Multiple global manufacturers prevent equipment downtime with predictive maintenance platform
A large, unnamed global manufacturer faced frequent unplanned equipment downtime due to fragmented and siloed data systems that obscured real-time insights. The company's vast IoT sensor network and extensive maintenance records were not effectively integrated, resulting in reactive rather than proactive maintenance approaches. By collaborating with Mutually Human and deploying Microsoft Fabric, the manufacturer centralized its data in a lakehouse architecture (OneLake), unified sources across ERP, sensors, and logs, and automated pipeline refreshes with Data Factory. Azure Machine Learning models, trained on years of failure events, delivered daily risk scores and live predictions for over 200 critical assets. Maintenance teams could act quickly using insights visualized on Power BI dashboards. Over a 90-day pilot, the centralized system enabled timely, actionable alerts, significantly reducing costly downtime. The platform now operates across multiple plants, transforming asset management, boosting efficiency, and supporting broader digital factory initiatives.
- Organization
- Multiple global manufacturers
- Industry
- Manufacturing
- Location
- Global
- Published
- August 2025
Reported outcomes
Strategic outcomes
Primary read
Use case focus
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- 1Predictive Maintenance for Industrial Equipment
- Siloed IoT, maintenance, and ERP data created disjointed views of equipment health.
- Delays in manual data preparation prevented timely asset performance analysis.
- Reactive maintenance led to high costs and unpredictable equipment failures.
- Maintenance teams lacked real-time insights and predictive risk scoring.
- Adopted Microsoft Fabric to centralize and unify all plant data.
- Used OneLake lakehouse for integrated data storage and governance.
- Automated data ingest and transformation via Data Factory pipelines.
- Developed and trained Azure Machine Learning models to predict equipment failures.
- Deployed Power BI dashboards for real-time, actionable asset health insights.
- Enabled predictive maintenance and real-time analytics for 200+ assets.
- Reduced downtime and improved maintenance planning across multiple facilities.
- Accelerated data pipeline refresh from weeks to minutes/hours.
- Enhanced asset lifecycle insights and operational efficiency.
Architecture
The architecture unified disparate sources (IoT sensor, ERP, logs) in a Microsoft Fabric lakehouse (OneLake), automated data refreshes with Data Factory, and fed custom Azure Machine Learning models to score risk. Power BI dashboards displayed predictions and alerts for over 200 assets, providing near real-time insight to maintenance staff.
Implementation partners1
Sources & evidence1
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