bill hardgrave john aloysius sandeep goyal information systems department university of arkansas
TRANSCRIPT
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RFID-Enabled Visibility and Inventory Record Inaccuracy: Experiments in the Field Bill Hardgrave John AloysiusSandeep Goyal
Information Systems DepartmentUniversity of Arkansas
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Research Questions
Will RFID technology improve inventory record accuracy? (Study 1)
Can RFID technology ameliorate the effects of known causal predictors of inventory record inaccuracy? (Study 1 / Study 2)
What are the characteristics of product categories for which RFID technology is effective in reducing inventory record inaccuracy? (Study 2)
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Study 1 All products in air freshener category
tagged at case level Interrupted Time-series design Data collection: 23 weeks 13 stores: 8 test stores, 5 control
stores Mixture of Supercenter and Neighborhood
Markets Daily physical counts 10 weeks to determine baseline Same time, same path each day
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Study 1 Results: Linear Mixed Effects (Discontinuous growth model for test stores)
Results of Linear Mixed Effects
Variables Effect
(Intercept) 8.004***
Sales Volume -0.953**
Variety -0.003
Item cost 0.040*
Dollar Sales 0.000
PRE 0.138**TRANS 1.875***
POST - 0.345***
Notes p<.05, **p< .01, ***p<.001 Velocity = Number of units sold per day; Item Cost Cost of an item in cents; Sales Volume = Item Cost X Velocity; Variety = Number of unique SKUS carried in a store; PRE: Periods numbered consecutively for 40 day window around the adjustment; POST: Periods numbered O to 20 days before the adjustment, numbered consecutively after; TRANS: Numbered 0 before the adjustment, numbered 1 after Notes p<.05, **p< .01, ***p<.001 Velocity = Number of units sold per day; Item Cost Cost of an item in cents; Sales Volume = Item Cost X Velocity; Variety = Number of unique SKUS carried in a store; PRE: Periods numbered consecutively for 40 day window around the adjustment; POST: Periods numbered O to 20 days before the adjustment, numbered consecutively after; TRANS: Numbered 0 before the adjustment, numbered 1 after
Notes p<.05, **p< .01, ***p<.001
Velocity = Number of units sold per day; Item Cost: Cost of an item in cents; Sales Volume = Item Cost X Velocity; Variety = Number of unique SKUS carried in a store; PRE: Periods numbered consecutively for 40 day window around the adjustment; POST: Periods numbered O to 20 days before the adjustment, numbered consecutively after; TRANS: Numbered 0 before the adjustment, numbered 1 after
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Study 1 Results: Discontinuous growth
model (Interrupted time series for test stores)
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Study 1 Results: Linear Mixed Model for Test versus Control stores (post period)
Variables Fixed effects
(Intercept) 5.654***
Sales volume 2.356***
Variety 0.000
Item Cost 0.001
Dollar sales -0.002
Test -1 .630**
Period -0.008
Notes: ***p<.001, **p<.01
Sales volume = Number of units sold per day; Item Cost = Cost of an item in cents; Dollar Sales = Item Cost X Velocity; Variety = Number of unique SKUs carried in a store; Test: Dummy variable coded I for test stores and 0 for control stores; Period: Day 1 starting when RFID auto-adjust was made available in test store.
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Study 1: Discussion
• PI accuracy improved 23%•Results were essentially what we
expected• Insight from DeHoratius and Raman
(2008) variables•Raises the question: what about other
categories?
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Study 2
• Untreated Control Group design with pretest and post-test
• Matched Sample• 62 stores: 31 test stores, 31 control stores• Mixture of Supercenter and Neighborhood Markets• Spread across the United States
• Looked at both understated PI and overstated PI • 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
PI: Perpetual Inventory
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Study 2 (contd.)
• Five general merchandise categories• Floorcare• e.g., Powerforce vacuum, tough stain pretreat, Woolite gallon
• Air freshener• e.g., Glade plugin, Febreeze paradise, Glade oil
• Formula• e.g., Pediasure chocolate, Nutripal vanilla
• Ready to assemble furniture• e.g., computer cart, pedestal desk, executive chair
• Quick cleaners• e.g., wood floor cleaner, Readymop, Swiffer floor sweeper
PI: Perpetual Inventory
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Study 2 (contd.)
•Data collection• Two waves (Pre and Post implementation), two months apart
•Same time, same path each wave•Stock physical counts
• conducted over 5 days in each wave by an independent company
•Dependent variable: PI Absolute = |PI – Actual|
•Looked at both understated and overstated PI
Pre-implementation Post-implementation
RFID Implementation
5 days 2 Months 5 days
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Study 2 (contd.)
