Logistics
In logistics, margin is lost quietly: a damaged pallet here, a badly closed route there. Good data turns invisible loss into a decision.
Talk to a specialistThe loss that never shows on the P&L
A logistics operation has an enormous volume of events and very little consolidated visibility. Delivery status is in one system, freight cost in another, and the damage report in a spreadsheet filled in by hand at the end of the shift.
The result is that the loss exists and nobody can say where it starts. The symptom gets treated more checking, more meetings without ever addressing the cause.
The questions we answer
If you cannot answer these with what you have today, the problem is data, not operations.
- Which route, which driver and which cargo type concentrate most of the damage?
- What is the real cost per delivery, including redelivery and returns?
- Where is the SLA being lost: pickup, transfer or last mile?
- Which customer brings volume but destroys margin after cost to serve?
- How many hours a month does the team spend consolidating reports instead of operating?
The indicators of this industry
The ones that show up in most projects in this industry. The final set is always agreed with you an indicator nobody uses is dead weight on a dashboard.
- Cost and margin. Real cost per delivery, cost per kilometre driven, redelivery and return cost, freight as a share of revenue, margin per customer after cost to serve, fleet idle time.
- Service level. On Time In Full (OTIF), SLA adherence by stage, average delivery time, redelivery rate, dock dwell time, pickup window met.
- Damage and incidents. Damage rate by route, by driver and by cargo type, average cost of damage, loss rate, incidents per thousand deliveries.
- Productivity. Deliveries per route, vehicle load factor, empty kilometres, productivity per checker, hours of manual consolidation.
The solutions we use most here
- Business Intelligence. The foundation: integrating TMS, ERP and field reporting into a single flow.
- Integration & AI Automation. The bots that remove incident typing and manual consolidation.
- Data Analytics. Forecasting delay and damage risk before the load leaves.
How we run it
We start by integrating the sources that already exist: TMS, ERP, incident spreadsheets and field reports. Then we model the indicators with the operations team, not apart from it the definition of "damage" has to be the same on the dashboard and on the warehouse floor, or the number will never be accepted. With a reliable base, the dashboards by role come in and, where it makes sense, the predictive models.
How we measure results
The most frequent indicators in this industry are reduction in damage loss, real cost per delivery, SLA adherence by stage and consolidation hours given back to the team.
At a last-mile operator in Mato Grosso, this path removed R$ 480,000 of annual loss from damaged goods.
About this industry
Our incidents are recorded on paper. Does it work?
It works, and it is the most common scenario. The paper record comes in as a source via scanning or a simple form on the checker’s phone and that is usually the first automation to pay for itself, because it removes the retyping and the error that comes with it.
We already have TMS reports. Isn’t that enough?
A TMS report shows what happened inside the TMS. What is almost always missing is crossing it with cost, with the customer and with incidents it is in the crossing that you see which customer brings volume and destroys margin.
How long until the first result?
The first dashboard usually goes live within three to six weeks. The initial assessment, which already points to where the biggest loss sits, comes in the first week.
What size of operation do you serve?
We work with operations of very different sizes. What changes is the scope, not the method: in a smaller operation the gain usually comes from consolidation automation; in a large one, from profitability analysis by route and customer.
Where is your invisible loss?
Thirty minutes of conversation is usually enough to find the first one.
We understand processes before recommending technology. Automation, data, artificial intelligence, and custom software for companies seeking efficiency, control, and scale.