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

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

 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)

 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)

 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)

 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)

 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)

 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

 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)

RFID Deployment Inventory Visibility Inventory Record Inaccuracy -Stockouts -Customer Service

 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

WeekStore TypeStockoutsTotal # of SKUs % StockoutsSignificance Week 1Control %-3.55% *** Test % Week 2Control %-4.03% *** Test % Week 3Control %-9.26% *** Test % Week 4Control %-1.39% *** Test % Week 5Control %-3.97% *** Test % Week 6Control %-4.98% *** Test %

WeekStore TypeStockoutsTotal # of SKUs % StockoutsSignificance Week 1Control %4.76% ** Test % Week 2Control %4.07% * Test % Week 3Control %5.26% *** Test % Week 4Control %5.25% *** Test % Week 5Control %5.30% *** Test % Week 6Control %7.02% *** Test %

PeriodStore Type StockoutsTotal # of SKUs % Stockouts % Change (Control- Test) Net Change Overall Change BaselineControl %-4.56%9.83%48.36% *** Test % TreatmentControl %5.27% Test %

 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

PI: Perpetual Inventory

 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

 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?

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

 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?

Bill Hardgrave Sandeep Goyal John Aloysius

 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

Backroom Storage Sales Floor Door Readers Backroom Readers Box Crusher Reader Receiving Door Readers

 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

VariableMeanStd. Dev Sales Volume Item Cost ** 3. Dollar Sales *** 0.125*** 4. Variety *** 0.146*** 0.160*** 5. Treatment **0.059*** 6. PI- Inaccuracy *** ***0.182***0.030 Notes: ***p<.001, **p<.01

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

MeanStd. Dev PI_ABS Cost ** 3Category Variety ** -.198** 4Sales Volume ** -.032** -.037** 5Dollar Sales **.356** -.177**.648** 6Density **-.217**.263**.170** -.114**