Roster building & demand planning
How this is scored
How a plan gets built across locations, roles and qualifications — templates, staffing requirements, forecasting from sales or occupancy, and whether the product proposes a roster or only draws one.
0 — A shared calendar of shifts; no templates, no roles or qualifications, and no notion of how many people a shift needs.
3 — Shift templates and a weekly grid per location, with roles attached, but staffing needs and qualifications are the planner's memory rather than the system's.
5 — Templates, roles and qualifications, staffing requirements per shift, open shifts, multiple locations and departments, and coverage gaps shown before publication.
8 — Demand-based planning from a named data source (POS revenue, footfall, occupancy or bookings), automatic roster proposals the planner can accept or adjust, labour cost shown against budget while planning, and cross-location staff sharing.
10 — Planning closes the loop: forecast, proposed roster and actual hours compared in one place, the proposal explains which rule or demand figure drove each assignment, and the vendor documents the forecasting method rather than calling it AI.
The Works Council Advocate
Automatic schedule building is shown end to end: Shopify sales and order data feed demand prediction, schedules are optimised for hourly rates, qualifications and availability, and a live dashboard shows wage cost against demand. The forecasting engine is sold as an "AI Scheduling Agent" with the method otherwise undocumented — an algorithm that distributes hours and overtime is precisely what a works council must be able to interrogate. We found no public information on cross-location staff sharing. 1 2 3 10