SAS^® Size Optimization

Retailers deliver or die by how they manage the details of the business. For apparel and footwear retailers, excelling at fulfilling size-level demand has been the final frontier.

For years the approach of most large retail chains was to determine an "average" size profile and apply it to all stores. Since there are few "average" stores this typically resulted in early-season stock-outs and excessive end-of-season markdowns for the same merchandise in different stores - not a viable strategy in today's hypercompetitive retail world with ever more demanding customers.

SAS Size Optimization uses powerful analytics to transform historical sales data into valuable size-demand intelligence. The solution accurately predicts future sales and inventory needs by size, and determines case-pack supply to optimally meet this demand. When integrated with existing merchant systems, it systematically enables the application of this intelligence to purchasing and allocation workflows.

The result is optimal case-pack orders and allocations that meet each store's unique needs. This includes important details such as when to ship inner packs, the number of case packs required to meet inner pack shipment needs and case-pack mix by delivery.

Matching Store-Level Supply to Size-Level Demand
SAS Size Optimization uses a combination of SAS Size Profiling and SAS Pack Optimization to determine size-level demand at each store for any set of merchandise. By matching pack-level supply with size demand, retailers are better able to address the unique merchandise needs of individual stores. The solution includes:

SAS^® Size Profiling

• Intuitive User Interface - adds tremendous flexibility and sophistication to the size-profiling process through an intuitive user interface.

• True Historical Demand Determination - the power of SAS Analytics are employed to estimate size/store/week sales data that is either missing or constrained by previous supply conditions.

• Intelligent Store Clustering - leverages world-class analytics to cluster stores exclusively by size-demand ratios. The result is a set of size profiles specific to particular styles or even style colors, without a loss of store-level accuracy.

• Optimal Profile Generation - automatically captures the critical differences in size-demand ratios within a selected set of merchandise by creating multiple profiles at lower levels of the product hierarchy.

• Hierarchy and Attribute-Based Profiling - enables users to specify that merchandise at distinct points in the product hierarchy or with distinct values of a chosen attribute be profiled independently.

• Precise Size-Set Management - enables the automated identification of meaningful size sets and the accurate merging of size-level sales histories in order to create profiles.

• Optimized Case-Pack Level Purchase Recommendations - transforms style-level buys into optimized pack-level order recommendations.

• Optimized Case-Pack Level Allocation Recommendations - integrates with retail allocation solutions to create comprehensive pack-level allocation recommendations.