1. Bill Hardgrave Sandeep Goyal John Aloysius Information Systems Department University of Arkansas

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

3.  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.  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.  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.  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.  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.  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.  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

11.  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. WeekStore TypeStockoutsTotal # of SKUs % StockoutsSignificance Week 1Control13181716.03%-3.55% *** Test16282719.59% Week 2Control14081517.18%-4.03% *** Test17582521.21% Week 3Control14281417.44%-9.26% *** Test21982026.71% Week 4Control11778114.98%-1.39% *** Test12978816.37% Week 5Control11779014.81%-3.97% *** Test14878818.78% Week 6Control11078713.98%-4.98% *** Test14978618.96%

13. WeekStore TypeStockoutsTotal # of SKUs % StockoutsSignificance Week 1Control15878320.18%4.76% ** Test12178515.41% Week 2Control16377920.92%4.07% * Test13278316.86% Week 3Control17476822.66%5.26% *** Test13577617.40% Week 4Control17177522.06%5.25% *** Test13177916.82% Week 5Control17277422.22%5.30% *** Test13278016.92% Week 6Control19276924.97%7.02% *** Test14078017.95%

14. PeriodStore Type StockoutsTotal # of SKUs % Stockouts % Change (Control- Test) Net Change Overall Change BaselineControl757480415.76%-4.56%9.83%48.36% *** Test982483420.31% TreatmentControl1030464822.16%5.27% Test791468316.89%

15.  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. PI: Perpetual Inventory

17.  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

19.  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.  Improved inventory inaccuracy  Decreased on-shelf stockouts thus improving product availability  Influence is not consistent across all products

21.  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.  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. Backroom Storage Sales Floor Door Readers Backroom Readers Box Crusher Reader Receiving Door Readers

25.  Two comparisons:  Discontinuous growth model (Pre-test/Post-test)  PI = b 0 + b 1 *PRE + b 2 *POST + b 3 *TRANS  Linear mixed effects model (Test/Control)  Random effect: Items grouped within stores  Statistical software: R  Hardware: Mainframe

26. VariableMeanStd. Dev12345 1. Sales Volume1.131.18 2. Item Cost171.8975.71-0.305** 3. Dollar Sales21.7820.26 0.650*** 0.125*** 4. Variety294.0874.15 0.078*** 0.146*** 0.160*** 5. Treatment0.520.5-0.038 0.001-0.076**0.059*** 6. PI- Inaccuracy5.018.38 0.076***-0.080 0.121***0.182***0.030 Notes: ***p<.001, **p<.01

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

29. MeanStd. Dev. 12345 1PI_ABS3.1611.38 2Cost47.9911.96 -.049** 3Category Variety 795.31464.01.015** -.198** 4Sales Volume52.40184.95.400** -.032** -.037** 5Dollar Sales735.312786.83.201**.356** -.177**.648** 6Density100.8493.10.159**-.217**.263**.170** -.114**