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Forecasting and Stock Control for Intermittent Demands
Abstract
Exponential smoothing is frequently used for the forecasts in stock control systems. The analysis given shows that intermittent demands almost always produce inappropriate stock levels. Demand for constant quantities at fixed intervals may generate stock levels of up to double the quantity really needed. A method of overcoming these difficulties is described, using separate estimates of the size of demand, and of the demand frequency. The rules for setting the safety stock levels have also to be adjusted before consistent protection can be obtained against being out of stock.
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Cited by
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Benidis, K., Rangapuram, S. S., Flunkert, V., Wang, Y., Maddix, D., Turkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., et al. (2023) · ACM Computing Surveys · doi:10.1145/3533382 · Cited by 382
Supply chain forecasting: Theory, practice, their gap and the future
Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.11.010 · Cited by 328
Forecasting and operational research: a review
Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A. (2008) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602597 · Cited by 287
Sgarbossa, F., Peron, M., Lolli, F., & Balugani, E. (2021) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107993 · Cited by 110
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Kourentzes, N., Li, D., & Strauss, A. K. (2019) · Journal of Revenue and Pricing Management · doi:10.1057/s41272-017-0117-x · Cited by 9
Metadata
- Authors
- Croston, J. D.
- Venue
- Journal of the Operational Research Society
- Publisher
- Informa UK Limited
- Published
- 1972-09
- Volume
- 23
- Issue
- 3
- Pages
- 289-303
- ISSN
- 0160-5682 (ISSN-L)
- 1476-9360
- OpenAlex
- W1989787309
- Cited by
- 753
- References
- 0
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
