Industrial Dynamics to Systems Thinking

Prof. Jay W. Forrester pioneered industrial Dynamics. It enabled the management scientists to understand well enough the dynamics of change in economics/business systems. Four basic foundations on which System Dynamics rest were discussed. The thought process prevailing and their shortcomings are pointed out and the success story of System Dynamics was explained with the help of Production-Distribution model. System Dynamics graduated to Learning Organisations. Senge with his concept of integrating five distinct disciplines of Systems Thinking, Personal Mastery, Mental Models, Shared Vision and Team Learning succeeded in bringing forth the System Dynamics to the reach of large number of practitioners and teachers of management. However, Systems Thinking part of the Learning Organisation fails to reach out because it lacks the architecture needed to support it. Richmond provided the much-needed architectural support. It enables the mapping language to be economical, consistent and relate to the dynamic behaviour of the system. Progression from Industrial Dynamics to Systems Thinking has been slow due to different postures taken by the professionals. It is suggested that Systems Thinking has a lot to adopt from different disciplines and should celebrate synergies and avail cross-fertilisation or opportunities. Systems Thinking is transparent and can seamlessly leverage the way the business is performed. ★ A. K. Rao is Member of Faculty at Administrative Staff College of India, Bellavista, Hyderabad 500 082, India. E-mail: akrao@ascihyd.org and A.Subash Babu is Professor in Industrial Engineering and Operations Research at Indian Institute of Technology, Bombay 400 076, India E-mail: subash@me.iitb.ernet.in Industrial Dynamics to Systems Thinking A.K.Rao & A. Subash Babu Introduction: In the year 1958, the first words penned down by the pioneer of System Dynamics (then Industrial Dynamics) Jay W. Forrester were “Management is on the verge of a major breakthrough in understanding how industrial company success depends on the interaction between the flows of information, materials, manpower and capital equipment”. The article titled “Industrial Dynamics: A Major Breakthrough for Decision Makers” in Harvard Business Review attracted attention of management scientists. Several controversies arose when further articles appeared subsequently. Today, 40 years since the first article in the field of System Dynamics appeared in print, the progress when evaluated evokes mixed response. If it were a major breakthrough for decisionmakers, then why did it not proliferate into the curriculum of business schools as common as that of Principles of Management or Business Statistics or any other standard subjects of study? The purpose of this article is to critically review three seminal works in the field of System Dynamics: Industrial Dynamics by Jay W. Forrester (1960), Fifth Discipline: The Art and Practice of Learning Organisations by Peter Senge (1990) and Systems Thinking by Barry Richmond (1997) and to understand the pitfalls in reaching out to the large body of academia and practising managers. Forrester in his work raised a few fundamental issues way back in early 60’s that most of the corporate managers are able to comprehend only now. He clearly answered the question on what is the next frontier of our knowledge. The great advances and opportunities in the future he predicted would appear in the field of management and economics. The shift from technical to the social front was evidenced in the way global competition and the rules of the game changed. The test of leadership is to show the way to economic development and stability. The leading question therefore is whether we understand well enough the dynamics of change in economic/business systems to pioneer this new frontier? Forrester offered the much-needed solution: System Dynamics. The foundations for a body of knowledge called system dynamics were the concepts of servomechanism, controlled experiments, and digital computing and better understanding of control theory. Servomechanism of information feedback theory was evolved during the World War II. Till then, time delays, amplification effects and the structure of the system were taken for granted. The realisation that interaction between components is more crucial to the system behaviour than the components themselves are of recent origin. The thesis out of information-feedback study led to the conclusion that information-feedback system is all pervasive in the nature. It exists whenever the environment changes, and leads to a decision that results in action, which in-turn affects the environment. This leads us to an axiom that everything that we do as an individual, as an organisation, as an industry, as a nation, or even as a society irrespective of the divisibility of the unit is done in the context of information-feedback system. This is the bedrock philosophy of system dynamics. The second foundation is the realisation of the importance of the experimental approach to understanding of system dynamics. The standard acceptable format of research study of going from general analytical solution to the particular special case was reversed to the empirical approach. In this format a number of particular situations were studied and from these generalisations were inferred. This is the basis for learning. The activity basis for learning is experience. Some of these generalisations were given a name by Senge (1990) as Nature’s Templates. The third foundation for progress of system dynamics was digital computing machines. By 1945, systems of twenty variables were difficult to handle. By 1955, the digital computer appeared, opening the way to the simulation of systems far beyond the capability of analogue machines. Models of 2000 and more variables with out any restrictions on representing non-linear phenomena could easily be simulated on a digital computer at costs within the reach of the academia and the research organisations. The simulation of information feedback models of important managerial and economic questions is an area demanding high efficiency. A cost reduction factor of ten thousand or more in computation infrastructure placed one in a completely different environment than that existed a few years ago. The fourth foundation was better appreciation of policy and decision. There is an orderly basis that prescribes most of our present managerial decisions. These decisions are not entirely adhoc but are strongly conditioned by the environment. This being so, policies governing decisions can be laid down and their effect on economic/business behaviour can be studied. Forrester’s Postulates and Applications : The idea that economic and industrial systems could be depicted through linear analysis was the major stumbling block to begin thinking dynamically. Most of the policy analysis goes on to define the problem on hand as narrowly as possible in the name of attaining the objective of being specific and crisp. On one hand, it enables the mathematics of such analysis tractable but unfortunately, it ignores the fact that almost every factor in the economic or industrial systems is non-linear. Much of the important behaviour of the system is the direct manifestation of nonlinear characteristic of the system components. Social systems are assumed to be inherently stable and that they constantly seek to achieve the equilibrium status. While it is the system’s tendency to r each the equ ilibrium in its inanimate consideration, the players in the system keep working towards disturbing the equilibrium conditions. Perfect market is the stated goal of the simple economic system with its most important components the supply and the demand trying to equal each other in the long run. But during this period, the players in the market disturb the initial conditions by several means such as inducing technology, introducing substitutes, differentiating the products etc. which makes that the seemingly achievable perfect market an impossible dream. Therefore, the notion of sustainable competitive advantage is only fleeting in nature. The analysis used for solving the market problems with an assumption of stable systems in thus not valid. There appears ample evidence that much of our industrial and economic systems exhibit behaviours characterised by instability. Mathematical economics and management science have often been more closely allied to formal mathematics than to economics or management. The difference of orientation is glaringly evident on comparison of business literature with publications on management science. Another evidence of the bias towards mathematical rather than managerial motivation is seen in preoccupation in optimum solutions. In the linear analysis, the first action that is performed is to define the objective function. Thus specifying the purpose of a model of an economic system being its ability to predict specific future action. Further, it is used to validate the model. Models are required to predict the character and the nature of the system in question so that redesign could take place in congruence with the desired state. This is entirely different and more useful than the objective functions, which provide the events such as specific future times of peaks or valleys such as in a sales curve. It is a belief that a model must be limited to considering those variables, which have generally accepted definitions and must have objective value, attached to them. Many undefined concepts are known to be of crucial importance to business systems, which are known as soft variables. Linear models are not capable of capturing these details in the traditional methodology of problem solving. If the subjective matters are considered to be of crucial importance to the business system behaviour, it must be conceded that they must some how be incorporated in the model. Therefore, it is necessary to provide legi