Stock The sign (+) in this equation will be used in the CSU receptor, while the sign (-) will Fig. 3.8 Equivalence of a manufacturing process and a be used in the CSU sender. In conclusion, rearrangement of inventory the holding costs related to a product can be estimated dynamically in the simulation As it can be deduced from this figure, the process as long as one keeps track of the variable costs associated with each of these previous inventory level and the actual processes will essentially be the costs of amount of the product that is exchanged transforming R and P products into P+ between the CSU sender and the CSU products. On the other hand, those costs receiver. This was the method chosen in the associated with labour and with the space present simulation approach, since no occupancy of the production area can be fractionary delivery was considered. considered of the fixed type. Notice that 3.4.4 Manufacturing costs the costs related with the stocking process that supports the manufacturing activity are In our perspective of modelling the CSU, precisely the same referred to in the we consider the factory a facility similar to previous section, since the inventory of the the warehouse, with the differences that CSU is here considered a common resource the factory will handle three kinds of for all the processes running in the facility. products (R for raw materials, P for parts The transforming costs must be specified and P+ for secondary products) in its for each product P+ produced in the factory inventory and run a process responsible for and given per batch of production (lot). the transformation of R and P products into These costs must be afforded to the CSU at P+ products. This process is the the end of each production cycle. Thus, manufacturing. Obviously, P+ products are after a certain period of time t, the related with R and P products by means of a contribution of the manufacturing process MRP tree. So, the factory can be seen as a for the overall variable costs of the CSU will warehouse with a frequent rearrangement of products in its inventory. Such a 3 This can also be called production process.

112 Chapter3 – The Flexible Supply Chain Approach be given by: reached:

ManufactCost(t)=å [transforming(t)]i (3.10) Fuel(ds) = (VehicleFuelConsumption/PricePerLitre)*ds Roads(ds) = (PriceOfRoadPerKilometre)*ds th where i refers to the i product P+ Driver(dt) = (DriverPricePerUnitTime)*(ds/v) manufactured during this period of time. (3.12)

3.4.5 Delivery costs Where lubricants and maintenance are We consider the delivery costs a direct considered included in the cost of the fuel. consequence of the existence of a fleet of Notice that when the node corresponds to a vehicles attributed to the CSU. Once again, node where the driver will rest or have a these costs are the result of the following meal, the constant costs of “resting” or of contributions: the occupancy, which is “meal” must be added as well. related with what is needed to maintain the fleet, like garages, machinery, technical Notice also that hiring the services of an personnel, etc.; and transport, which external fleet can simply be modelled by includes the costs of drivers, fuel and keeping the costs of occupancy null, if those lubricants for the vehicles, road charges, services are contracted as variable, or by meals, delays, material handling, etc. specifying those costs as fixed and included Similar to what was considered in the in a sort of occupancy, if they result from a stocking cost, the occupancy cost is seen as contract of a fixed amount per month, for a fixed cost, and therefore given per unit of example. time (usually monthly). The transport costs, however, as a sort of variable cost, will be 3.4.5.1 Another method for transport costs computed during the simulation process The previous method for assigning the each time a vehicle reaches a new node in transport costs to the CSU implies a detailed the transport path network. These costs will knowledge about the vehicles used in the then be assigned to the respective CSU delivery process, as well as the itineraries when the material reaches its destiny. We for the customers. Although it is not so hard therefore have: in practice to obtain this type of information when modelling a case of DeliveryCosts = occupancy + transport(t) (3.11) distribution, that is, when the model is built from the point of view of the sender, The transport component includes the same is not true when modelling from aspects like fuel, lubricants, maintenance the perspective of the receiver, that is, in a and roads, as costs depending on the typical case of procurement. The reason for distance travelled (ds); drivers, depending this is that in the first case the transport on the time travelled (dt); and meals and resources belong to the system to be delays, as constant costs. As an example, if simulated, while in the second case they a vehicle is going from node A to node B usually do not, and this kind of information with a speed v, the following transport costs is difficult to obtain from external will be added to its CSU when the node B is companies (the suppliers). In such cases,

113 Flexible Supply Chain Simulation the transport costs are in fact usually to the prices of the products. “unknown” for the customer, and merely Notice that any data about road charges, reflected in the unitary cost of the product drivers, meals, fuel consumption, etc., is purchased. The price of a unit of product now unnecessary. Models can be created decreases when ordering a higher quantity even without considering such specific of product units, and vice-versa. Of course, information. This is part of the flexibility of the transport costs are indirectly reflected the present modelling approach, in what in the cost of the product for the CSU concerns the representation of Supply Chain client, but no detailed data is available as systems in diverse situations. in the latter case. 3.4.6 Management costs To model these cases we have decided to Management costs, which can include the attribute to each vehicle a table of prices costs of managers, analysts, secretaries, relating the space occupied by the materials consultants, marketing, etc., will be here ordered and the overall space available in considered a kind of fixed costs, and, in this the vehicle. In reality, this is how things first approach, will be integrated in the usually work in practice, and so this will fixed costs of the main resource of the CSU, definitely be useful in several situations. that is, the inventory. These costs will also So, each vehicle belonging to the fleet of be afforded to the CSU as a continuously the CSU supplier will have a list of echelons growing cost. with the respective prices for transportation to a certain destiny, as depicted in figure 3.4.7 Total and global costs 3.9. Each of the previous costs are recorded during the simulation as instantaneous values, thus, the calculation of their totals will automatically result from the simple summation of those values along the time domain. For instance, considering the variable transport costs the time series of

values T(ti) computed at the instants ti, which can also have the form of a data record put out by the simulation, the total variable transport costs will simply be given Fig. 3.9 Echelons of transport costs to the customer by the accumulation of these values along the time, that is: As we can see from this figure, we have decided to distinguish 5 different echelons Total = åi T(ti) (3.13) between vehicle empty (0%) and vehicle full (100%), leaving the user free to specify for This is used in this modelling approach as each of them, both the percentage and the a simple and efficient method of recording price per unit of product transported. The the relevant variables along the simulation transport costs are by this way transferred run, and allows the simplification of many

114 Chapter3 – The Flexible Supply Chain Approach future calculations. For instance, it makes base price4 for each product available in the the calculation of global costs extremely Supply Chain; and (2) assign to each CSU a simple, not only in a particular CSU but typical business margin5 for its product even in the entire network of the Supply exchange activities (by default 10%). This Chain. In effect, it holds for any sort k of way, the number of product configurations costs in the CSU: needed is practically reduced to P, the number of products in the system, if most of

Globalcsu = åk Totalcsu,k (3.14) the CSU margins are kept the default. This also introduces a kind of chain of value for and: the materials moving in the direction of the last customers, another interesting aspect

GlobalsupplyChain = åcsu Globalcsu (3.15) that can be a subject for future studies.

