item-level rfid f or the apparel industry: three field experiments
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Bill Hardgrave Sandeep Goyal John Aloysius Information Systems Department University of Arkansas. Item-level RFID f or the Apparel Industry: Three Field Experiments. Agenda. Business problem Scientific motivation Research gap Study 1 methodology and results - PowerPoint PPT PresentationTRANSCRIPT
Item-level RFID for the Apparel Industry: Three Field Experiments
Bill HardgraveSandeep GoyalJohn Aloysius
Information Systems DepartmentUniversity of Arkansas
Agenda
Business problem Scientific motivation Research gap Study 1 methodology and results Study 2 methodology and results Study 3 methodology and results Contributions
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)
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)
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)
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)
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)
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
Research QuestionsWill 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)
Research Model: Making the Business Case for ITEM-level RFID Tagging
RFID Deployme
ntInventory Visibility
Inventory Record
Inaccuracy
-Stockouts-Customer Service
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
Study 1 Results: Stockouts (Baseline)
Week Store Type Stockouts Total # of SKUs
% Stockouts
Significance
Week 1 Control 131 817 16.03% -3.55% ***
Test 162 827 19.59%Week 2 Control 140 815 17.18% -4.03% ***
Test 175 825 21.21%Week 3 Control 142 814 17.44% -9.26% ***
Test 219 820 26.71%Week 4 Control 117 781 14.98% -1.39% ***
Test 129 788 16.37%Week 5 Control 117 790 14.81% -3.97% ***
Test 148 788 18.78%Week 6 Control 110 787 13.98% -4.98% ***
Test 149 786 18.96%
Study 1 Results: Stockouts (Treatment)
Week Store Type Stockouts Total # of SKUs
% Stockouts
Significance
Week 1 Control 158 783 20.18% 4.76% **
Test 121 785 15.41%Week 2 Control 163 779 20.92% 4.07% *
Test 132 783 16.86%Week 3 Control 174 768 22.66% 5.26% ***
Test 135 776 17.40%Week 4 Control 171 775 22.06% 5.25% ***
Test 131 779 16.82%Week 5 Control 172 774 22.22% 5.30% ***
Test 132 780 16.92%Week 6 Control 192 769 24.97% 7.02% ***
Test 140 780 17.95%
Study 1 Results: Stockouts (Overall)
Period Store Type
Stockouts
Total # of SKUs
% Stockouts
% Change(Control-Test)
Net Change
Overall Change
Baseline Control
757 4804 15.76% -4.56% 9.83% 48.36% ***
Test 982 4834 20.31%Treatment
Control
1030 4648 22.16% 5.27%
Test 791 4683 16.89%
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
Study 2 Results: Comparison of PI versus Actual Stockouts
PI: Perpetual Inventory
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
Study 3 Results: Inventory Record Discrepancy
Baseline Treatment
Shoes Bras
Disc
repa
ncy
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?
Contributions
Improved inventory inaccuracy
Decreased on-shelf stockouts thus improving product availability
Influence is not consistent across all products
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?
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
Read points - Generic Store
Backroom Storage
Sales FloorSales Floor
Door Readers
Backroom Readers
Box Crusher Reader
Receiving Door Readers
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
Study 1 Results: Descriptive statistics (all stores, pooled across pre-test/post-test periods)
Variable Mean Std. Dev 1 2 3 4 5
1. Sales Volume 1.13 1.18
2. Item Cost 171.89 75.71 -0.305**
3. Dollar Sales 21.78 20.26 0.650*** 0.125***
4. Variety 294.08 74.15 0.078*** 0.146*** 0.160***
5. Treatment 0.52 0.5 -0.038 0.001 -0.076** 0.059***
6. PI- Inaccuracy 5.01 8.38 0.076*** -0.080 0.121*** 0.182*** 0.030
Notes: ***p<.001, **p<.01
Study 1 Results: Post Hoc Analysis
Study 2: Statistical Analyses Comparisons:
Linear mixed effects model (Pre-test/Post-test)
Random effect: Items grouped within stores
Statistical software: R
Hardware: Mainframe
Study 2 Results: Descriptive Statistics
Mean Std. Dev.
1 2 3 4 5
1 PI_ABS 3.16 11.382 Cost 47.99 11.96 -.049*
*3 Category
Variety795.31 464.01 .015** -.198**
4 Sales Volume 52.40 184.95 .400** -.032**
-.037**
5 Dollar Sales 735.31 2786.83
.201** .356** -.177** .648**
6 Density 100.84 93.10 .159** -.217**
.263** .170** -.114**