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Size Optimization Made Easy With Machine Learning and Analytics
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"Size Optimization Made Easy With Machine Learning and Analytics"

The Challenges of Poorly Optimized Assortments

In today’s retail omnichannel world, data is growing exponentially from multiple sources and different formats. As a result, the challenge of managing millions of requirements for size and location has magnified, and the results have not been readily scalable. While technology and vast amounts of data have helped create more localized assortments based on consumer preferences, product attributes and location performance, it’s been nearly impossible to simplify the assortment planning process to create relevant assortments at the size level in an omnichannel marketplace. 

Results have been inconsistent and imprecise. Organizations spend lots of time and effort creating detailed and targeted assortments only to water down those assortments by applying corporate averages.

Some get sizing right by location, but miss seasonal opportunities where size demands are different. This inevitably leads to consumer disappointment due to missing sizes. Additionally, poor sell-through due to a lack of size precision can inadvertently train consumers to delay their purchase (even when you have the right sizes) because they know it will soon move to the markdown or clearance racks.

The problem intensifies with the introduction of packing configurations. Vendors and factories often offer (or impose) certain pack configurations (case packs). While case packs increase supply chain efficiencies, they tend to fall short of meeting size-level demand. When you combine these factors – location, size and case packs – the mathematical possibilities for configurations to meet the demand are overwhelming.

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