Pameet Singh and
University of Maryland
College Park, MD 20742
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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