Since each facility in the Supply Chain model has this structure of costs integrated 3.5 Information and money flows in its CSU object, the computation of such Other things that flow in the Supply Chain quantities becomes extremely easy in any are information and money, which in the moment of the simulation process. present approach we consider associated to quite simple operations. The information 3.4.8 The base price of the products will be mainly related with communications, Costs would never be useful without a either between CSUs during the ordering of previous definition of the value assigned to materials, or inside the CSU in any process the products. On the other hand, in reality that implies the transfer of data, for different members of the Supply Chain can instance when filling the time gap between establish different values (prices) for their the arrival of a new order and the beginning products, based on indexes like demand and of its processing. This is modelled by means concurrency, for example. Therefore, the of a typical information delay associated to price of a product in the modelling process the CSU, a stochastic variable. Although we would have to be dependent on the CSU, were expecting to include in this simulator meaning that different prices would have to some additional features about information be inserted in each CSU when creating the sharing, in particular regarding the transfer model of a Supply Chain. If the Supply Chain of the demand upstream in the network, a would have N facilities and P different procedure claimed to improve the Supply products, the number of initial product Chain performance (Mason-Jones & Towill, configurations needed would simply be N x 1999), the fact is that no effective analytic P. Taking N = 10 and P = 100, for example, this would lead to 1000 different product 4 This base price is the cost of the product at the facility that configurations, and a lot of time needed for makes it enter the Supply Chain system. After that, the inserting the initial conditions into the product price follow a chain of value as the product travels in the direction of the last customer. models. To surpass this problem, we decided to 5 Defined as 100x(price-cost)/cost of a product, and choose a simpler perspective: (1) establish a assumed a constant of the CSU in the present version of the simulator.

115 Flexible Supply Chain Simulation processes were found in the present Supply cash flow or turnover, for example. Chain Management (SCM) that could be used to implement such ideas. It is, therefore, an issue that remains open for future research. 3.6 Products and product-boxes It is now necessary to describe how the On the other hand, the flow of money products will be handled in the present will be modelled as an expression of the perspective of modelling. The properties of process of payment to the supplier for the the products could in principle be specified materials received. Considering that money the moment these were to be inserted in always flows in the opposite direction of the the stock of each facility, but this would materials flow, each time one receives turn to a redundancy since different materials one has to pay back its price to facilities may use the same product in their the supplier. It is based on this elemental inventories. To obviate this problem and process of exchange (Fig. 3.10) that the make simpler the process of configuring the Supply Chain maintains its activity through model, we have decided that the products the time. The payment is considered to be belong to the entire Supply Chain system, instantaneous or, at maximum, affected by and that each resource of the stock type the typical information delays of the CSUs. consists of a collection of boxes where products are maintained, each box being money associated to a single product. We call this kind of box a product-box (Fig. 3.11). CSUi information SYSTEM PRODUCTS CSUj P1 P2 materials . . . PN

Fig. 3.10 Basic exchanging processes between supplier CSUi and customer CSUj . . .

box1 box2 boxm As we have noticed earlier, introducing . . . CSU this economic aspect is essential to allow j box1 box2 box3 boxk the analyst to study the progress of the CSU Supply Chain not only on a technical base i but also taking into account economical aspects, as in fact happens in reality. Fig. 3.11 System products and the CSU product-boxes The money flow will be triggered each time a customer receives material from a Thus, any stock resource is primarily seen supplier. As the chain continues its activity, as a collection of product-boxes where such flow will spread to all its facilities, system products may later be added to or allowing the instantaneous calculation of subtracted from. This led to the following variable costs, incomes, total costs, etc., as two-step procedure concerning the well as any other economical indexes, like configuring of products in the Supply Chain:

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(1) products are first created in the system that particular area. and their characteristics specified independently of any CSUs; (2) then the CSU appropriate products are linked to the each manager CSU by the creation of an empty product- box for each product. Fleet Stock Production Notice that these boxes will later be Manager Manager Manager filled in with the required quantity of product (initial level of stock) before the simulation starts. stock CSU Production fleet 3.7 Modelling the CSU resources section The internal dynamics of the CSU are the complex activity resultant of the response of the facility to external and internal Fig. 3.12 Resources and management in the CSU requests, a response normally distributed by five dynamic processes: purchase, stocking, Notice that these blocks of management manufacturing, delivery and managing. As form part of a more general process of we have seen, this sort of organization is administration named CSU-manager. typical and implies the usage of certain typical resources, which mainly are: a stock, 3.7.1 The stock a fleet, a production section and a section As previously said, the stock of the CSU is of management. The general CSU will make a collection of product-boxes created and use of all these resources, and the tailoring linked by the user to the products formerly to a particular configuration will enable or launched in the system. Related to this disable the appropriate ones. The purpose resource are four levels of data needed to of this section is to describe in more detail be filled in so that the stocking and the how theses resources are modelled in this purchase processes, both related with the simulation approach. stock, can express their entire functionality: (1) general information, (2) ordering policy, We will consider physical resources those (3) technology and (4) stocking costs. This that in principle require a physical space in last aspect was already discussed in the the CSU to operate, which are the stock, section 4 of this chapter. the production section and the fleet, as illustrated in figure 3.12. The management 3.7.1.1 General information does not necessary imply such a space, so it This is the first type of data needed to will be represented on a different level. As configure the stock, and is basically related depicted in this figure, each of these to the creation and the linkage of product- physical resources has an associated block boxes to the products of interest, that is, to of management which specifically analyses those products with which the CSU will the problems and search for solutions in operate. Then, each product-box is

117 Flexible Supply Chain Simulation configured with its maximum allowable inventory must be replenished in certain inventory level as well as with the inventory cases; and the maximum level of inventory level at the start of the simulation (initial (Qmax), which imposes the upper limit of condition). allowable space in the stock for a particular product. Other important parameters are 3.7.1.2 Ordering policy the safety stock (ss), used to protect the The ordering policy assigned to a certain inventory from demand variations, and the product-box will characterize the method Economical Order Quantity (EOQ), which is by which its product will be reordered from expected to minimise Q considering the the supplier (or suppliers). To characterize demand figure, the costs of ordering and the inventory state and define the moment the costs of holding (see equation 2.1). Most and the quantity of product to order we of the current methods of reordering can be have considered the general parameters seen as particular cases of this generalised shown in figure 3.13, where tj are the scheme. moments when the product arrives from the supplier. Given that in this simulation approach we propose a continuous calculation and update Q of LT(t), the supplier lead time, as well as of the accumulated demand between Qmax Qs supplies, DTL(t), parameter that aims to measure the lead time demand, both the Q(t) Q(t) Q(t) reorder level and the quantity to order can

r(t) also be updated the moment that new LT(t) LT(t) LT(t) material arrives, and so be made dependent Qb on the time. This means that the number of t1 t2 t3 time different (r, Q) policies that can be selected with this scheme is the combinations

