The StoreCadence Workforce Efficiency Method™
A repeatable, data-first path from a roster built on habit to one that tracks demand store by store, with a measured baseline so the saving lands in your P&L. The same five stages run behind every service we offer.
The StoreCadence Workforce Efficiency Method turns the data your stores already produce into demand-matched rosters. It runs in five stages: diagnose against a baseline, forecast demand, optimise the roster, pilot in a few stores, then roll out and hand over. Every stage is measured, so you can see the saving before you scale it.
Most rostering fails in the same way. A store runs a fixed weekly template, lightly tuned by a manager, that bakes in a gap between when staff are paid and when customers actually arrive. The gap is invisible on any single day and expensive across a year. Our method exists to find that gap, quantify it, close it, and keep it closed.
It is deliberately low-risk: data first, a small pilot before any wider change, and a handover so your team owns the result. We work inside the tools and contracts you already have.
Diagnose, and set a baseline.
We run an efficiency scan across a representative set of stores, benchmark them against comparable chains, and agree the baseline of labour cost-to-sales and service that every later outcome is measured against.
A grocer believed its mornings were the problem. The scan showed mornings were fine; the real leak was a 90-minute evening checkout peak met by a roster that barely moved all day. The diagnosis changed where every later hour was spent.
- Labour cost-to-sales by store, format and daypart
- Demand-versus-cover gap analysis for sample stores
- A benchmark against chains of similar format and size
- An indicative saving range, and the agreed baseline
Forecast demand, store by store.
Machine-learning models turn your sales, transaction and footfall data into an hour-by-hour demand forecast for every store and daypart. The model learns each store own rhythm and the signals that move it: day of week, season, paydays, weather, promotions and local events.
Accuracy is measured against actuals, so you know how far to trust the forecast before you staff to it. This matters because, as operations research shows, labour only converts traffic into sales when it is matched to that traffic; a forecast accurate at the store and daypart level is what makes the match possible.
A chain-wide average hides the peaks and troughs you actually staff to. A rainy Tuesday in the school holidays and a sunny payday Friday are different stores; the model treats them that way.
Optimise the roster against the forecast.
A store with mixed shifts, breaks, skills and contracts has more possible weekly rosters than a planner could ever compare by hand. Mathematical optimisation searches that space under your real constraints and returns the lowest-cost roster that still covers forecast demand.
The output is reusable roster templates configured inside the WFM tool you already own. Planners keep their workflow; they simply start from a far better plan.
Pilot, and prove it against the baseline.
We pilot in a small group of stores and compare results against the agreed baseline before any wider rollout. The pilot de-risks the change for operations and gives finance a measured number rather than a projection.
If the pilot does not move the baseline, the design changes before anyone scales it. That is the point of piloting.
Embed, measure, and hand over.
We roll out across the estate, train your planners, and hand over the method so the savings continue after we leave. Labour cost-to-sales and service are tracked against the baseline on an ongoing basis.
The goal is independence: your team running a method they understand, not a dependency on us.
A benchmark and data engine, not a slide deck.
Two assets sit under the method. The first is a benchmark dataset that lets you see how your labour efficiency compares to chains of a similar format and size, so targets are grounded in what is achievable, not a generic ideal. The second is the forecasting and optimisation engine that does the heavy lifting on every engagement.
Both are owned and improving. The benchmark sharpens as more estates are measured; the models learn as more data arrives. You can read more on the benchmark report and the forecasting service.
Stores adopt what is simple and proven.
A better roster that store managers will not run is worth nothing. We keep change small and evidence-led: pilot first, give managers templates that are already tuned to their store, and show the result against the baseline so the change earns trust rather than demanding it.
Demand-matched rosters also tend to help staff, fewer understaffed peaks and fewer erratic, last-minute shifts, which supports retention. That aligns with research on higher-productivity store operations, where investing in stable, well-designed work lowers cost and improves service at the same time.
Questions about the method
Sources and further reading
- Ton, Z. The Good Jobs Strategy and the Good Jobs Institute (MIT Sloan) Research linking understaffing and poor execution to lost sales, and a model for higher-productivity store operations.
- Perdikaki, Kesavan & Swaminathan (2012), Effect of Traffic on Sales and Conversion Rates of Retail Stores (SSRN) Finds labour moderates how store traffic converts into sales.
- Chuang, Oliva & Perdikaki (2016), Traffic-Based Labor Planning in Retail Stores (Production and Operations Management) A heuristic for matching labour to actual store traffic.
External links are provided for reference and do not imply endorsement. Figures attributed to StoreCadence on this site are illustrative placeholders pending the firm's own published data.
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.