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RFID-enabled Visibility and Inventory Accuracy: A Field Experiment

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. RFID-enabled Visibility and Inventory Accuracy: A Field Experiment. John Aloysius

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RFID-enabled Visibility and Inventory Accuracy: A Field Experiment

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

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

  4. 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

  5. Causes of Inventory Inaccuracy

  6. 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

  7. Examples – Cashier error

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

  9. Receiving Door Readers Shipping Door Readers Distribution Center Conveyor Readers Read points - Generic DC

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

  11. RFID Data

  12. 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

  13. 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

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

  15. Results - Descriptives

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

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

  18. 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

  19. 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

  20. 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

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

  22. Results: Known Causes * p < 0.05 ** p < 0.01 *** p < 0.001

  23. Results: Interaction Effects

  24. Results: Interaction Effects

  25. 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 …

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