Deep Learning Based Purchase Forecasting for Food Producer-Retailer Team Merchandising

Expired foods turning into waste has always been an important issue. In Taiwan, more than 36,000 metric tons of unopened expired food, worth more than $130 million, are thrown away from retail stores as waste each year. Insufficient inventory results in the loss of business prospects for retailers, whilst excessive inventory results in abandoned merchandise. Foods with a short shelf life are particularly vulnerable. Typically, food producer and retailer would form team merchandising (MD). The team MD mechanism is responsible for ensuring safety and quality, not for forecasting demand. This study uses artificial neural networks (ANNs) to analyze sales data to forecasting purchase volume in response to changes in store environment, weather, events, and consumer attributes. The study object is a sort of cream puff with a short shelf life cobranded by a retailer. According to the experimental results, the adopted proposed model in this study effectively reduces the error in purchasing; the mean-square percentage error (MSPE) of the forecast values is less than 6%. The importance of this study is on promoting the team MD’s green energy management capabilities in food production and verifiably achieving the goal of environmental sustainability.