Fig. 3.13 General behaviour of a time dependent (r, Q) resulting from assuming r and Q either as inventory system with r = r(t) and Q = Q(t), showing a little fixed or as variable. For instance, at least 8 stockout around t = t2 different situations could be chosen taking into account that r can be: 6 Notice that both the reorder level (r), the quantity to order (Q) and the supplier · fixed by the user as r = ROP lead time (LT) are given the chance to be · automatically updated to r(t) = DTL(t) + ss dependent on the time. This lets us count with rather more degrees of freedom in the and Q: problem than usual. Other parameters are the base order quantity (Qb), representing · fixed by the user generically as Q = Qo the base ordering unit, thus any order must · fixed by the user as Q = EOQ be a multiple of this quantity; the fill level · automatically updated to Q(t) = (Qs – Q) of inventory (Qs), as the level to which the · automatically updated to Q(t) = DTL(t)

6 Also referred to as reorder point (ROP).

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Notice that if ss = 0 the option (r = DTL + from assuming Q as: ss, Q = DTL) can be made to represent the common two-bins inventory control system, · fixed by the user generically as Q = Qo which is also the base of the Kanban system. · fixed by the user as Q = EOQ · automatically updated to Q(t) = (Qs – Q) The previous considerations were valid to · automatically updated to Q(t) = DTL(t) those inventory control systems in which the moment of ordering is triggered by the level For obvious reasons, this type of policy is of inventory, and therefore we call these also useful for inserting the demand related “byLevel” policies. On the other hand, to last customers into the simulator, that is, systems that trigger the ordering of the to establish the input demand to the Supply materials by regular periods of time, hence Chain. As such demand is frequently given using what we will call “byCycle” policies, as a Normal distribution, the simulator will will be modelled considering the parameters let the user enter data not only for Q but shown in the general inventory scheme also for the standard deviation associated represented in figure 3.14. with it (sQ), another form of ordering to add to those previously shown. In reality, the Q possibility of associating a Normal behaviour and a initial phase shift to the period of Qmax Qs ordering (To) was included later as well, by adding sTo and tstart respectively, thus Q(t) Q(t) allowing that the variability can also be Q(t) LT(t) LT(t) LT(t) introduced along the time axis, as suggested LT(t) by figure 3.15. Qb t1 t2 t3 time Q Induced variability To To To

dQ Q Fig. 3.14 General (R, Q) inventory system with R = To and Q = Q(t)

Here, To is the regular period of time tstart To time dTo between consecutive orders to the supplier, while the other parameters have the same Fig. 3.15 Possibility of inducing variability in volume, and meaning as in the last case. phase shift and variability in the time scale, with a “byCycle” Since To is assumed fixed and previously ordering policy defined by the analyst, the number of different inventory control methods that The last customer is modelled as a CSU can be modelled with this type of policies is with no stock, no production section and no solely dependent on the degrees of freedom fleet, and solely represents a generator of of Q, that is, on how Q will be computed. In demand by means of this type of ordering fact, different stocking dynamics will result policy. Notice, however, that in any of

119 Flexible Supply Chain Simulation these ordering policies the events can also of inputting materials into the CSU and the be generated by historical data, if available, inverse process of outputting, the number and retrieved form a file in the appropriate of bays for loading/unloading the vehicles, format. In that case, the events of ordering the serving speed associated to the stock the product will be previously scheduled in operational performance, and some other the simulation for the proper times, and indexes, most of them given in terms of these ordering policies will, naturally, be time delays. In the process of inputting it is disabled. generally considered the utilization of the following basic sectors: input bays for the Apart from this, there are some other vehicles to queue and unload; marshalling important questions associated with the zone where the orders are disassembled and ordering policy. The first is “how to order?”, the respective products prepared to be which in this approach results in the option transferred to the stock; and a stocking immediate, if the order must be placed to system, that is, a sector structured and the supplier in the precise moment that the prepared to receive the products and keep need is detected; and in the option them perfectly ordered and monitored, as management, if the order must first wait for represented in figure 3.16. that a single order can be placed to the supplier in the end of the day, for example. Marshalling The second question is “from whom to order?”, and it implies that the user links each product-box to the respective CSU Input bays supplier (or various suppliers). Finally, the simulator also uses the parameters one-order-cost (or setup cost), to represent the cost of placing an order to the supplier; and the “time for full Stock satisfaction7”, related with equation 2.7, which specifies the maximum expected Fig. 3.16 Basic sectors associated with the process of supplier lead time for the product in inputting material into the stock question. Notice that there will be a zone of 3.7.1.3 Technology marshalling both for the process of inputting Technology is another category of data and for the process of outputting. The associated with the stock operation in this parameters particularly related with the simulation approach. It is basically related input process are the following: with the technical specifications that are indirect expressions of the actual stocking Number of input bays: the number of unloading technology in use in the CSU. Through such bays available to receive vehicles. data the user specifies both for the process Preparing bay time (min/bay): the average time

7 and standard deviation needed to prepare a free bay This parameter is presented in the simulator application by to receive the next vehicle, measured in minutes. the term “cool-time”, as it is a more compact expression.

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As previously stated, the vehicle types Unload time (min/unit): the average time and that can be chosen to compose the fleet standard deviation for unloading the vehicle a stock keeping unit (SKU) of material, measured in minutes. include road vehicles, trains, boats and airplanes, as we will soon see in the chapter Marshalling time (min/unit): the average time and dedicated to the simulator application. standard deviation in minutes needed to prepare a SKU to be moved into the stock. 3.7.3 The production section

Max stock input rate (unit/hour): the maximum As in the previous case, the space rate and standard deviation at which the stock can be occupied in the CSU by the production operated, reflecting its internal technology. section only indirectly is known by the fixed costs of occupancy associated with it. The Stock input function (min/unit): the average time and standard deviation in minutes needed to production section, where manufacturing effectively input a SKU to the stocking structure. processes take place, is modelled as a resource linked to the stock facility, and A similar list of parameters is used to interfacing with it precisely in the same way characterize the process of outputting. All the external clients do, that is, by means of theses parameters are suggested to the user orders to output or input material (Fig. with default values, in a strategy to help in 3.17). These orders must wait for the usual reducing the time spent on configuring the processing resources to become available. model.

3.7.2 The fleet Production section The fleet is viewed as a list of vehicles Stock dedicated to the delivery and linked to the CSU. The physical space occupied by the fleet in the CSU is only indirectly known through the fixed costs associated to this resource. In principle, if these costs are null, it implies that the tasks associated with the delivery are hired and will be only expressed in terms of variable costs. Clients Each type of vehicle belonging to the fleet is configured in accordance to the Fig. 3.17 Production section as a regular client of the three following categories: (1) costs, where stock facility information about the driver costs and distance dependent costs are recorded; (2) In the same sense that the products are speed, where the speed of the type of handled in the inventory by means of vehicle is characterized; and (3) handling, product-boxes, each manufacturing process where data concerning the transport (refer again to figure 3.8, if necessary) capacity of the vehicle and the typical times assigned to the production section is here for handling materials while loading and represented by a product-plan in which the unloading are specified. information needed to produce the

121 Flexible Supply Chain Simulation respective product P+ is specified. Notice 3.7.3.1 Manufacturing policy that only products of P+ type can be linked Unlike the ordering policy of the stock, to a production section. A product-plan can where data has been separated from the then be seen as the specific procedure to data of “technology”, the manufacturing execute the manufacture of such type of policy was made to also incorporate the items. The product-plan is linked to the data related to the “technology” operating product-box of the P+ product, which in at the production section and the variable turn is linked to the P+ product, and this to costs related to the productive activity. This the respective sub-products in the form of a is clearly due to the fact that while the MRP description tree (or bill of materials), stocking process in principle has the same as shown in figure 3.18. characteristics to all the products (thus it makes sense to distinguish “technology” Pi from “policy”), the manufacturing process Product-plan P+ Rj is commonly very dependent on the specificities of the P+ product being P n manufactured. That is, each product-plan

Rm represents a single and independent manufacturing process, therefore it makes Product-box sense that data like the related with the

PRODUCTION moment to start producing, how to produce, STOCK SYSTEM SECTION etc., is alongside data representing the production rate and the costs of producing, Fig. 3.18 The association between a product-plan, a for example. This is our perspective of product-box, a system product and its sub-products modelling.

