Day 1: Preparing Data for Statistical Control

Process Measures and Sampling

• Decision points for process measures
• Sampling evidence informing quality decisions
• Defect patterns requiring root-cause analysis
• Prevention actions after recurring defects

Data Stability and Integrity

• Validation checks for data stability
• Acceptance evidence retained for review
• Control limits highlighting unusual variation
• Failure patterns requiring deeper investigation

Control Limits and Baselines

• Control boundaries around control limits
• Leading indicators of process drift
• Quality measures tied to outcomes
• Measurable acceptance criteria for quality

Day 2: Enhancing Control with AI Signals

Pattern Detection Beyond Limits

• Control boundaries around pattern detection
• Corrective actions tied to evidence
• Process variation before customer impact
• Verification checks prior to final release

Multivariate Process Signals

• Control boundaries around multivariate process signals
• Missing values needing deliberate treatment
• Data lineage supporting result traceability
• Common definitions for core measures

Early Warning Threshold Design

• Application limits around early warning
• Visual outputs informing decision clarity
• Explanation limits communicated to decision-makers
• Validation checks prior to using results

Day 3: Using Alerts for Quality Intervention

Signal Validation and Diagnosis

• Ownership expectations for signal validation
• Outliers reviewed prior to model use
• Data quality before analytical interpretation
• Assumptions tested prior to final conclusions

Escalation and Corrective Response

• Review criteria for corrective response
• Business question linked to decisions
• Uncertainty boundaries around analytical conclusions
• Query logic checked against requirements

Intelligent Quality Control Dashboard

• Evidence requirements for intelligent quality
• Ownership of approved AI outputs
• Oversight roles throughout the lifecycle
• Human oversight at high-impact decisions

• Case analysis examining data stability in workplace decisions
• Simulation testing multivariate process signals under operational pressure
• Guided exercise applying signal validation to workplace cases
• Group review evaluating intelligent quality against evidence