Based on this discussion and our subsequent team analysis, I want to share our recommended approach for balancing automation with quality oversight in non-conformance disposition workflows.
Risk-Based Categorization Framework: Implement a three-tier categorization system that evaluates each non-conformance on multiple dimensions before determining automation eligibility. Tier 1 (Low Risk): Defects with no safety impact, no customer visibility, severity level 3-4, and quantity under threshold limits. These are candidates for full automation. Tier 2 (Medium Risk): Defects with potential customer impact, severity level 2, or quantities exceeding thresholds but still within acceptable limits. These use automated recommendations but require human approval. Tier 3 (High Risk): Any safety concerns, customer complaints, severity level 1, or regulated product characteristics. These always route to full MRB review regardless of other factors. The categorization algorithm should be documented, validated, and periodically reviewed.
Workflow Branching Strategy: Design your Arena workflow with intelligent branching that routes non-conformances based on the risk categorization. The workflow should automatically evaluate incoming non-conformances against your criteria matrix and branch to appropriate disposition paths. Low-risk items flow through automated disposition states with minimal human interaction. Medium-risk items get automated analysis and suggested disposition but pause for engineer approval before finalizing. High-risk items immediately route to MRB scheduling. Include exception handling paths for edge cases where automated categorization is uncertain - these should default to manual review rather than risking inappropriate automation.
Disposition Rules for Automation: Define very specific, objective criteria for automated disposition decisions. For ‘Use As Is’ automation: defect must be cosmetic only, no functional impact, within specification tolerances, and no regulatory reporting requirements. For ‘Scrap’ automation: defect makes item unusable, repair cost exceeds replacement cost, and no special handling requirements. For ‘Rework’ automation: only when standard rework procedures exist, rework cost is reasonable, and success rate is historically high. Never automate ‘Return to Supplier’ or ‘Deviation’ dispositions as these require negotiation and special approvals. Document the business rules clearly and make them accessible to all stakeholders.
Escalation Criteria: Build in multiple escalation triggers that override automation when certain conditions are met. Quantity-based escalation: if non-conformance quantity exceeds defined thresholds, escalate to manual review even if individual items would normally auto-disposition. Pattern-based escalation: if similar defects occur multiple times within a rolling time window, escalate to investigate potential systemic issues. Cost-based escalation: if disposition cost impact exceeds dollar thresholds, route to management approval. Customer-based escalation: any non-conformance related to customer-supplied material or customer-specific requirements goes to manual review. These escalation rules should be configurable and adjustable as you gain experience with your automation performance.
Automation Accuracy Monitoring: Implement comprehensive monitoring to validate that automation is making appropriate decisions. Create a real-time dashboard tracking automated disposition decisions with key metrics: number of auto-dispositions by type, cycle time savings, cost impact, and any subsequent issues or reversals. Conduct monthly audits where quality engineers review a statistical sample of automated decisions and rate them as appropriate or questionable. Track the false positive rate (automation made wrong decision) and false negative rate (automation escalated unnecessarily). Set acceptable error thresholds - we recommend starting with 5% maximum error rate and tightening as your rules mature. When errors are found, analyze root cause and refine your automation criteria. Also monitor for automation bias - are certain defect types or product lines having higher error rates? Use this data to continuously improve your disposition rules.
The balance between efficiency and oversight comes from starting conservative and expanding automation based on demonstrated success. Begin with the most clear-cut scenarios where disposition decisions are essentially binary and obvious. Monitor performance rigorously for the first 6 months. As confidence builds and error rates remain low, gradually expand automation to additional scenarios. Always maintain the ability to override automation and route items to manual review when needed. The goal isn’t to eliminate human judgment but to focus human expertise on the cases that truly require it while automating the routine decisions that follow clear, objective criteria.