Obviously, a product-plan can only be It is now important to present a clear linked to a product-box that has previously idea about the kind of model we will use to been associated to a P+ product. represent the production line, since the

required data is obviously dependent on it. To schedule and to control its internal Here we consider that the production line activities, the production section needs to can only produce multiples of a certain be configured in terms of: (1) general predefined quantity of product units (which information, which in this case is reduced to we call the lot size) at a time, and also that simple association of a P+ product-boxes to a production rate (pRate) characterizes the a product-plan; (2) manufacturing policy, maximum output rate of the system. The where information about when to produce, raw process time (rpTime) is considered the how to produce, etc., is to be recorded; and time needed to produce a lot when there is (3) fixed costs, which in this case include no need for queuing. This approach is labour and occupancy, as mentioned in equivalent to considering the manufacturing section 3.4.4. process a sort of conveyor with length equal to the raw process time and where the lots are allowed to “enter” with a maximum

122 Chapter3 – The Flexible Supply Chain Approach frequency equal to the production rate (Fig. ByCycle: when the P+ product is to be 3.19). produced regularly. The model also allows starting the process after a certain initial THE PRODUCTION LINE delay, therefore introducing different shifts in the production of different products. The dt = 1 / pRate Manufacturing process quantity to be produced is defined by the pre-fixed lot size and the frequency is the value set in the production rate (pRate). This makes the system work in a kind of continuous production mode.

rpTime OnDemand: the P+ product is to be

produced the moment it is removed from Fig. 3.19 The model used to represent a production line the inventory. In fact, once in this option, the required quantity of P+ is obtained As it can be deduced from this figure, directly from the inventory while the same when producing a series of lots one needs to quantity starts being produced to replace wait the raw process time only for the first the inventory as close as possible to the lot; the others will follow it, appearing in previous level. The quantity (q) removed the output with the frequency pRate. The from the stock is dependent on the demand, costs of producing those lots will be easily and so, the number of lots to produce is: computed once the cost of producing one lot is known. This is another parameter of nLots = q / lotSize (3.16) configuration taken into account. Frequently this does not represent an Finally, the information about when to integer number of lots, and a strategy for start to produce must be appended to this rounding the value is usually adopted, by data. Concerning this last aspect the excess or by defect. following possibilities (or policies) had been considered: 3.7.4 The management section This resource is considered a pure ByLevel: if the P+ product must start to abstraction in this approach. The costs be produced each time its quantity in the associated with it can be appended to the inventory gets smaller than a certain fixed costs of keeping the stock facility in predefined value (equivalent to ROP). When the form of a new item, for example. This this happens, all the sub-products needed was our option. The moment the user fills in for producing the quantity of one lot are the fixed costs associated to the stocking, retrieved from the inventory and the new the user also fills in those associated to the P+ product is produced. At the end of the management of the entire CSU facility. production the quantity of P+ produced is The management structure is, in any sent to the inventory. case, distributed, the CSU-manager being the first responsible for the management in

123 Flexible Supply Chain Simulation the entire CSU, composed of sub-blocks 3.8 Modelling the CSU activity dedicated to more specific areas, such as Till now we have discussed the static part the Stock-manager, the Fleet-manager and of the Supply Chain modelling, that is, those the Production-manager. Each of these issues and elements that are important to “expert managers” is responsible for the definition of the aspect, the structure, executing the policies of its respective or, in one line, the general topology of the section, for example, as well as to resolve system. It is obvious that this structure the particular situations occurring in any strongly interferes with the dynamics that tasks related to its domain, as figure 3.20 will develop in the simulation when the suggests. model will be made to run, since in reality it represents the physical constraints of the Stock Manager problem. Even so, what in fact drives the Fleet advance of the simulation in time is the Manager Production sequence of events that represents each of Manager the processes running in the CSU and the CSU operational rules they contain. The present manager section is dedicated to this topic.

stock As we know from the last sections, in each CSU there are some processes running tasks fleet and interfacing with one another, that way tasks tasks creating the complex dynamics of the unit. Production These processes are, as we know, the CSU space section purchase, the stocking, the manufacturing, the delivery and the management. Except Fig. 3.20 Supervised-distributed management in a CSU for the management, which we will consider an instantaneous process coincident with This approach can be considered of the any possible events of the CSU, each of the “top-down” kind, with the CSU-manager other processes is set to contribute to the working as a distributor of management overall CSU’s state with a certain number of jobs. The advantage is that in any problem states (we will call live states those states related to the CSU communications are of which the duration can previously be always established with only one and the computed or known; and dead states those same “entity”, that is, the “supervisor” states where this cannot be done, as in the CSU-manager, which then will analyse the case of waiting states or queues, for situation and distribute a job to the most instance (Brito & Feliz-Teixeira, 2001)). The appropriate “manager”. As we will see collection of such states, and the possible later, when a CSU event needs to make connections among them, form the certain decisions, the control is sent to the complete state diagram of the CSU, which CSU-manager, and the process waits till it in fact is the core of the model. So, to receives some answer. simulate the Supply Chain we first have to design and specify the state diagram of the

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CSU, and then to convert it into a sequence of events which can finally be translated Supplier into a computer programming code. In our case this code will be the C++, a few of e_order() e_arrive() which will be presented in the next chapter. While creating the Supply Chain model in wait the simulator application, the user will not e_startUnload() need to think on these internal CSU states, UNLOAD as in this approach they become endogenous to the CSU object. The Supply Chain Fig. 3.21 State diagram of the purchase process representation will therefore appear as a game played with visual CSU objects that Notice that the supplier lead times are must be appropriate interconnected by initially unknown, since they result from the paths of transportation and of information. internal dynamics of the CSU supplier and As we have noticed earlier, from the point depend on the transport speed and of view of the user, each modelling task will availability, for example. Also notice that be a kind of “real objects” exercise. *** the event e_arrive() can be thought the 3.8.1 Process of purchase (or ordering) beginning of a subsequent live state which in fact represents the UNLOAD activity. This This process is related to the ordering of will let us later substitute the event materials to the supplier, and is due to e_arrive() for the event e_startUnload(). begin whenever one of the following conditions is observed: (1) the present stock As we have seen, the event of ordering control policy determines it is time to e_order() is strongly linked to the ordering reorder; (2) there is an order from a client policy previously assigned to the product in that cannot be fulfilled directly from stock cause, and can be generated anywhere in and, in the case of accepting backorders, the simulation. Anyhow, when the policy is new material must be ordered. one of the “ByCycle” type, this event is After creating and preparing the new basically generated by a circular ordering order with the list of materials required to process that will maintain the activity by the supplier, any of these “conditions” will itself along time. This process will be trigger the purchase process, which can running independently for each product simply be described by the two major configured that way. This is the method events: (1) transmission of the new order to used to generate de demand at the ultimate the respective supplier, which we will name customer level, that is, the leading demand e_order(); and (2) receiving the material to the Supply Chain. that arrived from the supplier, which we will name e_arrive(). Between these events 3.8.2 Process of stocking the purchase process will fall into a dead For reasons of simplicity, we will regard state, where it waits for the materials to the stocking process as the meeting of two arrive from the supplier, as represented in other distinct processes: the input process figure 3.21. and the output process. As the names

