Generative AI in Supply Chain Management

Abstract

A new age of creativity and efficiency is ushered in by the integration of Generative Artificial Intelligence (AI) into supply chain management. This in-depth study examines the diverse effects of generative artificial intelligence on supply chain operations, including risk management, inventory optimization, procurement, logistics, and more. Given the predictive capacity of generative AI, traditional methods have been completely modified, enabling companies to anticipate demand, maximize inventory, and expedite procurement procedures with previously unheard-of accuracy. Real-time adaptation is made possible by its dynamic decision-making skills, which also help to promote resilience against interruptions and enable proactive reactions to changing market conditions. However, there are some challenges in implementing generative AI in supply chains. Obstacles requiring strategic navigation and organizational preparedness include skill gaps, ethical considerations, scalability issues, and data integration complexity. Future directions for generative artificial intelligence in supply networks are extremely promising. Substantial improvements are expected to be driven by advances in explainable AI, predictive analytics, seamless integration, and ethical frameworks. Redefining supply chain models could be facilitated by autonomous supply chains, adaptive resilience to disturbances, and increased transparency in decision-making.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 33 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.