# Infosys Whitepaper 
Title: Leveraging advanced validation techniques for retail size optimization 
Author: Infosys Limited 
Format: PDF 1.7 

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 WHITE PAPER LEVERAGING ADVANCED VALIDATION  TECHNIQUES FOR RETAIL SIZE  OPTIMIZATION - Divya Mohan C, Chitra Sylaja 

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 Introduction In today’s highly volatile business  environment, retailers that want to  remain profitable must be able to predict  customer demand and ensure availability  of the right products in the right store at  the right time. This is a challenging task  when merchandise such as apparel and  footwear are offered in a range of sizes.  To maximize revenue and profitability,  retailers need a strategy that allows them  to sell goods at full price while reducing  markdowns.<|endoftext|>External Document © 2018 Infosys Limited 

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 How does size optimization  help retailers Size optimization transforms historical  sales and inventory data into size-demand  intelligence. This enables smart buying  and allocation at the size level to match  customer needs at each store. The size  profile optimization (SPO) application  provides optimized size curves for products  at the store level. SPO uses past sales  history to deliver the right sizes to the right  stores and reduces the imbalance between  consumer demand and inventory.  By leveraging a combination of back-end  data crunching technologies and front- end size profiling tools, SPO enables  allocators, buying coordinators and buyers  to leverage sales and inventory data and  create precise style/color size scales for  each store. This degree of specificity can  help buyers and allocators create size  profiles that recover lost sales, improve  the size balance across the retail chain and  launch new products and categories based  on previous successes.<|endoftext|>Key challenges in size  optimization Retailers find it challenging to procure and  maintain the right mix of merchandise in  the right size at the right store. The store- specific size profile plays an important role  in helping retailers make assortment and  allocation decisions. As the competition  evolves, new challenges arise and retailers  need suitable approaches to counter these  challenges.<|endoftext|>Ability to understand  consumer demand Customer demand varies across stores.  Understanding the size level demand at  each store for different products is critical  to meeting each unique customer demand.  This requirement can be accurately  captured and analyzed through granular  data that represents size profiles for every  store/product.<|endoftext|>Historical performance is not  true customer demand As size profiling is based mainly on sales  history, a cleansed history is necessary to  generate an accurate size profile. Typically,  historical data comprises of profitable sales,  lost sales and markdowns. To get relevant  size profiling data, it becomes necessary  to filter out inventory data related to  stock-outs, markdowns, margins, etc., from  the consolidated data. This ensures that  the right data set is used to design better  analytical models and prevent biases  arising from extreme data points.<|endoftext|>Analyze and process large  volume of data Gathering information at the granular level  of store and size can generate large data  volumes that are difficult to handle and  analyze. Retailers may find it challenging to  derive impactful business decisions from  such large data sets.<|endoftext|>Key influencers in size  optimization: Predicting  customer decisions Consumers are always looking for specific  merchandise; say for instance, a shoe that  has the name of a basketball legend. In  case the store does not have this particular  product, they should be able to offer the  customer a suitable alternative through  smart product grouping. Size profile  optimization can help retailers predict  similar products and position them  appropriately within the store.<|endoftext|>External Document © 2018 Infosys Limited 

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 Region-specific demand for  products Certain products have high customer  demand in specific regions. Size profile  optimization can analyze historical data  to ensure adequate stock of high demand  products in these stores.<|endoftext|>Reducing imbalance between  consumer demand and  inventory Customer demand for merchandise  depends on factors such as holiday  seasons, upcoming special events such as a  marathon, etc.  Size optimization can predict the type of  merchandise that needs to be in stock  during these seasons. This ensures that the  inventory is never out-of-stock for such  products.<|endoftext|>Recovering lost sales  On the other hand, owing to inaccurate or  excessive merchandise allocation, some  stores are forced to conduct clearance  sales at the end of a season to reduce  their inventory. SPO can assist retailers in  allocating accurate size profiles, thereby  ensuring only the required inventory is  stocked to meet existing business demand.<|endoftext|>Allocate new products/ new stores based on similar  products/stores Size profile optimization goes beyond  allocating size profiles based on  historical sales and inventory data.  Retail merchandising mandates that the  allocation of a new article to any store is  profitable to the company. The SPO engine  can map a new article to similar existing  articles and create profiles based on these.  Similarly, SPO can map a new store to  similar existing stores and create relevant  profiles.<|endoftext|>Demand Transfer Walk Switch Point Product Type -  Apparel for Men Class Men - Top 3 2 1 Navigate maximum up to Class Level Style 123 Style-Color 123-143 Style-Color/Size S, M, L, XL, XXL SHORT SLEEVE Top Style 132 Style-Color 132-145 Style-Color/Size S, M, L, XL, XXL LONG SLEEVE  Top External Document © 2018 Infosys Limited 

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 Functional testing Functional testing is performed to ensure  that size profiling is done according to  business expectations. There are four key  ways to conduct functional testing: 1. Validate pre-load data 2. Analyze data in SPO  3. Validate of the size profile engine based  on business rules 4. Validate business intelligence reports Validation of pre-load data Data from various sources such as point-of- sale (POS) terminal, digital stores, etc., are  loaded into the database. The raw data can  exist in any form (flat file or XML).  To verify that the right data is fed into the  database, different validation techniques  can be used. These include:  • Comparing data received from different  input source systems through XML/flat  file format with data available in the  intermediate database • Ensuring that data is loaded according to  business rules defined in the system • Ensuring the data loaded from the  source to intermediate databases  is according to the mapping sheet  specified in the requirement  Analysis of data in spo Retail merchants possess large amounts of  historical data accumulated over several  years that can be fed into the size profile  engine for profile generation.<|endoftext|>Testing teams should ensure that the  correct data and possible scenarios are  sampled and transferred to the size engine.  a Data from POS, Digital Stores Intermediate DB d b c SPO Why validation is critical for  size optimization? Previously, retailers were unaware of the  importance of size optimization. They  would randomly determine an average size  profile and apply it across all stores and,  occasionally, across geographic locations.  Retailers lacked the right approach to  leverage the large amount of historical  data readily available. This often resulted  in early season stock-outs in some stores  and markdowns for the same merchandise  in other stores. Additionally, retailers  struggled to make intelligent data-driven  decisions owing to the lack of automated  approaches and techniques to validate  data quality and integrity issues.<|endoftext|>Validation strategy  Validation involves processing a large  amount of data from various sources  according to the format specified by the  size profile engine. A validation strategy  can help retailers meet customer demand  by accurately projecting each store’s future  sales and inventory needs. It can support  the business goals of the retailer, i.e.,  reduce markdowns, increase full price sales  and drive higher revenue.  Besides validation, size profiling also  includes functional and performance  testing.<|endoftext|>External Document © 2018 Infosys Limited 

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 Non-functional testing Any size profile optimization project  involves processing a large volume of  structured data. Performance testing  ensures that size profile engines perform  optimally and that size profiles are  generated within the stipulated time  limits to support business needs. For  best results, the test environment for  performance testing should be similar to  the product environment. Further, if the  performance service level agreement (SLA)  is not met, then the advantages of size  profile optimization are lost. Performance  can be monitored by different tools that  are available in the market. The typical  performance testing check-points are: • Ensure data movement in each stage is  completed according to the SLA • Monitor system performance on  maximum data load Validation of reports Once the size profiles are generated,  business users can compare the profiles  for different