Let’s get your location configured in 5 quick steps.
Combine your HotSchedules export with your daily sales to see staffing vs. productivity in real time.
This creates all required tabs in your spreadsheet:
schedule_history, sales_history,
sales_curves, config,
weekly_summary, and app_cache.
It’s safe to run multiple times — existing tabs are never overwritten.
Set your location name and the email address that will receive weekly labor summaries.
Two time-based triggers run automatically each week so you never have to touch the spreadsheet manually.
All 7 days start with the same default curve that distributes your daily sales estimate across the 19 tracked hours. The app refines each day’s curve automatically once CFA data starts flowing in Chunk 3.
You can edit any day’s curve in Settings → Sales Curves at any time.
Cockrell Hill — Analyze staffing with sales data & productivity metrics
Upload your raw HotSchedules export to generate an hourly CSV.
| Hour | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
|---|
| Hour | ||||||
|---|---|---|---|---|---|---|
|
Upload a schedule to get started
Use the FOH / BOH file inputs above, or click Load Demo Data
| ||||||
| Week | Avg SPLH | vs Goal | Peak Hour | % Below Goal | Hours Scheduled |
|---|
Each day’s 19 hourly weights determine how daily sales are distributed across the day. All active weights per day must sum to 1.000. 5 AM and 11 PM are outside CFA data range.
Managed automatically — change only if advised.
Time-based triggers run automatically every week. Install once — they persist across deployments.
Predictions use exponential smoothing on your historical daily sales. More weeks of data = higher accuracy. Sunday is excluded.
This is a statistical estimate, not a guarantee. Actual sales depend on weather, promotions, and other factors.
Cockrell Hill — Staffing Analysis & Labor Optimization
Version 3.1 · Built for non-technical operators
Schedule Counter combines your HotSchedules export with daily sales data to show exactly where you’re overstaffed, understaffed, or on target — hour by hour, day by day. It replaces the guesswork of “do we have enough people?” with a color-coded grid, actionable insights, and automated weekly forecasts. Everything runs inside Google Sheets with no external services required.
Converts your raw HotSchedules “Schedule Report” export (Excel/HTML format) into a clean hourly CSV that Schedule Analysis can read. Upload the file, click Convert & Download, and save the CSV. You need to pull FOH and BOH separately from HotSchedules to get per-department analysis.
The main analysis screen. Upload your FOH and/or BOH CSVs from Step 1, enter daily sales estimates (or let the forecast fill them), and click Generate Analysis. You get:
Week-over-week performance tracking. Shows SPLH trends, goal distribution over time, weekly total sales history, and a drill-down for any individual week. Also includes forecast accuracy flags that detect when your sales curves are consistently over- or under-estimating for specific days. Data populates automatically as you upload schedules and as the Monday pipeline archives real CFA sales.
All configuration lives here. Nothing is hardcoded — operators can adjust everything:
A standalone monthly productivity view. Upload or enter monthly SPLH figures to track long-term trends and compare before/after periods (e.g., before vs. after implementing schedule changes). Includes a trend chart and statistical breakdown.
Predicts daily total sales for the week you’re scheduling (2 weeks out). Uses a linearly-weighted moving average with outlier exclusion and trend adjustment across your last 8 weeks of CFA sales data. Each day shows a predicted total, trend direction, and how many weeks of data were used. More history = more accurate forecasts.
Three time-driven triggers run automatically each week. Install them once from Settings → Automation.
Sales curves control how your daily sales estimate gets distributed across hours. Each day (Monday–Saturday) has its own curve of 19 hourly weights that sum to 1.0.
The Forecast tab predicts total sales for each day of the week you’re scheduling (2 weeks out).
forecast_weeks in the config sheet).All data lives in Google Sheets tabs. No external databases or services.
| Sheet Tab | Purpose | Written By |
|---|---|---|
config |
All settings (goals, email, shares, alpha, bias flags) | Settings tab / pipeline |
schedule_history |
Hourly staffing per day (FOH, BOH, combined) per week | Report generation |
sales_history |
Hourly sales per day per week (estimated + actual/api) | Report generation / Monday pipeline |
sales_curves |
Per-day hourly weight distributions (19 hours × 6 days) | Monday pipeline / manual edit |
weekly_summary |
One row per week: avg SPLH, peak hour, goal %, total hours | Report generation |
app_cache |
Pre-computed JSON blob for fast app load (~200ms vs 2–3s) | Monday pipeline |
Sheet1 |
Raw CFA sales data landing zone (cleared after pipeline runs) | CFA sync / manual paste |