Decision AI for Supply Chain Excellence

The Customer

A Fortune 500 Pharmaceutical Manufacturer

A Fortune 500 global pharmaceutical manufacturer with formulation and API sites across 5 countries, supplying 200+ SKU market variants into regulated and institutional tender markets.

The client supplies the US, EU and other ICH aligned jurisdictions, as well as institutional tender markets. Supply decisions run across SAP ERP, demand planning, warehouse and transport systems and distributor sell out data, with a vendor base qualified under DMF/CEP. Planning, procurement, manufacturing, QA and logistics each own a slice of the network: a footprint representative of most large pharma supply chains today.

In pharma, a supply failure is not just a cost. An expired batch is a binary write off, a stock out can trigger tender penalties, and a supplier switch is gated by filings and audit history.

The Ground Reality

Why Pharma is Different for the Data Office

Pharma data must carry rules and evidence.

There was no shared ontology between the business and data teams. SKU, batch, site and supplier meant different things across ERP, planning, WMS, TMS and quality systems. Tool sprawl made it worse: dashboards, spreadsheets and one off studies each held a slice, and none explained why.

Getting data AI ready by hand was estimated at 12 months, based on the annual CDO plan, with ongoing data transformation initiatives to de risk along the way. And AI noise was real. Every business leader brought up a new agentic tool that promised to solve a particular use case at POC, but lacked governance, infrastructure and provenance. Controlling those initiatives and educating business teams on the risks and rewards was a continued challenge.

The Decisioning Challenge

AI and Adoption Challenges

Trust in AI, skepticism and change management had hit a dead end for internal AI initiatives so far.

For the AI team, the problems were structural. AI hallucinates without context. It doesn't have pharma domain and compliance intelligence. And existing intelligence doesn't self learn or compound.

For adoption, the bar was higher still. Process teams expected sharp signals with the why and the next best action. They were skeptical of AI's ability to hold tribal knowledge, and every answer had to be trusted and explainable. Bringing the data to be AI ready to the business value expectation seemed like a 12 month project at this scale.

The Causal Chain

From Inputs to OTIF Failure, Node by Node

No single node caused the failure. It was the combination, exactly the pattern a causal chain view is built to catch.

The Architecture

Enterprise Brain for your Supply Chain

On the way in, the brain reads planning and transaction systems (SAP ERP, demand planning, WMS and TMS), distributor and CFA sell out, supplier and compliance records including vendor CoAs, DMF/CEP and audit, and the tribal knowledge held by planners, procurement, logistics, distribution and CFA teams. An adoption program captured that knowledge through process interviews, SOPs and manuals, because 60% of the knowledge sits with people.

Ontology agents build the data, process, domain and decision layers autonomously, and the client's Data and AI team reviews, governs and judges them in the Ontology Studio. On the way out, decisions arrive in the flow of work: AI reasoning, blindspots surfaced 24x7, AI tracking boards and AI reports to collaborate on, each with lineage on every value.

One connected, self learning supply chain brain, governed by the customer's own Data and AI teams.

Time to Value

Each Decision Went Live Faster Than the One Before

An AI ready foundation in weeks, not a year. Every new use case is a decision agent on shared context rather than a new build.

Built by hand, the foundation was estimated at 12 months with a 40 person team. With DecisionX, it reached a governed, auditable go live in 45 days. The first decision, OTIF root cause, went live on that foundation, and the next two were added on the same ontology a week apart. ERP and planning suites stay in place; DecisionX reads from them.

The Outcome

Real Impact at Scale

One decision layer in place of many disconnected tools, with every answer traced to source.

Decision dashboards that showed what happened but never why were retired, replaced by chat boards that give reasons. Spreadsheet S&OP models were replaced by governed, traceable models, and control tower exceptions by continuous causal signals. Separate risk and cost reports were merged into one shared view, saving about 1.5 Mn$ a year in licences and effort. Answers reached 88% contextual accuracy against 12% for a generic LLM, with 100% traced to source and a 9.2/10 user CSAT.