Classification for forecasting and stock control: a case study

Abstract

Different stock keeping units (SKUs) are associated with different underlying demand structures, which in turn require different methods for forecasting and stock control. Consequently, there is a need to categorize SKUs and apply the most appropriate methods in each category. The way this task is performed has significant implications in terms of stock and customer satisfaction. Therefore, categorization rules constitute a vital element of intelligent inventory management systems. Very little work has been conducted in this area and, from the limited research to date, it is not clear how managers should classify demand patterns for forecasting and inventory management. A previous research project was concerned with the development of a theoretically coherent demand categorization scheme for forecasting only. In this paper, the stock control implications of such an approach are assessed by experimentation on an inventory system developed by a UK-based software manufacturer. The experimental database consists of the individual demand histories of almost 16 000 SKUs. The empirical results from this study demonstrate considerable scope for improving real-world systems.

Keywords:

• categorization
• forecasting
• inventory
• intermittent demand
• case study

Acknowledgements

We acknowledge financial support for this project from the company involved and the DTI. The empirical findings of the paper emerged from a Knowledge Transfer Partnership between the company and Buckinghamshire Chilterns University College. Also, we thank the participants in the Intelligent Management Systems in Operations (IMSIO) III conference (Salford, June 28–29, 2005) for their comments on an earlier draft of this paper.