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

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A Field Experiment

John AloysiusSandeep Goyal

Bill Hardgrave




A Field Experiment

Sandeep Goyal



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 visibilitywill improve inventory accuracy

RFID Visibility

Inventory accuracy

Out of stocks

Excess inventory

Read points - Generic DC

Receiving Door

Readers

Shipping Door

Readers

Conveyor Readers

Distribution Center

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 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 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

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

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

Results: Discontinuous Growth Model

• Model of Understated PI Accuracy over Time

Intervention

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

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 …

rfid-enabled visibility and inventory accuracy: a field experiment

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