Single-period newsvendor problem under random end-of-season demand

Abstract

Newsvendor problems, which have attracted the attention of researchers since 1950s, have wide applications in various industries. There have been many extensions to the standard single-period newsvendor problem. In this paper, we consider the single-period, single-item and single-stage newsvendor problem under random end-of- season demand and develop a model to determine the optimal order quantity and expected profit. We prove that the optimal order quantity and expected profit thus obtained are lower than their respective values obtained from the standard newsvendor formulation. We also provide numerical examples and perform sensitivity analyses to compute the extent of deviations of the ‘true’ optimal solutions from the newsvendor solutions. We observe that the deviations are most sensitive to the ratio of the means of the demand distributions. The deviations are also found sensitive to the contribution margin, salvage price, coefficients of variation of the demand distributions and correlation between seasonal and end-of-season demands. We provide broad guidelines for managers as to when the model developed in this paper should be used and when the standard newsvendor formulation would suffice to determine the order quantity.

Keywords:

• Newsvendor problem
• end-of-season demand distribution
• optimization
• stochastic modelling

Acknowledgement

The authors thank the Editors and two anonymous reviewers for their valuable comments and suggestions that helped improve the quality of the paper. The authors are grateful to Indian Institute of Management Calcutta (IIMC) for funding this research. The authors thankfully acknowledge the contribution of Nishant K. Verma, a former doctoral student of IIMC, in helping solve the numerical problems. Thanks are also due to Prof. Rahul Mukerjee of IIMC for his valuable advice and guidance.

Notes

Please note this paper has been re-typeset by Taylor & Francis from the manuscript originally provided to the previous publisher.