Leading European Shoe Retailer (anonymized)

Operating 980 stores across 23 countries, this leading European shoe retailer had outgrown the limits of manual inventory management. No off-the-shelf tool could accommodate the retailer's operational complexity — including sorter capacity constraints, shipment window limitations, and package prioritization across a multinational distribution network. Planners manually allocated stock across hundreds of locations with limited visibility into real-time demand signals, creating persistent availability gaps that translated directly into lost sales. At the same time, excess inventory accumulated in the wrong locations, generating carrying costs and markdown exposure that manual reallocation processes could not resolve at the required speed or scale.

invent.ai deployed two purpose-built AI modules — Store Replenishment and Transfer Optimization — both powered by machine learning and predictive analytics. The replenishment solution continuously ingests demand signals and current stock positions, calculating optimal replenishment quantities for each store while respecting the retailer's specific constraints around sorter capacity and shipment windows. The transfer optimization module complements this by identifying inventory imbalances across the 980-store network and generating recommendations to redirect slow-moving stock toward higher-demand locations. Both solutions were integrated with the retailer's existing operational systems without a phased pilot — a full enterprise rollout across 23 countries completed within six months, demonstrating the platform's scalability and deployment efficiency.

The deployment delivered measurable gains across every tracked dimension within the implementation period:

• $21.4M in additional sales revenue — the combined outcome of improved availability and optimized stock positioning across the network
• 11.95% reduction in lost sales — directly reflecting fewer stockout events at the store level
• 8.8% improvement in on-shelf availability — the operational driver behind the revenue lift
• 4% increase in the additional sales representative unit ratio — attributed specifically to inter-store transfer optimization

Beyond headline metrics, automated workflows reduced the manual planning burden on store operations teams, freeing capacity for higher-value decisions and improving overall workforce efficiency across the network.

• Enterprise-scale AI inventory rollouts (980+ stores, 23 countries) are achievable within six months when solutions are purpose-built to a retailer's specific operational constraints rather than adapted from generic platforms.
• Treating replenishment and inter-store transfers as a unified system compounds impact — availability gains reduce lost sales while transfer optimization captures revenue that replenishment alone cannot recover.
• Sorter capacity, shipment windows, and package prioritization are not edge cases at this scale; solutions that cannot model these constraints will underperform in complex distribution environments.
• Establishing lost sales as a baseline KPI before implementation creates the clearest path to measuring ROI and sustaining organizational buy-in post-launch.