Results / 17% Reduction in Unplanned Downtime·Case study

17% Reduction in Unplanned Downtime Across All Global Facilities

$200M+ US-Based Manufacturer

17% Reduction in Unplanned Downtime Across All Global Facilities (Operating Leverage)
What we did:
AI Engineering Data Engineering NLQ Product Management Systems Integration
Industry: Manufacturing

Business Situation

A US-based manufacturer operating extensive IoT sensor networks across its global production facilities, with senior maintenance technicians holding decades of domain expertise held largely in their heads.

The Challenge

Sensor data was accumulating faster than the organization could act on it, and the technicians who understood the equipment best were nearing retirement. Left unaddressed, that expertise would leave with them while maintenance stayed reactive.

Why It Matters

Compose built an integrated GenAI copilot on the client’s existing IoT infrastructure: a predictive engine detecting anomalies and forecasting failures from live sensor data, a natural-language interface for querying any instrumented asset, and a knowledge system rendering senior-technician expertise as repair guidance.

What Compose Delivered

  • Discovery audit of existing IoT infrastructure and data flows
  • Predictive analytics engine reading sensor data in real time
  • Anomaly detection flagging equipment issues before failure
  • Natural-language query interface for asset status and trends
  • Knowledge capture turning technician expertise into repair steps
  • Integration with enterprise applications for unified visibility
  • [Ongoing model refinement and performance monitoring post-launch]

Outcome

17% reduction in measured unplanned downtime across all global facilities. Expert repair guidance now reaches every technician.

Technicians receive step-by-step repair guidance generated from captured senior expertise, and managers see real-time analytics across every instrumented asset in the estate, not a sampled subset.

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