FLO, one of Europe’s largest footwear retailers, operates in 25 countries across three continents. With over 800 stores and a multi-brand e-commerce platform, the company manages millions of SKUs each season to cater to every need. Balancing local fashion cycles, promotional calendars and clearance assortments across both physical and digital channels requires precise inventory planning—ensuring the right styles, sizes and quantities are in the right place at the right time to delight customers and protect revenue.
At the same time, stock operated in silos. Central warehouses, regional hubs and the online fulfillment team guarded their own inventory pools, competing for the same limited inventory rather than collaborating to meet total demand. Without a unified view of available stock and expected demand across its channels, FLO missed opportunities to transfer excess inventory from one location to another, resulting in markdown-driven clearance and lost revenue.
FLO needed more than a static spreadsheet. They needed a demand-forecasting engine that worked in real time, a single view of inventory across every channel and an automated way to shift stock or time markdowns for maximum revenue. In short, they needed to turn fragmented, reactive planning into a seamless, predictive, revenue-driven machine.
Key requirements of the solution included:
At the heart of this system is a financial optimization engine that looks beyond standard fill-rate metrics. It calculates the trade-offs between potential lost sales and inventory holding costs, then recommends the best allocation and restocking moves to maximize revenue. When unexpected demand pops up the system flags which stores are running low and which have excess. It recommends transfers from overstocked locations to those in need, helping keep inventory balanced. And, as products near the end of their shelf life, the platform figures out the best markdown strategy, so clearance happens efficiently without taking a hit on revenue.
Invent.ai’s agentic AI also takes on a tricky challenge: size optimization. It groups stores with similar size-selling patterns and then fine-tunes case packs to match each cluster. This leads to fewer odd sizes left over and stronger sell-through. On top of that, invent.ai helps FLO model different distribution network setups—adjusting DCs, hubs and store routes to see how changes would affect speed, cost and service before making any real-world moves.
Key capabilities introduced:
By bringing these capabilities together, FLO has moved from reactive planning to a more agile model—keeping shelves stocked with what customers actually want while protecting revenue.
Since implementing invent.ai, FLO has experienced a transformation in how it manages inventory, meets customer demand and drives growth. The shift from manual, spreadsheet-based planning to an automated, predictive system has delivered measurable improvements across the business including:
Today, FLO and invent.ai continue to deepen their partnership. New efforts are underway to refine size-level assortment planning, further automate seasonal transitions and unlock additional revenue through advanced forecasting and optimization tools. The goal is not just to react faster, but to get ahead of shifts in customer demand and turn planning into a competitive advantage.
By transforming fragmented workflows into an integrated data-led approach, FLO has strengthened both its operational efficiency and its ability to grow in a fast-changing retail landscape. With intelligent planning now at the core, FLO is better equipped to serve customers, scale efficiently and protect revenue—no matter what comes next.