Retail
Solving problems for inventory optimization, demand forecasting, and pricing optimization.
PROBLEM
In the highly competitive retail market, success depends on having the right product at the right time and the right price. Current computing technology imposes significant limitations on true delivery optimization.
SOLUTION
This immense computational load, currently shouldered by quantitative analysts and classical computing pipelines, is a perfect fit for the flexibility, power, and speed that the Quantum Trifecta™ offers.
Inventory Optimization
Deciding what inventory to stock and for how long can be a daunting task for businesses. Variable demand across thousands of items must be considered, and wrong decisions can result in major bottlenecks for retail. Even with accurate demand forecasts, the scale of this computational problem makes it very difficult for classical methods to generate optimal solutions. The Quantum Trifecta™, with its combination of ML and quantum computing, can tackle this complexity and guide inventory management plans and adjust them in response to new market data.
Demand Forecasting
Quantum machine learning can be used to analyze time-series market data to uncover useful patterns in demand curves over time. Businesses, in turn, can use these insights to inform their actions as they plan to meet future demand, helping them to avoid costly operational bottlenecks while keeping profit margins in mind.
Pricing Optimization
Deciding how to price products can be framed as an optimization problem where pricing strategy must maximize profit while considering relevant market data and knowledge of one’s own operational costs. Quantum algorithms in the near- and mid-term will prove highly effective at performing these calculations, especially with the added flexibility that Al and human elements would provide under the Quantum Trifecta™ model.
