Performance measurement based on reality
The challenge
Traditional performance evaluation relies heavily on supervisor observation and subjective assessment. Two operators performing identically might receive different evaluations based on when supervisors happened to observe them or personal relationships. This subjectivity creates inconsistency in performance management, training decisions, and resource allocation.
Why it happens
Supervisors can't observe every station continuously, so they form impressions based on periodic observation and overall output numbers. These impressions, while often accurate, carry inherent bias—supervisors notice problems more than consistent good performance, recent events overshadow historical patterns, and personal rapport influences perception. Without objective measurement, there's no alternative to these subjective assessments.
Our solution
OpenSeam provides objective performance data for every station based on measured behavior: actual stoppage frequency, measured pace consistency, calculated proficiency scores, and observed stamina patterns. These metrics derive from continuous sensor data, not human observation. A station's performance rating reflects what the machine actually did, not what a supervisor remembers seeing.
The outcome
Management discussions shift from opinion to evidence. When evaluating operator performance, you see objective metrics that show exactly how that station performed throughout the shift. Training decisions get made based on measured skill gaps rather than general impressions. Resource allocation targets stations with documented constraints rather than perceived problems. The system doesn't replace supervisor judgment—it gives supervisors reliable data to inform that judgment.
How we deliver
Performance metrics appear alongside station identification in all dashboards. You can compare operators working on the same operation type with standardized metrics, accounting for task difficulty differences. Historical performance shows whether an operator is improving, declining, or maintaining consistency. The data supports fair evaluation by removing guesswork and providing evidence for both recognition of strong performance and identification of genuine improvement needs.