This paper is a case study on the introduction of systems thinking tools into a research group within a large information service company. The central dynamics involved in this learning process was a continuous goal shift. We address the realities of trying to develop a shared dynamic problem definition, and show how would-be practitioners internalize the material in unexpected and often paradoxical ways.
The paper proposes a methodology, of building system dynamics models for queuing systems. The methodology is applied to a variety of queuing systems and it is observed that, the models so developed are more transparent than conventional state-transition diagram and incorporation of real life complexities are easier. In effect working out the transient and steady state behaviour of a wide variety of queuing system becomes easy without going into much mathematical tedium.
The paper review the experience of a consultancy in the company called BETA. Two goals are pursued: cognitive and methodological. Cognitive goal refers to the System Dynamics methodology applied to a concrete case of the company growth and strategy making within a traditionally dominated accounting framework. Based on symbolic (though keeping similarity to real) data, the article presents the ithink™ model construction and simulation within 3 strategic scenarios: optimistic, realistic, and pessimistic. The methodological objective contains the use of the Partitioning and Tearing Method in the problem conceptualization and model preparation. Although the scope of the paper excluded a possibility of its detailed description, it is argued that this method has proved to be very useful in working with complex problems containing many variables.
Periodically, at different times of its history, the Argentine economy has been dominated by a vicious circle, well known among developing countries. The Central Bank pays interest on money and such interest is financed through emission of more money thus, causing inflation. In one of these periods: the corresponding to February 1981-July 1982, the accumulated inflation increased to 250 per cent. In 1982, the government decided to reduce the interest rate abruptly, in order to achieve a quick reduction of the inflation rate. However, the year 1982 witnessed the failure of the application of this financial reform. Although the growth rate of liquid assets declined, the inflation rate of July 1982 duplicated the precious month rate. This article reformulates a small economic model, in the Cagan tradition, due to Rodriguez (1986). It was conceived to explain the historic dynamics of the financial indicators, after the reform. Hopefully, the readability of the model should improve, when compared with the original version. And, instead of attributing the dynamics globally to the complex behavior of the system, the paper identifies the cause of this dynamics throughout the causal structure that produced it.
The purpose of this paper is fourfold: 1) to survey the literature on evolutionary economics in general; 2) to survey the literature on evolutionary economics modeling in particular; 3) to outline the contribution that system dynamics can make to evolutionary economic modeling; and 4) to present two original, evolutionary, system dynamics models.
Innovation is a topic that has received much attention in the literature in recent years. For the most part, these articles have not solved an important problem facing the managers in today’s large organizations -- how to manage a portfolio of interactive product- and process- innovations, addressing the interrelated forces, including monetary constraints, manpower planning & technology capability, to a dynamic environment. By systems thinking of these problems, the author first set up a generic S.D. model as a Microcosm for portfolio analysis of technological innovations. Based on this Microcosm, an experiment aimed at pattern selection of product-& process- innovations was conducted, drawing the conclusion different from the famous Abernathy/Utterback’s. Finally, the mechanism of group decision on project selection of innovation portfolio using the Microcosm was explained, and the group decision support system was constructed.
By the thought of coordinative development between Science & Technology, economy, education and finance, this paper first concerns the problems facing China on the resource allocation of Scientific Research. A comparative study on both developed and developing countries is made. In the meantime, the mechanism of the coordinative development between Science & Technology, economy, education and finance, the coordinative development between Scientific Research (Basic Research), Applied Research & Development as well as the priority of Scientific Research in different stages of social & economic development, a system dynamic model is constructed, focusing the analysis of scale & speed of resource allocation for Scientific Research in China.
This paper describes what is meant by modelling at Sunderland and how System Dynamics fits into this ethos. The teaching and the examples covered in this System dynamics module are different the usual course and the paper deals with our experience in these areas. The reaction of Eastern European ( Bulgarian ) students to this type of teaching is discussed. Students must complete a project in a work placement to obtain a masters qualification. The reaction of companies to the use of System Dynamics ( a new experience for most ) is discussed and examples of the type of projects that have been completed are given. The paper concludes with a description of a Hypercard project which extends the use of System dynamics to Engineering students.
The development and diffusion of innovations is a highly dynamic phenomenon. It is influenced by various factors like price, product quality, and market entry time. The paper discusses the impact of pricing strategies on R&D performance and the diffusion of innovations. It is based on a comprehensive decision support model in the field of innovation management. The model consists of two components: (1) an evolution algorithm modeling the processes of corporate R&D, and (2) a DYNAMO-based modul mapping corporate policy making and the structural fundamentals of market dynamics. The integrated model is used to analyze the dynamic consequences of different pricing strategies on research and development, the readiness for market entry and the resulting competitive advantages.
This paper presents a system developed to design strategies for organizational expansion based on system dynamics and expert system methodologies. The tool was especially built to plan the expansion of a computing system network.
