000001624 001__ 1624
000001624 005__ 20190411205441.0
000001624 0247_ $$2DOI$$a10.1007/s12597-016-0248-7
000001624 022__ $$a0030-3887
000001624 037__ $$aARTICLE
000001624 041__ $$aeng
000001624 245__ $$aOptimal ordering policy for newsvendor models with bidirectional changes in demand using expert judgment
000001624 260__ $$c2016
000001624 269__ $$a2016-09
000001624 300__ $$a23 p.
000001624 506__ $$avisible
000001624 520__ $$aDemand forecast is a critical determinant of order quantity under newsvendor problem (NVP) framework and warrants major revision in the event of changing circumstances or happening of some unforeseen events having potential to alter the demand. Retailers of single period products such as fashion apparels are required to pass their orders far ahead of selling seasons and apply preseason two-stage ordering procedure, where an initial order (first stage) is followed by a final confirmed order (second stage). The enterprise forecasting experts may get additional information related to the occurrence of some unforeseen events that may significantly impact the initial demand estimation. In this paper, the potential impact of such events is combined using a weight factor to obtain revised demand forecasts. In this context, this paper develops inventory models under NVP framework to determine the optimal order quantity and weight factor on the basis of revised forecasts. Considering the bidirectional changes in demand, we formulate a unique objective function that operates as a profit maximization function for the positive demand adjustment and turns into a cost minimization function for the negative demand adjustment. Models developed without constraints at first instance are extended subsequently by incorporating constraints of budget limits, storage space capacity and required service level. Near closed form expressions of decision variables for four demand distributions with multiplicative demand forms are presented. The results demonstrate economic benefits of using revised demand through models developed, negative impact of constraints, and role of demand distribution entropy in determining the order size and expected profit.$$9eng
000001624 592__ $$aHEG - Genève
000001624 592__ $$bCRAG - Centre de Recherche Appliquée en Gestion
000001624 592__ $$cEconomie et Services
000001624 6531_ $$ainventory$$9eng
000001624 6531_ $$anewsvendor problem$$9eng
000001624 6531_ $$aexpert judgment$$9eng
000001624 6531_ $$ademand forecasting$$9eng
000001624 6531_ $$acontextual information$$9eng
000001624 655__ $$ascientifique
000001624 6531_ $$aconstraints$$9eng
000001624 65017 $$aEconomie/gestion
000001624 700__ $$uShailesh J. Mehta School of Management, Indian Institute of Technology Bombay, Powai, Mumbai-400076, India$$aNagare, Madhukar
000001624 700__ $$uShailesh J. Mehta School of Management, Indian Institute of Technology Bombay, Powai, Mumbai-400076, India$$aDutta, Pankaj
000001624 700__ $$aCheikhrouhou, Naoufel$$uHaute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale
000001624 773__ $$tOpsearch$$g2016, vol. 53, no. 3, pp. 620–647
000001624 8564_ $$uhttps://hesso.tind.io/record/1624/files/cheikhrouhou_optimalorderingpolicy_2016.pdf$$s662497
000001624 909CO $$pHEG_GE_ARTICLES_SCIENTIFIQUES_ECO$$pHEG_GE_ARTICLES_SCIENTIFIQUES$$pGLOBAL_SET$$ooai:hesso.tind.io:1624
000001624 906__ $$aGREEN
000001624 950__ $$aI2
000001624 980__ $$ascientifique