Beyond Mere Automation: A Techno-functional Framework for Gen AI in Supply Chain Operations

Sreyoshi Bhaduri, Pavan Nithin Mullapudi, Shannon Dietrich, Scott DeWaters, Raj Ratan, Brajesh Kashyap, Rajanikanth Mandava, Lu Guo, Hungjen Wang, Vykunth Ashok, Abhilasha Katariya, Rohit Malshe, Ankush Pole, Arkajit Rakshit

Keywords: Generative AI, Supply Chain, Retail

TL;DR: This paper looks beyond hype to deliver a techno-functional roadmap for Gen AI in retail supply chain: mapping real-world applications, identifying gaps, and proposing a scalable GenAI architecture grounded in human workflows, not just model metrics.

Abstract: As Generative AI (Gen-AI) continues to evolve rapidly, its potential to transform supply chain operations remains largely unexplored. Narrowing in on retail supply chain, this paper presents a taxonomy diagram that categorizes trends in Gen-AI adoption across various functions thereby mapping current Gen-AI capabilities and identifying immediate opportunities and potential challenges. We identify several key patterns in Gen-AI integration, including the automation of routine cognitive tasks, and enhancement of human decision-making capabilities. We posit that while Gen-AI shows immense promise in improving supply chain efficiency and resilience, successful implementation requires careful consideration of existing workflows, user capabilities, and organizational readiness. Finally, we present a cohesive vision for scaling Gen-AI in Supply Chain operations. Ultimately, this position paper provides insights for both practitioners looking to implement Gen-AI solutions and researchers exploring the future of AI in and for supply chain management.

Submission Number: 20