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 42 of 68

PARTNERING SCIENCE, DATA, AND ASSET PROTECTION TO TACKLE RETAIL SHRINK deliveries per week, the risk tier for the store determined by the asset protection team, store type, inventory, charges, ratio of sales to inventory, net sales, and customer satisfaction score. The first four explanatory variables were determined to have no significant effect on the shrink percentage. The following explanatory variables were all found to be significant and are discussed in more detail below. In these relationships, the impact of these variables is expressed "cetereis paribus," or when all other things are equal. Due to the confidential nature of company records, actual figures will be replaced with X, Y, or Z. The actual value of X, Y, and Z varies for each factor. Q Risk Tier. Based on various metrics at the discretion of the asset protection team, there are four risk tier categories: low risk, medium risk, high risk, and max risk. Our model found no significant differences between shrink rates at low- and medium-risk stores. High-risk stores are expected to have X basis points more shrink, and max-risk stores are expected to have Y basis points more shrink, compared to low- and medium-risk stores. Q Store Type. Our analysis included five store types, categorized 1–5, where store type 1 corresponded to the most upscale stores, and store type 5 corresponded to the least upscale stores. Transitioning from store type 1 to 5, each change in store type corresponds to a reduction in shrink by X basis points. For example, consider a store that is type 1 and has 600 basis points of shrink. If all other explanatory variables (risk tier, inventory, and so forth) are held constant, but the store type changed to type 2, the expected average shrink would be (600-X) basis points. If the store type changed to type 3, it would be (600-2*X) basis points, and so on. Q Inventory, Net Sales, and Charges. These explanatory variables are expressed in thousands of dollars per period. The shrink increases by X basis points for each additional $1 million inventory, decreases by Y basis points for each additional $1 million net sales, and increases by Z basis points for each additional $1 million sales. Q Sales Per Inventory. Sales per inventory is expressed as a ratio of sales during a given period ($) to the value of the inventory on display at the end of the period ($). For example, if there were $800 in sales during the period, and $1,000 of inventory was on the shelves, the sales per inventory figure would be 0.8. For each unit increase Dashboard: Which stores in this division perform the best? Commodity Category 1 Commodity Category 2 Commodity Category 3 Commodity Category 4 Commodity Category 5 Commodity Category 6 Commodity Category 7 Commodity Category 8 Commodity Category 9 Commodity Category 10 Commodity Category 11 Commodity Category 12 Commodity Category 13 Commodity Category 14 Commodity Category 15 Commodity Category 16 Commodity Category 17 Commodity Category 18 Commodity Category 1a Commodity Category 2 a Commodity Category 3 a Commodity Category 4 a Commodity Category 5 a Commodity Category 6 a Commodity Category 7 a Commodity Category 8 a Commodity Category 9 a Commodity Category 10 a Commodity Category 11 a Commodity Category 12 a Commodity Category 13 a Commodity Category 14 a Commodity Category 15 a Commodity Category 16 a Commodity Category 17 a Commodity Category 18 a 42 SEPTEMBER–OCTOBER | LOSSPREVENTIONMEDIA.COM

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