Publications

Thompson, Bruce M.

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Thompson, Bruce M.

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Thompson, Bruce M.

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Thompson, Bruce M.

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Thompson, Bruce M.

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Thompson, Bruce M.

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Thompson, Bruce M.

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Cranwell, Robert M.; Thompson, Bruce M.

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Thompson, Bruce M.; Lawton, Craig R.

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Cranwell, Robert M.; Campbell, James E.; Anderson, Dennis J.; Thompson, Bruce M.; Lawton, Craig R.; Shirah, Donald N.

Analyzing the performance of a complex System of Systems (SoS) requires a systems engineering approach. Many such SoS exist in the Military domain. Examples include the Army's next generation Future Combat Systems 'Unit of Action' or the Navy's Aircraft Carrier Battle Group. In the case of a Unit of Action, a system of combat vehicles, support vehicles and equipment are organized in an efficient configuration that minimizes logistics footprint while still maintaining the required performance characteristics (e.g., operational availability). In this context, systems engineering means developing a global model of the entire SoS and all component systems and interrelationships. This global model supports analyses that result in an understanding of the interdependencies and emergent behaviors of the SoS. Sandia National Laboratories will present a robust toolset that includes methodologies for developing a SoS model, defining state models and simulating a system of state models over time. This toolset is currently used to perform logistics supportability and performance assessments of the set of Future Combat Systems (FCS) for the U.S. Army's Program Manager Unit of Action.

Campbell, James E.; Thompson, Bruce M.; Thompson, Bruce M.

This paper focuses on fully automated analysis of failure event data in the concept and early development stage of a semiconductor-manufacturing tool. In addition to presenting a wide range of statistical and machine-specific performance information, algorithms have been developed to examine reliability growth and to identify major contributors to unreliability. These capabilities are being implemented in a new software package called Reliadigm. When coupled with additional input regarding repair times and parts availability, the analysis software also provides spare parts inventory optimization based on genetic optimization methods. The type of question to be answered is: If this tool were placed with a customer for beta testing, what would be the optimal spares kit to meet equipment reliability goals for the lowest cost? The new algorithms are implemented in Windows{reg_sign} software and are easy to apply. This paper presents a preliminary analysis of failure event data from three IDEA machines currently in development. The paper also includes an optimal spare parts kit analysis.