AI can optimise a route it cannot cost.
AI is moving fast through logistics: dynamic route optimization, demand forecasting, warehouse automation, last-mile orchestration. Every one of those decisions is only as good as the cost data underneath it. An algorithm that minimises distance while ignoring stop time, liftgate and cash handling optimises the wrong number. The operators who get value from AI are the ones who already know their true cost to serve.
Cost and Profitability Consulting · 150+ models since 2010 · TDABC
AI in logistics improves route planning, demand forecasting, warehouse automation and last-mile orchestration, but it can only optimise on the cost data it is given. Since last mile is 40 to 53 percent of cost and cost to serve varies 5 to 10 times between customers, an AI that optimises distance instead of true cost to serve will confidently make the wrong call. A TDABC cost base, the real minutes per stop, per pallet, per delivery, is what turns AI from a distance minimiser into a margin optimiser. People matter as much as the model: the planners reading its output need to understand cost to serve to override it well.
Four shifts, one dependency.
Dynamic route optimization
AI re-plans routes in real time. It only protects margin if its objective is cost to serve, not distance.
Demand forecasting
Better forecasts smooth fleet and warehouse load, letting capacity match real need instead of a flat plan.
Warehouse automation
Automation changes the cost mix of receiving and picking. TDABC is how you see by how much, and whether it paid.
Last-mile orchestration
The 40 to 53 percent of cost that is last mile is exactly where a wrong objective does the most damage.
Give the algorithm the right number to chase.
An AI told to minimise distance will minimise distance, and confidently route a driver into a high-touch, cash-paying, liftgate stop it has priced as cheap. Feed it a TDABC cost base instead, the real minutes per stop, per pallet, per delivery, and the same algorithm becomes a margin optimiser. This is not a regulatory countdown; it is decision quality. Budget the human side honestly: the planners who read the output need to understand cost to serve well enough to override the model when distance and margin disagree. A tool the team cannot interrogate is cost without control.
Frequently asked questions
- How is AI changing cost in logistics?
- AI improves route planning, demand forecasting, warehouse automation and last-mile orchestration, but it can only optimise on the cost data it is given. Since last mile is 40 to 53 percent of cost and cost to serve varies 5 to 10 times between customers, an AI that optimises distance instead of true cost to serve will confidently make the wrong call.
- Can AI replace cost-to-serve modelling?
- No. AI optimises the objective it is given. A TDABC cost base, the real minutes per stop, per pallet, per delivery, is what turns AI from a distance minimiser into a margin optimiser. Without it, a sophisticated model just reaches the wrong destination faster.
- What do teams need to use AI well in logistics?
- The planners reading the model's output need to understand cost to serve well enough to override it when distance and margin disagree. People and training matter as much as the algorithm; the operators who get value from AI are the ones who already know their true cost to serve.
Get the cost base AI needs underneath it.
The Profit Check needs no data upload. It shows whether your cost data is ready to point an AI at margin instead of distance, and what to fix first.
- Duration
- 12 to 15 minutes
- You receive
- Score, 7 dimensions, sector benchmark
- Price
- Free, no email needed
Who you would be talking to
Miguel Guimarães, Founding Partner
Cost and profitability practitioner for 25+ years. Lectured alongside Professor Robert S. Kaplan at the CFO conference in Amsterdam (2009).
Call +351 910 313 731
