Abstract:

This paper describes a methodology for forecasting technology insertion concurrent with design refresh planning. The optimized parameter is the life cycle cost of the system. The resulting analysis provides a design refresh schedule for the system (i.e., when to design refresh) and predicts the design refresh content for each of the scheduled design refreshes. The best design refresh content is determined using a hybrid analysis scheme that utilizes Monte Carlo methods to account for uncertainties (in dates) and Bayesian Belief Networks to enable critical decision making once candidate refresh dates are chosen.  The methodology described in this paper has been implemented within a tool called MOCA (Mitigation of Obsolescence Cost Analysis). MOCA has a design refresh planning engine that manages the selection of candidate refresh plans and a cost analysis engine that determines the life cycle cost of the candidate plans. MOCA has been extended to construct Bayesian Belief Networks (BBNs) for critical components from pre-built network fragments that are coupled together (component-to-component and refresh-to-refresh) to determine the optimum design refresh content at candidate refresh dates.

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