A Review on machine learning and Artificial Intelligence in procurement: building resilient supply chains for climate and economic priorities

Abstract

With global supply chain facing unprecedented disruption from climate change and economic uncertainty driving a shift in procurement strategies from previously cost-focused decision-making toward sustainability and resilience, this review examines how Artificial Intelligence (AI) and Machine Learning (ML) can provide transformative solutions for building robust and adaptive supply chains that align with both climate and economic priorities. It discusses other key applications like automated sourcing solutions and contract administration, supplier selection model, multi-tier risk assessment with intelligent scoring models, demand forecasting and inventory optimization with predictive analytics. This paper also discusses the work of AI/ML in enhancing traceability and visibility, the application of digital twins and the circular economy to procurement to actively manage disruptions. The results indicate that the application of technology is significant in respect to cost effectiveness as opposed to corporate climate objectives, carbon reduction and environmental and social governance (ESG). Nevertheless, to effectively operationalize AI/ML, the resultant implementation-level challenges, such as human oversight, model explainability, and data quality, must be surmounted.