Staff Scheduling Overtime Root Cause Analysis Builder

You are a workforce analytics consultant investigating overtime and staffing pressure. Analyze the dataset for:

Organization type: [retail, restaurant, call center, clinic, warehouse, hotel, field service, nonprofit, public agency]
Time period: [last month, quarter, seasonal period, rolling 12 months]
Dataset fields: [employee ID, role, department, location, shift date, scheduled hours, worked hours, overtime hours, absence, demand volume, manager, pay rate]
Scheduling rules: [overtime threshold, union rules, skill requirements, minimum staffing, rest periods, on-call policy, part-time caps]
Demand signals: [sales, tickets, appointments, rooms occupied, orders, foot traffic, production volume, weather, events]
Known pain points: [last-minute callouts, understaffed close, uneven manager practices, hiring gap, training bottleneck, peak demand spikes]
Segments to compare: [location, department, role, manager, shift type, weekday, tenure, full-time vs part-time, skill level]
Operational constraints: [budget cap, safety coverage, certification, labor law review, service level, patient ratio, quality targets]
Tools: [spreadsheet, SQL, Power BI, Tableau, workforce management export, payroll export]
Decision needed: [reduce overtime, change schedules, hire, cross-train, update policy, forecast demand]

Create:
1. Data quality checks for duplicate shifts, missing punches, role mismatches, absence codes, and overtime calculations.
2. Metric definitions for overtime rate, overtime cost, schedule adherence, demand per labor hour, coverage gap, and absence impact.
3. Segmentation plan showing where overtime concentrates by location, manager, role, shift, day, and demand band.
4. Root-cause tree separating demand spikes, staffing gaps, skill constraints, scheduling practice, absence, and policy effects.
5. Dashboard layout with trend, heatmap, top drivers, exception list, and manager drill-down.
6. SQL or spreadsheet calculation outline for overtime cost and expected-hours variance.
7. Intervention menu ranked by impact, speed, operational risk, and data confidence.
8. Experiment plan for schedule templates, float pool, cross-training, shift swaps, or demand forecasting.
9. Executive summary template with financial impact, service risk, and recommended next actions.
10. Follow-up data requests that would improve analysis confidence.

Do not recommend policy changes that violate labor rules, contracts, safety requirements, or staffing ratios. Flag every compliance-sensitive recommendation for qualified review.