Transcript Data Envelopment Analysis (DEA) and Its Applications

Performance Evaluation
and Benchmarking Using
DEA
Joe Zhu
Department of Management
Worcester Polytechnic Institute
Worcester, MA 01609
[email protected]
www.deafrontier.com
Outline
• What is DEA?
• New Models/Uses
• Two-Stage Model
• Context-dependent DEA
• Benchmarking
• Books
Data Envelopment Analysis
Joe Zhu
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DEA & Banking

The Banking industry has been the subject of DEA
analysis by researchers in various areas and
probably is the most heavily studied business


Branches
Banks across countries
Inputs
FTE in dollars
Premise/IT expenses
Other Expenses
Bank
Branch
Outputs
Loan Balances
Deposit Balances
Securities Balances
Gross Revenue
Source: Paradi et al. 2004
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DEA

Deals with multiple performance measures
(inputs and outputs) in a single integrated
model

Includes any necessary measures related to the
characterization of banking performance
Identifies a “base-line” for comparisons in
continuous improvement program
 Provides specific targets for improvement
(over time)

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Why DEA?
Output
DEA Best-Practice
Frontier
6
6 6
6
6
6 6
6
6
6
- Regression can accommodate
Multiple inputs or
outputs but not both
6
6
- Regression requires a
functional relationship
between in/outputs
Data Envelopment Analysis Joe Zhu
predicted
average
behavior
6
Input
- Regression provides only
average relationships
not best practice
5
Basic DEA Benchmarking Information

DEA gives




Efficiency rating, or score, for each DMU
Efficiency reference set: peer group
Target for the inefficient DMU
Information on how much inputs can be
decreased or outputs increased to make the
unit efficient – improving productivity &
performance
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DEA & Performance Improvement
Output
DEA Best-Practice
Frontier
D
6
D
6
6
6
6
Output
augmentation
K
D
Input reduction
Input
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Benefits

The establishment of the efficient frontier
consisting of the best performing DMUs
 A projection to the efficient frontier - a guide
to “what to do” for the DMU managers
 The identification of the peer group, a
reasonable argument why it is a FAIR
comparison
 An indication of how important a particular
DMU is as a role model
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How DEA works?
Supply Dollars
450
400
350
B1
B4
300
T1
250
200
B2
B5
150
100
50
B3
0
0
20
40
60
Teller Hours




5 branches
Three (B1, B2 & B3 are efficient – best practice frontier)
B4 and B5 are inefficient
Target for B4 is T1 (decrease inputs)
Data Envelopment Analysis Joe Zhu
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How DEA works?
450
H1
400
Market Share
350
H2
H5
300
250
T2
200
H4
150
H3
100
50
0
0
100
200
300
400
500
Sales




5 branches
Three (H1, H2 & H3 are efficient – best practice frontier)
H4 and H5 are inefficient
Target for B4 is T2 (increase outputs)
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More Information on DEA

Web



Books




www.deafrontier.com
…
Cooper, W.W., Lawrence M. Seiford, and K. Tone. 2000. Data
Envelopment Analysis: A Comprehensive Reference Text with
Models, Applications, References, and DEA-Solver Software.
Kluwer Academic Publishers, Boston
Zhu, J. 2002. Quantitative Models for Performance Evaluation
and Benchmarking: Data Envelopment Analysis with
Spreadsheets. Kluwer Academic Publishers, Boston
…
Softwares


DEA Excel Solver (DEAFrontier)
…
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DEA & IT
• Indirect impact of IT on productivity
• Two Stage DEA Model
• Chen, Y. and Zhu, J., Measuring information technology’s indirect
impact on firm performance, Information Technology &
Management Journal, Vol. 5, Issue 1-2 (2004), 9-22.
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What is Benchmarking?
... a process of defining valid
measures of performance
comparison among peer units,
using them to determine the
relative positions of the peer
units and, ultimately, establishing
a standard of excellence.
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Acceptance System Decision Rule


Trout et al. (1996, COR, Vol 23, 405-408)
– acceptance/rejection of credit risks
Seiford & Zhu (1998, COR, Vol. 25, 329332)
Benchmarking
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Approach
Output
DEA Best-Practice
Frontier/Benchmarks

6
6
6
6T
T
66
new
activities
T
T
Data Envelopment Analysis Joe Zhu
Input
15
Business Process Reengineering
• Compare new bank branches to the
traditional best-practice frontier.
performance
ss
s
traditional
best practice
ss
s
s
Data Envelopment Analysis Joe Zhu
time
16
Benchmarking results
New branch best-practice

Overall, new
branches’
performance is
improving
traditional branch bestpractice
Cook, W.D., Seiford, L.M. and Zhu, Joe, Models for
performance benchmarking: Measuring the effect of ecommerce activities on banking performance, OMEGA,
Vol.32, Issue 4 (2004), 313-322.
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Context-dependent DEA
• Context-dependent
DEA
• Consumer’s choice
is influenced by the
context
• The performance of
DMUs should also
reflect “context”
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Journal of Marketing
Research
• Book Review
– contextdependent DEA
(identifying
possible
moderating
results) intriguing
and, conceivably,
breathtaking.
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Joe Zhu
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Service Productivity
• D. Sherman and J. Zhu, Service
Productivity Management: Improving
Service Performance Using Data
Envelopment Analysis (DEA)
Springer, Boston, 2006, ISBN 0-38733211-1.
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DEA Handbook
W.W. Cooper, L.M.
Seiford and J. Zhu
Handbook on Data
Envelopment
Analysis, Springer,
Boston, 2004, ISBN
1-4020-7797-1
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Modeling Issues
 W.D. Cook and Joe
Zhu, Modeling
Performance
Measurement:
Applications and
Implementation
Issues in DEA,
Springer, Boston,
2005, ISBN 0387-24137-X.
Data Envelopment Analysis
Zhu
Joe
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DEA & Finance
• Mutual funds
• CTAs
• Hedge Funds
G. Gregoriou and Joe Zhu, Evaluating
Hegde Funds and CTA Performance:
Data Envelopment Analysis Approach,
John Wiley & Sons, New York, 2005,
ISBN 0-471-68185-7 .
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