Logistics has never had much room for error, and that room is shrinking. The American Transportation Research Institute (ATRI) found that the average cost of operating a truck in 2024 was $2.260 per mile, with non-fuel costs at the highest level it has ever recorded. Truckload carriers averaged an operating margin of -2.3 percent. These are US figures, but any Canadian fleet running cross-border lanes will recognize the pressure.
When margins are that thin, software is no longer a nice-to-have. But not every project pays back at the same speed. A new routing engine, a warehouse system and an AI forecasting model can each be the right investment, just not in the same year. The question is where to start.
Three tests for ranking a logistics software project
Before comparing tools, score each idea against three questions:
- Is the data already there? Projects that use data you already capture (timestamps, GPS pings, order records) move fast. Projects that need new data collection move slowly.
- How big is the leak? Put a dollar figure on the problem: hours lost, miles driven empty, calls answered, fees not collected.
- How fast can it go live? A tool that one team can adopt in weeks beats a platform that needs every depot to change at once.
Run the usual candidates through those tests and a clear order appears.
1. Dock scheduling and detention capture
This is often the fastest win, because the leak is large and the data is simple. ATRI’s study of truck driver detention found drivers were detained at 39.3 percent of all stops in 2023, costing the US industry $3.6 billion in direct expenses and $11.5 billion in lost productivity. Even more telling: while 94.5 percent of fleets charge detention fees, they are paid for fewer than half of those invoices.
Most of that loss comes down to missing evidence. Appointment booking, automatic arrival and departure timestamps from geofences, and detention invoices that attach the proof cut both the waiting and the disputes. For shippers and warehouses, the same scheduling data smooths dock labour and reduces yard congestion.
2. Shipment visibility that flags exceptions
Visibility is the investment logistics teams ask for most. In Descartes’ 2024 benchmark survey of more than 630 companies across the US, Canada and Western Europe, real-time transportation visibility was the top transportation IT investment for the seventh year in a row.
The payback does not come from a map full of moving dots. It comes from exceptions: an alert when a load will miss its window, sent early enough to rebook a dock or warn a customer, and fewer “where is my truck” calls for your team to answer. Most fleets already have the raw data in their ELD or telematics platform and their carriers’ tracking feeds. The work is joining it to orders and deciding which exceptions deserve a person’s attention.
3. Routing and load planning
ATRI reports that empty miles rose to an average of 16.7 percent in 2024. Every one of those miles carries the full cost per mile and earns nothing. Better route planning, load consolidation and backhaul matching go straight at that number, which is likely why fleet routing jumped from eighth to third place in the same Descartes survey.
Routing ranks third rather than first because it depends on clean inputs. Optimization is only as good as your addresses, delivery windows, vehicle constraints and driver rules. If those live in spreadsheets and dispatchers’ heads, the first project is getting them into a system, and the optimizer comes next.
4. Warehouse visibility and slotting
Inventory accuracy, slotting and pick paths can return a lot in high-volume buildings, but the payback depends heavily on scale and on the systems already in place. For many operators the winning move is not a new warehouse management system. It is connecting the existing one to transportation and order data, so the warehouse knows what is arriving and the dispatcher knows what is ready to leave.
5. AI prediction, once the data can support it
Predictive ETAs, demand forecasting and dynamic pricing are where the headlines are, and they can be very valuable. They rank last here not because they matter less, but because they feed on the clean, connected data that steps one to four produce. As we covered in overcoming roadblocks to AI in supply chain, layering AI on a fragmented foundation tends to lock in the problems it was meant to solve. Build the data first and the models get much easier.
Connect before you replace
A common thread runs through all five: the biggest returns rarely come from ripping out a TMS or WMS. They come from the integration layer between transportation, warehouse, telematics and ERP systems that currently do not talk to each other. Our guide to designing applications for integration covers the patterns. In practice this often means a custom software layer that pulls events from each system, applies your business rules and pushes the result where people work.
A 90-day starting plan
- Weeks 1 to 2: size the leaks. Pull six months of detention, empty-mile, late-delivery and customer-call data and put a dollar figure on each.
- Weeks 3 to 4: pick one. Choose the leak with the best mix of size and data readiness. For many teams that is detention or exception alerts.
- Weeks 5 to 10: build a focused proof of concept for one lane, one customer or one depot, connected to your real systems.
- Weeks 11 to 13: measure and decide. Compare hours, miles or fees recovered against the baseline, then scale it or move to the next leak.
Build it with 247 Labs
247 Labs builds custom software and integrations for operators who need their systems to work together, from dock scheduling and visibility tools to the data foundation that makes AI practical. If you know where your margin is leaking, talk to our team about scoping a first project.
