Logistics
Haulage, Government Contracts, Transportation Hub
Logistics
With transportation problems going from bad to worse, CEO Keith Dyer needed solutions fast.
Data Science
Data Science finds a solution to the bottleneck in this company's distribution nightmare.
Background
Keith Dyer is CEO of a well-established logistics company which oversees a transportation hub.
Problem
The current system was failing the organisation, with empty incoming trucks waiting around for loading and outgoing loaded trucks getting delayed in congestion. Keith could see the transport hub was inefficient which was causing delays, frayed tempers and congestion.
With a fixed terminal size, there was no option to expand and Keith realised that solving the problem depended on developing a more efficient system.
Action
After connecting with Jose, Keith engaged him to find the best allocation of shipments to backhauling trips using AI and mathematical modelling.
Outcome
Jose implemented a hybrid model that allocates shipments to trucks, while optimising these for the best economic outcome.
How we helped
Jose immediately identified that every forecast indicates an increase in transportation needs for the future (scaling with population growth). This meant that the transportation and congestion problems were only going to get worse.
To solve this problem, he created a heuristic optimisation model which improved the assignment of shipments to backhauling trips. However, the speed and efficiency of the assignments were impractical, so after discussion it was agreed the process needed simplifying.
From here Jose developed a model that could produce answers in a timely fashion. The result is a hybrid system that allocates shipments to trucks, whilst optimising the economic result of the allocation.
Risk free
exploratory session to evaluate data
Mathematical modelling
implemented and validated
Development of modelling
tool for practical application
Less congestion and delays
The model is currently being implemented as a solution, the expected benefits are reduced economic and environmental costs, improved use of the surface area of the transportation hub and more efficient allocation of logistic resources in the network.
