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Assessing the effects of using demand parameters estimates in inventory control and improving the performance using a correction function


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  • Janssen, Elleke
  • Strijbosch, Leo
  • Brekelmans, Ruud


Inventory models need some specification of the distribution of demand in order to find the optimal order-up-to level or reorder point. This distribution is unknown in real life and there are several solutions to overcome this problem. One approach is to assume a distribution, estimate its parameters and replace the unknown demand parameters by these estimates in the theoretically correct model. Earlier research suggests that this approach will lead to underperformance, even if the true demand distribution is indeed the assumed one. This paper directs the cause of the underperformance and quantifies it in case of normally distributed demand. Furthermore the formulae for the order-up-to levels are corrected analytically where possible and otherwise by use of simulation and linear regression. Simulation shows that these corrections improve the attained performance.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Production Economics.

Volume (Year): 118 (2009)
Issue (Month): 1 (March)
Pages: 34-42

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Handle: RePEc:eee:proeco:v:118:y:2009:i:1:p:34-42

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Keywords: Unknown demand parameters Inventory control Normal distribution Service level criterion;


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  1. Erik W. Larson & Sunil Sharma & Lars J. Olson, 2000. "Optimal Inventory Policies When the Demand Distribution is Not Known," IMF Working Papers 00/183, International Monetary Fund.
  2. Bulinskaya, E. V., 1990. "Inventory control in case of unknown demand distribution," Engineering Costs and Production Economics, Elsevier, vol. 19(1-3), pages 301-306, May.
  3. Katy S. Azoury, 1985. "Bayes Solution to Dynamic Inventory Models Under Unknown Demand Distribution," Management Science, INFORMS, vol. 31(9), pages 1150-1160, September.
  4. Zhaohui Zeng, Amy & Hayya, Jack C., 1999. "The performance of two popular service measures on management effectiveness in inventory control," International Journal of Production Economics, Elsevier, vol. 58(2), pages 147-158, January.
  5. Strijbosch, L. W. G. & Heuts, R. M. J., 1992. "Modelling (s, Q) inventory systems: Parametric versus non-parametric approximations for the lead time demand distribution," European Journal of Operational Research, Elsevier, vol. 63(1), pages 86-101, November.
  6. Strijbosch, L.W.G. & Moors, J.J.A., 2006. "Modified normal demand distributions in (R, S)-inventory control," European Journal of Operational Research, Elsevier, vol. 172(1), pages 201-212, July.
  7. Strijbosch, L.W.G. & Moors, J.J.A., 1999. "Simple Expressions for Safety Factors in Inventory Control," Discussion Paper 1999-112, Tilburg University, Center for Economic Research.
  8. Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
  9. Katy S. Azoury & Bruce L. Miller, 1984. "A Comparison of the Optimal Ordering Levels of Bayesian and Non-Bayesian Inventory Models," Management Science, INFORMS, vol. 30(8), pages 993-1003, August.
  10. Strijbosch, L.W.G. & Moors, J.J.A. & Kok, A.G. de, 1997. "On the interaction between forecasting and inventory control," Research Memorandum 742, Tilburg University, Faculty of Economics and Business Administration.
  11. Strijbosch, L. W. G. & Moors, J. J. A., 2005. "The impact of unknown demand parameters on (R,S)-inventory control performance," European Journal of Operational Research, Elsevier, vol. 162(3), pages 805-815, May.
  12. Silver, Edward A. & Rahnama, Mina Rasty, 1987. "Biased selection of the inventory reorder point when demand parameters are statistically estimated," Engineering Costs and Production Economics, Elsevier, vol. 12(1-4), pages 283-292, July.
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Cited by:
  1. 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.
  2. Halkos, George & Kevork, Ilias & Tziourtzioumis, Chris, 2014. "On the convexity of the cost function for the (Q,R) inventory model," MPRA Paper 55675, University Library of Munich, Germany.
  3. Halkos, George & Kevork, Ilias, 2012. "The classical newsvendor model under normal demand with large coefficients of variation," MPRA Paper 40414, University Library of Munich, Germany.
  4. Halkos, George & Kevork, Ilias, 2013. "Forecasting the optimal order quantity in the newsvendor model under a correlated demand," MPRA Paper 44189, University Library of Munich, Germany.
  5. Halkos, George & Kevork, Ilias, 2012. "Evaluating alternative frequentist inferential approaches for optimal order quantities in the newsvendor model under exponential demand," MPRA Paper 39650, University Library of Munich, Germany.
  6. Halkos, George & Kevork, Ilias, 2012. "Unbiased estimation of maximum expected profits in the Newsvendor Model: a case study analysis," MPRA Paper 40724, University Library of Munich, Germany.


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