Assortment Optimization

The right products in the right stores. Not the chain average.

AI-driven assortment optimization that tailors your product mix, shelf space, and planograms to each store cluster.

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Most retailers optimize assortments for the chain average.

Every store gets the same range, regardless of who shops there or how much shelf space is available. The result: wasted facings on slow sellers in some stores, out-of-stocks on top sellers in others. SKU rationalization without demand modeling is guesswork. You need to know what happens when a product leaves the shelf.

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Store-specific assortments, not chain averages

AI clusters stores by shopper behavior, demographics, and performance. Each cluster gets an optimized product mix tailored to local demand, within the shelf space actually available.

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Know what happens before you cut a SKU

Demand transference models predict substitution and cannibalization when products are added or removed. You see the projected impact on sales and margin before making the change.

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Planograms generated automatically, at scale

Store-specific planograms are built from your optimized assortment, shelf dimensions, and merchandising rules. One retailer manages 7,500 planograms across 450 stores with 2-3 FTEs.

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Real outcomes, validated in production

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+4.6% sales, +5.2% margin
at Leading UK Co-operative Retailer across categories including soft drinks, baby, and snacking.
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175+ convenience stores
replaced a legacy on-premise solution with CINDE Assortment Optimization.
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17,000 hours saved
annually at Systembolaget (Sweden). 7,500 planograms across 450 stores managed by 2-3 FTEs, with quarterly update cycles completed in 5-6 weeks.
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300+ planograms personalized
at Foodstuffs (New Zealand), developed through a joint PepsiCo-SymphonyAI clustering project. "We grouped stores into shopper-led clusters and optimized each shelf to match." (Jan Haluza, Manager, Data Science and Analytics, PepsiCo Europe)
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+8% incremental sales
lift at PepsiCo Northern Europe
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35% faster shelf reset execution
AI-driven planogram automation replaced static planograms at 96% market penetration across 1,000+ retail locations
What is assortment optimization in retail?

Assortment optimization is the process of selecting the right product mix for each store or store cluster based on local demand, available shelf space, and financial targets. AI-driven approaches use demand transference models to predict what happens when products are added or removed, replacing manual spreadsheet-based range reviews.

How does AI improve SKU rationalization?

Traditional SKU rationalization cuts slow sellers without modeling what happens next. AI predicts substitution and cannibalization effects, so you know the projected impact on sales, margin, and customer satisfaction before making changes.

What is the difference between assortment optimization and space planning?

Assortment optimization decides which products need to be carried in each store. Space planning decides where they need to be placed on the shelf and how much space they get. The best results come when both are connected: the assortment is optimized within the space actually available.

Stop optimizing for the average store.

See how it works
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