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Canned Food

assortment planning & optimization

Fulfill customer needs and increase store sales, profit, inventory turnover with optimal product mix. 

We developed AI software which ensures Optimal Assortment development  - both at macro and SKU levels. It is achieved using Data Science and Machine Learning algorithms enabling at the same time concider customer needs, store financial targets, product merchandising rules, capacity constraints, suppliers' cooperation terms, market oportunity. 

Macro – level product mix

  • Prepare category assortment strategy 
    SUMATUS will recommend quantity of assortment matrices in each category, their width and structure at product attribute level (price, taste, size, color, model, etc.)

  • Allocate each store space to different merchandise categories
    SUMATUS will recommend allocate appropriate store space to each department, section, category as well as assign appropriate category assortment matrix

  • Allocate appropriate category assortment matrix in the new store
    SUMATUS will recommend grouping existing stores into category specific clusters according to store size and location attributes in order to select the most appropriate assortment matrix.

  • Be sure about the optimal assortment mix at any point of time
    SUMATUS will provide automatic alerts about the need of corrective actions when significant and permanent deviations from existing assortment mix will be identified.

SKU – level product mix

  • Choose correct SKUs in each category assortment matrix
    SUMATUS will provide listing/delisting recommendations in order to maximize shopper satisfaction and sales considering customer localized demand, assortment strategy, product substitution rules. 

  • Allocate appropriate space for each SKU
    SUMATUS will provide space allocation recommendations in order to maximize store financial targets considering SKU profitability, commercial terms with suppliers, product merchandising and elasticity to space rules.


  • Prepare in-store visual merchandising guidelines 
    SUMATUS will recommend how many facings to give to SKUs in order to maximize shelf space productivity, ensure visibility, appropriate shelf stock and avoid Out of Stock.

  • Take informative decisions in all the times 
    SUMATUS will help simulate “what if…” scenarios and evaluate decision’s effect to sales, gross profit, inventory turnover before its implementation – listing/delisting, shelf space changes, understand purchase power during negotiations with suppliers, etc.  


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Optimization options

  • sales

  • gross profit (incl. retro discounts, logistics and WC cost)

  • inventory turnover

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Forecasting base demand considered

  • phantom inventory

  • Out of Stock

  • promo cannibalization

  • unusual items

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Recommended decisions and estimated effects

  • simulation of scenarios "what if…"

  • estimate of decision outcome before its implementation 

  • automatic early warnings, insights, recommendations

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Cooperation terms with suppliers considered

  • retro discounts

  • shelf share

  • order and delivery terms

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Spotted rules & effects

  • item substitution

  • item elasticity to merchandising space

  • item promo cannibalization


Store space and fixtures considered

  • capacity constrains

  • efficiency


Defines category and store optimal assortment

  • assortment strategy - quantity of assortment matrices, their width and structure at product attribute level

  • optimal assortment at SKU level

  • visual in-store merchandising guidelines with facings and shelf stock;

  • local assortments & regional SKUs

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Product merchandising rules considered

  • replenishment frequency

  • all stock just in sales area

  • merchandising in units, secondary packaging

  • stacking on each other

  • supplier/brand/SKU shelf share

  • product lifetime


Shopping Basket

Satisfied customers:


  • Localized assortment 

  • Satisfied maximum shopper needs

  • Higher On-Shelf Availability 

Investment Chart

Better financial results:

  • Higher sales up to 4%

  • Higher gross profit margin up to 2.5 pp

  • Less capital invested in stock for about 10 – 20%

Financial Report


  • Faster data - driven decision making. Automated category manager's work

  • Efficient utilization of store and shelf space 

  • Shoppers' needs satisfied with less quantity of SKUs

  • Evaluation of decision effectiveness before its implementation

  • Understanding purchasing power during negotiations with suppliers

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