Field service and workforce
Labor Forecasting & Shift Scheduling
Demand forecasts by interval drive a roster that respects labor rules and availability, published to your scheduling tool and adjusted during the day.
The problem
Schedules are copied from last week and adjusted by feel, so some intervals are overstaffed while others leave customers waiting. Overtime fills the gaps, and late changes wear on the staff who absorb them.
Who owns it
The head of operations for the sites, with site managers who publish and adjust the schedule, and workforce or HR owners of the labor rules.
What it learns from
- Point-of-sale transactions, foot traffic, or contact volume by interval
- Past schedules and hours worked
- Staff availability, skills, and preferences
- Labor rules and agreements
- Promotions, events, and holidays
How it works
Predict, decide, act, measure
The same four steps in every solution, in this order, each one visible to the team that owns it.
- 01
Forecasts demand by interval
Models forecast demand for each interval of each day at each site, from sales, traffic, or contact volume, with the promotions, events, and holidays that move it.
- 02
Decides the roster through an optimizer
An optimizer builds the roster that covers forecast demand within labor rules, staff availability, skills, and the labor budget. Fairness rules spread unpopular shifts, and limits on late changes protect staff.
- 03
Acts in the scheduling tool
The schedule is published to the workforce or scheduling tool your managers already use. During the day, intraday adjustments are suggested to the site manager, who decides.
- 04
Measures with a phased rollout by site
Sites adopt the schedule in phases, and the sites not yet on it are the comparison. Labor as a share of sales, overtime, and understaffed intervals are tracked for each.
What it moves
The business levers
Labor cost
Labor as a share of sales held where it should be, with less overtime.
Coverage
Fewer understaffed intervals, so customers wait less.
Manager time
Managers review a schedule instead of building one.
Staff experience
Fairer shifts and fewer late changes.
Proof
How the return is proven
The return is the change in labor cost, overtime, and understaffed intervals at the sites on the schedule against the sites not yet on it, stated against the return model agreed up front. Late changes and fairness of shift allocation are reported beside it.
What you see: the ROI readout
Every solution produces the same readout, assembled from the system's own records rather than built by hand.
Correctness
The checks the pipeline passed on your own data: leakage, point-in-time, and entity resolution.
Incrementality
The incremental effect against the holdout or equivalent design, with its confidence interval.
Return
The measured return against the investment, set beside the return model agreed before work started.
Guardrails
Complaints, cost, margin, fairness, and contact limits, each shown against its bounds.
Audit extract
Every score, action, and outcome, with the model and policy versions behind it.
Governance
What is governed
Access, approvals, and the audit trail are part of the solution, not a review step added at the end.
Identity-scoped access
Schedules and staff data are visible by site and role through your single sign-on.
Approval gates
Site managers approve each published schedule, and changes to labor rules or the budget need workforce approval.
Audit trail
Every schedule version and change is logged with who made it and why.
Labor and scheduling law
Working-time limits, rest periods, minor-labor restrictions, and predictive-scheduling rules where they apply are hard constraints in the policy.
Why it holds up
Trained on your data, framed by the outcome
This solution trains models on your own data, checks every stage from data to decision, and is measured against the outcome it was built for. Because its predictive power is measured, it also shows the drivers behind the metric, so your team can fix causes as well as act on scores.
Why solutions hold upStart with one decision
Bring the problem and the systems your team works in. We scope the first solution with you, agree how its return will be measured, and run it in production with your people.