In a chain retail environment, each store serves a distinctive trade area with its unique customer profiles. One of the most common challenges in retail is accounting for the various demand patterns. For example, the same product sells very differently across sizes; each store has its own distinct size-demand patterns. Misinterpreting a store’s unique pattern will decrease its performance. Missing multiple patterns, and the entire operation may be affected.
Top retail brands constantly push to reinforce their operational capabilities. They optimize inventory allocation to accurately align with real local demands. When a size distribution is optimized, it can easily increase sales, reduce markdown rates, and significantly improve profitability. Therefore, accurately analyzing and optimizing size demand by store, category, and product is critical for refined retail management. When stores carry the right size mix, they can:
- Minimize sales losses from out-of-stockedpopular sizes
- Reduce replenishment costs
- Avoid excess inventory from slow-moving sizes
- Improve full-price sell-through and margin performance
When a single-store profitability improves, the entire retail network benefits. These benefits grow much more noticeable when scaled to every store.
Strategic Precision: The Reconstructing of Unconstrained Demand
The hallmark of a high-performance retail is its optimization of effective profiles. Manual predictions can only scale so far before they hit the barrier of human capabilities, becoming difficult or even unmanageable. 7thonline moves beyond historical patterns; it reconstructs the Unconstrained Demand Profiles to recover missing data, effectively filling in the gaps where the “lost sales” were, in reality, caused by store stockouts. Leveraging multiple sophisticated operational research models, the 7thonline system syncs inventory allocation with true localized consumer demand at the store-SKU level. This transformation not only maximizes full-price sell-through, but also recaptures latent market demand that biased traditional spreadsheets often overlook.
After setting up size optimization, stores can experience:
- Better supply and (real) demandalignment
- Reduced stockouts in key sizes
- Lower replenishment costs
- Fewer leftover inventory and markdown pressure
- Improved inventory flow andoptimal store profitability
Over the years, 7thonline focused on delivering tailored size-optimization consultations to numerous apparel and footwear brands, designed for each unique needs and proving the value of this approach.
Service Scope
- Utilize POS data to quantify sales lost due to size breaks and markdowns, cleansing and correcting historical data patterns
- Analyze store-level consumption patterns and determine optimal size ratios
- Recommend optimal combinations of pre-pack sizes by category,fine-tune allocation, and replenishment efficiency
- Automatically update the optimized size packs into supplier orders
Core Value
- Provide storeswith tailored size mix reflecting localized patterns, improving customer satisfaction and store performance
- Avoid in-season emergency replenishments, decrease costs caused by missing sizes
- Lower inventory pressure from slow-moving sizes
- Reduce size break occurrence (up to 15%)
- Increase full-price sell-through rate (up to 14%)
Customer Success Case: Macy’s
Company Background
Macy’s, a premier U.S. department store chain widely recognized for apparel, footwear, and home goods, as well as its commitment to customer service. At the time of the project, Macy’s operated over 1,000 stores nationwide and was ranked #417 on the Fortune Global 500 list.
Project Scope
- Selected 8 classifications for size optimization
- Excluded 5 classifications due to insufficient data or one-size-only products
- Focus on products sold at no less than 75% of full price
- Analyze each classificationwith two size pack scenarios: 3-pack and 5-pack combinations
- Analysis based on fall season POS data
Optimization Results
After implementing size optimization recommendations:
- Average size break per style per store reduced from 2% to 4.9%
- Full-price sell-through increased from 8% to 68.4%
- Reduced significantlyin supply and demand miscalculation
- Lower markdown rates and replenishment costs
About 7thonline
With 26 years of global retail consulting experience and industry-leading methods in merchandise management, 7thonline combines advanced data modeling and machine learning to tailor automation solutions for many business scenarios. Through its AI + BI cloud platform, 7thonline assists brands in automating merchandise planning, driving refined operations, and supporting intelligent decision-making for digital and operational transformation.
7thonline’s clients include Alexander Wang, BIRKENSTOCK, Bestseller Group, Canada Goose, PVH, Jimmy Jazz, Calvin Klein, Michael Kors, Nautica, Colony Brands, Phillips Van Heusen, VF, and many more.
