Supply chain software has spent the last decade fragmenting. What was once a monolithic SAP or Oracle stack has splintered into dozens of best-in-class point solutions: Coupa for procurement, Kinaxis for S&OP, separate systems for demand planning, warehouse management, and everything in between. Each tool is excellent at its job. None of them talk to each other well.
This is the problem Andy Kohm, CEO of SCIP (Supply Chain Intelligence Platform), set out to solve. A biomedical engineer by training, Kohm first encountered the pain of fragmented data while sourcing vendors for R&D projects. The information existed somewhere in the organization, but it was inaccessible, outdated, or trapped in systems he couldn’t reach. That experience became SCIP’s founding insight: the gap in supply chain technology isn’t another application. It’s the intelligence layer that sits across all of them.
SCIP ingests data from ERPs, PLMs, warehouses, and procurement systems, then aligns it against roughly 200 universal supply chain attributes. The platform doesn’t replace existing tools. It activates the data already living inside them. Where AI enters the picture, Kohm is deliberately pragmatic. Agents handle data cleansing, manufacturer matching, and part-number resolution, but they operate behind the curtain. The customer never builds or manages an agent. AI creates the rules; deterministic logic applies them at scale.
The business model is equally deliberate. No seat licensing. Four modules (visibility, data health, optimization, and risk scoring), priced by company size. The logic is simple: more users means more interaction, more feedback, and a stronger proprietary data layer that compounds across the customer base. That data, anonymized and enriched over time, is the moat. Software can be rebuilt. The accumulated context of millions of cross-customer data points cannot.
