A Comparative Study of Bootstrapping Techniques for Inventory Control

Abstract

Setting correct inventory levels is an important business consideration in order to minimise inventory investment while at the same time ensuring sufficient inventory levels to meet customer demand. Inventory management has a significant impact on both financial and customer service aspects of a business. Selecting appropriate inventory levels requires that products’ lead time demand be accurately estimated in order to calculate the reorder point. The purpose of this study was to empirically determine whether bootstrapping methods used to estimate the lead time demand distribution and reorder point calculation could match or even outperform a standard parametric approach. The two bootstrapping methods compared in this research included variations of those presented by Bookbinder and Lordahl [1989] and do Rego and de Mesquita [2015]. These were compared to the standard parametric approach common in practice which makes use of the Normal distribution for modelling lead time demand. The three reorder point calculation methods were each incorporated into the inventory policy simulations using data supplied by a South African automotive spare parts business. The simulations covered a period of twelve months and were repeated for multiple service levels ranging from 70 to 99 percent. Results of the simulations were compared at a high level as well as for groups of items identified using segmentation techniques which considered different item demand and lead time characteristics. Key findings were that the Normal approximation method was far superior in terms of the service level metric, while the variation of the Bookbinder and Lordahl [1989] method adopted in this study presented possible cost benefits at lower service levels