Labor & Scheduling Optimization

R Studio Analytics

Labor & Scheduling Optimization

Statistical modeling and predictive analytics powered by R — turning your POS data, covers, and labor hours into precise staffing recommendations that cut costs without sacrificing service.

See the Analysis

labor_optimization.R — RStudio

# ── TableStandards Labor Model ──

> library(tidyverse)

> library(forecast)

> library(lubridate)

> covers <- pos_data %>%

group_by(day_of_week, hour) %>%

summarise(

avg_covers = mean(covers),

p90_covers = quantile(covers, 0.9)

)

> optimal_staff <- predict_staffing(

covers, labor_model,

target_cplh = 22,

service_level = "full"

)

# Projected weekly savings: $1,840

How It Works

From Raw Data to Optimized Schedules

We connect directly to your POS and scheduling systems, run the numbers in R Studio, and deliver actionable staffing models — not just reports.

Data Ingestion

We pull historical POS data, labor schedules, covers, revenue by daypart, and event calendars. All data is cleaned and normalized in R for statistical consistency.

Statistical Modeling

R Studio forecasts demand by hour and station using time-series analysis, seasonal decomposition, and regression models tuned to your specific operation.

Schedule Output

The model generates optimal staffing grids with labor cost projections, identifies overstaffed dayparts, and flags scheduling gaps before they impact service.

Capabilities

What the Model Delivers

Every output is built in R and reproducible — no black boxes, no guesswork.

📈

Demand Forecasting

Predict covers per hour by day of week using ARIMA and STL decomposition, accounting for holidays, events, and seasonal trends.

⏱️

Covers-per-Labor-Hour

Calculate and benchmark CPLH by station and daypart. Identify where labor dollars are being wasted versus where you're understaffed.

🔥

Staffing Heatmaps

Visual labor demand heatmaps generated in ggplot2 — showing exactly when you need bodies and when you're burning payroll.

💰

Cost Scenario Modeling

Run what-if scenarios: wage increases, reduced hours, cross-training impacts. See projected P&L effects before making changes.

🎯

Overtime & Compliance

Flag schedules that trigger overtime thresholds or violate local labor regulations before they hit payroll.

📊

Weekly Variance Reports

Automated R Markdown reports comparing scheduled vs. actual labor, with variance analysis and rolling trend lines.

Sample Outputs

What Your Dashboard Looks Like

Real R Studio output formats — these are generated from your data weekly and delivered as interactive HTML reports.

staffing_heatmap.R — Labor Demand

Low

High

labor_variance.R — Fri Staffing

11 AM

12 PM

5 PM

7 PM

9 PM

Optimal

Actual

Overstaffed

weekly_kpis.R — Performance

CPLH (BOH)

18.4

▲ 2.1 vs last wk

CPLH (FOH)

24.6

▲ 1.3 vs last wk

Labor %

28.2%

▼ 1.8pp

OT Hours

6.5hrs

▼ 4.2 hrs

Console — Model Summary

> summary(labor_model)

# Weekly Labor Optimization Report

# Period: Jan 20 – Jan 26, 2026

Scheduled Hours: 412.5

Optimal Hours: 378.0

Variance: +34.5 hrs

Est. Savings: $1,242/wk

# Top overstaffed dayparts:

Tue 2-4PM +2.0 staff

Wed 3-5PM +1.5 staff

Sun 11-1PM +1.0 staff

Typical Results

What Operators See in 90 Days

Based on engagements with full-service restaurants running 150–400 covers per day.

8–12%

Reduction in labor cost as % of revenue

30+hrs

Weekly hours eliminated from overstaffing

60%

Reduction in overtime hours

3wks

Time to first actionable staffing model

Stop Guessing, Start Modeling

Send us four weeks of POS data and your current schedule. We'll run the analysis and show you exactly where you're leaving money on the table — no commitment required.

Request a Free Analysis