Reduce Understaffing and Overtime With Shift Optimization

Understaffing and overtime feel like opposite problems, so managers tend to fight them one at a time — add hours to cover a gap, then cut hours when the wage bill spikes. The trouble is they share a root cause: a schedule that does not match staffing to demand precisely enough. Solve that, and both improve together.

Why manual scheduling drifts toward both

When you build a rota by hand, you round. You cannot mentally solve for twelve people, their availability, their max hours, and your coverage minimums at once, so you pad the shifts you are unsure about and lean on your reliable people for the rest. Padding creates overtime; leaning on the same few creates burnout and, eventually, gaps.

What optimization actually does

A shift optimizer treats scheduling as a constraint problem. You give it the rules — coverage per shift, each person's availability and hour limits, required roles, rest between shifts — and it searches for a schedule that satisfies all of them at the lowest cost. It does not get tired, and it does not have favourites.

• Coverage minimums are met exactly, so gaps do not slip through.
• Hour caps and overtime thresholds are respected as hard limits.
• Work is spread across the whole team, not the reliable few.

The overtime side

Because the optimizer knows everyone's hour limits and your overtime thresholds, it will fill coverage from people who still have regular hours available before it pushes anyone into overtime. When overtime genuinely is the cheapest option, it tells you — so overtime becomes a decision you make, not a surprise on the payroll run.

The understaffing side

The same engine guarantees your coverage minimums are met before it optimises anything else. If your available staff cannot cover the rules you set, it surfaces the gap while you can still act — post the shift, call in a part-timer, adjust the target — instead of you discovering it when the shift starts short.

A worked example

Take a store that needs three staff at peak and two off-peak, with six part-timers each capped at 25 hours a week. By hand, you would likely over-cover the peaks to be safe and rely on two people who are always available. An optimizer fills the peaks exactly, spreads the off-peak hours across all six, and keeps everyone under their cap — cutting the wage bill and the burnout at the same time.

This is the core of what Jadwala does: it holds coverage and cost as goals and hour limits as hard constraints, then builds the schedule that meets your coverage for the least overtime — and flags the trade-offs instead of hiding them.