How ERP Route Planning Cuts Fleet Miles on Field Projects

A regional propane distributor running 30 delivery trucks cut its average route length from 210 miles to 150 miles per truck per day after moving delivery scheduling out of a dispatcher's mental map and into ERP-driven route planning. At roughly $0.65 per mile in fuel, maintenance, and driver time, that 60-mile reduction across 30 trucks over a 250-day operating year works out to about $292,500 in annual operating cost — a number the dispatcher's intuition had never been able to see, because no single person can hold 30 trucks' worth of overlapping delivery windows in their head at once.

What route planning inside ERP actually optimizes

Consumer mapping apps solve for the shortest or fastest path between two points. ERP route planning solves a harder problem: sequencing dozens of stops for multiple vehicles simultaneously, subject to real constraints — customer delivery time windows, vehicle capacity limits, driver hours-of-service regulations, and delivery priority for time-sensitive accounts. It's a variant of what's known in operations research as the vehicle routing problem, and the "optimal" answer changes the moment any one constraint shifts, which is why doing it by hand doesn't scale much past a handful of trucks.

Tying route planning to project scheduling

For field-service organizations — utilities, HVAC service companies, telecom installers — route planning isn't just about delivery trucks. Each service call functions as a small project with its own scope, required parts, and estimated labor hours, and the ERP treats crew dispatch as an extension of project scheduling rather than a separate system. A utility company assigning crews across a service territory is effectively solving the same optimization problem as the propane distributor: which crew, in what sequence, minimizes total drive time while still hitting each customer's committed service window.

The math behind route optimization

A simple example illustrates why sequencing matters as much as the individual legs. A driver visiting five stops in the order they happen to appear on a delivery list (nearest-neighbor, essentially — always go to whichever stop is closest next) might cover 68 miles total. Solving for the truly optimal sequence across those same five stops can bring that down to 60 miles — an 12% reduction just from reordering the same set of deliveries, before factoring in traffic patterns or time windows at all. Multiply that percentage across a full fleet and a full year, and the savings compound quickly.

Telematics integration

Route planning only stays accurate if it's checked against what actually happened. GPS telematics feeding real-time location data back into the ERP lets a dispatcher compare planned routes against actual routes driven, flagging variance — a driver taking a longer path than planned, or idling for 20 minutes at a stop that should have taken five. That feedback loop is also how route plans improve over time: a route that looks efficient on paper but consistently runs long because of a specific intersection's traffic pattern gets corrected in the next planning cycle instead of staying wrong indefinitely.

Mobile access for drivers

None of the planning matters if drivers are still working from a printed manifest. Mobile ERP access lets a driver see their route on a phone or tablet, get turn-by-turn navigation tied to the actual planned sequence rather than a generic map app, and mark stops complete in real time so the office has live status instead of finding out at end of day. It also means a last-minute urgent stop can be inserted into an in-progress route with the driver getting an updated sequence immediately, rather than a phone call and manual recalculation.

A worked scenario with full numbers

Back to the 30-truck propane distributor: before route optimization, each truck averaged 8 stops per day across 210 miles. After optimization — accounting for delivery windows, tank capacity per truck, and driver hours limits — the same 8 stops averaged 150 miles, a 29% mileage reduction. At an estimated $0.65 per mile in fuel, maintenance, and driver time, that's $39 saved per truck per day, or $1,170 across the fleet per operating day. Over a 250-day operating year, that's $292,500 — enough to cover more than half the cost of a new delivery truck, generated entirely from better sequencing of the same deliveries the fleet was already making.

Where the savings actually come from

It's worth being clear that route optimization doesn't reduce the number of deliveries or the distance between any two fixed points — it reduces the wasted distance created by suboptimal sequencing and manual guesswork. For an operation running fewer than five or six vehicles, a good dispatcher can often approximate this by hand well enough that the software investment isn't justified. Past that point, the number of possible route combinations grows fast enough that manual planning reliably leaves money on the table, which is exactly where ERP-driven optimization starts paying for itself.

Driver hours-of-service compliance

Any fleet subject to Department of Transportation hours-of-service rules has a hard constraint route planning can't ignore: commercial drivers are generally limited to 11 hours of driving within a 14-hour on-duty window before a mandatory rest period. A route plan that looks efficient on paper but would push a driver past that limit isn't actually usable — it just creates a compliance violation and a logbook problem. ERP route planning that factors HOS limits directly into the optimization, rather than treating them as a constraint dispatchers have to check manually afterward, avoids building routes that have to be broken apart and redone once someone notices the driver would run out of legal hours two stops from the end.

Re-optimizing for seasonal and daily demand swings

A static route plan built once and reused every day works fine for stable, predictable delivery patterns, but plenty of fleets don't have that luxury. The propane distributor's delivery volume roughly triples between October and February compared to summer months, and running the same fixed routes year-round during that swing means either badly underutilized trucks in summer or overloaded, inefficient routes in winter. Re-optimizing routes daily or weekly based on current order volume, rather than reusing a template built for average conditions, is what lets a fleet actually capture the mileage savings across the full range of demand it sees over a year, not just on an average day.

When the investment doesn't pay off yet

Route optimization software isn't free, and for a fleet of two or three trucks running the same handful of regular stops every week, the manual planning a dispatcher already does by habit may leave very little money on the table to begin with. The economics tend to turn favorable once a fleet is large enough, or its delivery patterns variable enough, that no single person can reliably hold the full optimization problem in their head — usually somewhere in the range of eight to ten vehicles with daily-changing stops, though it depends heavily on how much stop-to-stop variability the operation actually has.

Proof of delivery tied back to the ERP record

Route planning gets a fleet to the right stops efficiently; proof of delivery closes the loop on what happened once the driver arrived. Capturing a signature, a timestamp, and a photo of the delivered product directly on a driver's mobile device, tied automatically to the original sales order in the ERP, removes a recurring source of billing disputes — a customer claiming a delivery never happened, or happened short of the invoiced quantity, is resolved by pulling up the exact record instead of relying on a driver's memory of a stop from three weeks earlier. For fleets that also handle high-value or regulated deliveries, that same record often becomes part of the compliance documentation auditors or customers ask for directly.