An Evaluation of End of Maintenance Dates and Lifetime Buy Estimations for Electronic Systems Facing Obsolescence

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Konoza, Anthony
Sandborn, Peter
The business of supporting legacy electronic systems is challenging due to mismatches between the system support life and the procurement lives of the systems' constituent components. Legacy electronic systems are threatened with Diminishing Manufacturing Sources and Material Shortages (DMSMS)-type obsolescence, and the extent of their system support lives based on existing replenishable and non-replenishable resources may be unknown. This thesis describes the development of the End of Repair/End of Maintenance (EOR/EOM) model, which is a stochastic discrete-event simulation that follows the life history of a population of parts and cards and operates from time-to-failure distributions that are either user-defined, or synthesized from observed failures to date. The model determines the support life (and support costs) of the system based on existing inventories of spare parts and cards, and optionally harvesting parts from existing cards to further extend the life of the system. The model includes: part inventory segregation, modeling of part inventory degradation and periodic inventory inspections, and design refresh planning. A case study using a real legacy system comprised of 117,000 instances of 70 unique cards and 4.5 million unique parts is presented. The case study was used to evaluate the system support life (and support costs) through a series of different scenarios: obsolete parts with no failure history and never failing, obsolete parts with no failure history but immediately incurring their first failures with and without the use of part harvesting. The case study also includes analyses for recording subsequent EOM and EOR dates, sensitivity analyses for selected design refreshes that maximize system sustainment, and design refresh planning to ensure system sustainment to an end of support date. Lifetime buys refer to buying enough parts from the original manufacturer prior to the part's discontinuance in order to support all forecasted future part needs throughout the system's required support life. This thesis describes the development of the Lifetime Buy (LTB) model, a reverse-application of the EOR/EOM model, that follows the life history of an electronic system and determines the number of spares required to ensure system sustainment. The LTB model can generate optimum lifetime buy quantities of parts that minimizes the total life-cycle cost associated with the estimated lifetime buy quantity.