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International Journal of Systems Science Volume 39, 2008 - Issue 7: Production Coordination and Inventory Policies
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Original Articles

Extended beta-binomial model for demand forecasting of multiple slow-moving inventory items

Alexandre Dolgui Centre for Industrial Engineering and Computer Science, Ecole des Mines de Saint-Etienne, Saint Etienne, FranceCorrespondence[email protected]
&
Maksim Pashkevich CISNET Modeling Team, Stanford University, Stanford, USA
Pages 713-726 | Received 10 Dec 2007, Accepted 03 Feb 2008, Published online: 19 May 2008

Citations (5)

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Read on this site (2)

Peng Jiang, Yibin Huang & Xiao Liu. (2021) Intermittent demand forecasting for spare parts in the heavy-duty vehicle industry: a support vector machine model. International Journal of Production Research 59:24, pages 7423-7440.
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Steven Prestwich, Roberto Rossi, S. Armagan Tarim & Brahim Hnich. (2014) Mean-based error measures for intermittent demand forecasting. International Journal of Production Research 52:22, pages 6782-6791.
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Articles from other publishers (3)

Saba Sareminia & Fatemeh Amini. (2023) A reliable and ensemble forecasting model for slow-moving and repairable spare parts: Data mining approach. Computers in Industry 145, pages 103827.
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Neal Wagner, Zbigniew Michalewicz, Sven Schellenberg, Constantin Chiriac & Arvind Mohais. (2011) Intelligent techniques for forecasting multiple time series in real‐world systems. International Journal of Intelligent Computing and Cybernetics 4:3, pages 284-310.
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George Nenes, Sofia Panagiotidou & George Tagaras. (2010) Inventory management of multiple items with irregular demand: A case study. European Journal of Operational Research 205:2, pages 313-324.
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