A Bayesian approach to demand estimation and inventory provisioning

Abstract

AbstractThis article addresses the problem of explicitly taking into account uncertainty about the demand for spare parts in making inventory procurement and stockage decisions. The model described provides for a unified treatment of the closely related problems of statistical estimation of demand and resource allocation within the inventory system, and leads to an easily implemented, efficient method of determining requirements for spare parts both in the early provisioning phase and in later periods of operations when demand data have accumulatedAnalyses of the model's theoretical foundations and of sample outcomes of the model based upon data on parts intended for use in the F‐14 lead to conclusions of great importance to both support planners and operations plannersFinally, of particular significance is the ability afforded the planner by this model to quantify the impact on inventory system costs of varying levels of system reliability or management uncertainty as to projected system performance. This will provide an economic basis for analysis of such alternatives as early deployment, operational testing, and equipment redesign.