Maximize uptime, reduce risk at Amgen
Get context related insights for Amgen's facilities powered by cerebre's leading industrial knowledge graphs.

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What we heard from you
From our recent conversations, we understand that:
- Unplanned downtime due to equipment failure, human intervention, and inefficient changeovers is a major concern.
- Critical equipment (e.g., freeze dryers, vacuum pumps) can cause batch loss or quality deviations if not perfectly reliable.
- Data is fragmented across Maximo (CMMS), P-Historian, IoT devices, process control systems, and document management tools.
- Investigating non-conformance events (NCs) and preventative maintenance planning is difficult due to a lack of integrated context.
- There is a significant opportunity to optimize reliability and scale visibility into asset health and process interdependencies.

Amgen current challenges
Amgen is facing systemic barriers to operational reliability:
- High-value products at risk from fragile, aging, or hard to monitor equipment.
- Human interventions in sterile filling and packaging lines introduce variability.
- Systemic issues like glass breakage, line stoppages, or sensor alarms aren't easily traced to root causes.
- Knowledge is siloed between Engineering, Ops, and Quality, slowing down resolution.
- Your current CMMS (Maximo), and IoT systems aren’t contextually connected, making downtime mitigation and planning reactive.

What cerebre brings to the table
cerebre connects the dots across systems, processes, and people with a semantic, graph-based platform that creates shared understanding and proactive visibility:
- A unified knowledge graph of your plant: assets, operations, documents, events, and sensors, fully contextualized.
- Integration across Maximo, SAP EAM, P-Historian, IoT, DeltaV, Viva, and PLM systems.
- Predictive triggers based on asset health, sensor drift, or weak signals—before they escalate to NCs.
- Asset-centric views for Maintenance, Engineering, and Quality teams to collaborate using the same operational truth.
- Foundation for AI agents to proactively reason over data (e.g., “Is there any open work order on this pump showing signs of failure?”)

Related resources
No context, no intelligence –
The case for a plant data model
Why AI fails without structure and how a data model gives your plant the context it needs.
Digitizing the plant –
The cerebre presentation
See how cerebre helps you transform plant schematics into searchable, smart assets.
We enable your AI –
An overview document
Learn how PlantGraph powers AI with data that’s contextual, connected, and usable.
Example use case for Amgen
Before cerebre
- A critical freeze dryer’s vacuum pump degrades mid-batch
- The issue isn’t caught until there’s a product loss
- Downtime triggers a CAPA investigation, and weeks are lost
After cerebre
- Pump sensor readings start to drift from baseline
- cerebre’s knowledge graph flags no active work order
- System recommends inspection before batch start
- Maintenance is completed proactively, batch saved
Let’s co-design a demo
We’d like to show Amgen how cerebre can:
- Demonstrate a working knowledge graph model of your environment
- Build a realistic reliability use case from your asset and process landscape
Book the next meeting to explore the platform and see how this could look for Amgen