Predictive Network Optimisation
One network, no single view of it. Build the digital twin that tests how the network should change before anyone changes it.
EMX is the logistics arm of the 7X Group — the delivery operator behind this challenge. What you build here has a direct pathway into EMX and the wider 7X ecosystem.
The problem
A logistics network is a living system of hubs, vehicle types, courier fleets and service models spread across the country. Each part keeps its data in its own system, so nobody sees the whole picture at once.
That makes simple questions hard. Can the network absorb a new customer? Which hub has spare capacity? What does it really cost to serve one emirate versus another? Answering just one of those takes around eight hours of manual work, and the answer changes depending on who you ask.
The bigger question never gets asked. What happens if we move a hub, change the fleet mix, or run two delivery waves instead of one? 7X is looking for a network digital twin: a live model that watches how the network performs, simulates changes before they happen, and recommends the most efficient shape for the network as demand grows.
What we're seeing
The numbers to move
- 5%
Reduce average network cost-to-serve by approximately 5% through better network design
- 80%
Enable at least 80% of capacity and network planning decisions to be made through the platform rather than manual analysis
What a winning build looks like
Build a prototype that demonstrates measurable improvement in network visibility and strategic decision-making over the current manual baseline using the provided dataset. Your solution should show how AI can give EMX a unified view of its network, simulate the impact of changes before they're made, and generate clear, explainable recommendations on what to do next. Judges will evaluate based on the depth of AI implementation, how well the solution addresses the problem, technical execution, and real-world viability.