Plan Optimization


Build Robust Assortments for Seasonal and Replenishment Goods

Celect Plan Optimization helps you build robust assortments for seasonal and replenishment goods, specifically optimized for the foot traffic in each individual store. Filter, analyze and make sense of an overwhelming amount of data from disparate sources.

For seasonal products, such as apparel, Celect intelligently determines the optimal mix of style attributes (such as color and fabric) to stock in a store.

For non-seasonal and replenishment products, Celect discovers new revenue opportunities across aisles and categories, while considering space constraints — store level SKU assignments to a specific aisle, fixture, or shelf.

The result is improved merchandise forecasting, rapid category growth, and increased inventory turns — helping you make better decisions while leveraging your honed intuition across the merchandise planning process.



Optimized Merchandise Planning

Accurately predict the optimal vendor or product matrix for the current or upcoming seasons. For retail executives, view assortment recommendations at any level of the merchandise hierarchy and set realistic spend constraints. For retail planners, filter down to a specific department (across classes and sub-classes) — adjusting spend based on overall department budget. 

Improved Space Utilization

Discover new revenue opportunities for products across data-driven aisles. Understand how various products impact the performance of the overall assortment. Remove poor performers in favor of higher demand products in each aisle or shelf, while considering pack size and availale space.


Rapid Category Growth

Easily see which categories are primed for growth, and which are being overallocated. Make changes dynamically based on your own constraints, limitations, or goals. 

Optimized Store Assortments

Determine the optimal mix (breadth and depth) of the store assortment, across all locations, while uncovering hidden opportunities to carry new classes, styles, and brands. Create data-driven store clusters where customers make similar choices and stores have similar demand patterns. 

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