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.
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References
Boylan JE and Syntetos AA (2006). Accuracy and accuracy-implication metrics for intermittent demand. Foresight: Int J Appl Forecasting 4: 39–42.
Croston JD (1972). Forecasting and stock control for intermittent demands. Opl Res Q 23: 289–304.
Eaves AHC (2002). Forecasting for the ordering and stock holding of consumable spare parts. PhD thesis: Lancaster University.
Eaves AHC and Kingsman BG (2004). Forecasting for the ordering and stock-holding of spare parts. J Opl Res Soc 55: 431–437.
Gardner ES (1990). Evaluating forecast performance in an inventory control system. Mgmt Sci 36: 490–499.
Gelders LF and Van Looy PM (1978). An inventory policy for slow and fast movers in a petrochemical plant: a case study. J Opl Res Soc 29: 867–874.
Johnston FR and Boylan JE (1996). Forecasting for items with intermittent demand. J Opl Res Soc 47: 113–121.
Kobbacy KAH and Liang Y (1999). Towards the development of an intelligent inventory management system. Integrated Manuf Syst 10: 354–366.
Ritchie E and Kingsman BG (1985). Setting stock levels for wholesaling: Performance measures and conflict of objectives between supplier and stockist. Eur J Opl Res 20: 17–24.
Sani B and Kingsman BG (1997). Selecting the best periodic inventory control and demand forecasting methods for low demand items. J Opl Res Soc 48: 700–713.
Silver EA, Pyke DF and Peterson R (1998). Inventory Management and Production Planning and Scheduling, 3rd ed. John Wiley & Sons: New York.
Strijbosch LWG, Heuts RMJ and van der Schoot EHM (2000). A combined forecast-inventory control procedure for spare parts. J Opl Res Soc 51: 1184–1192.
Syntetos AA and Boylan JE (2001). On the bias of intermittent demand estimates. Int J Production Econ 71: 457–466.
Syntetos AA and Boylan JE (2005). The accuracy of intermittent demand estimates. Int J Forecasting 21: 303–314.
Syntetos AA and Boylan JE (2006a). Comments on the attribution of an intermittent demand estimator. Int J Forecasting 22: p. 201.
Syntetos AA and Boylan JE (2006b). On the stock-control performance of intermittent demand estimators. Int J Production Econ 103: 36–47.
Syntetos AA, Boylan JE and Croston JD (2005). On the categorization of demand patterns. J Opl Res Soc 56: 495–503.
Watson RB (1987). The effects of demand–forecast fluctuations on customer service and inventory cost when demand is lumpy. J Opl Res Soc 38: 75–82.
Williams TM (1984). Stock control with sporadic and slow-moving demand. J Opl Res Soc 35: 939–948.
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.
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Boylan, J., Syntetos, A. & Karakostas, G. Classification for forecasting and stock control: a case study. J Oper Res Soc 59, 473–481 (2008). https://doi.org/10.1057/palgrave.jors.2602312
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DOI: https://doi.org/10.1057/palgrave.jors.2602312