Little’s Law as a Reality Check on Bottleneck Narratives

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1Decision-Support Boundaries and Human Decision Ownership 2Risk Tiers for Trust, Override, and Escalation 3Input and Output Contracts for AI-Assisted Ops Artifacts 4Decision Logs That Capture Evidence, Critique, and Overrides 5Ops Data Readiness and Ground Truth Definitions 6Lightweight QA, Stop Rules, and Automation Creep Controls

7Minimum Viable Demand History for Planning Forecasts 8Naive and Seasonal-Naive Baselines as Default Comparators 9Rolling-Origin Backtests and Accuracy vs Bias Measures 10Uncertainty Ranges and Planning Buffers From Prediction Intervals 11Evidence-Cited Forecast Narratives and Driver Hygiene 12Override Decisions and Forecast Pack Sign-Off

13Event Log Readiness for Flow Analysis 14Cycle Time Decomposition and Bottleneck Signals 15Variant and Rework Loop Discovery With AI That Must Show Evidence

16Little’s Law as a Reality Check on Bottleneck Narratives

17Conformance Checks Against the SOP and Controlled Intervention Selection 18Automation as a Controlled Change With Exceptions and Monitoring

19Service Targets as Operational Promises and Tradeoffs 20Uncertainty-Driven Review Cadence and Buffer Logic 21Lead Time Behavior, Variability, and Evidence Requirements 22Structured Policy Recommendations With Constraints Up Front 23Stress Tests, Escalation Criteria, and the Audited Decision Memo

24AI-Assisted SOP Drafting With Controls, Exceptions, and Citations 25SOP Validation for Hallucinations, Control Gaps, and Change Risk 26Document Control That Ties SOP Changes to Measured Issues 27Incident Signals and Thresholds That Create Owners, Not Noise 28Runbooks and Alert Tuning With Post-Incident Feedback Loops 29SLA Scorecards, Evidence Packs, and the Integrated Control Loop

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AI Decision-Support for Operations: From Data Readiness to Audited Decisions
Little’s Law as a Reality Check on Bottleneck Narratives