Abstract
Intermittent Demand forecasting is one of the most crucial issues of service parts inventory management, which forms the basis for the planning of inventory levels and is probably the biggest challenge in the repair and overhaul industry. Generally, intermittent demand appears at random, with many time periods having no demand. In practice, exponential smoothing is often used when dealing with such kind of demand. Based on exponential smoothing method, more improved methods have been studied such as Croston method. This paper proposes a novel method to forecast the intermittent parts demand based on support vector regression (SVR). Details on data clustering, performance criteria design, kernel function selection are presented and an experimental result is given to show the method’s validity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Silver, E.A.: Operations research in inventory management: A review and critique. Operations Research 29, 628–645 (1981)
Syntetos, A.A., Boylan, J.E.: On the bias of intermittent demand estimates. International Journal of Production Economics, 457–466 (2001)
Bartezzaghi, E., Verganti, R., Zotteri, G.A.: Simulation framework for forecasting uncertain lumpy demand. International Journal of Production Economics 59(1-3), 499–510 (1999)
Croston, J.D.: Forecasting and stock control for intermittent demands. Operational Research Quarterly 23(3), 289–303 (1972)
Rao, A.V.: A comment on: forecasting and stock control for intermittent demands. Operational Research Quarterly 24(4), 639–640 (1973)
Willemain, T.R., Smart, C.N., Shockor, J.H., DeSautels, P.A.: Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston’s method. International Journal of Forecasting 10(4), 529–538 (1994)
Johnston, F.R., Boylan, J.E.: Forecasting for items with intermittent demand. Journal of the Operational Research Society 47(1), 113–121 (1996)
Wilcox, J.E.: How to forecast lumpy items. Production Inventory Management Journal 11(1), 51–54 (1970)
Willemain, T.R., Ratti, E.W.L., Smart, C.N.: Forecasting intermittent demand using a cox process model. In: INFORMS Meetings, Boston, USA, pp. 1-14 (1994)
Watson, R.B.: The effects of demand-forecast fluctuations on customer service and inventory cost when demand is lumpy. Journal of the Operational Research Society 38(1), 75–82 (1987)
Zhao, X., Lee, T.S.: Freezing the master production schedule for material requirements planning systems under demand uncertainty. Journal of Operations Management 11(2), 185–205 (1993)
Lee, T.S., Adam, E.E.: Forecasting error evaluation in material requirements planning (MRP) production-inventory systems. Management Science 32(9), 186–205 (1986)
Sridharan, S.V., Berry, W.L.: Freezing the master production schedule under demand uncertainty. Decision Science 21(1), 97–120 (1990)
Wemmerlov, U.: The behaviour of lot-sizing procedures in the presence of forecast errors. Journal of Operations Management 8(1), 37–47 (1989)
Vapnik, V.N., Golowich, S.E., Smola, A.J.: Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 9, 281–287 (1996)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Schmidt, M.: Identifying speaker with support vector networks. In: Interface 1996 Proceedings, Sydney (1996)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernet. 3, 32–57 (1974)
Medasani, S., Kim, J., Krishnapuram, R.: An overview of membership function generation techniques for pattern recognition. International Journal of Approximation Research 19, 391–417 (1998)
Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Systems 3(3), 370–379 (1995)
Rezaee, M.R., Lelieveldt, B.P.F., Reiber, J.H.C.: A new cluster validity index for the fuzzy c-means. Pattern Recognition Letters 19, 237–246 (1998)
Haykin, S.: Neural networks: a comprehensive foundation. Prentice Hall, Englewood Cliffs (1999)
Zhang, G.Q., Michael, Y.H.: Neural network forecasting of the British Pound=US Dollar exchange rate. Omega 26(4), 495–506 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bao, Y., Wang, W., Zou, H. (2005). SVR-Based Method Forecasting Intermittent Demand for Service Parts Inventories. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_64
Download citation
DOI: https://doi.org/10.1007/11548706_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
eBook Packages: Computer ScienceComputer Science (R0)