• Data collection (contd.): Measures• Item cost• Cost of the item to the retailer
• Sales volume• Quantity of item sold for two month preceding measurement
• Dollar sales• Dollar amount of items sold for two month preceding
measurement
• Density• Total number of units in a category divided by linear feet of
shelf space for that category
• Variety• Total number of unique SKUs in a category
PI: Perpetual Inventory
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Study 2 Results: Ameliorating effects of RFID (Pre-test/Post-test)
PI~TREAT + COST + SALESVOL + DOLLARSA + DENSITY + CATVAR + TREAT_XXX
Treatment X Cost 0.0280 *
Treatment X Sales Vol. -0.0092 ***
Treatment X Dollar Sal. -0.0004 *
Treatment X Density -0.0132 ***
Treatment X Variety -0.0015 ***
*** p < .01, ** p < .05, * p < .10
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Study 2 Results:Effect size for Treatment, Linear Mixed Model
PI = β0 + β1*Treatment
Category Control Stores Test Stores Difference
Floorcare -0.208 * -0.899 *** 0.691**
Airfreshener -1.099 * -2.729 *** 1.63***
Furniture -0.061n.s. 0.168 n.s. -0.229 n.s.
Formula 0.894 n.s. -2.004 *** 2.898 ***
Cleaners 1.692 ** 1.319 ** 0.373**1. *** p < .01, ** p < .05, * p < .102. Significance of difference assessed by
interaction term of treatment (pre-post) and group (test-control)
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Study 2 Results:Characterization of Categories
CategorySales
Volume Item CostDollar Sales
SKU Variety
Inventory Density % Improve
Floorcare 16.06 20.21 366.52 736 24 45.15%**
Airfreshener 91.47 2.59 232.14 1123 224 29.56%***
Furniture 9.69 51.94 586.32 384 4 -60.64%n.s.
Formula 127.53 10.46 1499.14 282 130 81.60%***
Cleaners 80.46 6.14 559.47 120 72 16.86%**
*** p < .01, ** p < .05, * p < .10
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Contributions
▫RFID technology with case-pack tagging demonstrated to improve inventory inaccuracy by 16% to 81% depending on category characteristics
▫Evidence that RFID technology is effective in ameliorating the effects on inventory inaccuracy of item cost, sales volume, dollar sales, density, and variety
PI: Perpetual Inventory
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Contributions (contd.)
RFID technology is more effective in reducing PI inaccuracy in product categories which have: higher sales volume, lower item cost, higher dollar sales, greater SKU variety, greater inventory density
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Future Research Directions
What is the economic impact of improving inventory accuracy (with RFID)?
Imagine inventory accuracy with item-level tagging …
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Bill [email protected]
John [email protected]
Sandeep [email protected]
For copies of white papers, visithttp://itri.uark.edu/researchKeyword: RFID
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Business Problem and Motivation
Perpetual inventory (PI) record inaccuracy affects forecasting, ordering, replenishment PI is inaccurate on 65% of items (Raman et
al. 2001)
At any given time the retailer in this study manages about $32 billion in inventory
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Scientific Motivation
Firms are skeptical about implementing new technologies based on pure faith, but need value assessments, tests, or experiments (Dutta, Lee, and Whang 2007)
Such empirical-based research requires “a well-designed sample, with appropriate controls and rigorous statistical analysis”
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Research Model: Making the Business Case for RFID Technology
RFID Technology
Inventory Visibility
Inventory Record Inaccuracy
Costs/Profitability
Research Gap
Delen et al. 2007
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Research Gap
There is little empirical research in the field that demonstrates and quantifies the ability of RFID technology to improve inventory inaccuracy
There is no empirical research that characterizes product categories for which RFID technology may be effective in reducing inventory record inaccuracy
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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)
RFID-enabled auto-adjustment A system that leverages RFID technology to correct for
the absolute difference between physical inventory and the inventory management system inventory at any given time
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How does inventory inaccuracy occur?
Mechanisms which result in record inaccuracy
Results in overstated PI?
Results in understated PI?
Can case-level RFID reduce the error?
Incorrect manual adjustment
Yes Yes Yes
Improper returns
Yes Yes No
Mis-shipment from DC
Yes Yes Yes
Cashier error Yes Yes No
PI: Perpetual Inventory Source: Delen et al. (2007)
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Research Gap
There is evidence that RFID technology improves inventory visibility
Researchers assume that improved inventory visibility will result in improved inventory record inaccuracy and consequently impact costs and profitability
The current research experimentally manipulates inventory visibility in field conditions (by means of an RFID enabled auto-adjustment system) in order to assess the effect on inventory record inaccuracy
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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
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Read points - Generic Store
Backroom Storage
Sales FloorSales Floor
Door Readers
Backroom Readers
Box Crusher Reader
Receiving Door Readers
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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
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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
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Study 1 Results: Post Hoc Analysis
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Study 2: Statistical Analyses Comparisons:
Linear mixed effects model (Pre-test/Post-test)
Random effect: Items grouped within stores
Statistical software: R
Hardware: Mainframe
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Study 2 Results: Descriptive Statistics
Mean Std. Dev.
1 2 3 4 5
1 PI_ABS 3.16 11.38
2 Cost 47.99 11.96 -.049**
3 Category Variety
795.31 464.01 .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**