This Scalable Enterprise Systems Phase II project's primary goal is to develop models of emergent enterprises that capture the independent behavior of the organizations involved, as well as the effects of interactions among individual organizations, in order to accurately predict dynamics of the emergent enterprise and performance of the system in the long run. Participants in the enterprise can use these results to select appropriate operational strategies in order to improve local performance measures such as profit or cash flow over a fixed planning horizon. In addition, these models will provide insight into the mechanisms that result in effective alliances and organizational designs. The modeling approach has three steps. First, a representation scheme is developed for capturing characteristics of individual participants that incorporates task information, organizational relationships, local and system level goals, and possible changes in the environment. An enterprise is thus a collection of several of such detailed representations for each organization and the interactions between these organizations. Second, a micro-macro modeling approach is developed, where the details of each agent's tasks (represented in the micro-level simulation framework) are aggregated into a set of interaction parameters at the macro-level. The macro-level enterprise model is formulated as an interacting particle system. Finally, the third step involves validation of the theories developed using field data. Field data will be collected from different industries that have large number of participants in the supply network, such as the food industry and the automotive industry, to construct practical agent representations and to verify the theoretical performance estimates. Large-scale simulations using a multi-agent simulator will also be used to empirically estimate the performance of a given organizational configuration.

This research will allow planners to estimate a priori the overall enterprise performance under different operating conditions, and eventually, design optimal emergent societies. Moreover, systems composed of distributed decision-makers are encountered in various application domains, such as traffic on highway networks, air traffic management systems, design teams composed of dispersed designers, distributed sensor networks, multi-agent systems, etc. The fundamental advances made in this research will have significant impact on our ability to predict the performance of such systems, and therefore devise appropriate planning and control strategies.

National Science Foundation (NSF)
Division of Civil, Mechanical, and Manufacturing Innovation (CMMI)
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Stephen G. Nash
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University of Massachusetts Amherst
United States
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