AI-Augmented Decision Systems for Sustainable Supply Chain Management

Abstract

Environmental concerns, governmental restrictions, and customer demands for sustainable operations make supply chain sustainability more important than ever. Data and complexity are growing in supply networks. AI may improve production and distribution, cut carbon emissions, and save energy. Study suggests AI-augmented decision systems may increase supply chain sustainability. AI can help supply chains find affordable, green solutions. We use machine learning, predictive analytics, and sophisticated optimisation. WE study how AI-powered resource allocation, real-time environmental monitoring, and logistics decrease waste and pollution. 

This study shows how AI can detect supply chain challenges and modify solutions using historical and real-time data. AI estimates production, transportation, storage, and usage carbon footprints. The research shows how AI-powered optimisation may improve production and distribution energy efficiency. Renewable energy choices may benefit from prediction models. 

Various AI resource optimisation algorithms are investigated. RL, deep learning, and hybrid models provide flexible supply chain management. Models are influenced by unforeseen demand, difficulties, or availability. More sustainability and supply chain resilience. It also analyses how AI and digital twins may help businesses assess, predict, and implement sustainability. AI can enhance procurement, manufacturing, and route optimisation using real-time data analysis. Transport emissions and material utilisation may decrease. 

AI-augmented sustainable supply chain management decision systems are promising yet limited. Data availability and quality, AI implementation in diverse operating systems, and high computing demands for processing massive real-time data are concerns. The study raises ethical considerations regarding data privacy and AI-driven decision transparency, which might damage stakeholder confidence and regulatory compliance. Data harmonisation, scalable AI infrastructure, and unambiguous rules may solve these challenges. 

Examples show AI's supply chain sustainability benefits. These case studies show how AI systems optimised resources, lowered carbon footprints, and supported green automotive, consumer, and logistics supply chains. Report: AI advances the environment and helps firms meet Paris Agreement and SDG goals. 

This paper advises enterprises, academics, and governments implement supply chain AI together. Tech developers, logistics providers, and regulators must coordinate large-scale AI challenges. Future AI supply chain management breakthroughs are mentioned. Investigate AI-integrated blockchain for emissions monitoring, sophisticated machine learning for circular economy models, and pandemic and geopolitical environmental effect mitigation.