1 / 30

TALLER DE EFICIENCIA ( noviembre de 2011)

TALLER DE EFICIENCIA ( noviembre de 2011). Dr. Diego Prior. Plan de trabajo. Conceptos previos e introducción al Data Envelopment Analysis ( DEA ) Modelos DEA , extensiones y software EMS Aplicaciones con datos reales. Conceptos previos. Conceptos previos.

lisle
Télécharger la présentation

TALLER DE EFICIENCIA ( noviembre de 2011)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. TALLER DE EFICIENCIA(noviembre de 2011) Dr. Diego Prior

  2. Plan de trabajo • Conceptos previos e introducción al Data Envelopment Analysis (DEA) • Modelos DEA, extensiones y software EMS • Aplicaciones con datos reales

  3. Conceptos previos

  4. Conceptosprevios

  5. Introducción al Data Envelopment Analysis (DEA) • 1. ¿De qué hablamos? • Productividad: (Output/ Input) = (y/x) • Eficiencia: max. (y/x) • Si el input está definido en unidades monetarias: • Productividad: (y/x) = [1/(x/y)] = • = (1/tasa de costes) • PROBLEMA: ¿qué hacer cuando tenemos múltiples outputs e inputs?

  6. Introducción al Data Envelopment Analysis (DEA)

  7. Introducción al Data Envelopment Analysis (DEA)

  8. Introducción al Data Envelopment Analysis (DEA) • DEA evaluates relative efficiencies of a homogenous set of decision making units (DMUs) in the presence of multiple input and output factors • Efficiency is defined as the ratio of weighted sum of outputs to weighted sum of inputs • A DMU is considered efficient if it achieves a score of 1.00 • DEA identifies necessary improvements required in making inefficient DMUs efficient • DEA has extensively been applied in a variety of business and decision making environments that include banking, healthcare, transportation …

  9. Some DEA Models and Approaches • CCR Model (Primal and Dual) • BCC Model • Super Efficiency Model • DEA Models with Weight Restrictions • Cross-Efficiency Models in DEA • Benchmarking in DEA • DEA Windows Analysis • DEA with Ordinal and Cardinal Factors

  10. CCR Ratio Model: Primal Form

  11. CCR Ratio Model (Input-Oriented): Dual Form

  12. CCR Ratio Model (Output-Oriented): Dual Form

  13. CCR Model: Illustration

  14. Selection of Inputs, Outputs, and units in DEA • Inputs: resources (examples: workers, machinery, operating expenses, budget, etc.) • Outputs: actual number of products produced to a host of performance and activity measures (examples: quality levels, throughput rates, lead-time, etc.) • If there are m inputs and s outputs then potentially ms DMUs can be efficient. Thus, to achieve discrimination we need substantially more units than ms

  15. BCC Model • CCR model considers constant returns to scale (CRS) whereas the BCC model considers variable returns to scale (VRS) new constraint (convexity)

  16. Super Efficiency Model • Super efficiency model allows for effective ranking of efficient DMUs The DMU being evaluated is removed from the constraint set thereby allowing its efficiency score to exceed a value of 1.00

  17. DEA Model with Weight Restrictions • Unrestricted weight flexibility in DEA can be resolved through weight restrictions • Weight restrictions also allow for the incorporation of managerial input into DEA models

  18. Cross Efficiencies in DEA • Cross efficiency in DEA allows for effective discrimination between niche performers and good overall performers • Cross efficiency score of a DMU represents how well the unit is performing with respect to the optimal weights of another DMU • A DMU that achieves high cross efficiency scores is considered to be a good overall performer

  19. Cross Efficiency Matrix Efficiency score of DMU 2 when evaluated with the optimal weights of DMU 1

  20. Benchmarking in DEA • We discussed traditional DEA benchmarking in the illustrative example • Benchmarks may not be inherently similar to inefficient DMUs • Virtual benchmarks do not exist in practice • Benchmarking can also be performed based on the cross efficiency matrix. • Use of cluster analysis on cross efficiencies

  21. Windows Analysis in DEA • Evaluating the performance of a DMU over time by treating it as a different entity in each period • A DMU is compared to itself over time

  22. Questions

More Related