The objective of the paper is to show that the one of the main source of macroeconomic investment instability is similar to that which makes difficult managing supply line in famous Beer Game developed by the system dynamics group of MIT Sloan School of Management. It will be pointed out that ignoring production time delays causes instability not because economic agents simply ignore supply line delays, but because they adjust their expectations more rapidly than the delays involved in supply lines, whatever those delays could be. The paper is structured in three sections. In the first we present the classic Phillips argument about unintentional destabilizing effects of stabilization policy in modern dynamic system language, in order to show how to build a simplified macroeconomic supply line model for investment dynamics; in the second section, the macroeconomic model is developed and simulated. Third section concludes the paper suggesting that the inclusion of production time delays in macroeconomic models reopens the space to the control theory in stabilization policy debate
This paper introduces the notion of increasing returns to economic activity agglomeration and develops a formal system-dynamic model where this notion is used to explain the self-organizing nature of the spatial structure of industrial clusters. In this model, both pecuniary and external economies based on knowledge spillovers are considered.
Laboratory studies have shown that people cannot handle the time con-stants in dynamic tasks. Yet they obviously cope with such tasks with some success outside the laboratory. This study is one in a series of studies that examine the hypothesis that people cope by relying on heuristics that allow them to simplify the task. The heuristic studied here was that of relying on frequency differences, i.e., what Reason (1990) calls frequency gambling. It examines the effects of varying the relative frequency of scenarios that require different responding, and where relying on frequency rather than learning the actual time con-stants will lead to some success. The results show that the participants did not learn the time constants, that frequency had a strong effect on their decisions, but that their responding also seemed to be influenced by another heuristic identified in earlier studies, viz., that of rapid and massive responding. Implications of these findings for system dynamics modellers are discussed.
Organizations may fail to adopt sustainable solutions as a result of incomplete and/or inaccurate feedback into the decision making process. Events that cause harm - environmental, health, or social - are commonly the delayed effect of a prior course of action, itself the result of decisions that emerge from endogenous policy. By accelerating the cost of future harm into current period decisions, producers and purchasers have greater access to the quantity and quality of information that influence decisions to produce and consume. The creation of a financial policy structure that makes future, long-term costs of production, promotion, and consumption explicit in the decision process will correct a current deficiency in the analysis of costs and benefits made by producers and purchasers. Such a feedback loop would correct a structural market failure and could reduce the need for governmental regulation.
Environmental strategies such as Zero-to-Landfill are gaining increasing attention throughout the world. Product take back is a significant means of ensuring that products that have reached the end of their useful lives are reclaimed for reuse, remanufacturing, or recycling. Such a strategy is expected to minimize environmental impacts, reduce overall resource consumption, and provide economic value to manufacturers and consumers. The reverse logistics, however, can be quite complicated as product collection, product disassembly, processing, component returns, and component reclamation must be considered. Further, the costs and magnitude of the requisite system must be projected to support appropriate planning and execution. In this paper, we present a model of a reverse logistics system for a consumer product. The impacts of closed-loop logistics on product adoption rate, product costs, and component reliabilities are balanced against the cost of new infrastructure, shipping and tracking, and processing and inventorying of expended components. We illustrate how a reverse logistics approach may develop as a function of product adoption, the total value of returned components, product reliability, and product lifetime. A Zero-to-Landfill strategy has a significant potential to improve the triple bottom line people, planet, and profit of companies that adopt it.
This paper presents a dynamic hypothesis explaining the system dynamics underlying the identity theft epidemic. The causal loop structure synthesizes current understanding of the problem and suggests that any strategy to address the identity theft epidemic by primarily focusing on prosecuting thieves without effectively mitigating the underlying forces is doomed to failure. The causal loop diagram elucidates the dominant feedback structure ...a collection of rapid-feedback, self-reinforcing dynamics that generate ample opportunities for would-be thieves. Preliminary results from the analysis provide a foundation for exploring policy options through a full working model, yet to be developed.
PANEL: Andrei Borshchev, Xjtek,Russia; Nate Osgood MIT,USA; Mark Paich, Decisio Consulting, USA; Hazhir Rahmandad, Sloan School of Management, MIT, USA; Mark Heffernan, International System Dynamics, Australia; Sara Metcalf, University of Illinois, USA; Chris Johnson General Electric, USA;Geoff McDonnell UNSW Australia; Other users from industry.
There is increasing interest in combining agent based (AB) and system dynamics (SD) modeling methods. This workshop will demonstrate the differences between the AB and SD approaches using some popular examples from the Dynamics of Contagion and the Diffusion of Innovation, using the AnyLogic multi-method software. It will also walk through some practical examples of the use of combined methods in health, marketing and other industries. The workshop will conclude with a "warts and all" panel discussion involving experienced SD practitioners and researchers in SD, geography and computer science, all of whom are adding AB methods to their work.
Electric power systems are traditionally designed and developed with the assumption that demand is exogenous to the system. Connecting the feedbacks from the system to consumers will provide incentives for consumers to reduce demand during periods of high system prices. A system dynamics model is used to analyze the dynamics and long term implications of adoption of technology to enable demand response. The model includes the decision by consumers to adopt demand response technology along with decisions by investors to build generation capacity. The adoption process reduces overall system prices for peak demand periods, creating feedbacks with generation investment. The effects of technology improvement via learning, long term demand elasticity, and policies to promote adoption are considered. The results of the simulations show that diminishing returns to adopters and significant externalities in terms of free rider effects limit the attraction of individual adoption. A subsidy to alleviate the costs to individuals can be justified by the significant system level savings from widespread adoption. Several pernicious effects can emerge from large scale demand response, however, including increased price volatility due to a reduction in generation capacity reserve margin, an increase in long term demand, and increased emissions from the substitution of coal plants for natural gas and renewable generation capacity.
In todays economy all manufacturers need to pay attention on how to build strong and long-term relationships with their dealers chain. In fact, it has been demonstrated that short term policies aimed to provide dealers immediate benefits (e.g., price discounts) may prevent the development of long term and fruitful relationships. Also supporting dealers in promoting manufacturers products has been proved as a sustainable strategy in long run.
Another implication of manufacturers bounded policies refers to their inclination to reinvest significant amounts of their sales revenues in advertising and product portfolio improvement, without taking into account the need to invest in dealers human resources, to make their strategies sustainable.
Based of the above remarks, this paper aims to demonstrate the usefulness of a system dynamics approach in involving both manufacturers and dealers in strategic reasoning.
Empirical evidence arising from a research project conducted by the authors with a manufacture operating in a high-tech industry, shows that using system dynamics as a methodology to support communication and learning may act as a significant lever to design successfully long term oriented policies. Such policies ought to increase dealers skills and motivation, and improve potential customers awareness of product benefits, at the same time.
The evolution of fleet maintenance and management policies highlights the growing importance of maintenance issues in both private and public companies. The need to improve maintenance performance requires an accurate evaluation of the trade-off between costs and benefits related to alternative fleet maintenance and management policies. However, the complexity of maintenance system makes this evaluation a very difficult task.
More often a fleet manager deals with the following key issues:
is it more profitable to repair or to renew the company fleet?
Is it more convenient to reduce the average age of the different assets (e.g., by increasing investments in new bus) or to expand the maintenance activities (e.g., by rising repairing costs)?
In fact, fleet managers cannot ignore the impact of their decisions on both company service and financial performance over time.
Aim of this paper is to show how the System Dynamics approach can effectively support fleet managers in designing and evaluating their strategies. The simulation model here presented is based on the result of a project with two Italian city bus companies. Through such tool decision makers can test different fleet strategies and assess their effects on company performance.