RFID-enabled Visibility and Inventory Accuracy: A Field Experiment Bill Hardgrave John Aloysius...
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![Page 1: RFID-enabled Visibility and Inventory Accuracy: A Field Experiment Bill Hardgrave John Aloysius Sandeep Goyal University of Arkansas Note: Please do not.](https://reader035.fdocuments.us/reader035/viewer/2022070408/56649e6b5503460f94b69ffe/html5/thumbnails/1.jpg)
RFID-enabled Visibility and Inventory Accuracy:
A Field Experiment
Bill HardgraveJohn Aloysius
Sandeep Goyal
University of Arkansas
Note: Please do not distribute or cite without explicit permission.
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Premise
Does RFID improve inventory accuracy?
• Huge problem– Forecasting, ordering, replenishment based on PI– PI is wrong on 65% of items – Estimated 3% reduction in profit due to inaccuracy
• What can be done?– Increase frequency (and accuracy) of physical counts– Identify and eliminate source of errors
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Causes of Inventory Inaccuracy
PI inaccuracy causes
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
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Examples – Manual adjustment
PI = 12 Actual = 12 Casepack size = 12 Associate cannot locate case in backroom;
resets inventory count to 0 PI = 0, Actual = 12 (PI < Actual)
Unnecessary case ordered
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Examples – Cashier error
Product A Product B
PI 10 10
Actual 10 10
Sell 3 of A and 3 of B, but Cashier scans as 6 of A
PI = 4Actual = 7(PI < Actual)
PI = 10Actual = 7(PI > Actual)
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Proposition
RFID-enabled visibility
will improve inventory accuracy
RFID Visibility
Inventory accuracy
Out of stocks
Excess inventory
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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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RFID Data
Location EPC Date/time Reader
DC 123 0023800.341813.500000024 08-04-08 23:15 inbound
DC 123 0023800.341813.500000024 08-09-08 7:54 conveyor
DC 123 0023800.341813.500000024 08-09-08 8:23 outbound
ST 987 0023800.341813.500000024 08-09-08 20:31 inbound
ST 987 0023800.341813.500000024 08-09-08 22:14 backroom
ST 987 0023800.341813.500000024 08-11-08 13:54 sales floor
ST 987 0023800.341813.500000024 08-11-08 15:45 sales floor
ST 987 0023800.341813.500000024 08-11-08 15:49 box crusher
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The Study
• All products in air freshener category tagged at case level
• Data collection: 23 weeks
• 13 stores: 8 test stores, 5 control stores– Mixture of Supercenter and Neighborhood Markets
• Determined each day: PI – actual
• 10 weeks to determine baseline
• Same time, same path each day
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The Study
• 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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Results - Descriptives
12%
-1%
12% - (-1%) = 13%Numbers are for illustration only; not actual
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Results - Descriptives
Understated PI before auto-PI …
Understated PI after auto-PI …
Close (-1 or -2 units)
Inaccurate (> -2 units)
Perfect (PI = on-hand)
10% 30% 20%
Close (-1 or -2 units)
Inaccurate (> -2 units)
Perfect (PI = on-hand)
12% 17% 31%
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Random Coefficient Modeling
• Three levels– Store– SKU– Repeated measures
• Discontinuous growth model
• Covariates (sales velocity, cost, SKU variety)
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Factors Influencing PI Accuracy (DeHoratius and Raman 2008)
• Cost
• Sales volume
• Sales velocity
• SKU variety
• Audit frequency (experimentally controlled)
• Distribution structure (experimentally controlled)
• Inventory density (experimentally controlled)
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Results: Test vs. Control Stores
Linear Mixed Model of Test versus Control Stores
Variables Effects
(Intercept) 5.65446***
Velocity 2.35560***
Variety 0.00009
Item cost 0.00001
Sales Volume -0.00002
Test -1.62965**
Period -0.00762Test: Dummy variable coded as 1 - stores in the test group; 0 - stores in the control groupPeriod: Time variable with day 1 starting on the day RFID-based autoPI was made available in test stores* p < 0.05 ** p < 0.01 *** p < 0.001
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Variable Coding
For discontinuity and slope differences:
• Add additional vectors
to the level-1 model– To determine if the post
slope varies from the pre slope
– To determine if there is difference in intercept between pre and post
ID PRE TRANS POST1 0 0 01 1 0 01 2 0 01 3 0 01 4 0 01 5 0 01 6 1 01 7 1 11 8 1 21 9 1 31 10 1 41 11 1 5
ID PRE TRANS POST1 0 0 01 1 0 01 2 0 01 3 0 01 4 0 01 5 0 01 6 1 01 7 1 11 8 1 21 9 1 31 10 1 41 11 1 5
ID PRE TRANS POST1 0 0 01 1 0 01 2 0 01 3 0 01 4 0 01 5 0 01 6 1 01 7 1 11 8 1 21 9 1 31 10 1 41 11 1 5
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Results: Pre and Post AutoPI
Results of Linear Mixed Effects
Variables Effects
(Intercept) 8.00424***
Velocity -0.95251**
Variety -0.00345
Item cost -0.00040*
Sales volume 0.0000
PRE 0.13786**
TRANS -1.87477***
POST -0.34511***Pre: Variable coding to represent the baseline periodTrans: Variable coding to represent the transitions period—
intercept Post: Variable coding to represent the treatment period p < 0.05 ** p < 0.01 *** p < 0.001
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Results: Discontinuous Growth Model
• Model of Understated PI Accuracy over Time
Intervention
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Results: Effect on Known Causes of PI Inaccuracy
* p < 0.05 ** p < 0.01 *** p < 0.001
Influence of RFID-enabled Visibility on Known Predictors of Inventory Inaccuracy
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Intercept 8.79406*** 8.96105*** 8.57806*** 8.50871*** 8.63217*** 8.29307***
Test -2.38451*** -1.93159*** -1.96443*** -1.60594*** -1.8918*** -1.59360***
Cost/item -0.00003 -0.00002 -0.00003 -0.00004 -.00005 -.00005
Velocity -0.85846* -1.04432** -0.57057** -0.99065** -1.18576** -1.18576**
Variety -0.02180 -0.02546 -0.02079 -0.02079 0.02322 0.00232
SalesVol 0.00000 0.00002 0.00000 0.00000 0.00002 0.00002
Test X Cost/item
-0.00005***
0.00004*
Test X Velocity
-0.08735
0.59597
Test X Variety
-0.15657***
-0.00917**
Test X SalesVol
-0.00005 -0.00003
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Results: Interaction Effects
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Results: Interaction Effects
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Implications
• What does it mean?– Inventory accuracy can be improved (with tagging at
the case level)– Is RFID needed? Could do physical counts – but at
what cost?– Improving understated means less inventory; less
uncertainty• Value to Wal-Mart and suppliers? In the millions!
– When used to improve overstated PI: reduce out of stocks even further
– Imagine inventory accuracy with item-level tagging …