Engineering
Teaching AI to understand a logistics operation
How we are researching models that can form a coherent view from complex operational data.
A logistics operation is not one dataset. It is drivers, vehicles, routes, defects, disputes, service levels, capacity, cost and management decisions, all changing at the same time. The challenge for AI is not simply reading those signals, but understanding how they relate to one another. That is one of the core research problems behind Depot-1.
The operation has to be understood as a system
Most operational tools look at one part of the business at a time. A fleet system understands vehicles, a scorecard shows performance, a workforce tool tracks people and a finance system records cost. The operation itself sits across all of them.
If performance falls, the cause may not be visible in the metric that moved. A driver issue may begin with poor route allocation, a service failure may be linked to vehicle availability, and a recurring defect may point to a management process rather than an individual mistake. For an AI system to be useful, it needs to reason across those relationships rather than treat every signal in isolation.
Context changes the meaning
The same number can mean different things in different operations. A rise in defects may be serious in one environment and temporary in another. A driver who appears to be underperforming may be working the most difficult routes, while a capacity problem may actually be caused by attendance rather than fleet size.
Our research with Depot-1 explores how a model can combine current performance with historical patterns, operational constraints and management context to form a more complete view of what is happening. The aim is not simply to detect change, but to understand what that change means in context.
From signals to causes
Detecting a problem is relatively easy. The harder question is whether the model can identify what is actually driving it.
We are particularly interested in the difference between correlation and useful operational reasoning. If two things move together, that does not automatically mean one caused the other. A useful system should be able to weigh evidence, consider alternative explanations and recognise when the available information is not enough to support a confident conclusion.
Understanding what matters most
Even when several problems are real, they are not equally important. Managers have limited time and teams have limited capacity, so an operational model needs to do more than identify issues. It needs to help rank them.
Which problem is having the greatest effect? Which one is most urgent? Which one is most likely to improve if management acts now? That ability to prioritise is a major part of what we are researching with Depot-1.
Learning from outcomes
The strongest signal often comes after a decision has been made. If an intervention is introduced and performance improves, that result adds evidence. If nothing changes, the original diagnosis may have been incomplete.
Over time, that creates a learning loop:
State → decision → intervention → outcome
The more consistently those relationships can be captured, the better a model can become at understanding what tends to work under different operating conditions.
Why this matters
Logistics does not need AI that simply produces more analysis. It needs systems that can make sense of complexity, surface the right problems and help people decide what deserves attention next.
That is the standard we are aiming for with Depot-1: not just reading the operation, but understanding it.