Challenge

The plant generated high-volume telemetry on temperature, vibration, throughput, and energy usage, but insights were siloed. Engineers reconciled spreadsheets while supervisors lacked predictive visibility into bottlenecks or equipment drift.

How We Helped

We designed event-driven data pipelines that normalize sensor feeds, trained predictive models to flag anomalies, and launched dashboards for executives and operators. The solution maps copper flow end-to-end, highlighting stations most likely to impact yield before issues surface on the floor.

• Implemented Azure IoT Hub and Stream Analytics to orchestrate real-time ingestion and transformation.
• Developed machine learning models in Python that predict equipment drift and recommend interventions.
• Built Power BI and custom React dashboards tailored for leadership, process engineers, and floor managers.

Impact

Teams now prioritize maintenance using model-driven alerts, focusing on the processes with the highest risk to throughput. Time-to-insight dropped from days to minutes, keeping leadership aligned on where to invest.

The manufacturer has expanded the solution to new facilities, using Coders playbooks to onboard data sources and continuously improve predictive accuracy.