AI in Logistics & Supply Chain
Where it is genuinely working
Demand and capacity forecasting
Volume, staffing, vehicle and warehouse capacity planning.
Document automation
Bills of lading, customs declarations, invoices, proof of delivery — a document-dense sector where extraction and validation removes substantial manual keying.
Exception handling
Delays, damages, misroutes and address failures triaged and actioned automatically, with escalation on the cases that need judgement.
Customer communication
Tracking queries, delivery updates and rescheduling — extremely high volume and highly repetitive.
ETA prediction
Arrival estimates accounting for route, traffic, historical performance and conditions.
Route and network optimisation
Genuinely valuable, and usually a constraint-solving problem rather than a learning problem.
Practical constraints
Data is often spread across TMS, WMS, carrier systems and customer platforms that do not reconcile. Integration is usually the first phase.
Real-world variability is high — weather, traffic, customs, carrier performance — and models need to represent uncertainty rather than produce single-point predictions that operations then over-trust.
Customs and trade documentation carries compliance consequences for errors, so validation and human review thresholds matter more than in most sectors.
Services that apply most
Forecasting and ETA work is machine learning development. Document processing is computer vision. Exception handling is agentic AI & automation. Customer queries are NLP & conversational AI. System consolidation is AI data engineering and ERP & CRM integration.
Frequently asked questions
Route optimisation is usually a constraint-solving problem — vehicle capacity, time windows, driver hours — better served by operations research than machine learning. ML contributes by predicting the inputs, such as travel time and service duration. We recommend the right tool rather than the fashionable one.
Better than static estimates, and improving with historical data on your actual lanes and carriers. Uncertainty is inherent and the useful output is a range with confidence, not a false-precision timestamp.
Extraction and validation, yes, with human review on anything consequential. Error consequences in customs are significant enough that we design conservative review thresholds.
It is the first phase. Integration work usually delivers value on its own before any model is involved.
One of the highest-volume, most repetitive query types in any sector, and correspondingly well suited to automation. Usually the fastest visible win.
Document processing and customer query handling, yes, at almost any scale. Forecasting and optimisation need enough volume to learn from.