Size and Pack Optimization
Retailers selling products with multiple dimensions, such as sizes, colors or other variations of pre-packs, understand the need and difficulty of optimal planning.
Every consumer has their story about digging through stacks of jeans on the store shelf and learning their size was sold out.
Softvision incorporates Size and Pack Optimization algorithms throughout our application modules to help retailers improve the profitability of their merchandise investment. The proprietary algorithms and models identify lost sales opportunities, recommend pre-pack size and construction and stage new season assortments that correct historical sizing errors from previous seasons. Furthermore, the resulting optimized models can minimize stock outs and overstocks.
Our customers have found that the application of these ‘curves’ during the buying process have resulted in 10-20% improvements in sell-thru at full price.
The functions of Size Profiling are:
✓ Calculation of “Lost Sales” of items that have reached an out-of-stock condition in a size(s), where other sizes continue to record sales.
✓ Development of Size Contribution Tables by location using various performance metrics, including Lost Sales, to enable size optimization.
✓ Store Modeling using ‘sister stores’ for establishing Size Curves for new stores.
The functions of Pack Optimization are:
✓ The development of optimal Pre-Packs in the ‘Pack Builder’ function using Location based Size Curves. These Pre-Packs can be constructed to contain multiple iterations of a pack, e.g. 12A, 12B, 12C.
✓ The optimization engine develops a recommended allocation of packs by location utilizing the available Pre-Packs considered against the optimal unit quantities by location.
✓ The user can choose to assign with Packs Only or optimize considering both Packs and Pieces, with Pieces addressing the remaining units that do not fit within optimal Packs.
✓ When integrated with the retailer’s PO System, separate PO line items are applied to distinguish quantities ordered by Pack and by Pieces.