Retail · Case study
Putting AI to work in retail planning and pricing.
A retailer connected AI demand forecasting, store allocation and markdown decisions with a shared platform and new ways of working.
Client context
The business context.
A retailer was applying AI across its value chain. The work linked demand forecasting for assortment and volume decisions with product allocation to stores and markdown choices intended to improve sales and sell-through.
The transformation required more than individual models. The retailer needed a common technology platform, organizational capabilities and a way to test and deploy use cases repeatedly while refining its AI strategy as the work progressed.
The work
How the engagement was delivered.
Connect AI to retail decisions
Developed demand forecasting to inform product ranges and volumes, allocation models at store and product level, and markdown optimization. Addressed distinct decisions across procurement, distribution and sales.
Establish a common platform
Set up a cloud-based AI and analytics platform to support the use cases. Connected the technology work with the capabilities needed to apply AI across the business.
Make testing and deployment part of the operating model
Introduced iterative testing and deployment, defined organizational capabilities and new performance indicators, and supported a culture of experimentation. The strategy evolved as teams learned from implementation.
What changed
A coordinated approach to applying AI across retail operations.
The retailer brought forecasting, allocation and markdown use cases into an ongoing AI transformation supported by a shared cloud platform. New capabilities, performance indicators and iterative working practices provided a basis for continued deployment and refinement.
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