About

What is AI for Supply Chain?

AI for Supply Chain is a searchable directory of documented AI implementations in supply chain. Each record captures the use case, technology when classified, outcomes reported by the cited source, and the source link. Supply chain directors, VPs of operations, and logistics managers can inspect the details behind an implementation before evaluating a vendor.

How the directory works

Every case study goes through structured collection and automated checks before it is published. The checks cover required fields and source availability; they are not a human fact-check or an independent audit. We do not generate synthetic results, and reported claims remain attributed to their cited source.

Sources

Each entry retains the cited source name and link recorded during collection. Sources may include vendor case studies, industry publications, public filings, conference talks, and direct submissions. The directory does not assign a source type unless that provenance is explicitly recorded.

• Source identity — the recorded source name and a direct link to the cited page.
• Source timing — a publication date only when it exists in the record, plus a separately labeled source-link check date.
• Submission attribution — contributed records identify the submitting organization when that information is available.

Record labels

Each case study is assigned one of three quality levels:

• Verified — reserved for records whose status explicitly records a human review. Completeness and a reachable source alone do not earn this label.
• Contributed — submitted by a vendor or supply chain team and published with attribution; any additional review state is recorded separately.
• Scraped — programmatically collected from public sources. It may have shorter content sections.

Classification

Case studies use four comparison dimensions where the record supports them: industry (10 categories), use case type (12 categories), AI technology when available (10 categories), and company size when available. A missing technology stays marked as not available in the record rather than being inferred. The records can be compared by documented technology and use case without filling gaps in the source.

Editorial Standards

• Metrics are reported exactly as published by the source; we do not round, extrapolate, or reinterpret results.
• Every published case study links to the cited source recorded for that entry.
• We do not label a source primary, independent, or vendor-published unless that provenance has been explicitly classified.
• Records without quantifiable results are still included if they document an implementation with a named organization.

About Us

We maintain this directory for people evaluating AI in supply chain. Our background spans supply chain operations, data engineering, and logistics technology deployment.

Questions, corrections, or a case study to share? Contact us.