Identification of demand patterns for selective processing: a case study

Abstract

A basic function in the proper management of repair part inventories is the anticipation of demand. The US Navy maintains a database of univariate demand data for its repair part inventories using a quarterly time interval and a limited number of periods. Historically, the exponential smoothing procedure has been used for demand forecasting. This method is simple and robust, but it does not make use of any characteristics of the entire time series such as trend, cycles, presence of outliers or demand clustering. Sharper information may be available with the use of the Box–Jenkins system. Not all repair parts can capitalize on this and there is a problem in identifying those that do. Moreover the number of parts is quite large and the speed of identification is an issue. This paper addresses this problem. The research begins with the development of several simple, robust and dimensionless time series features. These are used to predict the suitability of Box–Jenkins (ARIMA) modeling. Two predictive models are considered: classical regression and a modern expert-system statistical package, ModelQuest™. Their strengths and weaknesses are compared. The result of either is a computationally simple means for determining which repair parts time series may benefit from the Box–Jenkins methodology for purposes of inventory management.