This question gets asked constantly, and it almost always gets answered badly. Someone quotes a figure from a trade publication, a target gets set, and technicians are measured against a number drawn from companies that do different work in different places.
The honest answer is that there is no transferable number, and that you can compute your own in about ten minutes. So let us do that instead.
The calculation
The daily capacity of one technician is governed by four inputs:
- S — shift length in productive hours (paid hours minus shop time, breaks, paperwork)
- J — average hands-on duration of one job
- D — average drive time between two consecutive jobs
- F — first and last drive of the day (depot out, depot back), if you pay for it
The maximum number of jobs N that fit is the largest whole number satisfying:
N × J + (N − 1) × D + F ≤ S
Rearranged, before rounding down:
N ≤ (S − F + D) ÷ (J + D)
That is the whole model. Everything else is commentary on the inputs.
Worked example
A company with a 9-hour paid day, of which 8 hours is genuinely available field time. Average job: 1.5 hours hands-on. Average drive between jobs: 25 minutes. Depot-out and depot-back total 40 minutes.
Four. Not six, not eight. And notice what happens if the average drive rises from 25 minutes to 40:
Fifteen minutes of additional average drive costs a quarter of the day's output. That is the entire argument for taking drive time seriously, expressed as arithmetic rather than opinion.
Why your real number is lower than this one
The formula gives a ceiling under ideal conditions. Reality subtracts from it in several predictable ways.
Jobs overrun. If 20% of jobs run 50% long, your effective J is higher than your average J. Use a realistic percentile, not the mean — the mean is dragged down by the quick ones and you do not schedule for the quick ones.
Not every hour is sellable. Customers who want mornings, a recurring commercial account that owns Tuesday afternoons, an apprentice who needs supervision. Constraints reduce packing efficiency in ways the formula does not see.
Access and parts. Waiting for a gate code, a tenant, or a part is time inside the shift that is neither job nor drive.
The day is not uniform. A 7:00 a.m. drive and a 4:30 p.m. drive across the same distance are different. If your D is a single average, it is wrong in both directions at different times of day.
A practical approach: compute the ceiling, then compare it against what your technicians actually complete. The gap is the thing worth investigating. If the formula says four and your crews do four, your scheduling is fine and growth means more trucks or shorter jobs. If the formula says four and your crews do two and a half, you have between one and two jobs per technician per day of recoverable capacity, and the next section is for you.
The ceiling is real. Some trades cap out on arithmetic alone. If your standard job is three hours hands-on and your productive shift is eight, then three jobs is the ceiling and no scheduling software will produce five. Be suspicious of any vendor — including us — whose case study implies otherwise without showing the service durations underneath it.
The input that moves the most
Look at the formula again: N ≤ (S − F + D) ÷ (J + D).
You have limited control over S — lengthening shifts has costs and limits. You have limited control over J — the work takes what it takes, and rushing it produces callbacks, which are negative capacity.
D is the variable that is genuinely in play, and it is the one most companies never touch, because it is not experienced as a decision. Nobody chooses to drive forty minutes between jobs. It is the residue of a sequence that was assembled one phone call at a time.
Three things reduce D, in ascending order of leverage:
- Sequence the day better. Given a fixed set of jobs on a truck, put them in a sensible order. Real, but bounded — the set was already chosen.
- Assign each job to the truck that will be nearest it at that hour. Note at that hour: proximity is a property of a technician at a point in time, not a fixed attribute, and it depends on what else is on their day.
- Do not offer times that create the drive. The largest lever, because it operates at booking rather than dispatch. If the system checks whether a qualified crew can physically reach the address by the requested time — computing from where that truck will actually be — then infeasible sequences never enter the schedule to begin with.
Using the number well
Once you have your own figure, resist turning it into a quota. A technician measured on job count will find ways to raise job count, and some of those ways are callbacks, skipped diagnostics and unhappy customers.
Better uses:
- Capacity planning. Jobs per day × technicians × working days tells you what you can sell before you need another truck.
- Hiring triggers. If you are consistently at the calculated ceiling and turning work away, that is a defensible hiring case. If you are well below it, hiring buys you a second underused truck.
- Territory decisions. Compute
Nseparately for each territory. A zone with a much lower ceiling is telling you something about density or drive distance that is worth acting on. - Pricing. If a job type has a duration and drive profile that limits the day to two, it has to carry more margin than one where four fit.
What to take away
- Published jobs-per-day benchmarks average across trades and territories with nothing in common — they will mislead you.
- Compute your own: N ≤ (S − F + D) ÷ (J + D), using honest service durations and real drive times.
- Drive time is the input most companies never touch and the one most available to change.
- Compare the calculated ceiling against actual completions — the gap is your recoverable capacity.
- Some trades cap out on arithmetic alone. Scheduling recovers lost hours; it does not create time.
- Use the number for capacity planning and hiring decisions, not as a technician quota.
Common questions
How many service calls should a technician complete per day?
There is no transferable number. It depends on average service duration, average drive between jobs, shift length, and how much of the day is lost to non-job activity. A company with forty-five minute jobs in a dense suburb and one with four-hour jobs across a ninety-mile radius have no comparable answer. Run the calculation with your own inputs.
Why do published benchmarks for jobs per day vary so much?
Because they average across trades, territory densities and job types that have nothing in common. A figure drawn from urban plumbing maintenance will badly mislead a rural HVAC install business, and vice versa. Benchmarks are useful for questions about your own trend over time, not for setting targets copied from someone else.
What is the fastest way to increase jobs per day?
Usually reducing drive time between jobs rather than working faster. Technicians are generally not the constraint — the sequence they were given is. Look at how much of the shift is travel before asking anyone to speed up.