LP Magazine

SEP-OCT 2018

LP magazine publishes articles for loss prevention, asset protection, and retail professionals covering shrinkage, investigations, shoplifting, internal theft, fraud, technology, best practices, and career development.

Issue link: http://digital.lpportal.com/i/1030193

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Page 40 of 68

PARTNERING SCIENCE, DATA, AND ASSET PROTECTION TO TACKLE RETAIL SHRINK O ver the course of the past half year, we have been working closely with The Kroger Co. and the asset protection team utilizing analytics to drive insights from data that lead to better overall understanding and decisions regarding a problem common to the entire retail industry—retail shrinkage, or shrink. The produce department, in particular, is susceptible to loss and represents a disproportionate amount of shrink relative to the entire Kroger enterprise. Addressing shrinkage within the produce department will help Kroger reduce costs and improve profitability. The goal of this capstone project is to utilize data science and analytics to better understand the relationship between inventory, sales, produce freshness, customer satisfaction, and shrink. Exploratory Data Analysis Our first step was to better understand the business and determine the need. Due to the lack of bar codes, the variety of products and vendors, and the perishable nature of the products, produce department data can be extremely problematic. We needed to understand the departmental structure at Kroger, financial data, how Kroger measures and records produce inventory, and how to calculate shrinkage at a granular level. This information played a crucial role in understanding which questions to ask next, which direction to take the analysis, and ultimately which recommendations to provide. Good practice dictates exploratory data analysis (EDA) when starting any analytics project to better understand your data. As a first step in our data analysis we decided to analyze data at the overall store level, focusing on the big picture and looking at trends that affected each location as a whole. Our questions included: Q Which stores were performing the best and worst in terms of shrink results? Q Do these stores have any clear physical relationship? Q How do average shrink results vary depending on store type, produce square footage, number of deliveries per week, and seasonal considerations? Q Are there correlations between produce freshness and wastage? This analysis resulted in some interesting takeaways. First, stores that carry more value-based items had shrink results higher than more upscale stores. On average, stores that received six to seven deliveries per week had better shrink results than those with fewer deliveries. Perhaps these stores order less per delivery and carry less on the floor anticipating that another delivery will be made soon. Further, on a seasonal basis, shrink as a percentage to sales tends to be lowest in February through May. This may be due to a difference in product mix or other seasonal effects. However, while the results of the preliminary analysis may display general patterns of shrink performance by varying characteristics, the purpose of the exploratory data analysis was simply to uncover major trends and answer relevant questions rather than determine causal relationships. Digging Deeper Our next step was to analyze more granular data based upon sales, inventory, and cost factors for item-level data. We uploaded over 300 item-level data files, calculated shrink at the item-level based on the given information, and calculated aggregate statistics at the subcommodity level. As an enterprise, Kroger anticipates which commodities and subcommodities result in the most waste based on the experience and expertise of the employees. Some store managers claim there are items that are restocked solely to throw away again. Through data analysis we can provide greater accuracy regarding the wastefulness of each product and use quantitative analysis to support employee experience and expertise. Where is Kroger's shrink in the produce department coming from? Our first objective was to determine which commodities contributed the most to Kroger's total shrink. The dashboard created will then allow Kroger to mine deeper into each commodity to see the true problem Dashboard: Which commodities contributed the most to Kroger's total shrink? Commodity 001 Commodity 002 Commodity 003 Commodity 004 Commodity 005 Commodity 006 Commodity 007 Commodity 008 Commodity 009 Commodity 0010 Commodity 0011 Commodity 0012 Commodity 0013 Commodity 0014 Commodity 0015 Commodity 0016 Commodity 0017 Commodity 0018 Commodity 0019 Commodity 0020 Commodity 0021 Commodity 0022 Commodity 0023 Commodity 0024 Commodity 0025 Commodity 0026 Commodity 0027 Commodity 0028 40 SEPTEMBER–OCTOBER | LOSSPREVENTIONMEDIA.COM

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