Comprehensive Analysis of RFID Performance in Retail Stores. University of Arkansas RFID Research Center. Justin Patton

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2 Comprehensive Analysis of RFID Performance in Retail Stores University of Arkansas RFID Research Center Justin Patton 2

3 Retail Benefits of RFID Hardgrave s Big Four 1. Inventory Accuracy 2. Out of Stock 3. Loss Prevention 4. Item Location First Wave: Inventory Accuracy Out of Stocks (Various Published Research Studies, 2005-present) Expanded Implementations: Loss Prevention Item Location

4 An RFID Story Tiger Apparel Believes in the Inventory Accuracy and Out of Stock Benefits of RFID Jumps into item level RFID pilot Handhelds and tags on sales floor Tiger doesn t do their homework, and implementation is shoddy Tiger doesn t know it, but RFID is only capturing 80% of inventory in store (wrong tags, poor training, unorganized tagging, and lots of untagged product) Tiger adjusts their PI and replenishes based on incorrect RFID data

5 What Happens? Tiger Apparel sees reduction in Out of Stocks Tiger Apparel sees a sales lift from increased In Stock So what s wrong? 20% of the shelf inventory is invisible Shelf is overstocked by 20% PI is inaccurate, but UNDERstated, not OVERstated Replenishment occurs FASTER than necessary

6 What Happens Next? Increased capital carrying costs of excess inventory 20% of shelf shrink and theft are masked When someone finally figures out the problem - Markdown Time

7 How well are we REALLY doing? RFID Store Audits Specialized Cycle Count Remove and Profile EACH article of clothing Barcode EPC Inlay Model Tagging Issues (no inlay, encoding problems, double inlays, loose inlays, etc.) Read Performance Issues (no read, dead inlay, weak inlay, no read on store cycle count, etc.) Compare Audit data to store Cycle Counts and Store PI

8 Audit Program so far Six months in operation - 24 Audits from 4 Retailers Full night per audit, usually 5-10 students in a store Multiple categories of apparel and nonapparel items Mainly in retail, tested in other RF inventory systems 8

9 So what does the data say? Full Report Forthcoming Sneak Peak Let s look at Denim only Aggregate data from 3 retailers, 7 stores Both New and Established RFID stores Multiple geographic locations 9

10 Aggregate 3 Retailers, 7 Stores

11 What are the problem areas? #1 Getting tags on the items (2.3%) - Retagging return items (1.1%) - Supplier Source Tagging (1%) Most retailers have store tagging capabilities, but the tagging processes need to take returns into account

12 What are the problem areas? #2 RF Performance (1.6%) - Dead Inlays: No physical damage (.2%) - Poor Performers (1.4%) Poor Performers means inlay didn t read on store cycle counts. Two contributing factors: difficult read environment, and/or wrong inlay.

13 Analyzing RF Performance Why didn t some of the tagged products read? Analysis of location of Poor Performers shows usual suspects: bottom of modulars, tightly packed areas, and metal shelving Analysis of inlay types of Poor Performers shows inlay models that comparatively underperform

14 Inlay Performance Report Inlay model names masked, Approved inlays available ARC website Green = ARC Approved Yellow = Under Review Red = Non-Approved Inlays

15 ARC Inlay Performance Freely available on UofA website Based on store audit performance Inlay Data Library collected from static testing Statistical analysis of field audits sets required performance thresholds

16 What does proper tagging look like? If unapproved/underperforming inlays are removed from current stores, Poor Performers drops to 0.7% RF System Performance improves to 99.3% Improved process, RF friendly store environments, and increasing gains in RF performance will yield additional future gains

17 Chipping Away at the Last 4% We can easily achieve RF System performance of 99%+ How can we tackle the other 4% of the data capture problem? Process, process, process Change management 17

18 Changing the PI model Pre-RFID ~65% Inventory Accuracy 35% inaccuracy is mostly OVERstated (System thinks store has more than actual) Post-RFID ~95% Inventory Accuracy 5% inaccuracy is mostly UNDERstated (System thinks store has less than actual) Not just increasing the accuracy, but also FLIPPING the inaccuracy 18

19 Living in an Understated World Sea change in how retail reacts to PI Traditional primary problems of in-stock and replenishment replaced by issues of hidden inventory and carrying costs Fundamental restructuring of forecasting, ordering, replenishment, and shelf capacity The competitive advantage for retailers is not just RFID adoption, it is who can most quickly comprehend, adapt, and capitalize on the new normals of store PI. 19

20 Justin Patton RFID White papers can be found under the research tab at:

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