T he purpose of the paper is to demonstrate the usefulness of (1) system dynamics as a structural theory for operations management and (2) system dynamics models as content theories in operations management. The key findings are that, although feedback loops, accumulation processes, and delays exist and are widespread in operations management, often these phenomena are ignored completely or not considered appropriately. Hence, it is reasoned why system dynamics is well suited as an approach for many operations management studies, and it is shown how system dynamics theory can be used to explain, analyze, and understand such phenomena in operations management. The discussion is based on a literature review and on conceptual considerations, with examples of operations management studies based on system dynamics. Implications of using this theory include the necessary re-framing of some operations management issues and the extension of empirical studies by dynamic modeling and simulation. The value of the paper lies in the conceptualization of the link between system dynamics and operations management, which is discussed on the level of theory.

Prefabricated computer-based simulations usually offer a user-friendly interface. This allows inexperienced users fast access to the simulation because they do not have to possess specific knowledge about simulation techniques. Thus, giving simulation models an easy-to-use interface increases the acceptance of the simulation tool and draws attention to it. Learners are not only able to examine the results of their decisions but also the causes of these results using powerful system dynamics diagramming techniques. This adds transparency to the former black-boxes, producing so-called transparent-box business simulators. This article reports on an experiment evaluating the relevance and effects of structural transparency. This experimental design also can be used to examine other types of business simulators. Hypotheses regarding the effectiveness of transparency were tested. Results show the necessity for further research and collaboration.Computer simulation models are powerful tools to support problem solving and learning processes. In particular, the iterative process of modeling and simulating-which leads to a simulation model, mapping the problem structure adequately and showing the behavior modes of reality-is important in promoting the understanding of complex systems. Partly because modeling and simulation require significant expertise, business simulators have been developed to allow easier access to a specified model and simulation through the implementation of a user-friendly interface. Although these devices are to a large extent designed as black-box simulators, they enable the user to experience the dynamics created by his or her policies and decisions and therefore facilitate learning about and understanding of complex systems. However, black-box simulators do not provide direct insight into the problem structure. There is no information about the internal feedback structure of the model available, and the users usually have not participated in the process of model development. Hence, black-box simulators are assumed to be of limited effectiveness and efficiency in supporting the learning and problem-solving capabilities.For several years, the idea of transparent-box simulation/gaming has been discussed in the system dynamics (SD) community. Adding features to provide structural AUTHORS' NOTE: Andreas Größler wishes to express his thanks to Pål Davidsen and J. Michael Spector for their help and encouragement in an earlier stage of the research project reported here.

The airline market is a highly cyclical business with relatively poor returns on invested capital. The fluctuations in the market put the carriers under severe economic pressure, and most of them lack strategies for cycle oriented behavior. The focus of the research conducted at Lufthansa German Airlines is the analysis of fundamental, cycle‐generating structures in the airline market and the identification of alternative strategies for effective “cycle‐management”. The system dynamics approach is combined with a statistical forecasting model—a combination that proved to be valuable for the analysis and management of airline business cycles. The article describes a successful system dynamics study in a complex and fast changing environment. Insights generated during the project work are now going to influence order policies for new commercial aircraft for the carrier. Copyright © 2001 John Wiley & Sons, Ltd.

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Purpose -Based on a conceptual framework of the linkages between strategic manufacturing goals and complexity, the purpose of this paper is to investigate adaptation processes in manufacturing firms to increasing external complexity. Design/methodology/approach -Hypotheses are tested with statistical analyses (group comparisons and structural equation models) that are conducted with data from the third round of the International Manufacturing Strategy Survey. Findings -The study shows that manufacturing firms face different degrees of complexity. Firms in a more complex environment tend to possess a more complex internal structure, as indicated by process configuration, than firms in a less complex environment. Also depending on the degree of complexity, different processes of adaptation to increases in external complexity are initiated by organisations.Research limitations/implications -Research studies taking into account the dynamics of adaptation processes would be helpful in order to draw further conclusions, for instance, based on longitudinal analyses or simulation studies. Practical implications -Depending on the level of complexity a firm has been confronted with in the past, different adaptation processes to further growing complexity can be initiated. Firms in high complexity environments have to re-configure their strategic goals; firms in low complexity environments have to build-up internal complexity to cope with demands from the outside. Originality/value -The paper distinguishes between adaptation processes in low and high complexity environments and provides explanations for the differences.