PHILADELPHIA — Invent.ai, a global leader in retail inventory optimization and multi-agentic AI, spotlights its size prepack optimization capabilities, helping retailers define demand-driven size profiles, configure prepack composition and align inventory deployment before purchase orders are created. While decisions about size profiles, prepack composition and inventory deployment have a significant influence on availability, sell-through and inventory productivity, many retailers continue to make these decisions using spreadsheets and fixed vendor pack structures.

Size prepack optimization by invent.ai addresses these considerations earlier in the planning process by helping retailers define demand-driven size profiles, configure prepack composition and align inventory deployment before purchase orders are created. As part of the invent.ai AI-Decisioning Platform, the capability uses AI to evaluate forecasted demand across products, store clusters, channels and regions, helping retailers determine how inventory should be structured prior to execution.

For large retailers, this can involve thousands of distinct size profiles across product categories, fit types, size families and demand clusters. Invent.ai uses AI to identify the appropriate planning granularity, extract meaningful demand signals even when data is limited and create demand-driven size profiles that reflect expected customer demand rather than historical inventory constraints.

“Inventory performance is often determined long before products reach stores,” said Gurhan Kok, Founder and CEO of invent.ai. “The way inventory is structured at the planning stage directly influences availability, sell-through and overall inventory productivity throughout the season.”

Instead of relying on fixed pack assumptions, retailers can establish size and pack decisions based on expected demand before inventory enters the network.

Size prepack optimization by invent.ai helps retailers:

• Define size profiles based on demand signals rather than historical averages
• Optimize prepack composition aligned to expected demand patterns
• Use AI to determine the exact composition of each prepack configuration, even across complex size runs and assortment structures
• Determine the number of prepack configurations required across the network
• Recommend buy quantities for each prepack configuration based on expected demand
• Determine inventory profiles for e-commerce demand and in-season replenishment inventory held in distribution centers
• Align inventory deployment across stores, e-commerce and distribution centers
• Evaluate planning scenarios before inventory is committed
• Automate purchase order creation and split recommendations

The platform also provides transparency into recommendations through Remi, invent.ai's retail merchandise intelligence agent, enabling planners to understand the demand signals and decision factors behind specific size profile, prepack and deployment recommendations.

By moving these decisions upstream in the planning process, retailers can reduce downstream adjustments and improve alignment between inventory structure and demand. For planners, this reduces manual effort across spreadsheets and purchase order creation. For retailers, it supports better size availability, fewer inventory imbalances and improved inventory productivity throughout the season.

“Retailers aren’t only deciding what to buy,” said Kok. “They are deciding how inventory should exist before it is bought. When those decisions reflect expected demand, inventory performs with greater consistency across the season.”