In the world of manufacturing, predictive maintenance holds big promise. The idea is simple: if you can detect equipment issues before they escalate, you can prevent downtime, reduce costs, and plan smarter. But for many manufacturers, that promise feels just out of reach—not because of a lack of technology, but because of messy, fragmented data.
One global manufacturer found itself in exactly that situation. Despite having years of IoT sensor data, maintenance logs, and ERP records, they were still stuck in reactive mode—scrambling to fix machines after failures instead of preventing them. Their story shows that predictive maintenance doesn’t start with machine learning models. It starts with the right data foundation.
The Hidden Obstacle: Data Silos and Delays
The manufacturer had sensors installed across hundreds of assets and collected maintenance data from multiple plants. But none of it worked together. Data lived in disconnected systems—sensor feeds in one place, inventory data in another, and operator shift reports in spreadsheets.
This lack of integration made it nearly impossible to analyze equipment performance in real time. Data scientists spent weeks manually preparing datasets. Maintenance teams relied on monthly reports. By the time problems were visible, it was often too late.
What they needed wasn’t more data—it was a way to unify it, make it trustworthy, and deliver insights fast enough to act.
A Platform Built for Predictive: Microsoft Fabric
To solve this, the company partnered with Mutually Human and adopted Microsoft Fabric, a unified data and analytics platform. The goal was to consolidate fragmented systems, automate data preparation, and enable predictive analytics that could scale across multiple plants.
They started with a 90-day proof of concept at one facility. Using Fabric’s lakehouse architecture and built-in governance, they connected over 15 TB of data—from real-time sensor readings to historical maintenance logs and quality metrics—into a centralized repository using OneLake.
Automated data pipelines built in Data Factory refreshed insights as frequently as needed, from nightly updates to near real-time. Within Azure Machine Learning, models trained on five years of failure history began generating daily risk scores for more than 200 assets—complete with explainability metrics to help teams understand the “why” behind each prediction.
Interactive Power BI dashboards made it all usable, surfacing current asset health, predicted failure timelines, and real-time alerts directly into existing workflows.
And thanks to OneSecurity, the company could confidently scale the solution—ensuring secure access, auditability, and compliance across the enterprise.
What Changed: From Reactive to Ready
After validating the approach, the company expanded the solution to four more facilities over the next four months. The results were immediate and measurable:
- 32% Reduction in Unplanned Downtime:Early detection of equipment issues saved over 200 production hours annually across multiple plants. The result: measurable improvements in OEE performance and on-time delivery.
- 18% Decrease in Maintenance Costs:Early risk insights and real-time equipment visibility enabled better planning—reducing emergency repairs, overtime labor, and the need for rush part orders.
- 86% Reduction in Data Preparation Time:Model-ready datasets were automatically generated through Fabric pipelines—cutting prep time from three weeks to under three days.
- Faster, Scalable Deployment:The initial proof of concept was completed in 90 days. The solution was then rolled out to four additional plants over the next four months, with consistent results.
- Improved User Adoption and Trust:Maintenance and operations teams were involved early, received hands-on training, and now rely on dashboard insights and model alerts for daily planning.
- Stronger Governance and Lineage:Fabric’s OneLake and OneSecurity enabled centralized control, data traceability, and auditability across all sites—boosting IT confidence and supporting future enterprise AI efforts.
The Takeaway
Predictive maintenance only works when your data does. By using Microsoft Fabric to unify, govern, and activate their data, this manufacturer finally moved from reacting to predicting—and built a foundation they can grow on.
Ready to make predictive maintenance a reality? Let’s talk about how Mutually Human can help you get started with Microsoft Fabric.
