Global General Merchandise Retailer Masters Hyper-Growth with AI-Powered Omnichannel Planning Across 500K SKUs

In retail, the gap between forecast and reality is measured in markdowns—and for a global general merchandise retailer operating 220 brick-and-mortar stores plus e-commerce channels across roughly 500,000 SKUs, that gap was costly. Fashion and seasonal merchandise require hyper-local demand intelligence: weather patterns, local demographics, pricing sensitivity, promotional timing, and assortment mix all shift demand at the store level. The retailer's incumbent Blue Yonder environment could not incorporate these external signals. Worse, assortment, financial, and supply planning ran on disconnected systems, creating siloed decisions. The result was excess inventory, elevated markdown rates, and stock imbalances that eroded gross margin.

The retailer replaced Blue Yonder with o9 Solutions' Enterprise Knowledge Graph, consolidating Forecasting, Allocation, and Replenishment onto a single AI/ML-based platform. The system ingests external demand drivers—weather, demographics, pricing, promotions, product assortment, and location—and synthesizes them into a unified forecast across 500,000 SKUs and 220 stores. The platform also supports both regular and cross-dock store allocation, with store capacities included in inventory planning decisions. Omnichannel planning spans brick-and-mortar and e-commerce on a single platform. By moving from disconnected legacy systems to o9's open-architecture platform, the retailer connected assortment, financial, and supply planning.

The deployment delivered a step-change in gross margin performance and reduced markdowns through localized forecast precision. Key outcomes included:

• Markdown reduction: Hyper-local demand signals cut excess inventory buildup.
• In-stock improvement: In-stock levels increased.
• Planner productivity: Planners became more productive.
• Sustainability gains: An improved forecast reduced inventory and supported efficient capacity utilization.

Collectively, these outcomes converted what had been a source of supply chain volatility into a competitive advantage in a high-SKU, high-seasonality environment.

• Unify planning before optimizing forecasts: Disconnected assortment, financial, and supply systems amplify forecast errors—consolidation onto a single platform is a prerequisite for AI to deliver margin impact.
• Hyper-local signals are non-negotiable for seasonal retail: Weather, demographics, and location-level data must feed the model directly; aggregate forecasts cannot substitute for store-level precision.
• Store capacity belongs in inventory planning: Ignoring physical storage constraints upstream creates downstream replenishment failures regardless of forecast accuracy.