Fashion Retail
From Season Plan to Store, in One Flow
Most planning systems were built for replenishable basics. Fashion is the opposite: most of the range is new every season, demand splits across colors and sizes, and the selling window closes fast. Every hand-off between planning tools costs time you don’t have.
Forecasts before the first sale. Dataviva forecasts every new style-color for every store and channel before you buy, from product attributes, with no like items to pick and no sales history needed. Forecasts correct themselves as soon as real sales arrive. See it at a fast-fashion retailer.
Sizes and packs, handled automatically. Size curves are built for every store and channel, even from messy size data and for brand-new items. They drive prepacks at both ends of the flow: the packs you order from suppliers, and the packs or loose units you send to each store.
One flow, from budget to store. Merchandise Financial Planning sets sales, margin and open-to-buy targets. Assortment & Category Planning builds the seasonal range at style-color level within them. Purchasing turns the buy into purchase orders within supplier terms and pack rules. Supply Chain Planning makes the first allocation and replenishes in season, and Pricing & Promotions manages the exit. Each step’s output is the next step’s input, on one platform, with nothing re-keyed and purpose-built AI driving every calculation.
Pre-Season Forecasting
Forecasts for every new style-color, store and channel before you buy, from product attributes. No like items, no sales history needed.
Automated Size Curves
Size curves for every store and channel, built automatically from messy size data, even for new and slow-selling items.
Prepack Optimization
Prepacks optimized from store-specific size curves and supplier pack rules, for what you buy and what you send to each store.
Merchandise Financial Planning
Sales, margin and open-to-buy targets by category and season, kept current as the season runs.
Seasonal Assortment Planning
Collections and drops planned pre-season at style-color level, with sizes handled automatically.
Purchasing
Buys turned into purchase orders within supplier terms, approved, issued and tracked to receipt.
First Allocation and Replenishment
First push to every store and channel, then in-season replenishment that follows what is actually selling.
Markdown Optimization
Multi-step exits by lifecycle phase, with markdown timing and depth optimized for sell-through or margin.
>25%
Lower end-of-season inventory
>25%
Lower in-store safety stock
>5%
Higher margin
Case Studies
Fast Fashion Pre-Season Forecasting
Fashion, Demand Forecasting, New Products
One of Europe’s largest fast-fashion retailers places most of its seasonal buy 10 months before the season opens. Dataviva Demand Forecasting forecast a full season from pre-season history alone, including brand-new style-colors with no sales history, and landed near the best end of the fashion industry’s accuracy range.
Fashion Forecasting & Replenishment
Fashion, Demand Forecasting, Allocation, Replenishment
Clothing and General Merchandise divisions of one of Western Europe’s largest hypermarket chains with more than 1,500 stores used multiple applications to provide their forecasting models and a reactive order-point system to move inventory from warehouse to store. With the aim of cutting down on time to plan and execute and improve supply chain efficiency, the company implemented Dataviva’s Demand Forecasting and Allocation & Replenishment solutions.
Clothing Assortment Planning
Fashion, Clothing, Footwear, Assortment Planning
The leading Eastern European footwear retailer has hundreds of stores spread across a large geographic area, with widely different climates and customer buying power. They carried a limited assortment of branded shoes, with a unique store distribution negotiated with each brand. However, they have most of their shoes made to order in China, involving thousands of samples to choose from.
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