Measuring Production Cycle Time Using AI Video Analysis

Choose unambiguous endpoints

Identify visible events such as fixture closure and part release. Document cases where a cycle aborts or produces multiple parts. The interval between two outputs includes a different meaning from time spent inside a machine. Draw a short event sequence and obtain production-team agreement before selecting detection rules.

Keep timestamps consistent

Use capture timestamps where available and account for dropped frames or stream buffering. If start and end come from different cameras, check clock synchronisation and measured offset. Determine whether the camera frame rate can resolve the required timing tolerance. More decimal places in a dashboard do not create more measurement precision.

Separate products and interruptions

Associate cycle records with product, recipe and machine mode. Mark incomplete cycles rather than turning them into extremely long normal cycles. Report distributions and percentiles alongside an average, and retain outliers for investigation. Compare like-for-like operating conditions before interpreting a change as process improvement.

Verify against a reference timeline

Label a representative set of video events independently and compare timing errors. Test back-to-back cycles, pauses, occlusion and restart. Define acceptable missed-cycle and timing-error limits with the process owner. Keep the measurement system observational unless a separate validated control integration is explicitly part of the project.

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