Logistics
Demand ForecastingMaturity: concept
Quantum-enhanced machine-learning concepts for complex demand patterns and supply planning.
concept — An idea we find credible. Nothing has been built or measured.
What this is
The problem
Forecasts drive purchasing, staffing and inventory. They are usually accurate in the steady state and wrong exactly when conditions change — which is when the decisions they inform matter most.
Where the current approach strains
Statistical methods extrapolate from history and cannot see a regime change coming. Machine-learning methods can capture more structure but need enough examples of the pattern to learn it, and disruptions are by definition rare.
What we are exploring
Quantum-enhanced machine-learning concepts for high-dimensional demand patterns with many interacting drivers. Also, less glamorously and more usefully: better uncertainty quantification, so a planner knows when the forecast is guessing.
What would have to be true
Backtesting against held-out history, including the disruptions — measured on the periods where the incumbent method failed, not on the average where it does fine.
Where it applies
Related
Real-Time Route Optimization
QUBO/annealing approaches for dynamic routing under traffic, capacity and timing constraints.
Warehouse Management
Slotting, picking, labour, inventory and order-flow optimisation.
Last-Mile Delivery Enhancements
Dispatch, vehicle routing, delivery windows, energy and customer-priority optimisation.