RFID-enabled Visibility and Inventory Accuracy: A Field Experiment

1. RFID-enabled Visibility and Inventory Accuracy: A Field Experiment Bill Hardgrave John Aloysius Sandeep Goyal University of Arkansas Note: Please do not distribute or cite without explicit permission.

2. Premise Does RFID improve inventory accuracy? • Huge problem • Forecasting, ordering, replenishment based on PI • PI is wrong on 65% of items • Estimated 3% reduction in profit due to inaccuracy • What can be done? • Increase frequency (and accuracy) of physical counts • Identify and eliminate source of errors

3. Causes of Inventory Inaccuracy

4. Examples – Manual adjustment • PI = 12 • Actual = 12 • Casepack size = 12 • Associate cannot locate case in backroom; resets inventory count to 0 • PI = 0, Actual = 12 (PI < Actual) • Unnecessary case ordered

5. Examples – Cashier error

6. Proposition RFID-enabled visibility will improve inventory accuracy RFID Visibility Out of stocks Inventory accuracy Excess inventory

7. Receiving Door Readers Backroom Readers Box Crusher Reader Backroom Storage Sales Floor Door Readers Sales Floor Read points - Generic Store

8. RFID Data

9. The Study • All products in air freshener category tagged at case level • Data collection: 23 weeks • 13 stores: 8 test stores, 5 control stores • Mixture of Supercenter and Neighborhood Markets • Determined each day: PI – actual • 10 weeks to determine baseline • Same time, same path each day

10. The Study • Looked at understated PI only • i.e., where PI < actual • Treatment: • Control stores: RFID-enabled, business as usual • Test stores: business as usual, PLUS used RFID reads (from inbound door, sales floor door, box crusher) to determine count of items in backroom • Auto-PI: adjustment made by system • For example: if PI = 0, but RFID indicates case (=12) in backroom, then PI adjusted – NO HUMAN INTERVENTION

11. Results - Descriptives -1% 12% Numbers are for illustration only; not actual 12% - (-1%) = 13%

12. Results - Descriptives

13. Random Coefficient Modeling • Three levels • Store • SKU • Repeated measures • Discontinuous growth model • Covariates (sales velocity, cost, SKU variety)

14. Factors Influencing PI Accuracy (DeHoratius and Raman 2008) • Cost • Sales volume • Sales velocity • SKU variety • Audit frequency (experimentally controlled) • Distribution structure (experimentally controlled) • Inventory density (experimentally controlled)

15. Results: Test vs. Control Stores Test: Dummy variable coded as 1 - stores in the test group; 0 - stores in the control group Period: Time variable with day 1 starting on the day RFID-based autoPI was made available in test stores * p < 0.05 ** p < 0.01 *** p < 0.001

16. Variable Coding For discontinuity and slope differences: • Add additional vectors to the level-1 model • To determine if the post slope varies from the pre slope • To determine if there is difference in intercept between pre and post

17. Results: Pre and Post AutoPI Pre: Variable coding to represent the baseline period Trans: Variable coding to represent the transitions period—intercept Post: Variable coding to represent the treatment period p < 0.05 ** p < 0.01 *** p < 0.001

18. Results: Discontinuous Growth Model • Model of Understated PI Accuracy over Time Intervention

19. Results: Effect on Known Causes of PI Inaccuracy * p < 0.05 ** p < 0.01 *** p < 0.001

20. Results: Interaction Effects

21. Results: Interaction Effects

22. Implications • What does it mean? • Inventory accuracy can be improved (with tagging at the case level) • Is RFID needed? Could do physical counts – but at what cost? • Improving understated means less inventory; less uncertainty • Value to Wal-Mart and suppliers? In the millions! • When used to improve overstated PI: reduce out of stocks even further • Imagine inventory accuracy with item-level tagging …