Industrial AI Is Making OEE Worse, Not Better
A major automotive supplier deployed machine learning to boost Overall Equipment Effectiveness and watched it drop 3.2 percentage points. The problem wasn't the AI. It was what everyone thinks AI should optimize for.
The plant manager at a Tier 1 automotive stamping facility outside Detroit had done everything right. He'd hired a systems integrator, spent $1.8 million on sensor infrastructure and an edge compute stack, trained a convolutional neural network on 18 months of production footage, and achieved what looked like a genuine technical win: 94.7% accuracy in detecting tool wear across 12 stamping presses. The model latency was 140 milliseconds per inference, well within the 500-millisecond window required for real-time intervention. By every measure that machine learning engineers measure things, the system worked.
His OEE collapsed by 3.2 percentage points in the first three months of production.
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