AI development
for logistics & supply chain.
Logistics is where machine learning meets genuinely difficult data. Timestamps are approximate, statuses are entered by hand, and the same event is recorded differently by three systems. Most of the work is not modelling — it is building a data foundation reliable enough that a forecast means something.
Discuss your projectHigh-value use cases.
Demand forecasting
Volume prediction by lane, SKU, and period. The highest-value application, because inventory and capacity decisions flow directly from it. Requires honest handling of promotions and seasonality.
ETA prediction
Learned arrival estimates that beat static transit tables by incorporating carrier performance, weather, and congestion. Directly improves customer communication and reduces support volume.
Exception detection
Flagging shipments likely to miss commitment early enough to intervene. An anomaly detection problem where the value is entirely in lead time.
Routing and load optimisation
Constraint optimisation for vehicle routing and container fill. Frequently a solver problem rather than a learning problem — the ML contribution is accurate inputs such as service-time estimates.
Document automation
Extraction from bills of lading, customs paperwork, and invoices. High volume, structured output, immediate savings.
Freight cost prediction
Rate forecasting and spot-versus-contract decisions, supporting procurement with a defensible view of where the market is going.
Sector-specific constraints.
Generic AI advice fails here for specific, predictable reasons. These are the constraints that shape every design decision we make in logistics & supply chain.
- Data quality is the project — expect most of the effort in reconciliation and entity resolution, not modelling
- Multi-party data — carriers, ports, and customers all hold pieces, with inconsistent formats and latency
- Long feedback loops — an ocean forecast takes weeks to validate, which slows every iteration
- Shock sensitivity — port closures and demand spikes break models trained on normal conditions
- Thin margins — the accuracy needed to change a decision is often high, so scope to decisions that move money
- Optimisation, not just prediction — many problems need a solver; ML supplies its inputs
AI in logistics & supply chain.
Our operational data is very messy. Is machine learning still viable?
Yes, but sequence the work honestly. Data reconciliation, entity resolution across systems, and defining what each timestamp actually means typically consume the majority of a logistics project, and no model recovers information that the data does not contain. The practical approach is to pick one decision with clear financial value, build a clean data foundation for just that decision, and prove the return before broadening scope.
How accurate can ETA prediction realistically be?
It depends heavily on mode and how much of the journey is observable. Road freight with telematics can reach useful accuracy within a narrow window. Ocean freight has irreducible uncertainty from port congestion and vessel scheduling, so the honest deliverable is a calibrated probability range rather than a single time. A well-calibrated range that customers can plan against is more valuable than a precise-looking point estimate that is quietly wrong.
Should we forecast demand with machine learning or statistical methods?
Start with strong statistical baselines — seasonal decomposition and exponential smoothing are hard to beat on stable series and are far easier to explain and maintain. Machine learning earns its place when you have many related series to learn across, meaningful external drivers such as promotions and weather, or non-linear interactions. Always benchmark against the statistical baseline; on many real series it wins, and knowing that saves considerable spend.
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ReadLet’s scope it
properly.
Tell us the problem you are trying to solve in logistics & supply chain and we will tell you honestly whether machine learning is the right tool.
Start the conversationor write to us at [email protected]