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, overpurchasing by retailers to avoid costly stockouts, by retailers setting minimum quality standards that lead to excessive amounts of "ugly produce," and by retailing strategies that induce impulse, or unplanned, purchases that are ultimately not consumed. Indeed, the rise of "sharing economy" firms that sell produce that is either below retail standards, or simply produced in excess of contractual obligations suggests a fundamental failure in the retail market for perishable food (Richards and Hamilton 2018). Our proposed research aims to apply new econometric techniques, developed in the machine learning literature, to three unique data sets ("big data") collected by retailers and independent data vendors to better forecast food inventory and replenishment methods, to develop insight into new quality-assortment strategies for fresh produce retailers, and to suggest how online retailing platforms can be designed to substantially reduce food waste in the supply chain. By focusing new analytical techniques specifically on the problem of food waste, our research promises to advance the state of knowledge on how fresh-food supply chains can be reconfigured to be both more sustainable, and profitable, for all stakeholders.
|Effective start/end date||6/1/19 → 5/31/21|
- USDA: National Institute of Food and Agriculture (NIFA): $499,999.00
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