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Evaluating the robustness of lead time demand models


  • Rossetti, Manuel D.
  • Yasin Ünlü


This paper examines the robustness of lead time demand models for the continuous review (r, Q) inventory policy. A number of classic distributions, (e.g. normal, lognormal, gamma, Poisson and negative binomial) as well as distribution selection rules are examined under a wide variety of demand conditions. First, the models are compared to each other by assuming a known demand process and evaluating the errors associated with using a different model. Then, the models are examined using a large sample of simulated demand conditions. Approximation results of inventory performance measures--ready rate, expected number of backorders and on-hand inventory levels are reported. Results indicate that distribution selection rules have great potential for modeling the lead time demand.

Suggested Citation

  • Rossetti, Manuel D. & Yasin Ünlü, 2011. "Evaluating the robustness of lead time demand models," International Journal of Production Economics, Elsevier, vol. 134(1), pages 159-176, November.
  • Handle: RePEc:eee:proeco:v:134:y:2011:i:1:p:159-176

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    References listed on IDEAS

    1. Katrien Ramaekers & Gerrit K. Janssens, 2008. "On the choice of a demand distribution for inventory management models," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 2(4), pages 479-491.
    2. Heuts, R.M.J. & van Lieshout, J.T.H.C. & Baken, K., 1986. "An inventory model : What is the influence of the shape of the lead time demand distribution?," Research Memorandum FEW 205, Tilburg University, School of Economics and Management.
    3. Eliezer Naddor, 1978. "Note--Sensitivity to Distributions in Inventory Systems," Management Science, INFORMS, vol. 24(16), pages 1769-1772, December.
    4. L W G Strijbosch & R M J Heuts & E H M van der Schoot, 2000. "A combined forecast—inventory control procedure for spare parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(10), pages 1184-1192, October.
    5. Tadikamalla, Pandu R, 1984. "A comparison of several approximations to the lead time demand distribution," Omega, Elsevier, vol. 12(6), pages 575-581.
    6. Teunter, Ruud & Dekker, Rommert, 2008. "An easy derivation of the order level optimality condition for inventory systems with backordering," International Journal of Production Economics, Elsevier, vol. 114(1), pages 201-204, July.
    7. Shore, Haim, 1999. "Optimal solutions for stochastic inventory models when the lead-time demand distribution is partially specified," International Journal of Production Economics, Elsevier, vol. 59(1-3), pages 477-485, March.
    8. Leven, Erik & Segerstedt, Anders, 2004. "Inventory control with a modified Croston procedure and Erlang distribution," International Journal of Production Economics, Elsevier, vol. 90(3), pages 361-367, August.
    9. Bartezzaghi, Emilio & Verganti, Roberto & Zotteri, Giulio, 1999. "Measuring the impact of asymmetric demand distributions on inventories," International Journal of Production Economics, Elsevier, vol. 60(1), pages 395-404, April.
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    Cited by:

    1. Cobb, Barry R. & Johnson, Alan W. & Rumí, Rafael & Salmerón, Antonio, 2015. "Accurate lead time demand modeling and optimal inventory policies in continuous review systems," International Journal of Production Economics, Elsevier, vol. 163(C), pages 124-136.
    2. Parsa, Payam & Rossetti, Manuel D. & Zhang, Shengfan & Pohl, Edward A., 2017. "Quantifying the benefits of continuous replenishment program for partner evaluation," International Journal of Production Economics, Elsevier, vol. 187(C), pages 229-245.
    3. Mekhtiev, Mirza Arif, 2013. "Analytical evaluation of lead-time demand in polytree supply chains with uncertain demand, lead-time and inter-demand time," International Journal of Production Economics, Elsevier, vol. 145(1), pages 304-317.


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