Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting

TL;DR

This paper proposes a Blockchain-based Federated Learning approach for demand forecasting in retail, enabling collaborative predictions without data sharing, thereby reducing waste and improving efficiency.

Contribution

It introduces a novel Blockchain-enabled FL framework for retail demand forecasting, enhancing privacy-preserving collaboration in supply chain management.

Findings

FL models perform nearly as well as data-sharing models

FL significantly reduces waste compared to isolated models

Blockchain ensures secure and transparent collaboration

Abstract

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.