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Shrink Analytics

Shrink Analytics. Michael Sanders Shrinkage Control Analyst J.C. Penney Company, Inc. $18B 1093 Stores 147K Associates. Basic Correlations and Flaws. *Shrink results grouped by audit score range. Quartiles. * Shrink quartiles with average audit score. Regression Analysis Result.

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Shrink Analytics

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  1. Shrink Analytics Michael Sanders Shrinkage Control Analyst J.C. Penney Company, Inc.

  2. $18B 1093 Stores 147K Associates

  3. Basic Correlations and Flaws *Shrink results grouped by audit score range

  4. Quartiles * Shrink quartiles with average audit score

  5. Regression Analysis Result

  6. Regression Analysis on the NFL

  7. INT Take INT Give Fumble Take Fumble Give Penalty YDS Pass YD/ATT Rush YT/ATT Completion % Rush YPG, Pass Yards PG 3rd Down Conversions 4th Down Conversions

  8. Pearson Correlation • A technique that determines the strength of a relationship between two variables. • +1 indicates they are perfectly related in a positive linear sense. Example: Caloric intake increases, weight increases. • -1 indicates they are perfectly related in a negative linear sense. Example: Car price goes down, as age goes up. • “zero” indicates there is no correlation. Example: The number of Red Sox fans named Steve to the number of wins the Red Sox win have this year.

  9. Defense & Offensive Stats -1 0 +1

  10. Pearson Correlation (Positive) – Wins/RSH YPG

  11. Correlation to Wins .48 Rush Yards PG -1 0 +1

  12. Pearson Correlation (Negative) – INT (Give)

  13. Correlation to Wins .48 Rush Yards PG -.43 INT Give -1 0 +1

  14. Pearson Results

  15. Correlation to Wins .55 3rd down conversions .04 4th down conversions .48 Rush Yards PG .30 Pass Yards PG .49 Completion % -.18 Fumble Take .56 Pass YD/ATT .15 Rush YT/ATT -.24 Fumble Give .03 Penalty YDS -.43 INT Give .21 INT Take -1 0 +1

  16. Multiple Buckets Merch Trends financial LP Metrics Human

  17. Financial Bucket • Cash Loss % • Refund % • Chargeback % • Scrap % • Markdowns • On-Hand Adjustments / Scrap

  18. Merchandise Trends Bucket • Months On Hand (COGS / Ending Inventory) • Inventory Turn • Markdowns • On-hand / Not Sold • Many of these can be evaluated as “whole store” or even more granular “by merchandise category”

  19. Human Bucket • Customer Survey Scores (total and by question) • Turnover • Tenure (Manager / Non-manager) • Training/Certification Compliance Rates • Workers Comp Rates • Payroll to plan (LP and sales separately) • Engagement Scores

  20. LP Staff Internals Externals LP Productivity Technology EAS Activations POS Exceptions Voids Dummy SKU usage No receipt refunds Line item voids Compliance / Process Store Self Inspection Score LP Audit Score LP Statistics

  21. Completing A Regression Analysis

  22. Regression Overview • Define Regression Analysis • Everyday life examples • Making the transition to Loss Prevention • Running a regression to predict shrink • Questions

  23. Regression • A method used to identify and measure the relationship between two or more variables • In regression there is always one “dependent” variable, and one or more “independent” variables. • The benefit of using regression, is that you can make reasonable estimates about expected results.

  24. Regression in Everyday Life

  25. Regression in Everyday Life Age Price Pearson = -.844 Lower - Higher Newer - Older Dependent Independent Mileage Price Pearson = -.734 Less - More Lower - Higher Dependent Independent

  26. Notice that both vehicles are listed at $17,499. Lowest Mileage Highest Mileage Regression in Everyday Life

  27. Regression in Everyday Life • It appears that at least one of the prices is too high. • How can we determine what the correct price should be? • We can pull sample data and run a regression analysis in Excel!

  28. In Excel Regression always put the dependent (price) variable to the left of the independent variables. The independent variables (age and miles) should be placed in the columns next to the dependent variable. Regression in Everyday Life Pulling Sample Data The order of the data is important.

  29. Start by selecting Tools on the top menu Then select Data Analysis… Regression in Everyday Life

  30. Scroll down in the dialogue box and select Regression. Regression in Everyday Life The Data Analysis dialogue box will open.

  31. In the box Input Y, we will define the range of our dependent variable including the title. Price is in column B. Next, in the Input X box we will select the range for the independent variables. Age and mileage are in columns C and D. Finally. Check the Labels Box Regression in Everyday Life The Regression dialogue box will open up.

  32. Regression in Everyday Life Here we can see the “Multiple R” is .859. Like the Pearson Correlation coefficient, the closer to 1 this number is, the more accurate the estimations made below. The area that we want to focus on is right here.

  33. Start with baseline price. Each year old subtract. Each 1K miles subtract. Regression in Everyday Life The Honda Accord Coefficients So how much should we expect to pay for a 2007 Honda Accord with no more than 20,000 miles on it?

  34. Regression in Everyday Life A 2007 Honda Accord is 2 years old and has 20,000 miles on it. • Per the Regression we should start with $20,100 as a base price. • For each year old the vehicle is we should subtract -$964.67. In this case our vehicle is two years old which equates to = - $1,929 (2 * -$964.67). • Finally for each 1,000 miles we should subtract -$28.77. For 20K miles we estimate on our vehicle, this would equate to -$575 (20 * -$28.77). • Therefore a 2007 Honda Accord with 20,000 miles should cost us about $20,100 - $1,929 - $575 or $17,596.

  35. FMV $17,624 $17,647 $17,735 $17,170 Regression in Everyday Life What is it really worth?

  36. How many games should a team win? Rushing YPG INT Give Pass YD/ATT 3rd Down Conversions Defensive Sacks

  37. Predicting Wins using Excel Regression

  38. Looking at the Multiple R we can see that the value is .870 which is very close to 1 and indicates that these five metrics combined have a strong correlation to victories. Again we only want to focus here. Predicting Wins Output

  39. Predicting Wins Regression Baseline

  40. Predicting Wins Regression • Pittsburgh won the Super Bowl. • According to the regression how many wins should Pittsburgh have gotten based on the following information? • How many should the Lions have won?

  41. Predicting Wins Regression

  42. Predicting Wins Regression

  43. Transitioning to Loss Prevention Dependant (Predicted) Variables • Price, Wins, Shrink % • Independent (Data) Variables • Age, Miles, Rush YPG, Def Sacks, Refunds, Over-Short Cash

  44. Predicting Shrink • Myths • You can predict shrink with 100% accuracy. • There are too many variables to provide an accurate prediction. • Where do I start?

  45. Shrink Predictor Tool Independent Variables Dependent Variable Shrink % External Apps per 100 Hours Over/Short Cash Customer Survey…Variety of Merchandise Store Associate Turnover

  46. Shrink Predictor Tool

  47. Shrink Predictor Tool

  48. Update your information in these 5 columns Build your own Shrink Predictor Report

  49. Shrink Predictor Tool From the Summary Output, Copy the highlighted cells and paste them into the Coefficient Updater Tab in the Shrink Predictor Workbook.

  50. Here we will take the information from the Summary Output and paste it into the yellow boxes on the Coefficient Updater tab. Once you paste the information in the shaded boxes click on the Shrink Predictor tool tab.

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