AI-Based Sustainable Supply Chain Analyzer

Abstract

Use of artificial intelligence-based solutions in managing supply chains has opened up many new opportunities for improving efficiency, resilience, and sustainability. Nevertheless, with more and more influence of AI-based analysis on the decision-making process within supply chain management, another problem has emerged, namely the possibility of producing incorrect, partial, or misguiding data because of the absence of cohesiveness or grounding of such data. In sustainable supply chain applications, where decisions have a direct impact on environmental sustainability, social responsibility, and economic performance, inaccuracies can result in suboptimal or even detrimental decisions. Conventional supply chain analytics may be based on static data sets, outdated assumptions, or singular optimization goals, making them less adaptable to dynamic sustainability constraints. In an attempt to overcome these challenges, this paper proposes an AI- based sustainable supply chain analyzer that dynamically contextualizes analytical results to be based on verified, up-to-date, and multi-source supply chain data. The proposed system combines intelligent retrieval techniques with analytical reasoning to ensure that sustainability analysis and suggestions are transparent, evidence-based, and contextually informed.