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Abstract:
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Analytical capacity planning models are key to the success of many resource constrained production systems. While these models are useful, they do not quantify risk or allow optimization based on risk factors. By treating the inputs and outputs of capacity models as Gaussian variables, analysis and mitigation of risk is possible using statistical methods. The paper will discuss the application of this approach to capacity planning models focusing on practical statistical methods to determine risk, mitigation and optimization strategies. It will include a discussion of the computation of capacity means, probability distribution of success, determination of assumption variance, and risk mitigation strategies. The approach will be illustrated with capacity planning examples from the US 2010 Census Decennial Response Integration System (DRIS).
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