Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/17277
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dc.contributor.authorAvijit, Khanra
dc.contributor.authorSoman, Chetan
dc.contributor.authorBandyopadhyay, Tathagata
dc.date.accessioned2016-01-07T09:40:25Z
dc.date.available2016-01-07T09:40:25Z
dc.date.copyright2014
dc.date.issued2014
dc.identifier.citationKhanra, A., Soman, C., & Bandyopadhyay, T. (2014). Sensitivity analysis of the news vendor model. European Journal of Operational Research, 239(2), 403-412.en_US
dc.identifier.issn0377-2217
dc.identifier.urihttp://hdl.handle.net/11718/17277
dc.description.abstractQuality 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.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectInventoryen_US
dc.subjectNewsvendor modelen_US
dc.subjectRobustness and sensitivity analysisen_US
dc.titleSensitivity analysis of the newsvendor modelen_US
dc.typeArticleen_US
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