Case Study: Multi-Factory OEE & KPI Monitoring

Overall Equipment Effectiveness (OEE) is the standard metric for manufacturing equipment productivity:

OEE = Machine Availability × Process Efficiency × Product Quality

An OEE of 85%+ is considered world-leading; most manufacturers land at 40–60%; below 40% is low. Different teams use it differently — shop floor teams use it to find lagging processes, executives use aggregates to judge overall performance and justify capital investment (ROIC).

The problem: OEE computation was traditionally manual. At multi-factory scale, that breaks two things at once — freshness (information needs to flow continuously with minimal latency) and consistency (every stakeholder needs to work off the same numbers, with the ability to drill into an OEE drift).

The pattern: incrementally ingest sensor/IoT data → clean and extract the needed fields → integrate workforce data from ERP systems → merge and aggregate in real time over a temporal window → surface KPIs. This maps directly onto Medallion Architecture: Bronze, Silver, Gold: Bronze (raw sensor ingestion), Silver (cleaned, joined with ERP data), Gold (the aggregated OEE/KPI layer that feeds dashboards).

RCA angle: when a reported OEE number looks wrong, the first question is which factor moved — availability, efficiency, or quality — and whether the drift is a real operational issue or a broken upstream transform.