System Dynamics (SD) would like to increase its influence and promote its professional approach to understanding and solving significant problems. This paper attempts to capture a snapshot of SD research topics, and publications 2000-2005 as a metric of the scope of SD domains and publication venues. The questions addressed by this paper follow: What are the frequently published topics? What may be the emerging topics? Where do SD authors frequently publish? To this end, 35, 920,686 documents were search. Of the total, 935 met the search criteria. After review and analysis of the 935 documents, only 302 qualified as relevant SD material for further analysis.
Nowadays, the performance measurement system has been well developed. And the relations between these performance measures are playing important roles in management science. However, the effective method to analyze these relations is still underdeveloped and attracts more and more concerns. After reviewing relevant research, by adopting and further extending the essential theories of Systems Thinking, we propose a three-dimensional Systems Thinking to achieve better analysis, control and decision-making. In this paper, the rationality of the three-dimensional thought is proved first, and the modelling method is then provided in theory. Finally, a manufacturing enterprise is illustrated as an example for practical implementation.
As part of the first authors PhD project, year 9 and 10 students were given a system dynamics model of the impacts of visitors on a National Park. The students were given a pre- and post-test to determine whether their knowledge of the environment changed. Students were randomly assigned to either the individual learning condition (students interrogated the model as individuals) or the collaborative learning condition (students interrogated the model with one other student). There was a significant increase in the environmental knowledge score for those students in the collaborative learning condition, but not in the individual learning condition. The implications of this finding for the use of system dynamics models in educational settings are discussed.
Goodwins A Growth Cycle [1967] represents a milestone in the non-linear modeling of economic dynamics. In terms of the two variables wage share and employment rate and on the basis of few simple assumptions, the Goodwin Model (GM) is formulated exactly as the well-known Lotka-Volterra system, with all the limits of such system, in particular the lacking of structural stability. A number of extensions have been proposed with the aim to make the model more robust. We propose a new extension that: a) removes the limiting hypothesis of Harrod-neutral technical progress: b) on the line of Lotka-Volterra models with adaptation, introduces the concept of memory, which certainly plays a relevant role in the dynamics of economic systems. As a consequence an additional equation appears, the validity of the model is substantially extended and a rich phenomenology is obtained, in particular transition to chaotic behavior via period-doubling bifurcations.
System dynamics has been successfully applied to the study of projects for many years. While this modeling has clearly defined the structures which create project dynamics, it has been less helpful in providing explicit policy advice to managers. To address this gap, we examine the effectiveness of three common project controls available to project managers to address deviations in project performance; (1) exerting pressure on project staff to work faster, (2) having staff work overtime, and/or (3) hiring additional staff. While the three project controls can have short-term benefits for project performance, their long-term impacts can be detrimental. The current work presents preliminary results of the research, focusing on the impacts of the three project controls on project rework and schedule performance. The work describes the development of project control feedback structures, the initial testing and use of a formal system dynamics model of the system, and preliminary results. The work concludes with a description of future project research efforts.
System dynamics models are often constructed to improve system performance by identifying and modifying feedback mechanisms that drive system behavior. Once identified, these feedback mechanisms can be used to design and test policies for system performance improvement. A preliminary step in developing policies is the identification of high leverage parameters and structures, the influential model sections that drive system behavior. The current work clarifies and extends the use of statistical screening (Ford and Flynn, 2005) as a model analysis tool with a six step process that identifies specific model sections for further analysis and development. The work also presents a method that clarifies the results of model analysis with statistical screening to practicing managers Statistical screening offers system dynamicists a user-friendly tool that can be used to help explain how model structure drives system behavior.
Crowd Control is a function generally associated with the police more than the military. However, the Canadian Forces are occasionally asked to intervene in riot situations either in Canada, in support to Federal, Provincial and Municipal Governments, or overseas, during Coalition operations. Thus, there is a need to understand crowd behaviour and to determine optimal intervention strategies for crowd control. The Canadian Forces have skills and resources that might be called upon if a situation gets out of control and must be prepared to deploy on short notice. A model has been developed that can be used to understand these events in the time dimension both inside the event and from event to event. The model has been developed theoretically and face validated using data from two case studies. The model is required to evaluate appropriate tactics such as the employment of non-lethal weapons, and as a training simulator for strategic and tactical commanders.
In the modern information based society, failure of software systems can have significant consequences. It has been argued that increased attention to testing activities during the software development process can mitigate the probabilities of system failure after implementation. However, in order to justify investments in improved testing, the economic impacts of improper testing should be identified. In this paper, we propose a systematic approach to the evaluation of the economic impacts of software testing. The main factors affecting software testing are identified, and a computer simulation model is developed to investigate different testing scenarios. Usefulness of the suggested approach is demonstrated through several exploratory simulations. The results prove the utility of the System Dynamics modelling approach in building better understanding of the impact of software testing. Implications for software development practitioners, researchers, customers of software products and software support organisations are also discussed.
In this paper, we propose a new approach to network bandwidth estimation based on System Dynamics modelling. The paper discusses existing approaches to bandwidth estimation and network capacity planning. Limitations of these approaches are presented and the case for using System Dynamics is made. Applicability of the proposed approach is demonstrated through a real world network planning project for a distributed logistics application. A practical computer simulation model was developed to predict bandwidth requirements for the projects network. This model provides system planners with the ability to test different possible scenarios in order to make informed decisions about the system architecture. We show through practical results of the simulation runs and the insights gained during the process that the System Dynamics approach offers an effective solution to the problem of network bandwidth estimation and system planning. The paper concludes with a review of the results and pointers for further research.
We will explore how to value using modern financial techniques the development of new alternative energy technologies (AETs) given uncertainty. Uncertainty in developing AETs derives from: (1) the reduction in installation cost of new generation capacity as experience with the technology is gained, i.e. the learning curve (2) oil and natural gas price cycles; and (3) other macroeconomic and geopolitical forces, particularly the behavior of national oil companies (Aramco, PDVSA, PEMEX, etc.). Evaluating a new AET properly requires representing these uncertainties as well as an investment valuation approach that works well under high uncertainty. In particular, we propose to adapt the real options methodology to value the potential return from developing AETs using stochastic system dynamics models representing the uncertainty in both the learning curve and the fossil fuel price cycles.