Quoting from a rate card nobody has tested
Rates were built from an assumed cost per mile that has not been checked against settled costs since it was written, so the commercial team is bidding blind on the lanes that matter most.

Logistics and freight
Freight businesses tend to know their revenue precisely and their cost per job approximately. Demurrage, waiting time, empty running, fuel surcharges and subcontracted legs land in accruals weeks later, by which point the quote that caused them is long forgotten. We join the operational record to the settled cost so a movement carries its full cost, and a lane, a customer and a contract can each be judged on what it actually returned.
Discuss your operationWhat we usually find
Rates were built from an assumed cost per mile that has not been checked against settled costs since it was written, so the commercial team is bidding blind on the lanes that matter most.
Detention, waiting time and subcontractor invoices land after the month has been reported, so the jobs that destroyed margin look fine at the point anybody was still paying attention.
It usually is not, but proving it needs the movements joined end to end, and the TMS holds legs rather than journeys.
Where we start
Scope depends on the state of your systems. These are the pieces that recur in this sector.
Linehaul, fuel, driver hours, subcontracted legs, detention and demurrage attached to the movement that incurred them, including the costs that settle weeks afterwards.
Profitability by lane, customer and contract, with the round-trip economics rather than the leg-by-leg view the operating system gives you.
What the last twelve months of settled cost says your rates should be, by lane, so the next negotiation starts from a defensible number.
The movements, customers and sites generating recoverable cost this week, ranked, with somebody named against each.
What changes
You own all of it: the code, the written definitions and documentation aimed at whoever maintains this after us.
The builds behind it
Your finance, customer and operational records joined into one dataset, with each figure defined once and traceable back to the system it came from.
Reporting built on top of the joined data, aimed at the few revenue and cost drivers that actually change the result.
One defined task, automated inside a process that already exists, measured against whatever it replaced.
Driver-based models built on the same definitions as your reporting, so the forecast and the actuals stop disagreeing.
Worth asking