Route Optimisation for Field Service: Less Driving, More Earning

Your dispatcher opens the board on Monday morning and starts assigning jobs. Ten techs, forty-something jobs, spread across a metro area. They know the rough geography — northside techs get northside jobs, southside gets south. Beyond that, the route order is a best guess based on appointment windows and gut feel.

By lunchtime, two techs are sitting in traffic crossing the city because a priority job came in on the wrong side of town. Another tech just drove past a job site they’re scheduled to visit tomorrow because today’s route has them zigzagging instead of looping. The fuel card bill at the end of the month will be eye-watering, and nobody will be able to explain exactly why.

This is what route planning looks like without optimisation. It works — in the sense that jobs get done — but it leaves a staggering amount of money on the road.

The Hidden Cost of Bad Routes

Most field service companies think of fuel as a fixed cost. You have vans, vans need diesel, diesel costs what it costs. But fuel is a variable cost — it scales directly with how many kilometres your team drives. And the number of kilometres they drive is a function of how intelligently their routes are planned.

Fuel. A field service van averaging 12 litres per 100km, driving 120km per day at $2.00 per litre, burns $28.80 in fuel daily. Across 10 vans and 250 working days, that’s $72,000 per year. If better route planning reduces daily distance by even 20%, you save $14,400 in fuel alone. That’s not theoretical — 20% is conservative for companies that have never optimised routes.

Windscreen time. Every minute your tech spends driving is a minute they’re not completing billable work. If a tech spends 2.5 hours per day driving instead of 1.5 hours, that’s an hour of lost productive capacity per tech per day. Across 10 techs, that’s 50 hours per week of billable time sitting in traffic. At $120 per hour charge-out rate, the opportunity cost is $6,000 per week — $312,000 per year.

Vehicle wear. Tyres, brakes, oil changes, and services all scale with kilometres. Reducing annual fleet kilometres by 15-20% has a measurable impact on maintenance costs and extends vehicle life. It won’t show up immediately, but your fleet manager will notice it over 12 months.

Manual Planning vs. Software-Assisted Optimisation

There’s a spectrum between “the dispatcher eyeballs it” and “an algorithm plans every route.” Most field service companies sit at the eyeball end. Understanding what’s available helps you decide how far along the spectrum to move.

The Dispatcher’s Mental Map

Most dispatchers are surprisingly good at route planning — for a small team. They know where customers are, they know traffic patterns, they know which tech lives near which suburb. When you have five techs and fifteen jobs per day, a good dispatcher can plan decent routes from memory.

The problem comes with scale. At ten techs and forty jobs, the number of possible route combinations is astronomically large. The dispatcher is no longer optimising — they’re satisficing. They’re finding a route that’s good enough, not the best available. And the gap between “good enough” and “optimal” widens as the number of jobs and techs increases.

Clustering by Zone

The simplest improvement over pure gut feel is geographic clustering. Divide your service area into zones and assign each tech to a zone for the day. Northside, southside, eastern suburbs, CBD, outer metro. This prevents the worst-case scenario of techs criss-crossing the city.

Zone-based planning is easy to implement, requires no software, and eliminates the most egregious route inefficiencies. But it’s blunt — it doesn’t account for job time windows, priority levels, or traffic patterns within a zone. And it can leave capacity imbalanced if one zone has 12 jobs and another has 4.

Route Optimisation Software

Dedicated route optimisation tools — like OptimoRoute, Route4Me, or built-in routing in platforms like ServiceTitan — use algorithms to calculate the most efficient sequence of stops for each tech. They factor in:

• Time windows. Customer appointments with specific arrival windows.
• Job duration. A 30-minute service call and a 4-hour installation need different scheduling.
• Traffic. Real-time or historical traffic data to estimate actual travel times, not just distances.
• Tech skills. Certain jobs require certain qualifications — the route planner only assigns jobs a tech can actually complete.
• Priority. Urgent callbacks or SLA-bound jobs get scheduled first; flexible jobs fill gaps.

The output is a sequenced route for each tech that minimises total drive time while respecting all constraints. The dispatcher reviews and adjusts rather than building from scratch.

Real-Time Re-Optimisation

Static morning routes are a good start. But field service days rarely go to plan. A job runs long. A customer cancels. An emergency call comes in. Each disruption invalidates the rest of the planned route.

Re-optimisation means the system recalculates routes on the fly when things change. The emergency job gets inserted into the nearest tech’s route at the point that causes the least disruption. The cancelled appointment is removed and the remaining jobs are resequenced. The tech who’s running behind gets their afternoon jobs redistributed to other techs with capacity.

This level of dynamic planning is where the gap between manual dispatching and software-assisted routing becomes enormous. A dispatcher managing ten techs across forty jobs cannot mentally recalculate optimal routes in real time. They make the best quick decision they can — which is usually sending the closest tech to the emergency and leaving everyone else’s route unchanged. The software recalculates everything in seconds.

The Dispatching Connection

Route optimisation doesn’t exist in isolation. It’s tightly connected to how you dispatch. If your dispatch process assigns jobs to techs one at a time as they come in, optimising routes is impossible — there’s no batch of jobs to optimise across. The most effective route planning happens when tomorrow’s jobs are grouped and sequenced the afternoon before, with same-day reactive jobs slotted into the existing route structure.

This requires a shift in how many companies think about dispatching. Instead of “which tech should do this job?” the question becomes “given all the jobs we need to complete today, what’s the best distribution and sequence across all available techs?” It’s a fundamentally different question, and it produces fundamentally different results.

Where to Start

Step one: Measure what you have. If you’re not tracking daily kilometres per tech, start. GPS data from your fleet tracking or even Google Maps timeline on work phones gives you a baseline. You can’t improve what you don’t measure.

Step two: Cluster by geography. If you’re not already grouping jobs by area, start with simple zone-based assignment. This is free, requires no software, and typically saves 10-15% on drive time immediately.

Step three: Sequence within zones. Once jobs are clustered, plan the order within each zone. Even manual sequencing — plotting the jobs on a map and choosing a logical loop — beats the default order of “whatever was booked first.”

Step four: Evaluate software. If you have more than eight techs and more than 30 jobs per day, the complexity of manual optimisation means you’re leaving money on the table. Run a trial with route optimisation software and compare actual results against your manual baseline.

The maths on route optimisation is straightforward: less driving means more jobs, lower costs, and shorter customer wait times. The companies that treat routing as a strategic lever — rather than a dispatcher’s best guess — consistently outperform on all three.