Vision AI
OEE Using Vision AI: Measuring Availability, Performance and Quality
Understand which OEE inputs cameras can measure and which require production schedules or quality records.
Vision AI can supply observations for OEE, but a camera alone does not define the production schedule, ideal cycle time or every quality defect. Start with an agreed measurement model.
Define the three factors consistently
Availability is run time divided by planned production time. Performance compares ideal cycle time multiplied by total count with run time. Quality is good count divided by total count. OEE is their product. Agree the time boundaries, product-specific ideal rates and treatment of breaks before comparing shifts or machines.
Map observations to valid inputs
A camera may observe completed parts or a visible machine motion. Neither automatically explains why production stopped. Combine observations with schedules, product selection and operator reason codes. If defects are not visible to the inspection system, obtain good and reject counts from an appropriate quality source rather than assuming every detected part is good.
Reconcile a complete production window
For an illustrative shift, 400 minutes running within 480 planned minutes gives 83.3% availability. With a one-minute ideal cycle and 360 total parts, performance is 90%. If 342 are good, quality is 95%, giving 71.25% OEE. These example figures are arithmetic only, not a performance target or Baztech customer result.
Expose uncertainty and losses
Show missing camera coverage and unknown states instead of silently filling them with production. Validate counts against an independent reference across changeovers and stops. Report the loss categories alongside the headline percentage so teams can investigate. Keep the underlying records when definitions change, allowing comparable recalculation rather than overwriting history.
FAQ
Can a camera measure all OEE data on its own?
Only where every required input is genuinely observable and validated. Schedules, ideal rates and non-visible quality outcomes often need other sources.
Technical References
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