An Evaluation of End of Maintenance Dates for Electronic Assemblies

The business of supporting legacy electronic systems is challenging due to mismatches between the system support life and the procurement lives of the systems’ constitute components. Legacy electronic systems are threatened with DMSMS-type obsolescence (Diminishing Manufacturing Sources and Material Shortages) and the knowledge of their system support lives based upon existing replenishable and non-replenishable resources may be unknown. This paper describes an End of Repair/End of Maintenance (EOR/EOM) model, which is a stochastic discreteevent simulation that follows the life history of a population of parts and cards and determines how long the system can be sustained based upon existing inventories of spare parts and cards, and optionally harvesting of parts off of existing cards to increase system support length. The model includes: part inventory segregation, part-specific degradation of inventories, user-defined inventory inspection periods, and operates from failure distributions that are either user-defined or synthesized from observed failures to date. 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 through a series of different scenarios: obsolete parts with no failure history never failing, obsolete parts with no failure history immediately incurring their first failures, and with and without part harvesting. For this example case with existing inventories, the model indicates for the 'best-case' scenario that the legacy system can be supported for approximately 20 years prior to its first EOM event. The immediate first failure assumption decreases the system support life by two years, while harvesting parts extends the system support life by two years. Index Terms sustainment, COTS, legacy systems, demand forecasting, EOR, EOM, harvesting, DMSMS