A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks

Socioecon Plann Sci. 2023 Feb:85:101417. doi: 10.1016/j.seps.2022.101417. Epub 2022 Aug 19.

Abstract

The unexpected emergence of the COVID-19 pandemic has changed how grocery shopping is done. The grocery retail stores need to ensure hygiene, quality, and safety concerns in-store shopping by providing "no-touch" smart packaging solutions for agri-food products. The benefit of smart packaging is to inform consumers about the freshness level of a packaged product without having direct contact. This paper proposes a data-driven decision support system that uses smart packaging as a smart product-service system to manage the sustainable grocery store supply chain during outbreaks to prevent food waste. The proposed model dynamically updates the price of a packaged perishable product depending on freshness level while reducing food waste and the number of rejected customers and maximising profit by increasing the inventory turnover rate of grocery stores. The model was tested on a hypothetical but realistic case study of a single product. The results of this study showed that stock capacities, freshness discount rate, freshness period, and quantity discounts significantly affect the performance of a grocery store supply chain during outbreaks.

Keywords: ANOVA, Analysis of Variance; COVID-19; Data-driven decision support system; Dynamic pricing; Grocery store supply chain; IoT, Internet of Things; Packaged produce; RFID, Radio Frequency Identification; Simulation; Smart packaging; Smart product-service system; TTI, Time-Temperature Indicator; smart PSS, Smart Product-Service Systems.