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Sensitivity analysis of the newsvendor model

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  • Khanra, Avijit
  • Soman, Chetan
  • Bandyopadhyay, Tathagata

Abstract

Quality of decisions in inventory management models depends on the accuracy of parameter estimates used for decision making. In many situations, error in decision making is unavoidable. In such cases, sensitivity analysis is necessary for better implementation of the model. Though the newsvendor model is one of the most researched inventory models, little is known about its robustness. In this paper, we perform sensitivity analysis of the classical newsvendor model. Conditions for symmetry/skewness of cost deviation (i.e., deviation of expected demand–supply mismatch cost from its minimum) have been identified. These conditions are closely linked with symmetry/skewness of the demand density function. A lower bound of cost deviation is established for symmetric unimodal demand distributions. Based on demonstrations of the lower bound, we found the newsvendor model to be sensitive to sub-optimal ordering decisions, more sensitive than the economic order quantity model. Order quantity deviation (i.e., deviation of order quantity from its optimum) is explored briefly. We found the magnitude of order quantity deviation to be comparable with that of parameter estimation error. Mean demand is identified as the most influential parameter in deciding order quantity deviation.

Suggested Citation

  • Khanra, Avijit & Soman, Chetan & Bandyopadhyay, Tathagata, 2014. "Sensitivity analysis of the newsvendor model," European Journal of Operational Research, Elsevier, vol. 239(2), pages 403-412.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:2:p:403-412
    DOI: 10.1016/j.ejor.2014.05.039
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    References listed on IDEAS

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    Cited by:

    1. Rabta, Boualem, 2017. "Sensitivity analysis in inventory models by means of ergodicity coefficients," International Journal of Production Economics, Elsevier, vol. 188(C), pages 63-71.
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    4. Mitra, Subrata, 2018. "Newsvendor problem with clearance pricing," European Journal of Operational Research, Elsevier, vol. 268(1), pages 193-202.

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