Products & Solutions / Size Optimization

SAS® Size Optimization: SAS® Size Profiling and SAS® Pack Optimization

An enterprise solution that affects both supply and demand

SAS Size Optimization uses powerful analytics to transform historical sales data into size-demand intelligence. The solution 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 enables the application of this intelligence to purchasing and allocation.

SAS Size Optimization helps retailers improve profitability by identifying and supplying the right sizes to the right stores at the right time. The solution systematizes this level of planning and execution by matching packs to size-level demand for each store. The result is higher store-level margins, lower operating costs, fewer stock-outs and reduced end-of-season markdowns.

Benefits

  • Improve sales.
  • Increase margins.
  • Decrease operating costs.
  • Improve planning team efficiencies.

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Features

  • Generate demand-based size profiles
  • Build and use attribute-specific profiles
  • Recommend pack portfolios
  • Design multidelivery strategies
  • Conduct what-if analysis

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" Because we are optimizing sizes, we will improve sales and margins, and reduce markdowns. These are the biggest benefits we are getting."

— John Kubo

Vice President and CIO, The Wet Seal

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Screenshot

Size profiling highlights meaningful variations in size-level demand across stores.



How SAS® Is Different

Only SAS provides:

  • A single enterprise solution that supports planning, buying and allocation.
  • An end-to-end workflow through a simple, business-friendly interface.
  • Retail buyers and allocators with market-leading analytics that account for lost demand and derive optimal supply chain strategies.  

Benefits

  • Improve sales. SAS Size Optimization assists retailers by more accurately identifying both size-level demand by location and determining pack-level quantities required to meet that need. As a result, there are fewer stock-outs across the stores and higher overall sales without increasing inventory risk.
  • Increase margins. SAS Size Optimization increases full-price early-season sales by better matching size profiles to forecasted demand. SAS Size Optimization also reduces the need for markdowns by reducing "stranded" inventory at store level. The result is higher profit margins. Determining just the right number of each pre-pack configuration needed also allows retailers to control product procurement costs.
  • Decrease operating costs. Determining just the right number of each available pre-pack configuration needed to economically meet demand helps maintain reasonable distribution costs. Retailers using this solution also reduce the need for costly break-pack and piece-pick activities within their distribution networks by raising the percentage of product supplied in multi-item pre-packs. Ultimately, this provides an opportunity to reduce labor requirements and improve distribution center throughput.
  • Improve planning team efficiencies. Retailers today are always looking for ways to streamline their teams and increase throughput and effectiveness while constantly fighting competitive pressures that drive down margins. SAS Size Optimization utilizes numerous automated processes and procedures to improve the efficiencies of planning teams, allowing them to play a more strategic role in the organization.

Features

Generate demand-based size profiles
  • Employ market-leading lost-sales analytics.
  • Create event-specific scenarios.
  • Automatically detect active size ranges.
  • Build profiles at multiple levels of the hierarchy.
Build and use attribute-specific profiles
  • Group products by hierarchy or multiple attributes.
  • Tag profiles with relevant attribute values.
  • Automatically match new products with attribute profiles.
Recommend pack portfolios
  • Enforce constraints on pack usage and variation.
  • Vary packs by size ratio and overall volume.
  • Optimally combine packs and bulk.
  • Consider supply-chain costs and efficiencies.
Design multidelivery strategies
  • Optimize packs over an entire season or life cycle.
  • Reflect changing lifecycle needs.
  • Incorporate promotional requirements.
Conduct what-if analysis
  • View real costs of pack strategy.
  • See merchandising mismatch levels.
  • Track handling costs.

Screenshots

Screenshot
Size profiling highlights meaningful variations in size-level demand across stores.

View Screenshot

System Requirements

Operating System Requirements

SAS Size Optimization use the SAS Merchandise Intelligence 4.2 architecture. The server tier is implemented in SAS as part of SAS Foundation 9.2.

Middle Tier

The SAS Merchandise Intelligence midtier server is a Java-based, middle-tier component. The middle-tier component enables automatic optimizing of data. The SAS Merchandise Intelligence midtier server can be installed in the following operating environments:

  • Windows Server 2008
  • AIX 5.3 or 6.1

SAS Size Optimization can be deployed behind the firewall or as a service.

Ready to learn more?

Call us at 1-800-727-0025 (US and Canada) or request more information.