Big Data and Food Loss Mitigation in the Supply Chain

Investigator:
Timothy J. Richards, Marvin and June Morrison Professor of Agribusiness, Arizona State University

Overview:
The amount of fresh food lost or wasted between the farm and retail levels results in a substantial loss in economic value. Retailers reject, discard, or donate some 19.5 million metric tons of edible, perishable food products every year, representing a considerable loss of economic, social, and ecological value (Buzby and Hyman 2012). Food waste at the retail level is created by, among other things, retailers’ minimum quality standards, over-purchasing by retailers to avoid costly stockouts, and by retailing strategies that induce unplanned purchases. Our proposed research aims to develop a set of empirical methods, applied to data on retail food purchase-and-sale transactions, that can help reduce the amount of food loss and food waste in the retail-consumer market. Our supporting objectives are to design a theoretical model of quality-based price discrimination, to empirically examine quality-assortment strategies for fresh produce retailers and the implications for retail food-loss, to develop a set of machine-learning algorithms to assist retailers in better matching demand-flow to wholesale purchases, and to analyze online and offline purchase patterns in order to better understand the implications of online purchasing for household waste. By focusing new analytical techniques specifically on the problem of food waste, our research seeks to advance the state of knowledge on how managing fresh-food supply chains can be both more sustainable, and profitable, for all stakeholders.