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Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance


  • Dolgui, Alexandre
  • Pashkevich, Maksim


Modeling the lead-time demand for the multiple slow-moving inventory items in the case when the available requests history is very short is a challenge for inventory management. The classical forecasting technique, which is based on the aggregation of the stock keeping units to overcome the mentioned historical data peculiarity, is known to lead to very poor performance in many cases important for industrial applications. An alternative approach to the demand forecasting for the considered problem is based on the Bayesian paradigm, when the initially developed population-averaged demand probability distribution is modified for each item using its specific requests history. This paper follows this approach and presents a new model, which relies on the beta distribution as a prior for the request probability, and allows to account for disparity in variance of demand between different stock keeping units. To estimate the model parameters, a special computationally effective technique based on the generalized method of moments is developed. Simulation results indicate the superiority of the proposed model over the known ones, while the computational burden does not increase.

Suggested Citation

  • Dolgui, Alexandre & Pashkevich, Maksim, 2008. "Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance," International Journal of Production Economics, Elsevier, vol. 112(2), pages 885-894, April.
  • Handle: RePEc:eee:proeco:v:112:y:2008:i:2:p:885-894

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    References listed on IDEAS

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    1. repec:wsi:apjorx:v:34:y:2017:i:04:n:s021759591750021x is not listed on IDEAS
    2. Kumar, Anupam & Evers, Philip T., 2015. "Setting safety stock based on imprecise records," International Journal of Production Economics, Elsevier, vol. 169(C), pages 68-75.
    3. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.
    4. Lu, Chi-Jie & Wang, Yen-Wen, 2010. "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, Elsevier, vol. 128(2), pages 603-613, December.
    5. Lindsey, Matthew & Pavur, Robert, 2009. "Prediction intervals for future demand of existing products with an observed demand of zero," International Journal of Production Economics, Elsevier, vol. 119(1), pages 75-89, May.

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