Staff to the demand curve, not the calendar.
We turn your sales and footfall data into demand-matched roster templates your planners run in the tool you already own, so the right people are on the floor in the right hour, store by store.
What is demand-matched staff scheduling?
Demand-matched scheduling builds each store’s roster around an hour-by-hour forecast of customer demand, instead of a fixed weekly template. Staff hours rise and fall with footfall and sales, so you stop paying for idle morning cover and stop under-resourcing the peaks that drive conversion.
Most chains roster from habit: the same shift pattern every week, lightly tuned by a manager who knows the store. It is fast, but it bakes in a structural gap between when staff are paid and when customers actually arrive. That gap is invisible on any single day and expensive across a year.
We close it by forecasting demand for every store and daypart, optimising the roster against that forecast, and measuring the result against a baseline of labour cost-to-sales and service. The schedule changes; the tool, the contracts and the rostering team stay the same.
Millions of possible rosters. We find the best one.
A 25-person store with mixed shifts, breaks and skills has more possible weekly rosters than there are seconds in thirty thousand years. No planner can compare them by hand.
Mathematical optimisation can. It searches that space under your real constraints, contracts, availability, skills and compliance, and returns the lowest-cost roster that still covers forecast demand.
For one fashion store, the engine evaluated over a million candidate rosters and cut a recurring Saturday over-staffing pattern the template had hidden for years, without touching service at the evening peak.
Share staff across stores that sit close together.
When two or three stores are a short distance apart, their peaks rarely line up perfectly. One can be slammed at lunch while another, ten minutes away, is quiet. Treating each store as an island wastes that difference: you over-cover one site while the next runs short.
Where staff are willing and contracts allow, letting people work across nearby locations is far more efficient. It turns several small, separate labour pools into one flexible pool that can flow to wherever demand actually is, so the group covers its combined peaks with fewer total hours.
Sharing staff across sites is a hard scheduling problem by hand: travel time, availability, skills, fairness and each store demand curve all interact. Optimisation handles it directly, building cross-store rosters that respect travel and contracts while minimising total cost. A worked example: three mall stores within walking distance, pooled into one roster, covered the same trading with noticeably fewer scheduled hours, and no one travelling more than a few minutes.
What the no-obligation scan gives you.
Every engagement starts with a free efficiency scan. For staff scheduling, you walk away with four concrete things, whether or not we work together.
A demand-vs-staffing gap analysis
We map actual demand against your current cover for a sample of stores and show where the roster and the trading day diverge.
A sample demand-matched roster
One store rebuilt to its demand curve, so you can see the change before committing to anything.
An indicative saving range
A first, conservative estimate of the labour cost-to-sales you could recover across the estate.
Your measurement baseline
The labour cost-to-sales and service baseline every later outcome is tracked against.
From scan to embedded saving.
Scan and baseline
We run the efficiency scan across a representative set of stores and agree the baseline we will measure against.
Forecast and optimise
We forecast demand per store and daypart, then the optimisation engine searches roster options for the lowest-cost one that still covers it.
Pilot
We pilot in a small group of stores, compare against the baseline, and refine before any wider rollout.
Roll out and hand over
We scale across the estate, train your planners, and hand the method over so the savings continue after we leave.
What good looks like.
Labour cost-to-sales at a top-5 Asia-Pacific grocery chain across 180 stores, service held flat (illustrative).
Roster-to-demand fit at a 200-store fashion retailer, with planner scheduling time roughly halved (illustrative).
Reduction in the time planners spend building schedules each week (illustrative).
Staff scheduling: common questions
Find your labour-cost gap in one conversation.
A no-obligation efficiency scan gives you a numbers-first picture of where your roster is leaking margin, and what it's worth to fix. No software to buy. No commitment.