Data-Driven Mechanisms for a Newsvendor Problem: A Case Study

Reducing food waste is paramount for a sustainable future as its implications are important to achievingsustainable development goals set by the United Nations. Inmanyindustrygroups,thepublicawareness ofreducingfoodwastethatmaypotentiallyemergealongfirms'operationshasgrown.IntheeraofBigData, one of the most pursued exercises of this escalating attention on reducing food waste is to utilize artificial intelligence techniques to incorporate sustainability concerns into the decision framework. Many firms embrace machine learning methods to build effective decision mechanisms that help make efficientand sustainable decisions. In this study, we analyze the impact of blending machine learning approaches with demand forecasting and order quantity decisions for a firm operating in a setting where the market demand is random, and the demand structure is not observable to the firm. The performance of the methodology is evaluated on sunflowerseed demand datatakenfromTad & imath;mcompany. Ourresultssuggest that thejoint consideration of forecasting and ordering decisions using the quantile regression approach can lead the firm to decrease its operational cost by 8,11% on average