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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

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data envelopment analysis

Data Envelopment Analysis

Chapter 1 Introduction - General Discussion

mission vision goals strategies a brief review
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
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 introduction1
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
1.2 Single input & single output

Efficient frontier envelops branches: why DEA

1 2 single input single output1
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 output2
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
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 case1
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 case1
1.4 One input & two outputs case
  • Improvement

“radial measures”