About
Stio
Stio is an outdoor lifestyle brand offering functional and versatile apparel, outerwear, and footwear. The company has evolved from a DTC eCommerce business into a multi-channel retailer, including eCommerce, retail, wholesale, outlet, corporate, and closeout. Over the past two years, Stio has doubled its brick-and-mortar presence, expanding to 10 stores, including two outlet locations.
With the rapid growth of its business, Stio needed a more precise way to manage inventory flow and reduce inefficiencies in stock allocation. By implementing Toolio, Stio was able to cut end-of-season returns by 30%, significantly reducing costly send-backs to its warehouse. This shift to a demand-driven allocation strategy has not only minimized excess inventory handling but also improved sell-through, allowing Stio to keep the right products in the right stores at the right time.
Use Cases
Allocation & Replenishment
Demand Planning
Inventory Optimization
Modules
Merchandise Planning
Assortment Planning
Allocation
Location
United States
30%
reduction in returns
90%
time savings
Increased
inventory visibility
Solution
From Inefficiency to Optimization
Before Toolio, Stio’s growing complexity in inventory management made Excel-based planning unmanageable, leading to inefficiencies in allocation and excess stock redistribution. Ensuring accurate channel roll-ups, reconciling demand across eCommerce, retail, and wholesale, and quickly responding to changing conditions were becoming increasingly difficult—resulting in costly end-of-season send-backs.
Toolio provided a centralized, automated solution that eliminated the inefficiencies of manual Excel planning. By aligning allocation with demand and introducing phased allocation strategies, Stio reduced end-of-season returns minimizing excess inventory handling and improving sell-through across channels.
Amanda Holland
Senior Manager, Retail Planning
“Toolio has gotten me out of Excel, out of time-consuming and mundane tasks, and focused more on strategy and refining my plan. I now have much more confidence in my allocation recommendations.”
A Closer Look at Metrics
- 30% reduction in returns Optimized allocations and phased replenishment strategies minimized excess inventory, reducing costly returns to the warehouse and improving sell-through.
- 90% time savings Automating allocations, store clustering and CSV generation through ERP integration, Stio cut processing time from hours to minutes, enabling a sharper focus on strategic decision-making.
- Increased Inventory Visibility Toolio’s demand-driven allocation ensures that high-performing products are prioritized in replenishment, preventing stockouts and excess inventory.
Data-Driven Decision Making
Prior to Toolio, Stio's allocation strategy was reliant on time-consuming manual docs and static presentation profiles, often leading to inefficiencies. The company used a replenishment model that maintained fixed inventory levels in stores regardless of demand, resulting in excess stock and inefficient distribution.
With Toolio, Stio now dynamically allocates inventory based on demand, allowing the team to focus on strategic decision-making rather than manual processes. Toolio's integration with Stio’s item plan ensures that stores receive replenishment aligned with actual sales performance rather than arbitrary thresholds. This shift has significantly enhanced inventory visibility and control, reducing stockouts and overages.
Driving Operational Excellence
As Stio continues to scale, Toolio remains a critical part of its operational strategy. The platform has helped the company refine allocation processes, improve in-season inventory management, and enhance overall decision-making.
By leveraging Toolio, Stio has transformed its inventory management, ensuring that its stores are stocked with the right products at the right time while minimizing operational inefficiencies. The strategic shift from manual allocation to automated, demand-driven processes has positioned Stio for continued success in a highly competitive retail landscape.
