§ 40 — Use Cases

Use Cases

Pattern-level analyses of what works and what fails when supply-chain AI is applied to specific functions and techniques (demand forecasting, inventory optimization, procurement automation, warehouse computer vision), synthesized from the Post-Mortems and corroborated by cited external research (e.g., MIT CTL, SupplyChainBrain, peer-reviewed studies) rather than recycled unsourced industry statistics. Every claim carries a named source and date. Distinct from Post-Mortems (single-deployment accounts) and Readiness (task checklists): this section answers whether a category of AI application actually delivers, and under what conditions it fails, at a pattern level.

What Peter Thiel's AI Prediction Means for Supply Chain Jobs

· Machine Learning Forecasting

Predictive Analytics in Supply Chain: Why 73% of Projects Fail and How to Avoid the 5 Root Causes

· Machine Learning

AI in Procurement: Which Use Cases Deliver Measurable ROI?

· machine learning, natural language processing, generative AI, agentic AI

AI Sales Forecasting vs. Traditional Methods: What the Benchmarks Actually Say

· Machine learning

The Supply Chain AI Companies Landscape: A Buyer's Guide to Every Category

· Machine learning, predictive analytics, generative AI

Why AI Agent Pilots Fail in Supply Chain — and How to Build One That Works

· Agentic AI