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 41 of 68

PARTNERING SCIENCE, DATA, AND ASSET PROTECTION TO TACKLE RETAIL SHRINK subcommodity. By reviewing the results for each commodity and subcommodity, Kroger can better determine what is happening in stores, investigate results that appear problematic, and make informed decisions based on the available data. We then addressed whether certain products were being restocked solely to be thrown away, as well as the shrink cost per commodity. As depicted in the Tableau visual above, we were able to identify certain products that experience a high shrink percentage in the produce department. While clearly problematic, this insight creates a valuable business opportunity based on a data-driven decision. Based on this analysis, Kroger has the opportunity to reconsider best practices and improve results at every location where the product is available and all new locations where the products will be stocked moving forward. Similarly, we created a second dashboard representing the percent of waste within each commodity and subcommodity allowing Kroger to review and compare the performance of each group of products. For example, upon determining that a large portion of a certain product goes to waste, data mining was able to reveal the primary problem by narrowing it down to a few select items with extraordinarily high shrink-to-sales ratios. This may lead to critical business decisions, including the possibility of adjusting delivery sizes or frequency for these products to better match inventory with demand and to reduce shrink. The Tableau dashboard can be a crucial tool allowing store managers to visually observe trends in their produce departments. The final dashboard (see next page) depicts a map of stores in a Kroger division, color coded by shrink results with red representing the worst-performing stores and grey representing the best. Using the dashboard, regional managers will be able to visualize how the division is performing and which areas need attention. Store managers can compare performance to that of neighboring locations and immediately identify problem commodities and subcommodities. Employees can click on individual commodities or subcommodities and visually identify strong performance and problem areas. This dashboard allows for easy comparison between stores and much faster identification of problems in the field, providing awareness and visibility into performance at a granular level and offering high-value information to Kroger. Predictive Modeling In addition to exploratory data analysis, predictive models were developed for the Kroger data to help answer key questions and provide additional visibility into Kroger operations. While this analysis can show what's happened in the past, predictive models will determine the average expected result (shrink percentage) for a given set of conditions. Additionally, a well-fitted predictive model will quantify the impact of a change in a factor (such as moving a store from a high-risk neighborhood to a low-risk neighborhood) when all other conditions remain equal. The first predictive model used was a multilinear regression. We used multiple linear regression to model the relationship between several explanatory variables (including store type, store area, delivery schedule, customer satisfaction scores, and more) and the desired response variable—produce shrink. The resulting regression describes how mean responses vary in response to changes in the explanatory variables. The model predicts whether shrink will increase or decrease when a given variable changes and quantifies the expected magnitude of change. Further, it can determine which variables impact shrink, allowing Kroger to better understand factors affecting shrink rates. We modeled shrink rate, expressed as basis points rather than the monetary value of shrink, so conclusions would be easier to translate from one store to the next. Explanatory variables included produce department square footage, store employee turnover rate, percentage of produce department area relative to overall store area, number of produce Dashboard: Which products experience a high shrink percentage? 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 41 LP MAGAZINE | SEPTEMBER–OCTOBER 2018

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