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Managing inventory systems of slow-moving items


  • Hahn, G.J.
  • Leucht, A.


Slow-moving demand patterns frequently occur with spare parts as well as items in decentralized retail supply chains with large assortments. These patterns are commonly called lumpy since they exhibit comparably high demand variation and a high fraction of zero-demand events. In this paper, we examine two distribution-based approaches to model lumpy demand processes for inventory control: (i) a generalized hurdle negative binomial model, and (ii) a worst-case non-parametric model that is derived using a test-based approach. Considering a base stock inventory policy, we examine a set of lumpy time series from the industry to exemplify the suitability and benefit of the proposed approaches for managing inventory systems of slow-moving items.

Suggested Citation

  • Hahn, G.J. & Leucht, A., 2015. "Managing inventory systems of slow-moving items," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 543-550.
  • Handle: RePEc:eee:proeco:v:170:y:2015:i:pb:p:543-550
    DOI: 10.1016/j.ijpe.2015.08.014

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    References listed on IDEAS

    1. J. B. Ward, 1978. "Determining Reorder Points When Demand is Lumpy," Management Science, INFORMS, vol. 24(6), pages 623-632, February.
    2. Snyder, Ralph, 2002. "Forecasting sales of slow and fast moving inventories," European Journal of Operational Research, Elsevier, vol. 140(3), pages 684-699, August.
    3. Tiacci, Lorenzo & Saetta, Stefano, 2009. "An approach to evaluate the impact of interaction between demand forecasting method and stock control policy on the inventory system performances," International Journal of Production Economics, Elsevier, vol. 118(1), pages 63-71, March.
    4. Janssen, Fred & Heuts, Ruud & de Kok, Ton, 1998. "On the (R, s, Q) inventory model when demand is modelled as a compound Bernoulli process," European Journal of Operational Research, Elsevier, vol. 104(3), pages 423-436, February.
    5. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    6. Ahmed, Shabbir & Cakmak, Ulas & Shapiro, Alexander, 2007. "Coherent risk measures in inventory problems," European Journal of Operational Research, Elsevier, vol. 182(1), pages 226-238, October.
    7. Teunter, R.H. & Syntetos, A.A. & Babai, M.Z., 2010. "Determining order-up-to levels under periodic review for compound binomial (intermittent) demand," European Journal of Operational Research, Elsevier, vol. 203(3), pages 619-624, June.
    8. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
    9. Strijbosch, Leo W.G. & Syntetos, Aris A. & Boylan, John E. & Janssen, Elleke, 2011. "On the interaction between forecasting and stock control: The case of non-stationary demand," International Journal of Production Economics, Elsevier, vol. 133(1), pages 470-480, September.
    10. Dunsmuir, W. T. M. & Snyder, R. N., 1989. "Control of inventories with intermittent demand," European Journal of Operational Research, Elsevier, vol. 40(1), pages 16-21, May.
    11. Panjer, Harry H., 1981. "Recursive Evaluation of a Family of Compound Distributions," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 12(01), pages 22-26, June.
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    Cited by:

    1. Rezaei Somarin, Aghil & Chen, Songlin & Asian, Sobhan & Wang, David Z.W., 2017. "A heuristic stock allocation rule for repairable service parts," International Journal of Production Economics, Elsevier, vol. 184(C), pages 131-140.
    2. repec:eee:ejores:v:265:y:2018:i:2:p:399-422 is not listed on IDEAS


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