rfid-enabled visibility and inventory accuracy: a field experiment
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RFID-enabled Visibility and Inventory Accuracy: A Field Experiment. Bill Hardgrave John Aloysius Sandeep Goyal University of Arkansas. Note: Please do not distribute or cite without explicit permission. RFID-enabled Visibility and Inventory Accuracy: A Field Experiment. John Aloysius - PowerPoint PPT PresentationTRANSCRIPT
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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.
![Page 2: RFID-enabled Visibility and Inventory Accuracy: A Field Experiment](https://reader035.vdocuments.site/reader035/viewer/2022062520/56815b1c550346895dc8d147/html5/thumbnails/2.jpg)
RFID-enabled Visibility and Inventory Accuracy:
A Field Experiment
John AloysiusSandeep Goyal
Bill Hardgrave
University of Arkansas
Note: Please do not distribute or cite without explicit permission.
![Page 3: RFID-enabled Visibility and Inventory Accuracy: A Field Experiment](https://reader035.vdocuments.site/reader035/viewer/2022062520/56815b1c550346895dc8d147/html5/thumbnails/3.jpg)
RFID-enabled Visibility and Inventory Accuracy:
A Field Experiment
Sandeep Goyal
University of Arkansas
Note: Please do not distribute or cite without explicit permission.
![Page 4: RFID-enabled Visibility and Inventory Accuracy: A Field Experiment](https://reader035.vdocuments.site/reader035/viewer/2022062520/56815b1c550346895dc8d147/html5/thumbnails/4.jpg)
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 visibilitywill improve inventory accuracy
RFID Visibility
Inventory accuracy
Out of stocks
Excess inventory
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Read points - Generic DC
Receiving Door
Readers
Shipping Door
Readers
Conveyor Readers
Distribution Center
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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 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 StoresVariables Effects
(Intercept) 6.05***
Velocity 1.92***
Variety -0.03
Item cost -0.01
Test -1.28**
Period 0.54***Test: 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
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Results: Pre and Post AutoPI
Results of Linear Mixed EffectsVariables Effects
(Intercept) 7.871***
Velocity -0.925***
Variety -0.003
Item cost -0.001*
PRE 0.131**
TRANS -2.038***
POST -0.298***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: Known Causes
Influence of RFID-enabled Visibility on Known Predictors of Inventory Inaccuracy
Variables Model 1 Model 2 Model 3 Model 4
Intercept 11.50*** 7.40*** 7.55*** 7.77***
Treatment -1.83*** -1.28*** -1.27*** -1.82***
Cost Per Item (in dollars) -0.55** -0.40 -0.39 -0.39
Velocity -0.83*** -0.87*** -0.90*** -0.89**
Variety -0.03 -0.02 -0.02 -0.02
Treatment X Cost Per item 0.38***
Treatment X Velocity -0.06
Treatment X Variety -0.15*** p < 0.05 ** p < 0.01 *** p < 0.001
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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 …