Introduction
There is no room for chance in logistics. When a product has to reach the customer in perfect condition, and that too abroad, in non-standard packaging, by three different carriers, every decision counts. And there are more and more of these: the type of transport, its availability, cost, risk of delay or damage. How not to sink in this chaos?
More and more companies are opting for prediction, decisions supported by data and AI models. A well-built machine learning system can predict which packages are most likely to be damaged and suggest the best shipping method. This is not futurism. It's already working.
Intuition is no longer enough
Until recently, many companies made logistics decisions based on the experience of the team and general guidelines. If a piece of furniture is large, it goes by dedicated transport. If a package is going to Germany, we choose company X because “they don't usually fail.”
But in the world of data-driven logistics, “usually” is not enough.
Variables such as:
- size and number of packages,
- product material,
- destination country,
- local carrier,
- history of complaints in the region
…can (and should) be recalculated. AI can pick up patterns that a human may not notice and pinpoint the decision with the highest probability of success.
How does AI-based logistic prediction work?
This is not a magic black box. Every predictive project in logistics is based on specific historical data.
Key steps:
- Data collection: from CRM systems, ERP, delivery tracking, claims history.
- Cleaning and preparation: normalization, error exclusion, feature engineering.
- Model training: on labeled data - e.g., whether a shipment arrived whole or damaged.
- Process integration: the model indicates a recommendation (e.g., “dedicated transportation” on a specific order) in the operator panel.
To podejście nie tylko zwiększa trafność decyzji, ale zmniejsza koszt błędów: reklamacji, zwrotów, reorganizacji transportu, utraty zaufania klienta.
It works. This is confirmed by our implementations
At one furniture company operating in a direct-to-consumer model throughout Europe, we implemented a predictive model to support logistics decisions. This company's products are individually designed by customers, so each shipment is unique.
The model, based on data from thousands of shipments, indicated the probability of damage with different transportation options. The result? Logisticians gained concrete hints - not just “what usually works,” but what will work in this particular case.
The results were:
- reducing the number of damages,
- more conscious use of dedicated transportation,
abandonment of carriers whose risks were too high.
What do you need to get started with AI in logistics?
Not every project needs to take off on a grand scale. But a few things are essential:
- Data - preferably from multiple sources: ERP, orders, transportation, claims.
- Domain knowledge - someone who knows which variables really matter.
- A team or partner - not every IT team needs to know everything. Sometimes it's better to focus on integration, not on building models from scratch.
Process openness - AI can change the way decisions are made. You have to be willing to accept that.
Conclusion: anticipate before you fix
AI in logistics does not eliminate all problems. But it allows you to make better decisions, faster. Minimize costs that were previously “included in the risk.” And create a system that learns from mistakes, and not just human ones.
Because sometimes all it takes is one less well-predicted package to complain about to make it all worthwhile to implement.
Want to learn how such models could work in your company?
See how we help operations teams connect data to decisions.
