PG010
Calibrate detection thresholds using historical investigation outcome data and apply risk-differentiated sensitivity by process category to maximise genuine detections within investigator capacity.
CyberTRIZ analysis · Process contradiction PG010 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Higher Non-Conformance Detection vs. Lower False-Positive Rate
Business Context. Sensitive quality controls detect more genuine non-conformances, but overly sensitive thresholds also generate more false positives, consuming investigator time on issues that turn out not to be genuine problems.
Process TRIZ Resolution. Rather than choosing a single fixed sensitivity threshold, organizations should calibrate detection thresholds using historical outcome data, tuning sensitivity to the level that maximizes genuine detections while keeping false positives within investigator capacity.
Applicable TRIZ Principles
Principle 35 (Parameter Changes) calibrates detection thresholds using historical outcome data.
Principle 23 (Feedback) uses investigation outcomes to continuously refine detection sensitivity.
Principle 3 (Local Quality) applies different sensitivity levels to different process categories based on risk.
Expected Outcome
High genuine detection rate
Manageable false-positive volume
Efficient investigator capacity use
Continuously improving detection accuracy
Decision Indicators
Investigators are overwhelmed by a high volume of false-positive alerts.
Detection thresholds have never been recalibrated using outcome data.
Genuine non-conformances are missed due to alert fatigue.
The same detection sensitivity is applied to every process category.
No feedback loop exists between investigation outcomes and detection tuning.
If several of these indicators are present, the contradiction is likely active and the Process TRIZ resolution above should be evaluated.