Labor Forecast & Productivity Dashboard

Purpose

This dashboard helps managers and schedulers make faster, smarter staffing decisions by showing where scheduled labor matches (or misses) forecasted demand, which roles are most productive, and where overtime or service risk is building. It combines clear KPIs, alerts, and a simple staffing-scenario simulator so you can compare a few realistic schedule options before publishing.

Primary Hungers Served

• Match staffing to demand while protecting service and margins.
• Spot upcoming overtime and coverage gaps early.
• Make trade-offs visible so shift leaders can choose staffing plans confidently.

What You'll See (Widgets)

1. Forecast vs Schedule Gaps — hour-by-hour chart showing forecasted covers or revenue vs. scheduled capacity (labor hours). Highlights under- and over-staffed periods.
2. Revenue per Labor Hour (RPLH) — rolling and period-to-date values, role-filterable (front-of-house, back-of-house). Shows trend and target band for quick assessment.
3. Average Covers per Server — covers / active servers for each service period; useful for balancing front-of-house staffing.
4. Cook Station Productivity — orders or menu-items produced per cook-hour, by station. Helps identify bottlenecks and where cross-cover or prep needs change.
5. Overtime Risk Alerts — upcoming scheduled overtime exposure by person and by shift; ranked by financial impact and frequency.
6. Staffing-Scenario Simulator (lightweight) — try a few staffing changes (add/remove hours, swap roles, change start/end times) and see immediate projected changes to labor hours, RPLH, and overtime risk.

Key Metrics, Definitions & Example Formulas

• Forecasted Revenue — expected sales by hour (from POS + reservations + historical pattern).
• Scheduled Labor Hours — sum of planned hours on the schedule for the period.
• Revenue per Labor Hour (RPLH) — Revenue / Total Labor Hours. Use role filters to see RPLH for servers vs cooks. Compare to your business-specific target band.
• Covers per Server — Covers / Number of Servers scheduled for the period.
• Station Productivity — Items or orders produced / Cook Hours by station.
• Overtime Exposure — Sum of hours scheduled beyond regular threshold (or predicted when forecasting call-offs) and estimated overtime pay impact.

How to Use This Dashboard (Manager Workflow)

1. Open the day or week view in the morning planning slot. Scan Forecast vs Schedule Gaps for high-risk hours.
2. Check RPLH and Covers per Server for key service periods. If RPLH is dropping while covers remain steady, consider reducing scheduled server-hours or improving table turns.
3. Open the Staffing-Scenario Simulator: try moving one server’s start time earlier, or swapping a cook between stations. Compare projected overtime exposure and RPLH impact before saving schedule changes.
4. Review Overtime Risk Alerts and follow up with affected employees or re-balance shifts to avoid costly overtime where possible.
5. After the shift, compare actuals to forecast in the dashboard to learn and recalibrate forecasting or role assumptions.

Suggested Alerts & Thresholds (customize for your operation)

• Flag any hour where scheduled capacity covers less than 90% of forecasted covers or revenue.
• Alert when projected overtime for a person exceeds X hours/week or when hourly overtime cost exceeds a configurable threshold.
• Notify managers if RPLH falls below the location’s baseline for two consecutive comparable service periods.

Data Sources and Mappings

For accurate KPIs, connect or map these systems:

• POS — sales and covers by time and category.
• Reservation/Booking system — covers and party sizes.
• Time & Attendance / Scheduling system — scheduled and actual clocked hours per person.
• Payroll rules — regular vs overtime thresholds, wage rates for cost calculations.
• Kitchen order system or ticketing — station and item throughput.

Customization & Localization

Targets, alert thresholds, and role definitions should be set per location. Use the domain’s adaptive toolkit model to create local copies of the dashboard and tailor role names, wage bands, and service period definitions.

Common Pitfalls to Avoid

• Relying on a single RPLH target without considering mix, covers, or weekend vs weekday differences.
• Ignoring station-level bottlenecks — hours may look fine overall while one cook station is overwhelmed.
• Letting the simulator be an academic exercise; always save and monitor one or two real changes as experiments so you learn what the forecasts miss.

Quick Implementation Checklist

1. Map POS, schedule, and payroll data fields and confirm hourly granularity.
2. Agree on role definitions and baseline targets for RPLH and covers per server.
3. Set initial alert thresholds and pilot the dashboard for two weeks with daily manager check-ins.
4. Collect scenarios managers actually try and store them (see capability notes) so the organization can learn which changes worked.

Next Steps & Continuous Improvement

Treat the dashboard as a learning tool: after each week, keep the scenario records, compare actuals, and adjust forecasting and staffing rules. Over time, collect high-impact experiments (what changed and what happened) into a living playbook for shift leaders.