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

Listed author(s):
  • 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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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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  1. Katy S. Azoury, 1985. "Bayes Solution to Dynamic Inventory Models Under Unknown Demand Distribution," Management Science, INFORMS, vol. 31(9), pages 1150-1160, September.
  2. 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.
  3. Larson, C. Erik & Olson, Lars J. & Sharma, Sunil, 2001. "Optimal Inventory Policies when the Demand Distribution Is Not Known," Journal of Economic Theory, Elsevier, vol. 101(1), pages 281-300, November.
  4. Heuts, R.M.J. & Strijbosch, L.W.G. & van der Schoot, E.H.M., 1999. "A Combined Forecast-Inventory Control Procedure for Spare Parts," Research Memorandum 772, Tilburg University, School of Economics and Management.
  5. 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.
  6. Strijbosch, L.W.G. & Moors, J.J.A. & de Kok, A.G., 1997. "On the interaction between forecasting and inventory control," Research Memorandum FEW 742, Tilburg University, School of Economics and Management.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  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. 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.
  13. 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.
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