Variability in the Demands for Aircraft Spare Parts: Its Magnitude and Implications
Rand Corporation, 1988 - 88 pages
Mathematical models of the logistics system are used to determine spares requirements, and they play an important role in evaluating logistics policies. The kernel of many, if not most, of these models is the modeling of the failure process and the resulting series of random demands on supply and maintenance. This report describes the assumptions of these models and quantifies ways in which the behavior of the data differs from the assumptions of the models. The differences are pervasive and important. In addition, an examination of the number of parts in the repair pipeline over time reveals even more variability than does the number of demands over time. These observations have two important consequences: (1) excessive demand variability substantially reduces the confidence we can put in our requirements and capability assessment models, and (2) highly variable repair pipelines with means larger than assumed by requirements models have a damaging effect on aircraft availability and wartime readiness. Depot policies, decisions, and goals should be aimed at reducing the number of parts in these pipelines and increasing aircraft availability and wartime readiness.
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1000 flying hours 19 parts data actuator Air Force aircraft availability assessment and requirement assumed assumptions average demands CALENDAR YEAR QUARTER capability assessment models compound Poisson distribution compound Poisson process compounding density computed Converter Programmer COSO Cost of LRU Cumulative VTMRs demand process demand rate demand stream demands per 1000 demands per quarter depot pipeline expected observed expected Gamma distribution heat exchangers high VTMRs Holloman AFB hydraulic LRU Pipeline Luke AFB Luke and Holloman maintenance mean ratio Fig MICAP 42 NEGATIVE BINOMIAL DISTRIBUTION number in repair number of demands observed expected Cost observed expected Fig observed expected observed observed VTMR Palm's Theorem Peculiar and Common percent pipeline contents pipeline quantities Poisson arrival process Poisson process Poisson random variable predict QUARTER Base 80-2 RADAR repair pipeline repair process requirements and capability requirements models result sample Serviceability of Components Serviceable LRUs tion unserviceable Variance to mean variance-to-mean ratio VTMR larger WRSK
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