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Recommended
Buy 
Quantity.

One of the most expensive questions in retail is: "How much should I buy?" 

 

Sizeo's New Buy Planning Module helps fashion brands and retailers answer this question - driving more sales, reducing markdowns, and improving the customer experience -  making sure Every Unit Counts. 

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AI-Driven Inventory Optimization

Recommended Buy Quantity is the first feature release in Sizeo’s new Buy Planning module.  

This module will support buyers and planners in determining what and how much to buy, a notoriously challenging prediction task in fashion merchandising. Sizeo is building upon years of fashion retail experience and using its proprietary size level demand determination in combination with cutting edge AI techniques to blend the art and science of buy planning in modern apparel and footwear businesses. 

Integrates with Existing Processes

Upload a preliminary assortment plan with budget and style-color details to generate demand-based unit needs and budget-aligned buy quantities (respecting your buy/pack rules).

 

Submit directly to the Buying module for packing or export the recommendations for your current process.

True Consumer Demand

Sizeo combines historical sales and inventory data with attribute-level demand signals and like-product inference, alongside financial, assortment, and merchandising strategies, and operational / supply chain constraints. On this foundation, Sizeo’s retail AI optimizations determine localized consumer demand at the item and size level.

Built for Fashion

Many of retail’s demand planning practices are borrowed from long-lifecycle (CPG) methodologies and have traditionally not been successful for fashion businesses.

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After years of effort to optimize inventory, retailers still face missed sales, unnecessary markdowns, and dissatisfied customers. Sizeo is fashion first and has lived the challenges it presents in predicting demand at the item level. 

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Let's Talk.

The Sizeo team consistently deploys our modern technology faster than the norm.  Our low-touch solutions accelerate adoption, while AI and ML rapidly identify areas of missed opportunity.

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