125 Flexible Supply Chain Simulation suggest, the input process is responsible for activity, and the order gets ready to be sent inputting into the stock the materials to the client. Anyhow, the CSU-manager arriving to the CSU, while the output (now directed to the Fleet-manager) once process will ensure the outputting of again called by the e_endMarsh() event will materials from the stock of the CSU. Figure decide whether or not the order will wait 3.22 gives an idea of the events and for later LOAD and transportation, keeping activities involved in the output process: it in a different waiting place depending on that decision. Once the transport to the from client is available and an output bay is free client e_orderin() e_startOutput() for loading, the LOAD activity can start. At MNGR the end of it, the event e_endLoad() will wait trigger the delivering process.

wait OUTPUT Concerning the input process, that is, the e_endOutput() e_endLoad() inverse of this process, we can find some

e_endMarsh() wait similarities with what was described before. LOAD As figure 3.23 shows, the diagram of this MNGR e_startMarsh() process can, in fact, be seen as a part of wait MARSH1 the diagram of the previous process with wait some new activities replacing the old ones. e_startLoad()

Fig. 3.22 State diagram (or activity diagram) related with e_endInput() Vehicles the stocking output process INPUT from client MNGR Basically, this process starts in response e_startInput() e_startUnloa() to an e_order() event sent by a CSU client, wait that is, an order to output material. Such UNLOAD event is received in this CSU in the form of e_endMarsh() wait an e_orderin() event, so that the two events MARSH2 can be distinguished. This event will then e_endUnload() ask the permission of the CSU-manager to e_startMarsh() satisfy such an order for outputting materials. The CSU-manager, by means of Fig. 3.23 State diagram of the input process its Stock-manager, will decide if the order is When a vehicle arrives from the supplier, to be immediately served, rejected as a the e_startUnload() event is responsible for stockout, or made to wait to be satisfied calling the CSU-manager for stock later when new material will arrive from the management. Then, the activity of UNLOAD supplier. As long as the order is ready to be replaces the activity of LOAD, the served to the client, the process enters the marshalling works in the opposite direction phase of outputting the materials from the and the activity of INPUT substitutes that of inventory. Then the material will be OUTPUT. prepared and packed at the MARSH1

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Despite these similarities, in this first significantly complex, principally when version of this modelling approach the input nodes of transfer have to be used for process has suffered some simplifications modelling connections between different which practically reduced it to a modified types of vehicles, or even when the delivery version of an UNLOAD activity. This was network is in itself complex. In any case, equivalent to consider that the material will the vehicle entities will themselves be be moved into the stock precisely in the responsible to handle the materials while activity of UNLOAD, greatly simplifying the they move along the network, making them modelling in terms of the need for practically “transparent” to the CSU synchronization between the input and the delivery process, as the same figure output activities while accessing the stock suggests. of the CSU. Although, since not-yet-fulfilled orders in the output process will have to e_startDelivery() queue while waiting for the OUTPUT Supplier activity, this simplification is expected not to lead to significant discrepancies in the e_startUnload() results, as it is as if the INPUT activity would, together with the marshalling, run in e_atHome() wait Client parallel with the OUTPUT activity and, on costs average, the material would already be in money the inventory when it must be taken out from there. On the other hand, to output Fig. 3.24 State diagram of the delivery process from inventory is usually significantly slower than to input, and so it is expected that this So, we consider that this process begins simplification does not induce grave with the event e_startDelivery(), which is distortions in the model. Of course, in this equivalent to e_endLoad(), and will end the provisional version certain times and mainly moment the vehicle is finally received by costs are still computed as if the activities the CSU client, that is, at an event of MARSH2 and INPUT would be present, but e_startUnload(). In the case of more than the issue is to be resolved in a next version. one vehicle delivering to the same client, they probably need to wait for a free 3.8.3 Process of delivery inputBay, as proposed in the same figure. In this process, transportation vehicles With this model, only after the end of will be carrying materials to the clients. As simulation can the lead times of the system it was earlier mentioned, the transport of be estimated. So, lead times are treated as those materials can be done in many outputs, and not as simulation inputs. different ways, from using normal roads to Notice that the true final part of the airlines, and therefore the efficiency of this delivery process is the return of the vehicle process will be strongly dependent on the to its CSU home, which will terminate with kind of transportation chosen. Although the the event e_atHome(). For reasons of delivery process is conceptually very simple, simplicity, however, the time for returning as shown in figure 3.24, it can also become home will be estimated as 75% of the time

127 Flexible Supply Chain Simulation spent to reach the client, so, this event will be in fact automatically scheduled when the e_startProduction() e_endProduction() vehicle reaches the CSU client, at the “end” of the delivery process. At that same PRODUCTION moment also the costs and other parameters wait related with the vehicle are computed and the respective payment is made to the right suppliers. Fig. 3.25 The simplified manufacturing process

3.8.4 Process of manufacturing As previously said, the start of the The manufacturing process is modelled manufacturing process will be decided here assuming no product mixing and only a either by the stock level of the product P+ line of production for each product P+ to be produced, or by using a production manufactured. This, of course, can be made cycle, as in the continuous production, or more complex in future, but for the directly triggered by the orders of the moment it will be treated in this simplified clients if operating under a on demand form, as our major concern is to focus policy. This will be established by the user. attention on the overall Supply Chain At the proper time, the necessary products behaviour and not particularly on and the respective amounts are removed production8. In a wide range of real cases, from the stock (using the output process) however, this model is expected to be and the new product P+ starts to be useful without significantly affecting the produced. At the end, the new product will consistency of the results. be added to the stock of the CSU and the The states diagram of such a model is associated costs computed. very simple and is represented in the next figure (Fig. 3.25). There is a waiting state (queue) for the orders waiting to enter the 3.9 Products, orders and vehicles production, an event e_startProduction() It is important at this moment to make responsible for the beginning of the activity, also some considerations concerning other and an event e_endProduction() in charge of elements of this modelling approach which the actions that must be executed when the do not specifically belong to the CSU PRODUCTION activity is finished. As structure, despite remaining basic for the indicated in the section 3.7.3.1, this model simulation. These are related to the uses the traditional concepts of production principal flows in the Supply Chain and rate, which establishes the maximum therefore they may be seen as playing the frequency at which the orders can enter the roles of messengers between CSUs. They are PRODUCTION activity, and the raw cycle the system products, the material-orders time, defining the time that each lot must and the vehicles for transportation. System spend in that same activity. products are in fact passed from CSU to CSU by vehicles in response to material-orders.

