Deploying AI in Supply Chain Management: Key Methods - Edvantis

Executive Summary

Artificial intelligence (AI) has made its way into every business process, with supply chain management being no exception. Forty-one percent of business leaders are making AI a key part of their supply chain innovation strategy, with a third already leveraging it for data visibility, 29% for quality control, and 26% for labor optimization. An impressive 72% of leaders are also deploying Generative AI in their supply chains. That said, the adoption of AI in supply chain management has been a steep climb. Primarily because it requires transformations in other areas like cloud infrastructure, data management, and cybersecurity, leading to lower ROI. So how and where can SCMs realize ‘quick wins’ from AI investments? Our bet is on deploying gen AI with retrieval-augmented generation, plus being precise with ML use case prioritization. Retrieval-augmented generation (RAG) is a generative AI framework that combines natural language generation with information retrieval to produce more accurate and context-aware responses. Instead of relying solely on its pre-trained “knowledge”, a large language model (LLM) actively searches a knowledge base or external documents to retrieve relevant information in real time. The retrieved content is then used to ground the generated output, improving factual accuracy and reducing model hallucinations.