RFID-enabled Visibility and Inventory Accuracy: A Field Experiment
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Transcript of RFID-enabled Visibility and Inventory Accuracy: A Field Experiment
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.
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
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
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
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)
Proposition
RFID-enabled visibility
will improve inventory accuracy
RFID Visibility
Inventory accuracy
Out of stocks
Excess inventory
Read points - Generic Store
Backroom Storage
Sales FloorSales Floor
Door Readers
Backroom Readers
Box Crusher Reader
Receiving Door Readers
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
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
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
Results - Descriptives
12%
-1%
12% - (-1%) = 13%Numbers are for illustration only; not actual
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%
Random Coefficient Modeling
• Three levels– Store– SKU– Repeated measures
• Discontinuous growth model
• Covariates (sales velocity, cost, SKU variety)
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)
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
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
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
Results: Discontinuous Growth Model
• Model of Understated PI Accuracy over Time
Intervention
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
Results: Interaction Effects
Results: Interaction Effects
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 …