8 “Manufacturing” and “production” are terms used with This section is reserved to describe each of equivalence in this context. We will use “production” more them in more detail. during the implementation phase, since it is a shorter word.

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3.9.1 About the system products Base price: the cost of a unit of product (SKU) The idea of system products was used to when it enters the Supply Chain for the first time. resolve certain conflicts related to the Most of the products enter through the primer attributes of products in different CSUs. For suppliers, but it does not have to be the case. The example, as was previously noticed, if the price of the product in the next CSUs will be dependent on the chain of value. products would be considered property of the CSU, the stock of each facility would Typical load/unload time: an estimation of the only use its very own items, transforming time needed to load or unload a product unit (SKU). such representation redundant and little This is basically an index that later can be used in a flexible, since in reality diverse facilities special situation, if necessary. use the same product in their inventories.

Another aspect was the need for relating Other parameters like the life-cycle, different ordering policies to the same intended to represent the period of time product if it would belong to different CSUs, that the product is considered valuable, for example. So, we obviously decided that which strongly interferes with its price, as the ordering policy would not be linked well as the next-generation, by which the directly to the product but to something in new versions of the product would be the stock that would contain the product, generated in the simulation, have not been that is, the product-box. System product is implemented in the present modelling therefore a term aimed to avoid confusion version, although they have been planed whenever necessary, but in fact represents since the beginning. the product. This product is the same item to all the Supply Chain system. 3.9.2 The material-order structure

The material-order (or simply order) is Some parameters have been ascribed to the element that triggers the flow of the the system product with the intent to materials in the Supply Chain. In this reference it in the Supply Chain model and version, the order is primarily composed by to define its attributes. These are: a list of materials. A material is seen as a

Reference: a unique identification of the product registry containing attributes related to the in the system. It is automatically attributed by the product to order, the required quantity, the system when the product is created and can be used in reference, the product name, etc., so that future searches. the supplier can clearly link to it the correct

Type: the type of the product. R for raw materials, item of its inventory (Fig. 3.26). P for parts and P+ for secondary products. Notice that only products P+ will be allowed to be linked to a material ref name quantity units/container manufacturing process (through a product-plan). MAT1 p(1) butter 100 1000 Name: a name to easily identify the product. MAT2 p(23) milk 300 500

Units per container: the number of product units MAT3 p(12) water 50 400 (SKUs) that can be transported in a normal container. All the measures in space occupancy are later made Fig. 3.26 Example of a list of materials based on this value.

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In the previous figure, a list of materials Notice that this is a basic and illustrative (a part of the order), is represented in the model. The one effectively implemented in form of a table. the simulator includes a list of materials, Since in the simulation the order is the delivery path to the destiny, 14 other always made to return to the CSU of origin attributes, and also some methods for (when not refused by the supplier), some executing certain operations. other attributes were also added to the material, like a link to the product-box of 3.9.3 Modelling the vehicles origin, the cost of the product meanwhile All vehicles are modelled here in the served by the supplier, and the average same basic form, that is, they are based on time that it was waiting before being the same abstract mould (or class) which we effectively delivered to the client. These have named Veiculo (vehicle in Portuguese). attributes are mainly used to help in This element has, as usual, diverse simplifying further calculations and to attributes, but also a transport path which ensure the coherence of the flow of is associated with it before the start of each materials between the facilities. new delivery job. Basically, this is how the vehicle gets to know the way to its clients, Then, the complete order, as considered and a form to be independent of the CSU, in this approach, is an element containing from then on. Although the Veiculo class the information of a list of materials and, in implemented in the simulator has almost 40 addition to that, some other attributes to attributes (it would make no sense to help identify the supplier and the client, present them in this context), here we will the moment when the order was placed, the only talk of those which are essential to vehicle currently transporting it, the total understanding how the vehicle dynamics number of SKUs, etc. We like to abstractly have been modelled. With that purpose, represent the order as a kind of another figure 3.28 shows a visual representation of “box” which is at first sent empty to the the concept adopted. supplier and then will return with different Basically, before starting the delivery, types of materials to its CSU of origin. This the vehicle inspects the first order it carries is what we intend to represent in figure and assumes as its job the transportation of 3.27. such an order to the respective client, by following the delivery path previously

Pi assigned to the order by the Fleet-manager. P j Notice that each order includes its own Pk delivery path. When the current order is delivered, the vehicle inspects the next Origin CSU Destiny CSU order and will repeat the previous Number of SKUs procedures till there are no more orders to Current vehicle delivery. At that moment, the vehicle returns home. Fig. 3.27 Basic model of a material-order

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CSU Supplier CSU node: when reaching this type of CSU Client(j) node the vehicle will act as at a normal node if this node is not the CSU client to list of paths to the clients where the current order is to be delivered. On the contrary, if the node corresponds to the CSU destiny, the vehicle enters that CSU and transfers the responsibility of the next CSU Max. volume actions to the CSU. In the proper time the Total of CSU will set it free, either to execute the kms next delivery job or to return home.

List of speed orders 3.10 About the flexibility (matrix)

Fig. 3.28 The vehicle and some of its attributes In the previous chapter some references were made to the concept of flexibility and The vehicle movement along the delivery the way it is currently seen by Supply Chain paths is basically driven by two events: managers. The present modelling approach e_start(), which corresponds to the moment is also claimed to introduce the possibility the vehicle enters a new path; and e_end(), of measuring the flexibility associated to a which is executed at the end of each path, certain Supply Chain structure by means of when a new node is reached. In this case, simulation. This new feature is based on some actions must take place, depending on finding a matrix related to the amounts of the type of the node: inventory in imbalance (rigidity) when steps of demand are injected in the Supply Chain. Normal node: the vehicle compares the The concept of rigidity is seen as the inverse next path of the current order with each of of flexibility, and it will be used in order to the paths going from the node. The vehicle simplify the representation of the matrix. In will follow the path that will lead to the this section we will present in more detail current client. such a proposal for measuring the Supply Chain flexibility, and explain the way it was Transfer node: the vehicle transfers to integrated in the simulator. More detailed the new vehicle all the materials it is information about this theory and the carrying and all the information needed for method of building the rigidity matrixes, as the later calculation of costs, delivery well as some practical results, can be found times, etc. Then, it returns home. in Feliz-Teixeira & Brito (2004).

