Predicting Shrink and Allocating Resources Strategically

Transcription

1 Predicting Shrink and Allocating Resources Strategically

2 Aashish Amin Walt Hall Office Depot, Inc.

3 Topics Predictive Modeling What, why, and how? Identifying Attribute Impacts and Importance Implementation of Change Opportunity Stores Target Stores Threshold Stores Store Visits Impact

4 Predictive Modeling What is it? Statistical process using patterns as well as current and historical data to predict future results Why use it? More science by incorporating more data How to use it? Identify the proper software to handle the data and analytics Applied Predictive Technologies (APT)

5 How did we use Predictive Modeling Find more efficient methods to reduce shrink in stores with the most opportunity for improvement

6 Shrink Reduction Phases Phase 1: Build an accurate model to predict shrink Phase 2: What can we learn from good performing stores to help understand under-performing stores?

7 Example How do you tell stores apart? Historical Shrink for 2 Random Retail Locations Store A ($85,000) ($83,000) Store B ($53,000) ($55,000) Should these stores be treated differently? How do I treat them differently? And still drive shrink reductions?

8 Attributes to Model Store level attributes are Controllable or Uncontrollable Shrink Audit Scores Population Distance to Highway Controllable Controllable Uncontrollable Uncontrollable

9 Shrink Reduction Phase 1 1. Determine attributes that are highly correlated to inventory loss Identified over 800 attributes per location Loss Prevention metrics Real Estate demographic data Human Resources metrics Store Traits Operational metrics Ideal model will have attributes based on statistical impact. 1. Combine key attributes into regression model to predict inventory loss by location 1. Determine controllable (actionable) attributes that impact inventory loss

10 Level of Accuracy with Historical Data Model predictions are highly accurate across all quartiles Model Results: Model correctly predicts year-over-year shrink change by location 90% accurately Looking at actual shrink % predictions, model was 86% accurate (+/- 0.10% shrink change)

11 Opportunities for Success How many locations underperformed versus their predicted inventory loss %? Less than half the stores If these locations achieved their predicted results, how would the Company s overall inventory loss results change? Impact could be worth Millions What do we need to focus on to reduce shrink?

12 Example Are the stores still the same? 2013 Savings Predicted Opportunity Store A ($90,000) ($85,000) ($83,000) ($5,000) Store B ($35,000) ($53,000) ($55,000) $18,000 Should they be treated differently? Why are they different?

13 Shrink Reduction Phase 2 What can we learn from good performing stores to help understand under performing stores?

14 Winner s Profile Determine What to Analyze Annual shrink % for all stores Identify Uncontrollable Correlates of Performance Which attributes outside of a store s control (i.e. trade area demographics) affect performance? Model Uncontrollable Attributes Quantify the impact of different uncontrollable characteristics 4 Apply the Model to the Entire Network 5 Model Impact of Controllable Attributes How much impact do these factors affect performance?

15 Identify Uncontrollable Attributes Higher Cap Index (Aggravated Assault) in locations with worse than predicted inventory loss results Proximity to larger households increased with stores that performed worse than predicted

16 Attribute Impact on Actual Shrink Versus Prediction Historical shrink % is highly indicative of future shrink results Audit scores are great indicators of operational compliance Overall operational accuracy is higher in stores with better performance Less-experienced managers have lower performing stores Experience in the position Tenure with the company Proximity to high traffic areas decreases shrink performance

17 Focus on Underperforming Stores Increase Audit presence where it s most needed Partner with Regional Vice Presidents and District Managers to improve performance and compliance Revised Audits for more focused reviews Training Site visits notes Recap each visit Record of visit for Operations and HR

18 Leveraging Data Opportunity Store Program Identify stores based on their predictive modeling shrink savings potential. Objective: Respond to potential shrink trends and proactively impact shrink.

19 Recognizing Shrink Target Store Program Identify stores by their actual inventory results and shrink. Opportunity and Target Stores share enhanced Loss Prevention services. Opportunity Stores: identified based on leading indicators (predictive modeling of shrink exposure) Target Stores: identified based on lagging indicators (spike in actual shrink results)

20 Field Staff Adjustments Store Visit Schedules Program stores have increased frequency Action plan review and follow-up Remote Management Skills Change in service protocol for stores Emphasis on communication Investigations Data mining Telephone Interviews Internal Customer Service

21 Opportunity / Target Store Visits Monthly Loss Prevention Audit Research Primary Focus Areas Review shrink performance and adjustment accuracy Follow-up on prior visit / audit exceptions Training Document store staff training each visit Communication Review trending with store staff Provided the District Manager a visit review and update the program database

22 Program Impact Quantifying Results Weekly status reports: Red / Yellow / Green progress indicators Program membership review cycles Accountability for performance Training and Staff Development Increased presence and training opportunities in program stores Focus provided for operational skills to impact shrink Catalyst for Loss Prevention Manager skill development Increasing Scope of Influence As stores improve widen the Threshold of participation

23 Review of Objectives Leverage Predictive Modeling Data Recognize areas of potential shrink Design programs to focus assets for best impact Identify Areas of Historic Exposure Supplement Predictive Modeling with market experience Maximize Impact of Assets Increase frequency of visits to emphasize training & behavioral change Reduce frequency of Loss Prevention visits in top performing locations Continue to Drill-Down

24 Questions?