The Problem With a Single Overtime Total

You pull the payroll report, see €2,400 in overtime for the month, and assume it's under control. But that number tells you almost nothing. Is it spread across 20 people logging an hour each, or is one team lead consistently hitting 15-hour weeks? Are your kitchen staff burning out while your front-of-house team has slack? One total hides all of that.

Role-level overtime tracking breaks the number down so you can act on it.

Why Roles Diverge

Different roles have structurally different overtime patterns. A few reasons:

  • Coverage requirements: Some roles can't leave a shift half-staffed. A security guard or a lone cook cannot walk out at 5 pm if their replacement hasn't arrived. Overtime in these roles is often unavoidable without a deep bench.
  • Skill scarcity: If only two people in your 25-person team can operate the forklift, those two will absorb every unplanned gap. Their overtime rate climbs while others stay flat.
  • Scheduling conventions: Supervisors and shift leads often stay late to close out, brief the next shift, or handle incidents. That extra 20-30 minutes per shift adds up fast across a full month.

What to Track Per Role

For each distinct role, track:

  • Total overtime hours in the period
  • Average overtime hours per person in that role
  • Number of people in that role logging zero overtime vs. 5+ hours
  • Overtime as a percentage of total hours worked
  • Cost of overtime vs. cost of hiring a part-timer for equivalent hours

That last metric - cost comparison - often surfaces the hire decision. If three supervisors each logged 12 overtime hours in a month at 1.5x pay, that's 54 hours of premium-rate labour. A part-time hire at base rate for 54 hours would cost significantly less. The data makes that visible.

Practical Thresholds to Set

Without thresholds, overtime data just accumulates. Set role-specific alerts:

  • Flag any individual in a role who logs more than 8 overtime hours in a week
  • Flag any role where overtime averages above 6% of total weekly hours for two consecutive weeks
  • Alert when a single person contributes more than 40% of all overtime in their role - that's a dependency risk

These numbers aren't universal. A busy restaurant kitchen in December might run 10% overtime as a baseline. The point is to set your own thresholds based on what's normal for each role, then track deviation.

Where Overtime Often Hides

Three places where overtime gets underreported or miscategorised:

  1. Clock-out rounding: Systems that round to the nearest 15 minutes can swallow small overtime increments. An employee who clocks out at 17:08 instead of 17:00 eight times a month has logged over an hour of unrecorded overtime.
  2. Role blending: When a cashier covers a supervisor absence, their hours may be tracked under the wrong role, distorting both sets of data.
  3. Split-shift overtime: An employee who works 5 hours in the morning and 4 hours in the evening may not hit the daily overtime threshold on paper, but their total weekly hours may still exceed the statutory limit.

Using the Data to Make Staffing Decisions

Role-level overtime data directly informs three types of decisions:

  • Hiring: Sustained overtime in one role points to structural understaffing, not just a busy week.
  • Training: If overtime concentrates in roles requiring specific certifications, training more people expands your coverage options.
  • Scheduling model: Some roles consistently generate overtime because of how shifts are structured - 8-hour shifts for a 10-hour operational window, for example. Switching to 10-hour shifts with staggered starts can eliminate overtime without adding headcount.

A Quick Audit to Do This Week

Pull last month's overtime data, split it by role. Find the top 3 roles by total hours. For each, check whether overtime came from too few people, specific days, or specific shift positions. That analysis - 30 minutes of work - often reveals the fix.

Try it at rezano.lv.