How a multi-site manufacturer gained real-time OEE visibility and AI-powered anomaly detection — catching equipment issues hours before they caused unplanned downtime.
Plant managers had no real-time visibility into machine downtime or Overall Equipment Effectiveness (OEE) across multiple production lines. Weekly Excel reports were outdated by the time they were reviewed, and unplanned downtime was costing the business significantly — with issues only discovered after the damage was already done.
Streamed real-time machine sensor data from Azure IoT Hub through Azure Data Factory into SQL Server, making it available as a live Power BI dataset with sub-minute refresh latency across all production lines.
OEE Score =
[Availability Rate] * [Performance Rate] * [Quality Rate]
Availability Rate =
DIVIDE(
[Run Time],
[Planned Production Time],
0
)
Performance Rate =
DIVIDE(
[Ideal Cycle Time] * [Total Count],
[Run Time],
0
)
A DAX-based semantic model calculating Availability, Performance, and Quality scores per machine, shift, and production line — with full time-intelligence measures for shift-over-shift and week-over-week comparisons.
Applied Power BI's built-in Anomaly Detection visual to time-series OEE data, automatically flagging deviations with Copilot-generated explanations. An Azure Machine Learning model scored each machine's failure probability daily, surfaced as a risk indicator directly within the dashboard.