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About

AI for Supply Chain is the most complete searchable database of real AI implementations in supply chain. Built for supply chain directors, VP of operations, and logistics managers evaluating AI adoption.

What is AI for Supply Chain?

AI for Supply Chain is the largest open database of real AI implementations in supply chain. We catalog what logistics providers, manufacturers, and retailers have actually done with AI — the use case, the technology, and the measurable results — so that supply chain directors, VP of operations, and logistics managers can make informed decisions based on evidence, not vendor marketing.

Our Methodology

Every case study in our database goes through a structured collection and verification process. We do not fabricate data, generate synthetic results, or accept unverified claims.

Data Sources

Case studies are collected from three categories of sources:

  • Vendor-published case studies — documented implementations from supply chain AI providers such as FourKites, Blue Yonder, project44, Kinaxis, and others.
  • Independent research — reports from industry publications like Supply Chain Dive, Gartner Supply Chain, and McKinsey Operations that document specific deployments.
  • Community contributions — case studies submitted directly by vendors and supply chain teams, verified by our editorial team before publication.

Quality Levels

Each case study is assigned one of three quality levels:

  • Verified — complete content with at least two quantified metrics, full taxonomy classification (industry, use case, AI technology), and a traceable source.
  • Contributed — submitted by a vendor or supply chain team, reviewed by our team, and published with attribution.
  • Scraped — programmatically collected from public sources. Contains structured data but may have shorter content sections.

Taxonomy & Classification

Every case study is classified across four dimensions: industry (10 categories), use case type (12 categories), AI technology (10 categories), and company size. This standardized taxonomy enables cross-comparison across implementations and helps surface patterns — for example, which AI technologies deliver the strongest ROI for specific supply chain functions.

Editorial Standards

  • Metrics are reported exactly as published by the source — we do not round, extrapolate, or reinterpret results.
  • Every case study links back to its original source when available.
  • We distinguish between vendor-reported results and independently verified data.
  • Case studies without quantifiable results are still included if they document a real implementation with a named organization.

About Us

We are a small team focused on making AI adoption in supply chain more transparent and evidence-based. Our background spans supply chain operations, data engineering, and logistics technology deployment.

Have questions, corrections, or a case study to share? Feel free to reach out.