Problem Statement
The client’s assortment planning process was centralized and fragmented, lacking store-level granularity to optimize SKU mixes by region. Decisions relied on aggregated data, resulting in poor SKU performance visibility, margin leakage, and suboptimal shelf allocation. Without simulation capabilities, the client could not quantify listing or de-listing impact, cannibalization, or incremental revenue, leading to assumption-driven recommendations and inconsistent assortment performance.
Impact
- Improved net revenue per store by $71M within 14 months of deployment through localized assortment plans
- Increased sales by ~2.5% within their top 3 clusters
- Optimized product selection, reducing underperforming SKUs by 13% through data-driven SKU rationalization
- Reduced the time taken to adjust plans by 70% through scenario planning capabilities
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