3.10.1 The rigidity concept Pause node: the vehicle simply stops and schedules the next event of e_start() for the As was referred in the previous chapter, proper time, obviously including the delay in the section devoted to facility related of the pause. The costs of the pause are metrics, the concept of rigidity is a way of also added to the costs of transportation. representing the energy spent on imbalance while the company is reacting to a sudden

131 Flexible Supply Chain Simulation step in the demand (Fig. 3.29). In the case due to stockouts. where the attention is focused in the So, considering Qout the number of SKUs inventory performance, the rigidity is leaving the inventory during the time measured in terms of SKU/day in imbalance. interval dTout, and Qin the number of SKUs In figure 3.29, this corresponds to the entering the inventory in the time interval excess and deficit in the quantity of product dTin, such a condition of equilibrium can be in inventory along the dashed area of the simply expressed as: Equilibrium function, divided by the time of imbalance Tq. Qout / dTout = Qin / dTin (3.17)

Supposing that we make the observations during the same time interval, that is, if we consider dTout = dTin, this leads to:

(Qout - Qin) / dTin = 0 (3.18)

which is another way to represent the Equilibrium function for the inventory, and

can be thought an “imbalance ratio”, Fig. 3.29 Concept of rigidity as an imbalance ratio measured in inventory units (SKU) per unit of time. This already corresponds to a This means that the more unstable the certain “energy” spent per unit of time, and response to a sudden change in the demand, thus gives us an idea of a “power spent on the more rigid the system. That is, the less imbalance”, which in fact can be seen as it is able to absorb shocks. the inverse of flexibility. By this definition, This is a general view of rigidity, which the system is more flexible if the rigidity can be applied not only to the stock gets lower, and a simple way to measure it reaction but also to any other class of is to integrate the Equilibrium function over responses, like to the costs, for example (in the time and then divide this value by the that case the rigidity could be measured in total time of imbalance. So, if for a certain

€/day). In any case, it is essential to write period j of time dTin[j] it holds that there the equations governing this process in was Qin[j] units of material going into the order to obtain quantitative results from node and Qout[j] units of material going out, such a metric. This is what we will do right the rigidity at the end of the observations now, for the example of the inventory. can be computed as:

We first suppose that the inventory will Qin[j] - Qout[j] å j be in equilibrium when the ratio of products RIGIDITY = (3.19) å dTin[j] going in, equals the ratio of products going j out. That means that the stock manager is in fact able to ensure the rotation of the And, from it, retrieve the flexibility: materials, or the flow of products, without incurring excessive costs of holding or costs

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1 FLEXIBILITY = (3.20) is, the didactic Supply Chain of the RIGIDITY “Cranfield Blocks Game” (Saw, 2002), which meanwhile was analysed with the present It is based on these expressions that the modelling concept (Feliz-Teixeira & Brito, matrix of the rigidity is computed in the 2004): Supply Chain simulator. 3.10.2 Matrix representation Supplier

The calculation of the rigidity is easily Factory achieved by counting the number of product Warehouse1 Warehouse2 units leaving and entering each facility between successive arrivals of materials Depot4 from the suppliers. This is in accordance Depot1 with the equation (3.19) and it is easily Depot2 Depot3 implemented in practice. We must notice, however, that till now C1 C2 C3 C4 we considered the rigidity of a single CSU (by its inventory), which can be represented Fig. 3.30 The Cranfield Blocks Game Supply Chain by a scalar. On the other hand, the rigidity of a Supply Chain emerges as a property of a This is a seven node Supply Chain (from collection of CSUs, therefore resulting in a Depot1 to the Factory) where the direct more complex mathematical element, as is demand (inputs) from customers (Cj) is the matrix. That is, the rigidity of a Supply injected at the depots level, and the Chain will have the form of a matrix Rij Supplier is considered an infinite source of where the rigidities of all the facilities are materials. It is a three level Supply Chain exposed under certain criteria. This matrix where, for simplicity, only one product is will characterize the Supply Chain only for considered. the specific conditions in which it was This system has four (4) inputs driving created, being obviously dependent on the certain output functions at each of its seven ordering policies adopted at each facility, (7) nodes. Thus, by means of injecting at as well as on the delivery policies, the each input a step of demand one will be in amplitude of the demand shocks, etc. It is, principle able to observe how any of those however, a good tool to form an idea about output functions (as inventory, costs, etc.) how the overall system will be able to respond to such conditions, obtain some answer to such a demand change. It is, in measures and then represent the rigidity fact, the multi-node step response of the matrix of the overall system. A simple way system, which contains extremely useful to write down this matrix is to plot the information about the system itself. individual rigidities in columns ordered by increasing levels of the facilities, as In order to clarify this subject we use suggested in the next figure. This let us fast again the example described in the final acquire an idea about how the chain is comments section of the last chapter, that susceptible to such input “shocks”.

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process, applying the step of demand to the inputs next depot, till all the depots have been “shocked”.

In the chapter dedicated to the results level 1 more about this example will be presented and discussed. level 2

level 3

Fig. 3.31 General rigidity matrix for the Supply Chain of the Cranfield Blocks Game

From this representation one can also realize that the rigidity of the chain will be the rigidity of each of its “arms” connecting the last customers to the primer suppliers. It is therefore unsurprising that a particular Supply Chain may show a high flexibility in serving certain customers while exhibiting a disastrous flexibility in serving others. A nice result from these considerations is that “the flexibility of an arm will be extremely dependent on the flexibility of its slowest node”, since the speed in a chain is the speed of the slowest element. So, it seems that cooperation is a need that naturally emerges from the idea of flexibility applied to the Supply Chain. In practice, the procedure leading to the rigidity matrix, by using simulation, is as follows: (1) simulate the system for a certain time with constant demand in all its inputs; (2) at a certain moment, provoke an instantaneous increment of demand in the first depot, keeping the others working as previously; (3) at the end of the simulation, measure the rigidities in all the facilities of the Supply Chain, using the formula (3.19), and record them in the form of a column; (4) repeat the simulation and the previous

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References:

Brito, A. E. S. C., & Feliz-Teixeira, J. M. (2001). Simulação por Computador. Porto, Portugal: PUBLINDÚSTRIA. Feliz-Teixeira, J. M., & Brito, A. E. S. C. (2004). On Measuring the Supply Chain Flexibility. Paper presented at the 2004 European Simulation and Modelling Conference, UNESCO, Paris, France. Gonçalves, J. F. (1999). Gestão de Aprovisionamentos (revista ed.). Porto, Portugal: PUBLINDÚSTRIA. Mason-Jones, R., & Towill, D. R. (1999). Using the Information Decoupling Point to Improve Supply Chain Performance. Journal of Logistics Management, 10 (2), 13-24. Saw, R. (2002). Cranfield Blocks Game: Centre for Logistics and Supply Chain Management (CSCM), University of Cranfield, UK. Sussams, J. E. (1992). Logistics Modelling. London, UK: Pitman Publishing.

