About

What is AI for Supply Chain?

AI for Supply Chain is an open evidence directory 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 when classified, and any measurable results reported by the cited source — so that supply chain directors, VPs of operations, and logistics managers can inspect the evidence and its source rather than rely on an uncited summary.

Our Methodology

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

Data Sources

Every entry retains the cited source name and link recorded during collection. The current data model does not classify source type, so the directory does not label individual sources vendor-published, independent, or primary. Instead, it exposes the evidence fields users can verify directly:

• Source identity — the recorded source name and a direct link to the cited page.
• Source timing — a source publication date only when it exists in the record, plus a separately labelled source-link check date.
• Community submissions — case studies can be submitted directly by vendors and supply chain teams, then published with attribution only after the applicable evidence checks.

Quality Levels

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. Contains structured data but may have shorter content sections.

Taxonomy & 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. This standardized taxonomy enables cross-comparison across implementations and helps surface patterns — for example, where documented technologies and use cases cluster within specific supply chain functions.

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.
• 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.