AI in Retail & E-commerce · AI in Merchandising & Store Layout

Can AI optimize product assortment for individual store locations?

AI can optimize product assortment for individual store locations by analyzing store-specific sales data, local demand patterns, and space constraints to recommend a tailored product mix and quantities for each store, rather than applying a single standardized assortment across an entire retail chain.

Key takeaways

Assortment optimization models weigh store-specific sales history alongside broader chain-level and category trends.
Physical space constraints at each store location are factored in, since assortment recommendations must fit available shelf and floor space.
AI can help identify underperforming products for removal and promising products for expanded placement at a specific store.
Assortment decisions are periodically revisited as models incorporate new sales data and changing local trends.

Getting Beyond a Single Chainwide Product Mix

For large retail chains, deciding exactly which products to carry at each individual store, and in what quantities, is a genuinely complex optimization problem. Carrying identical assortments everywhere is simple to manage but often leaves value on the table, since demand for specific products can vary meaningfully by location due to differences in customer demographics, regional preferences, and available space. AI has increasingly been applied to this problem specifically because it can process the volume of store-by-store data required to make tailored recommendations at scale, something that would be extremely labor-intensive to do manually across hundreds or thousands of locations.

The goal is to identify, for each store, the specific mix of products most likely to perform well given that location’s particular characteristics and constraints.

What Assortment Optimization Models Actually Weigh

These models typically combine several types of data: each store’s own historical sales performance by product, broader category and regional trends, and physical constraints like available shelf space or floor area. By analyzing this combination, a model can identify which products are strong performers worth expanding at a specific store, which are underperforming relative to the value of the space they occupy and might be candidates for removal, and which products from the broader catalog might be worth introducing at a store where they aren’t currently carried but show promising demand signals based on similar stores or regional patterns.

Incorporating physical space constraints is a particularly important part of making these recommendations practical, since a suggestion to carry more of a given product only makes sense if the store actually has room to display it without displacing something more valuable.

Assortment optimization isn’t typically treated as a single, static decision made once and left unchanged. As new sales data accumulates and local trends shift, models are periodically rerun to refine recommendations, allowing a store’s product mix to evolve over time in response to changing demand. Because this process still involves real commercial tradeoffs — supplier relationships, brand strategy, and broader chain-level merchandising priorities — recommendations generated by AI models are typically reviewed by merchandising and buying teams rather than implemented automatically without oversight.

This combination of data-driven analysis and human review reflects a broader pattern across AI applications in retail merchandising, where algorithmic recommendations serve as a starting point that’s refined with business judgment before being finalized.

Bottom Line

AI can meaningfully optimize product assortment for individual store locations by analyzing store-specific sales data, local demand patterns, and physical space constraints to recommend a tailored product mix. These recommendations are typically reviewed by merchandising teams and refined periodically as new data comes in, rather than applied as permanent, fully automated decisions.

Go deeper

Important caveats

Assortment optimization is generally most effective with a substantial history of store-level sales data, and less precise for newer stores or products.
Recommendations are typically reviewed by merchandising teams rather than implemented as fully automatic, final decisions.

Frequently asked questions

How is store-level assortment optimization different from general demand forecasting?

Demand forecasting predicts how much of a given product is likely to sell, while assortment optimization goes a step further to determine which specific products should be carried at all in a given store, and in what relative quantities, given space and demand constraints.

Does AI account for how much physical space a store actually has?

Yes, effective assortment optimization models incorporate store-specific space constraints, since a recommendation to expand a product's presence has to be realistic given the store's actual available shelf or floor space.

Can AI recommend removing a product from a specific store entirely?

Yes, if sales data consistently shows a product underperforming at a specific location relative to the value of the space it occupies, assortment optimization models can recommend removing it from that store while potentially keeping it at other locations where it performs better.

Sources

[1]Retail technology and merchandising coverage — Retail Dive
[2]Research on AI in retail operations — McKinsey & Company

Written by Editorial Team

Last updated July 28, 2026

Get one well-sourced answer a week

No spam. Unsubscribe anytime.