Retail staffing companyCase study
A month of scheduling, planned in about a second.
Their people work in retail stores for brands. Every month, a team built the schedule by hand from chat threads, a workforce app, retailer websites, and spreadsheets.
ChatWorkforce appRetailer websitesSpreadsheets
One place for the dataSchedulingBookingChanges with approvalOperations app
Client name withheld. Screens are illustrations with example data.
The shape of the problem01
Before: many tools.After: one system.
- The schedule was built by hand from chat, a workforce app, and spreadsheets.
- Booking store visits on retailer websites was a race against the clock.
- The same data lived in four systems, and they drifted apart.
What we built02
One system.Five parts.
Each part does one job the team used to do by hand.
- 01
One place for the data
Chat, the workforce app, retailer websites, and spreadsheets all feed one database.
- 02
Scheduling
It plans a month of work in about a second, using the rules the team used to hold in their heads.
- 03
Booking
It books visits on a retailer's website the moment the slots open. A person starts it and checks the exceptions.
- 04
Changes with approval
AI drafts changes across the four systems and shows a before and after. Nothing changes until a person presses Run.
- 05
Operations app
The screen the team uses every day to see coverage and fix gaps.
The result03
The work changed.It stays changed.
- In a test against eight months of past schedules, the system's plan covered 91% of the work. The team's own schedules had covered 77%.
- The team's work moved from copying data to checking decisions.
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