Item-level RFID f or the Apparel Industry: Three Field Experiments

1. Bill Hardgrave Sandeep Goyal John Aloysius Information Systems Department University of Arkansas Item-level RFID for the Apparel Industry: Three Field Experiments

2. Agenda • Business problem • Scientific motivation • Research gap • Study 1 methodology and results • Study 2 methodology and results • Study 3 methodology and results • Contributions

3. Business Problem • Poor store execution is a leading cause for customers leaving retail stores (e.g. DeHoratius and Ton 2009 ; Kurt Salmon Associates, 2002) • 24% of stockouts due to inventory record inaccuracy and 60% stockouts due to misplaced products (Ton 2002) • Inventory records are inaccurate on 65% of items(Raman et al. 2001)

4. Scientific Motivation • Firms are skeptical about implementing new technologies based on pure faith, but need value assessments, tests, or experiments • Such empirical-based research requires “a well-designed sample, with appropriate controls and rigorous statistical analysis” (Dutta, Lee, and Whang 2007)

5. Key Terms • Inventory visibility • Retailer’s ability to determine the location of a unit of inventory at a given point in time by tracking movements in the supply chain • Inventory record inaccuracy • Absolute difference between physical inventory and the information system inventory at any given time (Fleisch and Tellkamp 2005) • Store execution • Retailer’s ability to make a product available on-shelf or in-store when a customer seeks it (Fisher et al., 2006)

6. Prior Research • Pallet level tagging provides inventory visibility (Delen et al., 2007) • Case-level tagging reduces inventory inaccuracy (Hardgrave et al., 2010a) • Case-level tagging reduces stockouts(Hardgrave et al., 2010b)

7. Item-level Tagging: Beyond FMCG • For service level considerations, the variable cost of the tags is the factor that most influences the RFID-enabled retail sector (Gaukler et al., 2007) • “RFID in the apparel retail value chain is an item-level proposition, and the place to begin is in the store” (Kurt Salmon Associates, 2006)

8. Research Gap • Little empirical research examining the ability of RFID technology to improve inventory inaccuracy with item-level tagging • Little empirical research on how reduced inventory inaccuracy due to item-level tagging improves store execution • Little empirical research evaluating differences in the influence of RFID technology between on-shelf stock and backroom stock

9. Research Questions • Will item level RFID tagging improve inventory record accuracy? (Studies 1 and 3) • Will item level RFID tagging improve store execution with respect to on-shelf availability? (Study 1) • Will item level RFID tagging improve store execution with respect to in-store availability? (Study 2) • Will item level RFID tagging have similar influence on-shelf stock/backroom stock? (Study 3)

10. RFID Deployment Inventory Visibility Inventory Record Inaccuracy -Stockouts -Customer Service Research Model: Making the Business Case for ITEM-level RFID Tagging

11. Study 1 • Data collected at an upscale department store chain in the United States • All products in one apparel category (jeans) tagged at item level • Data collection: 12 weeks; 6 baseline and 6 treatment • 2 stores: 1 test store, 1 control store • Bi-weekly counts: using handheld RFID scanners (Test), handheld barcode scanners (Control) • Same time, same path each day

12. Study 1 Results: Stockouts (Baseline)

13. Study 1 Results: Stockouts (Treatment)

14. Study 1 Results: Stockouts (Overall)

15. Study 2 • Data collected at another upscale department store chain in the United States • All products in one apparel category (jeans) tagged at item level • Data collection: 13 weeks; 6 baseline and 7 treatment • 1 store • Bi-weekly counts: using handheld barcode scanners (baseline) and handheld RFID scanners (treatment) • Same time, same path each day

16. Study 2 Results: Comparison of PI versus Actual Stockouts PI: Perpetual Inventory

17. Study 3 • Data collected at another upscale department store chain in the United States • All products in two categories (shoes and bras) tagged at item level • Data collection: 12 weeks; 6 baseline and 6 treatment • 2 stores • Bi-weekly counts: using handheld barcode scanners (baseline) and handheld RFID scanners (treatment) • Same time, same path each day

18. Study 3 Results: Inventory Record Discrepancy

19. Discussion • Stockouts decreased by 48% in study 1 • PI system consistently underestimates the percentage stockouts—frozen stockouts • Results were essentially what we expected • Raises the question: what about other categories?

20. Contributions • Improved inventory inaccuracy • Decreased on-shelf stockoutsthus improving product availability • Influence is not consistent across all products

21. Future Research Directions • What is the impact of improved inventory accuracy (due to RFID tagging) on lost sales? • Are the results in this study generalizable to item level tagging in categories other than apparel?

22. Bill Hardgrave hardgrave@auburn.edu Sandeep Goyal sangoyal@usi.edu John Aloysius jaloysius@walton.uark.edu

23. Study 1 (contd.) • 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

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

25. Study 1: Statistical Analyses • Two comparisons: • Discontinuous growth model (Pre-test/Post-test) • PI = b0 + b1*PRE + b2*POST + b3*TRANS • Linear mixed effects model (Test/Control) • Random effect: Items grouped within stores • Statistical software: R • Hardware: Mainframe

26. Study 1 Results: Descriptive statistics (all stores, pooled across pre-test/post-test periods) Notes: ***p<.001, **p<.01

27. Study 1 Results: Post Hoc Analysis

28. Study 2: Statistical Analyses • Comparisons: • Linear mixed effects model (Pre-test/Post-test) • Random effect: Items grouped within stores • Statistical software: R • Hardware: Mainframe

29. Study 2 Results: Descriptive Statistics