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existing systems. This turns routine scans into more complete and actionable operational insights without adding steps for workers.
3. Exception and quality data: Quality checks, rework, small stoppages, and manual interventions are frequently logged on paper or not logged at all. Smart data capture lets workers scan and tag exceptions in the workflow- for example, flagging a mis-pick, recording a defect with a quick photo and barcode scan, or capturing a deviation from a work instruction. Over time, this builds a much richer dataset for continuous improvement and predictive maintenance.
How do you balance automation with human oversight in data capture systems? We see automation and human judgment as complementary. Automation should handle the repetitive, error-prone parts of the workflow, while humans stay in control of interpretation and handling exceptions. Our approach is to build a‘ co-pilot’ experience- the software guides the worker through the process, validates critical steps according to pre-set rules, and highlights anomalies, empowering the human to make the final call.
Have you got any specific examples / case studies from manufacturing that demonstrate this in action?
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