Abstract
Traditional computerised inventory control systems usually rely on exponential smoothing to forecast the demand for fast moving inventories. Practices in relation to slow moving inventories are more varied, but the Croston method is often used. It is an adaptation of exponential smoothing that (1) incorporates a Bernoulli process to capture the sporadic nature of demand and (2) allows the average variability to change over time. The Croston approach is critically appraised in this paper. Corrections are made to underlying theory and modifications are proposed to overcome certain implementation difficulties. A parametric bootstrap approach is outlined that integrates demand forecasting with inventory control. The approach is illustrated on real demand data for car parts.
Highlights
An understanding of key features of demand data is important when developing computer systems for forecasting and inventory control
A parametric bootstrap method is proposed for determining appropriate values for inventory control parameters
Comments Methods of forecasting that can be applied to both fast and slow moving inventories have been proposed in this paper
Summary
An understanding of key features of demand data is important when developing computer systems for forecasting and inventory control. A forecasting technique that allows for the possibility of zero values, but still works with fast moving inventories like Car Part 3, is most desirable. It eliminates the need to make artificial distinctions between slow and fast moving items, something that researchers (Johnston and Boylan, 1996b) have perceived as being a critical issue in applied forecasting. A parametric bootstrap method is proposed for determining appropriate values for inventory control parameters. They are compared using computed values of ordering parameters required for inventory control
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