Turning Machine Data Into Uptime for a Manufacturer

The manufacturer had done the hard part of instrumenting its production lines: sensors and machine controllers generated a constant stream of data about how the operation was running. What it had not done was make any of that data usable. The signals were generated and then effectively stranded, sitting in machine systems and historians that nothing else connected to, with no unified place to bring them together.

As a result, maintenance was reactive. Equipment ran until it broke, and a breakdown meant expensive, unpredictable downtime. Overall equipment effectiveness, the core measure of how well the lines were actually performing, was a number assembled by hand from disconnected sources and trusted by nobody. The company was sitting on exactly the data needed to run a tighter, more predictable operation, and could not reach it.

The purpose of the engagement was to unify the machine and sensor data into one governed operational layer, then add predictive maintenance and performance analytics on top, so the plant could move from reactive to predictive and finally see true line performance. The work had to integrate with existing industrial systems and respect the security of the operational environment.

Project duration

The engagement ran in three fixed-price stages over approximately four months.

Deliverables

• A governed operational data platform unifying machine, sensor, and production data
• A common data model across lines and equipment
• Predictive maintenance and anomaly-detection models with MLOps
• Trustworthy, real-time OEE and performance analytics
• IEC 62443-aligned segmentation and OT-aware monitoring
• Full lineage and continuous data-quality checks

The data existed, and the value was locked up because nothing brought it together. Machine and sensor data lived in disconnected systems, so there was no single operational picture to act on. That fragmentation forced two costly outcomes. Maintenance stayed reactive, because without unified, analyzable data there was no way to predict failures, so equipment ran to breakdown and downtime was unplanned and expensive. And OEE, the metric that tells you how well your lines are really doing, was unreliable, because it was stitched together manually from sources that did not agree.

The blocker was not a lack of data or a lack of AI ambition. It was the absence of a unified data foundation, the same root cause that stalls so many operational analytics efforts.

Our solution architect designed a staged roadmap that unified the operational data first, then layered predictive maintenance and analytics on top.

Stage one, unify the operational data. We integrated machine, sensor, and production data through OPC UA, MQTT, and historian connectors, with edge gateways at the line level, bringing it all into a governed data platform with time-series storage and a common data model across lines and equipment. We modeled and reconciled the data with dbt and added a data catalog with lineage, so the operational picture was finally unified and trustworthy.

Stage two, real-time OEE and performance analytics. With unified data in place, we built trustworthy, real-time OEE and performance analytics, replacing the manual, conflicting reporting with a clear, live view of how the lines were actually performing. This gave the plant immediate value and an accurate baseline.

Stage three, predictive maintenance. Our data scientists built predictive maintenance and anomaly-detection models on the unified data, deployed with MLOps (MLflow, a feature store, drift monitoring, and retraining), so the plant could catch developing equipment problems before they caused stoppages, moving from reactive to predictive operations.

We integrated the industrial data through OPC UA, MQTT, and historian connectors with edge gateways, normalizing it into a common machine and production data model in a governed data platform on AWS or Azure, provisioned through Terraform. Time-series storage held the operational data, dbt reconciled and modeled it into governed, tested tables, and a data catalog with lineage and continuous data-quality checks kept it trustworthy. The operational environment was protected with IEC 62443-aligned segmentation, OT-aware monitoring, and least-privilege access through IAM and MFA.

On top of the foundation, we built real-time OEE and performance analytics and developed predictive maintenance and anomaly-detection models in Python, operationalized through MLflow with a feature store, monitoring, and automated retraining. Because the models drew on unified data across the whole operation, they could detect patterns that no single machine system would have surfaced on its own.

• Unify before you analyze. The stranded data was the whole problem, so bringing it together into one governed model came first.
• OEE truth first, then prediction. Establishing a trustworthy real-time performance picture delivered value early and gave the predictive models an accurate baseline.
• Respect the operational environment. Integration and segmentation were done with OT realities and IEC 62443 in mind, not by treating the plant like an office network.
• Build models to stay accurate. Drift monitoring and retraining keep the predictions useful as conditions change.

Engagements of this type target the following outcomes:

The engagement achieved its goals:

1. Machine and sensor data is unified into one governed, trustworthy operational layer.
2. The plant moved from reactive to predictive maintenance, catching issues before they cause stoppages.
3. OEE and line performance are visible in real time and finally trusted.
4. The foundation supports further capabilities, from quality analytics to deeper process optimization.