Laboratory experiments of commodity markets have used the Cobweb design to investigate market dynamics. The predicted cycles of the Cobweb theory did not occur. Arango (2006) adds complexity and realism to the Cobweb model and observes stronger fluctuations and autocorrelation. He shows that these fluctuations are quite symmetric and similar to the behaviour observed in one category of markets. However the fluctuations are different from the asymmetric price behaviour observed in other commodity markets. We hypothesise that asymmetries could be caused by non-linear demand, different from the linear demand curve used by Arango. Consequently we replicate his experiment using a demand structure with constant price elasticity and dynamic adjustment. Similar to Arango, the supply side is complicated by capacity lifetimes and investment delays across treatments. Compared to the previous results, this experiment gives rise to larger fluctuations and stronger asymmetries
This poster describes research in progress undertaken by the Research Group on Comparative and Transnational Digital Government in North America, which is supported by the National Science Foundation Digital Government Research Program as well as by institutions in Canada, Mexico, and the United States. This research explores distribution networks that attach non-price information to products as a differentiation mechanism. Often this non-price information is transmitted through trusting networks or certifiable labels such as "Organic" or "Fair Trade." We call such networks Full Information Product Pricing (FIPP) Networks. Major objectives of the research are to explore how government policies and investment in information and communication technology can be used to promote FIPP networks and to assess what impacts on economic and local development will result. The first fair trade FIPP network selected for simulation is a coffee cooperative in Mexico, Tosepan Titataniske. Current modeling efforts are aimed at eliciting dynamic insights from the case by the application of established system dynamics knowledge related to commodity models and supply chains.
We present an end-to-end solution framework for addressing the various analytical challenges that are involved in developing an optimal deployment plan, from a business case development perspective. Our solution framework uses a judicious combination of system dynamics modeling, econometric modeling and mathematical programming based optimization modeling. A system dynamics model is used to estimate the dynamics of user adoption of the new technology, relative to deployment, which results from marketing effectiveness for the new technology, as well as the viral effect of word-of-mouth interactions among users. The model is also used to estimate the lag in benefits realization from the new technology deployment, arising from the above dynamics of user adoption, coupled with a lag in the maturity of the supporting Information Systems that enable effective functioning of the new technology. These estimates are then subsequently used in a mathematical programming model, which solves a multi-period, resource-constrained optimal deployment planning problem that is subjected to the lags in user adoption and benefit realization, which are estimated by the system dynamics model.
System Dynamics like other simulation methodologies is basically descriptive, in that it does not search for optimized set of policy variables. To render it an optimization capability one may augment it with a Genetic Algorithm (GA) input machine with a multi criteria objective function evaluator such as TOPSIS. Starting from a random population of policy variables, different simulation run will be performed one for every member of the population. Using GA operators and the evaluation motor a new population of policy variables will be constructed. The procedure is automatically repeated until the best combination of policy variables is formed. This paper presents this as an idea and gives an example of how this can be performed in practice.
A key feature of present day business is the fact that it is the supply chains that compete, not companies and the success or failure of supply chains is ultimately determined in the marketplace by the end consumer. Getting the right product, at the right time to the consumer is not only the linchpin to competitive success, but also the key to survival. Hence, customer satisfaction and market place understanding are critical elements for consideration when attempting to establish a new supply chain strategy. Based on the literature review, survey results, and discussion with experts, causal relationships among supply chain performance variables have been developed. On the basis of these causal relationships, a framework has been modeled using system dynamics approach to capture the dynamic impact of performance variables on the supply chain integration and responsiveness for a period of eighteen months. This framework is useful in analyzing the dynamic impact of different policies towards integration and responsiveness of a supply chain.
This paper explores the contribution and the influence of system dynamics in the development of management and organisation theory. It begins with a brief discussion of the contribution of computer simulation in theory development for the above areas. It then discusses the practice of theory development using Bourdieus concepts of Field and Habitus, and places the system dynamics methodology in their context by considering the influence that system dynamics scholars can have in these fields. The resulting conceptual framework is then demonstrated by presenting two different cases of operation strategy theory development using system dynamics modelling and simulation.
There exists a large body of literature on organizational change and on the puzzling effect of change failure. This paper adds the often missing element of combining several reasons for change inefficacy. These reasons for failure to change are low systemic mutability (inertia), insufficient political, rational, and emotional commitment (resistance). Participation and discourse are then presented as one solution to the problem, relieving resistance and enhancing employee commitment.
While large scale diffusion of alternative fuel vehicles (AFVs) is widely anticipated, the mechanisms that determine their success or failure are ill understood. Analysis of an AFV transition model developed at MIT has revealed that AFV diffusion dynamics are particularly sensitive to consumers decisions as influenced by social exposure to AFVs. While some empirical research in this area exists, uncertainty in these parameters remains high. Following principles of partial model testing in this paper we report on research that examines the social exposure parameters. We focus on empirical accounts of diffusion involving diesel passenger vehicles in Europe using the historical data of diesel vehicle registrations and installed base in six European countries - France, Germany, Italy, Spain, Sweden and United Kingdom. To complete diffusion data sets we generate synthetic data from 1970 to 2005. Confidence interval testing for model parameters is conducted using the bootstrapping method.
The results from the calibrations yield parameters that are in line with other marketing studies and help reduce uncertainty in the social exposure parameters. Further, the analysis suggests applicability of this model for alternative fuel vehicle markets and provides further lessons for other AFV technology transition. We discuss challenges and avenues for further research.
Despite its success and growing practitioner base, System Dynamics (SD) still lacks a strong and rich enough support toolbox, i.e. a set of formal mathematical tools that can support the modeler/practitioner in various stages including model identification, calibration, behavior analysis, policy design and sensitivity analysis. The study presented in this paper is an attempt towards developing such a support tool that can be used for pattern-based parameter search, which may be utilized in model identification, validation and policy analysis stages. The tool mainly incorporates a 2D pattern recognition algorithm and an optimization heuristic in order to search values for selected model parameters that yield a model behavior similar to the desired one in terms of pattern characteristics. The proposed tool is implemented, and a series of test experiments are conducted on three sample models in order to reveal the performance of it. Based on these experiments, the primary assessment about the proposed method is that its performance is quite satisfactory and it stands as a promising automated parameter search tool, which can be utilized even in the cases where data series representing the desired model behavior is missing.
Though world-of-mouth (WOM) communications is a pervasive and intriguing phenomenon, little is known on its effect in terms of macro-behavior. The purpose of this study is to investigate the WOM effect on macro-level marketing to explain the herd behavior of Chinese consumers. To achieve our goal, the system dynamics was applied build a simulation model for a popular herd behavior happening in Macao, Hong Kong, and Taiwan, i.e., buying Portuguese custard tarts. Both micro-behavior and macro-behavior were considered in this model and the linkage between micro-behavior and macro-behavior was specially emphasized. The results showed that the market of Portuguese custard tarts would crash quickly under fast distribution of WOM and herd behavior while considering the limitation of capacity.