From chaos to order
Scattered shift blocks align within minutes under the constraints; gaps and overlaps disappear.
Optimization Engine
Right person, right time, right place — by calculation, not guesswork.
Shiftup Optimization treats shift planning as a constraint-solving problem: demand, rules and people go in; the lowest-cost, fully covered and fair plan comes out.
What optimization does
Staffing that follows demand hour by hour
A manual plan runs overstaffed in the morning and understaffed in the evening. The engine builds staffing that hugs the hourly demand curve: no idle employees, no unserved customers.
Demand (hourly staffing need)Manual staffingShiftup staffing
Better with every iteration
The solver scans thousands of combinations and descends to a cheaper, better-covered plan at each step.
Gap-free coverage
Understaffed (red) and overstaffed (faded) hours of the manual plan vanish in the optimized plan.
How it works
Three layers of the engine
Inputs
Demand forecastSales, traffic, call volume, reservations; converted into hourly staffing need.
Employee poolContract hours, skills, locations, costs.
Rules & constraintsLegal limits, rest, leave, sick notes, preferences.
Solver
ConstraintsLimits never crossed: daily/weekly hours, rest time, leave, skills.
Objective functionWeighted goal: cost ↓, coverage ↑, fairness ↑, preference match ↑.
SearchThousands of combinations scanned within minutes; the best plan is chosen.
Outputs
Optimum planPerson × day × hour × location assignment, ready for approval.
AlertsUnmet demand, employees at their limit, missing skills.
ReportsPlanned vs. actual, cost, overtime, KPIs.
Constraints
Rules the engine never breaks
Every rule is defined as a constraint; the plan cannot step outside these limits. Assignments approaching a limit are flagged as warnings.
Legal working limits
Maximum daily and weekly hours, overtime cap, night-work rules.
Rest periods
Minimum rest between shifts, weekly day off, consecutive-day limit.
Leave and sick notes
Approved leave, sick notes and public holidays flow into the plan automatically.
Skills and training
Checkout, aisle, language or certificate requirements must be met before assignment.
Location and travel
Employees only at defined locations; travel allowance between stores.
Contract and time balancing
Part-/full-time hour commitments, balancing-period account.
Preferences
Days/hours an employee prefers to avoid are protected where possible (soft constraint).
Headcount limits
Budget ceiling, maximum/minimum people on duty at once.
Optimization lab
Change the rules, watch the plan change
Toggle constraints and slide the objective weight for a representative store. The results on the right show the change versus a manual plan.
CostCoverage
The lab is a representative model; the real engine runs on your data. Values are not a contractual commitment.
Optimize with my own dataManualShiftup
Overtime index——
Demand coverage %——
Legal violations per week——
Preference match %——
Labor cost index——
Hourly coverage map (optimized plan)
GapFull
Process
Optimum plan in four steps
- 1Demand
Hourly staffing need is generated from history and calendar; editable by hand. - 2Rules
Legal, contractual and operational constraints are defined once and applied to every plan. - 3Solve
The engine builds the plan within minutes and shows alerts and alternatives. - 4Approve & re-solve
The manager approves and publishes. On sickness, leave or demand change only the affected part is re-solved.
Living plan
Stays optimized after publishing
An employee calls in sick, campaign traffic is higher than expected, a new leave is approved: the engine re-solves only the affected days and keeps the rest of the plan intact. Changes are pushed instantly through the mobile app.
Sick noteA suitable, available person is proposed for the vacated shift.
Demand deviationHourly need is updated; surplus and gaps are balanced.
Leave approvalLeave is written into the plan, conflicts are resolved automatically.
Store opening/closingLocation staffing is redistributed within budget and skill limits.
Industry examples
Different goals per industry, one engine
Retail
Hourly staffing by store traffic and campaign calendar; integrated with incentives.
Call center
Call-volume forecast + skill-based assignment; service level as a constraint.
Healthcare
24/7 on-call balance, maximum consecutive days and rest rules; fair distribution.
Leave planning to the math
Let our experts run a sample optimization on your own data and see the difference in the first meeting.
Frequently Asked Questions
Which data does the engine use?
Historical sales/traffic/call data, the employee list (contract hours, skills, locations), leave and the rules you define. With ERP/HR integration this data flows automatically.
Who manages the plan — the engine or us?
You. The engine produces a proposal; the manager approves and can edit by hand. Manual edits are checked against the constraints instantly.
How are legal rules kept up to date?
Rules are parametric; when legislation or a collective agreement changes, the parameters are updated and applied to subsequent plans.
How many locations and employees does it handle?
From a single store to hundreds of locations and thousands of employees; multi-location businesses manage it from a central panel.