Industry
Logistics & Supply Chain
Routing, forecasting and warehouse problems where the constraint set changes faster than the plan does.
Problems we address
- Routing plans already stale by the time they reach the driver
- Demand forecasts that miss precisely when the miss is most expensive
- Warehouse layouts optimised once, years ago, for a different order mix
- Last-mile costs that resist every obvious lever
How we approach it
Quantum & Advanced Computing
Combinatorial problems formulated as QUBO and tested honestly against the classical baseline you already run.
AI & Automation
Forecasting and exception handling wired into the systems dispatchers actually use, not a parallel dashboard.
Product Engineering
Operational visibility across carriers, warehouses and orders.
Related research
Maturity labels are not decoration. Most of this is at concept stage.
Real-Time Route Optimization
QUBO/annealing approaches for dynamic routing under traffic, capacity and timing constraints.
Demand Forecasting
Quantum-enhanced machine-learning concepts for complex demand patterns and supply planning.
Warehouse Management
Slotting, picking, labour, inventory and order-flow optimisation.
Last-Mile Delivery Enhancements
Dispatch, vehicle routing, delivery windows, energy and customer-priority optimisation.
Quantum Powered Smart Mobility
Signal timing, fleet positioning and transit scheduling all interact, but the systems controlling them rarely do — local optimisation produces globally poor outcomes and nobody owns the global objective. We are looking at whether joint formulations across those layers are tractable, and whether the binding constraint is even computational. It may well be institutional: this needs a city willing to share data across operators and agree one objective, which is harder than the mathematics.