LITMIR.BIZ

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the simulators used in the book are now stored on the Learners' website in chapter-by-chapter learning support folders. They are accompanied by an evergreen link to the latest iThink modelling software. My thanks to Karim Chichakly at isee systems for providing this link and for implementing, within the evolving software, special functionality on which my book relies – in particular sketchable time charts and the capability for users to conduct instructive ‘tours’ of initial simulator conditions (by hovering-over model icons to view the numerical values they hold).

      My thanks also to the editorial and book production team at Wiley for their friendly and professional support throughout the 2^nd edition project. Jenny Ng worked diligently with me on design and content changes. She also expertly managed permissions and ensured everything was completed on time. Tessa Allen skilfully guided the book through it's multi-stage production process while Caroline Quinnell sharpened numerous phrases during copy editing. My secretary Suzanne Shapiro has steadfastly and cheerfully supported my work at London Business School for more than 25 years. During this period Suzanne and I have been based in two different subject areas: Strategy & Entrepreneurship (1986–1996); and Management Science & Operations (1997–present). Thank you Suzanne! Finally, I thank my wife Linda who provides the motivation, love, stability and perspective of family life that lies behind all my work.

       Preface from the First Edition 3

      I first became interested in models and modelling when I studied physics as an undergraduate at Bristol University. Or perhaps my interest was really awakened much earlier when, as a boy of nine or 10, my friend Alan Green introduced me to the board game of Monopoly. I soon became fascinated with board games of all sorts and accumulated a collection that included Cluedo (a detective murder mystery game, also known as Clue), Railroader (a game to build and operate your own wild-west railroad in competition with rival railway companies), and Buccaneer (a game of pirate ships and treasure collecting). I was intrigued by the colourful tokens, the chance cards, the rules and the evocative boards that showed city sights, a murder mansion, a treasure island or whatever was needed to fire the imagination. In Buccaneer, the game's clever distinction between the ‘sailing power’ and ‘fighting power’ of a treasure-seeking frigate is something I still appreciate today. And as a modeller I admire the game designer's artful representation of a pirate's world, set out on a blue-and-white chequered board that serves as an ocean.

      Later, after graduating from Bristol, I joined Ford of Europe's operational research department, where computational decision models replaced the abstract and elegant models of physics. There I worked on investment appraisal (justifying the decision to build a new Fiesta car factory in Spain) and dealer location (whereabouts within Bromsgrove, Bury St Edmunds, or other English towns and cities, to site new car dealerships). During the second of my three years with Ford, the company sponsored me on an MSc degree in operational research at London University's Imperial College. It was at Imperial that I first encountered system dynamics, albeit briefly in an elective course on quantitative methods, and this chance encounter eventually led me to apply to the doctoral programme at MIT's Sloan School of Management for a PhD in system dynamics. Hence began the journey that I have pursued ever since.

      When I look back over my 40-plus years in the field I see five different phases of work, all of which have contributed to the content of this book and led me to the friends and colleagues who have shaped my thinking. My names for these phases are: (1) manufacturing dynamics and information networks; (2) bounded rationality and behavioural decision making; (3) modelling for learning; (4) the dynamics of strategy; and (5) soft systems and complementary modelling methods.

      Manufacturing Dynamics and Information Networks

      The first phase coincided with my doctoral dissertation at MIT when I worked on manufacturing and supply chain dynamics in Cummins Engine Company and Harley-Davidson. I was fortunate, back then, to have Jay Forrester as my PhD thesis supervisor, Jim Lyneis as a collaborator/faculty adviser on the Cummins project, and Nathaniel Mass as a faculty instructor. I learned many valuable modelling skills from them and from MIT's intensive academic apprenticeship with its special educational blend of theory and real-world practice. I still remember the sense of excitement as a first-year doctoral student, arriving by plane in Columbus Indiana, headquarters of Cummins Engine Company. There, I worked on the Cummins manufacturing dynamics project and found myself applying the inventory control, forecasting and production planning formulations I had learned at MIT. The simple factory model in Chapter 5 contains echoes of these same formulations. Further archive material on manufacturing dynamics can be found in the learning support folder for Chapter 5 on the Learners' website (see the About the Website Resources section).

      My doctoral thesis topic arose from an on-the-job discovery that circumstance presented. I was working simultaneously on manufacturing models of Cummins and Harley-Davidson. When I set out the 10–15 page diagrams of these two models side-by-side on my apartment floor in Cambridge (Massachusetts), I noticed that the information flows which coordinated multi-stage production in the two factories were arranged in different patterns. Every stage of production in Harley, from final assembly of motorcycles to sub-assemblies and raw materials, was coordinated from a master schedule – a kind of top-down control. There was no such master schedule in Cummins's factory at the time. Stages of production followed local order-point rules. It turned out that Harley-Davidson was operating a computer-driven top-down material requirements planning (MRP) system, which was entirely new to manufacturing firms at the time (and, back then, had scarcely featured in the academic literature on operations management). My thesis compared the long-term dynamic performance of these alternative approaches to production planning and control. A striking result was that traditional order-point rules outperformed MRP (in terms of operating cost, production stability, inventory availability and lead-time predictability). Only under special and hard-to-achieve factory conditions was MRP superior, despite the cost-savings touted by advocates of MRP. And so my curiosity about information networks began.

      As an aside, I should mention that the basis for the manufacturing models in my thesis was the production sector of the MIT group's National Economic Model. The production sector was essentially a generic model of the firm, residing within a system dynamics model of the US economy. The premise of the group's research at the time was that the US economy could be conceived as a micro-economic collection of interacting firms, households and banks. Macro-economic behaviour arises from micro-structure. Jay Forrester was leading the National Model project, so he knew the production sector intimately. As my thesis supervisor he was able to swiftly critique and guide my efforts to adapt this generic model of the firm to fit what I had discovered from the company-specific models of Cummins and Harley. I learned a great deal about model formulation and behaviour analysis from those encounters. I also learned from other doctoral students in system dynamics who, at the time, included David Andersen, Alan Graham, Mats Lindquist, Ali Mashayeki, George Richardson, Barry Richmond, Khalid Saeed and Peter Senge; and then later Nathan Forrester, John Sterman, Jack Homer, Jim Hines and Bob Eberlein.

      It was while working with the production sector, which was a visually complex model, that I took to drawing boundaries around sets of model symbols that belonged with a given policy function, such as capacity utilisation or scheduling and ordering. This visual simplification procedure later led to policy structure diagrams as a high-level way of representing the coordinating network in system dynamics models. I use both policy boundaries and policy structure diagrams throughout the book.

      Bounded Rationality and Behavioural Decision Making

      My thesis showed that sparse and ‘simple’ information networks in firms can often deliver business performance that is superior to more complex and sophisticated information networks. This observation led me, as a newly-appointed junior faculty member at MIT Sloan, into the literature of the Carnegie School and Herbert Simon's work on bounded rationality. The idea that the ‘structure’ of a firm's information feedback network determines the firm's performance and dynamic behaviour is central to system dynamics.4 The Carnegie literature helps to bring the information network into clear focus and to explain why human decision makers, faced with complexity and information overload, prefer sparse information networks. People and organisations

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