Overtime and Hours Analysis

Overtime is one of the clearest signals of structural capacity problems. Occasional overtime is normal and expected. Persistent, recurring overtime in the same team or role is a data point telling you:

1. That team is under-resourced for its current workload

2. Or there is a process inefficiency that makes normal work take longer than it should

3. Or your revenue has grown beyond what current headcount can handle

The cost is also material: UK overtime is typically paid at 1.25–1.5× base rate. A team averaging 5 hours of overtime per week represents significant unplanned payroll cost.

Hours and overtime data typically comes from:

• Time and attendance systems — Clockify, Deputy, Rotacloud, BrightHR, or similar
• Payroll software — Xero Payroll, QuickBooks Payroll, Sage — export timesheet data
• Manual timesheets — for simple teams, a weekly CSV upload works

Once uploaded as a CSV (employee, week, regular hours, overtime hours, department), ask AskBiz:

• *'Which teams had the most overtime hours last month?'*
• *'Show me overtime cost as a percentage of base payroll by department over the last quarter'*
• *'Which weeks had the highest overtime hours and what was happening in the business then?'*

Consistent overtime in one team:

Investigate whether workload has grown permanently or temporarily. If permanent, build a business case for a hire using the overtime cost as part of the justification — often a new hire is cheaper than sustained overtime.

Overtime concentrated in one or two individuals:

Either those individuals have a disproportionate share of the team's workload (redistribution opportunity) or they are the only person who can perform certain tasks (single point of failure / training need).

Overtime that spikes at month-end or quarter-end:

Workload peaks are often smoothable. Review whether tasks are being concentrated unnecessarily at period-end and distribute them more evenly through the month.