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CHAPTER 4 137 IMPLEMENTATION AND OVERVIEW 137 4.1 INTRODUCTION 137 4.2 THE SIMULATOR STRUCTURE 137 4.2.1 The classic proposal 138 4.2.2 The OOP tendency 139 4.3 DISCRETE EVENT MODELLING 139 4.3.1 System states and time advance 140 4.3.2 Events versus activity cycles 140 4.3.3 The SimEvent object 141 4.4 THE BASIC ENTITY 141 4.4.1 The SimEntity object 142 4.5 LISTING THE EVENT IDS 143 4.6 CODING THE STATES (ACTIVITIES) 143 4.7 THE SIMULATOR OBJECT 144 4.7.1 The schedule method 145 4.7.2 Random Normal generator 146 4.7.3 The executive loop 146 4.7.4 The SimDlg loop controller 147 4.8 ENTITY MOTION (SIMMOVIE) 149 4.9 A DAILY MARK (SIMTIMEMARK) 150 4.10 SOME UTILITIES 151 4.10.1 The SPECTRUM class 151 4.10.2 The graph window (GraphDlg) 152 4.10.3 Visual modelling (Area) 153 4.11 SUPPLY CHAIN ENTITIES 154 4.11.1 Paths 155 4.11.2 Nodes 156 4.11.3 Vehicles 159 4.11.4 Material-order (Encomenda) 161 4.11.5 The product 162 4.11.6 Product-box 163 4.11.7 Product-plan 165 4.11.8 The CSU entity 166 4.11.8.1 Configuring the facility 167 4.11.8.2 Icon 167 4.11.8.3 States (activities) 167 4.11.8.4 Event handlers 168 4.11.8.5 Other methods 169 4.11.8.6 Main resources 169 4.11.8.7 Suppliers 169 4.11.8.8 Fleet 170 4.11.8.9 Graphs 170 4.11.8.10 Final metrics 170 4.12 THE SIMULATOR APPLICATION 171 REFERENCES: 175

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Chapter 4

Implementation and Overview

Coding the simulator in C++ and an overview on the final application

the most relevant issues, integrating them 4.1 Introduction in such a way that the overall exposition can This chapter will be organized in two come about positive and useful. major parts. At first, we will present and We would also like to note that while discuss the basis for implementing the some C++ code will be presented in the first simulator in a general purpose programming part of the chapter, constant attention will language, which in this case will be focused be given to the representation of the same in the C++. Then, an overview on the final ideas in the form of diagrams, whenever computer application for Supply Chain possible, in order to also allow non-C++ modelling and simulation meanwhile users to benefit from such information. developed will be offered, giving the reader Besides, whenever we consider it to be an idea about its particularities and useful, we will also include some brief potentials. explanations about any relevant aspects Since documenting in detail each of these related to discrete simulation. subjects would imply the writing of a large Concerning the second part of the amount of information, we decided to chapter, we decided to organise it as a sort reduce such exposition to a level considered of guided “tour” around the computer essential to the good understanding of the application which meanwhile was built with issues without the loss of any important this modelling approach. aspect. Principally concerning the C++ development, it is important to remind that the source code is distributed over 55 4.2 The simulator structure different object classes, several of which The present simulator was designed and having quite more than 10 attributes and implemented founded on the paradigm of various methods in their structure. In sequential objects, that is, using an object particular the CSU class makes use of almost oriented ambience where each object has 180 attributes of several kinds and 40 access to the others through a mechanism of methods, of which 20 are specifically shared memory, within a single application. dedicated to simulation events. A detailed Thus, the access to most of the variables description of such information would be and methods of the application can be made unreasonable in the present context. We by means of pointers, or references. will try, therefore, to focus the attention on Although this is just the classical Object

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Oriented Programming (OOP) scheme, it is block, as they are considered a constant for still the method with which the fastest the simulation of any model. executions of time-dependent models are achieved, as the basic information between EXECUTIVE objects is naturally passed in “parallel”. That is, there is no need for any protocol of - Time advance communication to transfer information between objects. Instead, the objects live OPERATIONS in a “sea of memory”, even if direct access - Model description to certain parts of that memory can be restricted. Such is the groundwork UTILITIES supporting our discrete event modelling - Simulation support approach.

4.2.1 The classic proposal The structure usually recommended for Fig. 4.1 The three basic blocks of a discrete simulator the implementation of a discrete simulator largely reflects the idea of dividing the Although this structure continues to be programme code into the three main blocks very useful for didactical purposes, the fact shown in the figure 4.1, each having is that it does not fit completely adequate different responsibilities: (1) the executive, to the development of certain simulators where the source code of the “engine” of using the OOP paradigm. In effect, it can be the simulator resides; (2) the operations seen as the result of an obvious strategy for block, where all the code related with the compiling the source code of an application implementation of the activity cycle in three separated blocks. That way, most diagrams of the model is, therefore where of the times the user only had to compile the system operations are described; and the operations block, as the others rarely (3) the utilities block, containing all the would need to be updated. types of resources, functions and methods On the other hand, the OOP languages used for supporting the simulation process, often structure the process of compilation like random number generators, histograms, object by object, as the methods (internal graphical tools, dialog-boxes, etc. functions) associated with a particular While the executive mainly controls the object are usually confined to the same time-advance in the simulation, that is, source code file. The code associated with ensures the rolling of the simulation loop, the model dynamics therefore has the and the block of utilities can simply be seen tendency to spread through some objects of as a repository of useful tools, it is in the the application, naturally betraying the operations block that the core of the model concept of a single operations block. The is described. Simulation languages, for approach described herein, being an OOP example, frequently hide from the user the proposal, is one of those cases. code of the executive and of the utilities

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4.2.2 The OOP tendency states belonging to the entities. Entities Under an OOP description, the model give states to the system, so, a system behaviour is dispersed through the diverse without entities is a system without states. objects that represent the active entities of Figure 4.2 represents this new type of the system. So, the structure of the structure. simulator naturally gets more flexible for adapting to each programmer style, without loosing the coherence of the previous case. S In a certain way, the simulator structure Model becomes horizontal. In our case, it was likely to follow the object structure of the Microsoft Visual C++, whilst the idea of the two step procedure was maintained: first model, then simulate. The model is created by means of

identifying and describing the appropriate entities of the system, which are seen as elements linked to their very own states (activities), and frequently containing their Other very own “operations block” too. The description of the dynamics of each entity eventually implies access to some resources Fig. 4.2 The simulator OOP general structure of support (those previously described in the utilities block), but most of these resources As one can see, it is still possible to find are now objects (as the dialog-boxes used similarities between this structure and the for configuring and ensuring the human classic proposal, if the elements are interface with the entities, for example), or separated by means of the three previous even methods belonging to other more criteria, but under a superior level of complex objects, as to the object abstraction. Also notice that now, for , which in this approach is also updating the model, it is only necessary to the “owner” of the executive, as we will update the model , that is, the see1. active objects in the simulation, those that This vision of entities owning their contribute with their states to the state of individual states, in effect contrasts with the system. the old tendency of viewing the states as belonging to the system, which was a typical perspective of straight programming. Here, 4.3 Discrete event modelling to the contrary, the states of the system are The method of simulation used here is seen to result from the collection of the the one usually known in literature as discrete event, or event-scheduling. At least 1 To avoid confusion with similar words along this text, we to clarify how this method “looks” to the will enclose objects between brackets, like in