The main objective of the MISTELA model is to integrate the different aspects of strategic planning of TELEFONICA DE ESPAÑA into one signal unit. By so doing one is obviously forced to give up many of the small details in order to be able to look at the larger picture. MISTELA uses a systemic approach to construct the model described in, this paper, Systems Dynamics was chosen, since this technique permits straightforward combination of different modelling procedures such as statistical inference, calibration by trail and error, linear and/or quadratic programming, etc. To give an idea of the size of the model, it handles about 1,500 equations, definition and identities. There are some 700 conceptual variables, and because many of these are vectors, in effect there about 4,000 scalar variables.
In almost all urban areas, existing infrastructure (transportation, water, sewer, social services) lags behind desired infrastructure. Planning new infrastructure depends on future land use forecasts. The distribution of future land use is also dependent of on available infrastructure. Due to this feedback, the infrastructure shortfall problem is resistant to solution through infrastructure improvement and local land use regulations. We have developed regional land use/infrastructure planning models that combine fairly simple system dynamics structures with spatially disaggregated databases. The models provide insights about the effectiveness of alternative policies, using detail of the local area that planners need.
Decision Support System (DSS) are commonly used in the manufacturing industry to assist management in decision making processes. There are several major types of DSS systems and each is useful for solving specific manufacturing problems. The development of intelligent DSS systems that can carry out high level reasoning is itself a challenge and a requirement by modern management. This paper illustrates the formulation of a DSS system (called Performance Decision System) that can be used for solving complex manufacturing problems. The DSS system is based on two major types of DSS; System Dynamics and Experts Systems.
This paper describes the work and experience gained by a team using a system thinking approach to developing a microworld to support the strategic planning of Athabasca University (AU), a fast growing opening university in western Canada. The opportunity for this experience arose from an invitation by the university President to teach an introductory course in Systems Thinking to a group of 30 senior management representing the faculty, administration, and the governing council. This work is intended to aid in understanding the dynamic forces which have allowed AU to double the number of courses registration in the past five years while lowering cost to government of providing access to AU from $1,179 to $635 per course registration (in constant dollars) since 1985. This work reports the experience of AU in building a Microworlds® system in order to accelerate organizational learning. The system is based on the system dynamics methodology and was developed using STELLA®. The system has been used to test different scenarios of strategic options which are almost impossible to evaluate otherwise. The system was validated against actual data and was used as management flight simulator to the system till year 2000. Repeated runs of the simulations have proved that quick fixes to one part of the system do not necessarily help its overall performance. It has been found that the process of constructing a simulation model is as valuable for problem solving as the final model itself.
The Beer Game is still today, one of the tools with the greatest impact in demonstrating that the behaviour of a system is generated by its structure. However, we believe that in its original form too much time is needed to play it and carry out a proper debriefing. In addition, it is not always easy to guarantee the hypothesis of isolation for the different positions within the game. Finally, we feel that participants often have difficulty in picking up quickly and clearly the process characterizing the game. To deal with these and other problems we have introduced certain modifications which, in our view, totally or partially resolve these difficulties.
A great number of System Dynamicists coincide in our belief that the methods and tools presently used in virtually all management education centres insufficient to cope with an ever more complex reality. For some years now there has been a significant movement within our field which aims to provide alternative ways and tools which will serve to fill the existing gap. Working along these lines we created a work group and started, within the EC Comett framework in 1990, a project termed “Learning laboratories in computer-aided Systemic Business Management”, sponsored by numerous European firms and institutions. The aims of the project are multiple and interrelated: production of learning tools based on System Dynamics, facilitating reflection on causes, design of learning laboratories in business management following a systemic approach, trying out the tools created and checking learning processes for different circumstances, development of training courses, promoting training of trainers.
The function of a competitive intelligence system is to generate a manufacturing strategy which is superior than the competition. A competitive intelligence system consists of a set of tools that capture and synthesize the competitors manufacturing strategies in order to generate the desired strategy. A competitive intelligence system that uses reference models is presented here and its use illustrated with a case study. A reference model is a generic system dynamics model which includes the cause-effect relationships that explain the current quality of the competitors products.
The savings and loan industry has been the primary source of home mortgages for American families since 1932. Since 1984, however, 25 percent of the savings and loans, approximately 700 out of 2800, have failed. Although the total costs associated with these failed savings and loans have yet to be determined, estimates range from $300 billion to $1 trillion. This paper discusses a system dynamics model of the effects of interest rate risk and default risk focusing on the savings and loan industry. Using the model to test the effects of policy initiatives specific to the prime interest rate and the default risk on loans, the authors demonstrate that the savings and loan crisis might have been lessened or even avoided if the regulators had a better understanding of the system’s structure and the effect of that structure on system behavior.
The diffusion of new technologies into the market is a critical factor in the success of any technology based company. This paper describes a system dynamics model which integrates a number of key concepts presently used to understand the diffusion process (e.g. technical progress functions, cost-experience curves). It shows how these concepts, together with management decisions regarding R&D investment, marketing, and pricing, drive the evolution of diffusion between technologies. It then illustrates how simulation can be used to understand the critical success factors in technology diffusion, and what this means for the management of technology-based companies.