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 PI inaccuracy causes Results in overstated PI? Results in understated PI? Can case-level RFID reduce the error? Incorrect manual adjustment Yes Improper returnsYes No Mis-shipment from DC Yes Cashier errorYes No

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 Product AProduct B PI10 Actual10 Sell 3 of A and 3 of B, but Cashier scans as 6 of A PI = 4 Actual = 7 (PI < Actual) PI = 10 Actual = 7 (PI > Actual)

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

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

8. RFID Data LocationEPCDate/timeReader DC 1230023800.341813.50000002408-04-08 23:15inbound DC 1230023800.341813.50000002408-09-08 7:54conveyor DC 1230023800.341813.50000002408-09-08 8:23outbound ST 9870023800.341813.50000002408-09-08 20:31inbound ST 9870023800.341813.50000002408-09-08 22:14backroom ST 9870023800.341813.50000002408-11-08 13:54sales floor ST 9870023800.341813.50000002408-11-08 15:45sales floor ST 9870023800.341813.50000002408-11-08 15:49box crusher

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

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 Linear Mixed Model of Test versus Control Stores VariablesEffects (Intercept)5.65446*** Velocity2.35560*** Variety0.00009 Item cost0.00001 Sales Volume-0.00002 Test-1.62965** Period-0.00762 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 Results of Linear Mixed Effects VariablesEffects (Intercept)8.00424*** Velocity-0.95251** Variety-0.00345 Item cost-0.00040* Sales volume0.0000 PRE0.13786** TRANS-1.87477*** POST-0.34511*** 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

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 …