Data Envelopment Analysis

1
Data Envelopment Analysis
2
Weights Optimization
3
Primal Dual Relations
4
Minimize Inputs Interpretation
5
Graphic Illustration
6
Conclusions From Optimality
7
Maximize Outputs
8
Conclusions From Optimality
9
Return to Scale
10
Modeling Return to Scale
11
Numeric Example
12
Numeric Example
13
Numeric Example
14
Numeric Example
15
Numeric Example
M3 is inefficient M2 and M4 are efficient peers
16
DEA Software
17
Summary, Pros and Cons

• Avoids arbitrary selection of weights.
• Uses the theory of mathematical programming.
• Need to solve n programs.
• Assumes deterministic measurements.
• Requires a sufficient number of units.

18
Summary, Pros and Cons

• Requires units to use the same exact variables.
• Insufficient conclusions for decision making.
• Use of discrete or even categorical values.
• Unchangeable values.