Store-Level Assortment Planning Software

Category and assortment planning is the retail process of deciding what to offer and how much of it each store or channel gets for an upcoming season. It covers collection design, localized buying, size and prepack optimization, and the management of limited-time promotional products.

Assortment localization gives markets, store clusters or regions different products or different timing, so each one matches its local customer demand. Solvoyo does this with store clustering and store-level forecasting. You can set different assortments, sizes, in-store dates and depth targets for each segment. For background, read our post on cluster analysis in retail.

Store-cluster assortment planning software builds store clusters from data and plans one range per cluster from store-level forecasts. Solvoyo groups stores by category sales, capacity, location type and customer profile, including demographics, so the same store can sit in different clusters for women’s wear and for activewear. Planners compare what-if results for the clusters, then plan the range and size ratios for each one. At DeFacto, which plans 670 stores, the planning director says each planner creates store clusters for their category in seconds and localized assortments for 30+ countries in minutes (DeFacto pre-season planning case study).

Assortment planning software moves the work that breaks in spreadsheets into one shared plan: store clusters, store-level forecasts for new items and repeat buys, size and prepack ratios, and the purchase orders that follow, all reconciled against the merchandise hierarchy targets. Each purchase order shows the reference products behind it, so planners can review the buy before approving it. DeFacto replaced spreadsheet-heavy pre-season planning with Solvoyo and reports a 96% user acceptance rate on its recommendations. At Penti, Cansu Aydemir, Senior Planning Manager, says the move away from manual methods brought faster analysis and decision-making.

Solvoyo forecasts a new product from the most similar items in its sales history, found with attribute-based models. It turns their sales into store-level demand estimates for the new item, based on its category, price point and product attributes. Similar item selection favors recent history, so the estimate reflects current demand trends. For apparel and fashion retailers, the forecast feeds initial allocation, which sets the right quantities and size ratios for each cluster from day one. Product attributes digitalized at the development stage in product lifecycle management flow into forecasting and buying, with no manual re-entry between development and planning.

Size and prepack optimization sets the size breakdown and prepack combinations for each store or region, based on size-selling analysis of similar items. It reduces size imbalances and inventory costs and improves on-shelf availability for the sizes customers buy. For more on the size problem in fashion, read solving the size dilemma with AI.

Solvoyo monitors every active item continuously against its sell-through trajectory and turns the result into recommendations. Items tracking ahead of plan get automated repeat-buy recommendations. Underperformers get markdown recommendations, sized against remaining selling life and exit dates. Solvi, Solvoyo’s AI planning agent, lists prioritized action items with their quantified business impact, so merchants start with the highest-consequence exceptions and skip building their own analysis from a WSSI report.

Yes. Solvoyo connects to ERP and PLM systems in real time. It generates purchase orders automatically, syncs master data and reconciles plans across systems, so nobody re-enters data by hand and errors go down.

Retailers using Solvoyo report planning cycles that shrink from days to hours. AI recommendation acceptance rates exceed 95%, and measurable business results show up within three to four months.