Data Envelopment Analysis

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# Data Envelopment Analysis - PowerPoint PPT Presentation

Data Envelopment Analysis. Chapter 1 Introduction - General Discussion. Mission, Vision, Goals, &amp; Strategies: A Brief Review. Mission statement: reasons to exist Vision statement: want to be Goals: Support mission/vision statements Long/short term goals

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### Data Envelopment Analysis

Chapter 1 Introduction - General Discussion

Mission, Vision, Goals, & Strategies:A Brief Review
• Mission statement: reasons to exist
• Vision statement: want to be
• Goals:
• Support mission/vision statements
• Long/short term goals
• Must be measurable, achievable, clear, …
• Strategies
• Ways or how to achieve goals
• A big assumption behind these terms: “Performance Measurement”
• Many approaches exist
1.1 Introduction
• Productivity measures: Output/Input
• Partial productivity measures
• Ex: Sales/Labor
• Increases in sales by other factors will lead to labor productivity increases even labor deteriorates
• Total factor productivity measures
• Must be compared to competitors/industry averages (this is the reason that productivity measures are relative: Jacobs & Chase)
• Many restrictions and limited insights
1.1 Introduction
• Data envelopment analysis
• A nonparametric approach
• Based on the production possibility set theory (Farrell, 1957) and solved with mathematical or linear programming (Charnes, Cooper, & Rhodes, 1978)
• Handles multiple inputs and outputs
• Measures relative efficiency
• Good for performance measurement along with benchmarking and what-if analysis

Q1 - DEA model automatically finds “weights” for inputs, which are impartial.

Q2 – Why “relative” efficiency? DEA compares only DMUs in the dataset. Thus, it is NOT the absolute efficiency score.

1.2 Single input & single output

Efficient frontier envelops branches: why DEA

1.2 Single input & single output
• Differences between DEA and regression analysis
• Regression analysis shows “average” or “central tendency”
• DEA identifies the frontier line for benchmarks (this line is based on the constant-returns-to-scale assumption)

y = 0.622x

1.2 Single input & single output
• Improving efficiency
• Move “A” to “A1” (decrease the input by holding the output at the same level) or “A2” (increase output by holding the input)
1.3 Two inputs & one output case
• y is normalized by dividing x1 and x2 by y respectfully for each store

Unitized axes

1.3 Two inputs & one output case
• Efficiency of A
• Line OP divided by Line OA = 4.2855/5.0000 = 0.8571
• Improvement of Store A
• Move A to A1: decrease x1 (the number of employees) or
• Move A to D: decrease x2 (Area) or
• Move to P or any point to the line A1D
• This case is an example of an input-oriented CCR model
1.4 One input & two outputs case
• Improvement