Human resource crises by collective retirement of hospital physicians are a critical issue in Japanese health care systems. System Dynamics modeling is a feasible way to understand these phenomena. Japanese health care system is confronted with not only exogenous environments but also endogenous feedbacks to build up the situation. Increasing busyness by physicians and risk of medical lawsuits and decreasing average productivity and quality of physician by hiring new physicians reinforce retirements of physicians and the retirements change the situation for the worse. To keep sustain level of physician we could find essential policies by simulation. First strategy is changing desired number of physicians with increasing of number of patients per physician. Second way is decreasing delay between retirement and hiring. This was accomplished by early recognition of physicians busyness by hospital managers and abundant of physicians in a health care system.
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.