In industries like retail, manufacturing, and logistics, accurate demand and supply planning is critical for minimizing waste, avoiding stockouts, and meeting customer expectations. Traditional forecasting models struggle with high variability, sudden demand spikes, or external disruptions.

The Challenge:

• Forecasting is often based on linear, historical trends

• External factors (weather, seasonality, global events) are difficult to incorporate

• Supply-demand mismatch causes lost sales, delays, or overstock

• Traditional models lack adaptability in volatile or high-dimensional environments

Quantum-Inspired Solution

Multi-Scenario Simulation

HessQ uses QUBO-based optimization to simulate thousands of demand-supply variations in seconds — even under uncertainty.

The model continuously recalibrates as new inputs (e.g., weather, holidays, market signals) arrive — unlike static rule-based systems.

Determines the most efficient distribution of resources across warehouses, stores, or production units — reducing waste and improving service levels.

Dynamic Forecast Adjustment

Optimal Inventory Allocation

The Results:

• +35% improvement in forecast accuracy

• Reduced excess inventory costs by up to 25%

• Shorter response times to demand changes

• Balanced inventory across the network, minimizing overstock/understock