Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting
This addresses food waste reduction for retailers dealing with perishable goods, though it appears incremental as it builds on existing federated learning methods.
The study tackled the problem of data privacy hindering collaboration for demand forecasting in grocery retail by applying a blockchain-based federated learning model, which achieved performance nearly equivalent to ideal data sharing and significantly reduced waste compared to isolated models.
Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, trained collaboratively across multiple retailers without direct data sharing. Our preliminary results show that FL models have performance almost equivalent to the ideal setting in which parties share data with each other, and are notably superior to models built by individual parties without sharing data, cutting waste and boosting efficiency.