AI scheduling predicts demand by 15-minute intervals so every shift has exactly the right staff
Their stores were either overstaffed (burning cash) or understaffed (burning customers). The average location was wasting $4,200/month in unnecessary labor while simultaneously losing $3,800/month in walkaway revenue from long lines. GMs were scheduling by gut feel and tribal knowledge, and it showed.
This 80-location premium fast-casual chain paid above-market wages ($19-22/hr) to maintain quality. But with turnover at 120% annually, most shift managers had less than 6 months of experience and couldn't predict demand patterns. Corporate's scheduling tool used simple historical averages — it didn't account for weather, local events, school schedules, or the fact that the location near the concert venue needed 8 extra staff on show nights.
We built a location-specific demand prediction model that forecasts customer volume in 15-minute intervals, 14 days out. The model incorporates weather, local events, school calendars, holiday schedules, nearby business patterns, and historical data. It generates optimized shift schedules that GMs can accept or modify. Our data team processes overnight data feeds and updates forecasts for the next day's operations.
The system told me I needed 3 extra people on a random Tuesday in October. I thought it was wrong. Turns out there was a teacher in-service day and a food truck festival nearby. We would have been crushed.— General Manager, Costa Mesa location
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