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.
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.
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.
Sustainable use of a natural resource ensures that the ecosystem associated with that use will also provide long term environmental services to society. Such services might include the provision of clean water, removal of excess CO2 from the atmosphere, flood protection, pleasant vistas, or enhanced biodiversity. These benefits are becoming less abundant as inappropriate resource uses hasten environmental degradation.
In theory, if beneficiaries pay for the environmental services received, and these payments are given to the resource users/owners to reward, or encourage, sustainable resource use, then such sustainable use will be assured. Schemes to implement such arrangements might be able to support conservation programs, and also supplement income of poor farmers and forest dwellers. Such payments are also seen as a means of encouraging better management of carbon dioxide in our atmosphere, by paying for forest practices which can store CO2.
How do such systems actually work? Can payments for environmental services encourage better resource management? Might they also create disincentives for management based on ethics, altruism, and stewardship? A generic system dynamics model was used to examine these questions.
Multiple objective optimisation (MOO) is an optimisation approach that has been widely used to solve optimisation problems with more than one objective function. The benefit of this approach is that it generates a set of non-dominated solutions which a policy maker can explore and evaluate before making a final optimal selection. This paper demonstrates that MOO can be used to assist policy makers explore a richer set of alternatives when deciding on a range of values for key parameters in their system dynamics model. In order to demonstrate the approach, a well-known case study The Domestic Manufacturing Company is used, and a stock and flow model and a multiple objective optimiser are designed and coded. The results show that valid solutions are generated, and that each of these solutions can be examined independently and hence give greater insight into the problem at hand - before a decision is made as to the most appropriate solution.
We analyze experimental data from the Beer Game in which the customer orders are constant (4 cases/week) and all the subjects are informed about this fact before the game starts. Even though the experimental settings disfavor oscillation and amplification, we still observe them. To analyze the decisions made by the subjects, we first estimate the decision rule used by Sterman (1989). This analysis suggests that typically subjects do not understand the time delays and the stock and flow structure of the Beer Game. Next, we relax some assumptions of this decision rule and use more sophisticated alternatives. These alternative decision rules do not yield overall improvement in terms of fit to the real data. However, for some subjects, these decision rules lead to significant improvement. Our analysis reveals strong evidence that these subjects were caught up in a reinforcing phantom ordering loop even though the experimental conditions strongly disfavor such behavior.
This paper discusses the benefits of having an interchange standard for system dynamics models, why XML is a good candidate on which to build such as standard, and how the development process may take place through community-wide participation. The paper also presents XMILE, a prototype model interchange standard, as a proof of the concept.
This report builds on a previous epidemiological model of a pneumonic plague outbreak that incorporated three behavioral responses as exogenous drivers and evaluated their importance in allowing us to replicate the actual outbreak (Heinbokel& Potash, ISDC-2003). The current paper describes our subsequent efforts to incorporate those critical and controlling behavioral dimensions into this model as critical feedback loops. We conceptually deconstructed the event into four segments: becoming aware of the outbreak, deciding to act in response, choosing a specific response, and returning to normal behavior. We utilized current psychological theories, such as the Psychometric Paradigm and Brunswiks Lens Model, to build small, conceptually clear, transferable, and combinable behavioral submodels to simulate the first three segments involving information and social networks, social trust, and risk perceptions. We believe these modeling efforts comprise first steps in a critical process of translating current, frequently static, risk theories to dynamically responsive vehicles that can be flexibly and quantitatively applied to reliably aid in understanding and influencing responses to such public health threats, other extreme events, and other dynamic risk scenarios in general.
The use of System Dynamic software tools are becoming a popular way of investigating complex problems. However, along with the use of these tools exists the risk of relying too heavily on the numerical part of the analysis and neglecting the preparation phase for analysis. Any modelling procedure in System Dynamic modelling goes through a conceptual phase that uses the Learning Loop approach. This phase is most often done unintentionally. Using the Learning Loop approach consciously facilitates the group modelling process to acquire four successive phases, i.e. Definition, Clarification, Confirmation and Implementation. This enables a clear structure in the process, from acquiring the task to documenting the results. Only by intentionally using the Learning Loop approach in a managed manner, can the full potential of the process be exploited. Qualitative analysis does not replace simulations with a computer model but simulations should serve as a continuation to reconfirm or refute qualitative hypothesis and a simulation should only occur when the mental model has been tested. Systems Analysis, including its thinking, analysis and dynamics, is not a method, but rather an adaptive learning behaviour. It is a behaviour that finds the optimally adapted method, applying at some times SD computer tools.