DAFTAR REFERENSI PENTING METODE DEA

REFERENSI PENTING DEA

SEBELUM 2001

1984 BANKER, CHARNES & COOPER Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis

1996 SANTOS & DULA Data Envelopment Analysis: A Tool for Measuring Efficiency and Performance

1996 TONE A Simple Characterization of Returns to Scale in Data Envelopment Analysis

1997 [EBOOK} DWG Data Envelopment Analysis: A Technique for Measuring The Efficiency of Government Service Delivery

1997 KORHONEN Searching the Efficient Frontier in Data Envelopment Analysis

1997 SENGUPTA A Dynamic Efficiency Model Using Data Envelopment Analysis

1998 CHERCHYE & PUYENBROECK Learning from Input-Output Mixes in DEA: A Proportional Measure for Slack-Based Efficient Projections

1998 LOTHGREN How to Bootstrap DEA Estimators: A Monte Carlo Comparison

1999 BOUYSSOU Using DEA as A Tool for MCDM: Some Remarks

2000 TALLURI Data Envelopment Analysis: Models and Extensions

12th International Conference on DEA, Kuala Lumpur 2014

We are pleased to announce that the International Data Envelopment Analysis Society (iDEAs) will hold the 12th International Conference on Data Envelopment Analysis in Kuala Lumpur, Malaysia from April 14 to 17, 2014. The conference will be held at the campus of the University of Malaya in Kuala Lumpur, the capital of Malaysia. The campus is located about 10 kilometers from the heart of the city. We are very pleased to extend a warm welcome and invite you to participate in the conference. Special hotel rates will be negotiated to make the conference trip budget friendly.

DEA 2014 plans to bring together scholars, researchers and practitioners interested in the development of DEA and its applications to performance management in public and private sectors. Theoretical and empirical papers on Data Envelopment Analysis (DEA) and other related fields such as econometric methods for efficiency and productivity analysis are solicited for presentation at the conference. We welcome papers on developing economies, banking, environment, education, energy, healthcare, transportation, tourism and other applications of DEA.

Selected conference papers will be published in:

A special issue of Annals of Operations Research (ISI, Q2 journal)

SCORE 2013 [SMART Consulting Research]

DEA Software: Overview DEAFrontier

DEAFrontier ^TM developed by Joe Zhu is a Microsoft® Excel Add-In for solving Data Envelopment Analysis (DEA) models. The software is developed based upon Professor Zhu's years of DEA research and teaching experience. The software is written by Professor Zhu in an effort to minimize the possibility of mis-presentation of DEA models during coding.

DEAFrontier uses Excel Solver as the engine for solving the DEA models. In order to run the DEAFrontier software, Excel Solver must be installed in the Excel. In Excel 2007 or 2010 or 2013, the user should see Solver in the Data Tab. Under Excel 2007 and earlier versions, the Excel Solver Parameters dialog box has to be displaced once before the DEAFrontier software is loaded. Otherwise, the DEAFrontier software may not run*.

Tentang Banxia Frontier Analyst

Enhance your efficiency and redefine performance measurement in your organisation with Frontier Analyst®. Using the technique known as Data Envelopment Analysis (DEA), perform objective, comparative efficiency analysis studies that take you beyond purely financial measures of performance. Ideal for use in retail, franchising, banking, health care, public services and many other business-unit based enterprises. Frontier Analyst® has the perfect mix of ease of use, power and functionality to help you achieve your goals.

Frontier Analyst® allows you to:

•  Identify star performers to locate best practice
•  Identify under-achievers
•  Set realistic, peer based improvement targets
•  Uncover greatest potential efficiency gains
•  Allocate resources more effectively
•  Visualise important information
•  Inform strategy development
•  Dig deeper than the “bottom line

The quest for greater efficiency is never ending as managers are always under pressure to improve the performance of their organisations. In the public sector, governments are constantly seeking better value for tax payers' money, while the emergence of a more global economy has intensified competitive pressures on commercial companies. The onus is therefore on managers to achieve better results from the resources available to them. Frontier Analyst® uses a powerful technique called Data Envelopment Analysis (DEA) to assist you in doing this.

DEA Bootstrap

DEA Bootstrap dilakukan melalui dua prosedur, yaitu menghitung skor efisiensi terlebih dahulu, kemudian mempergunakan analisis regresi untuk menjelaskan keragaman daripada skor-skor efisiensi tersebut. Regresi Ordinary Least Square (OLS) memiliki keterbatasan dalam analisa keragaman skor efisiensi DEA, dikarenakan skor DEA tersebut sangat berhubungan (berkorelasi) erat dengan variabel bebas pembentuknya (pada proses perhitungan skor DEA pada tahapan analisa data), sehingga nilai estimasi regresi dapat bias (Simar, 1992).

Di sisi lain, terdapat beberapa pendekatan untuk menyelesaikan permasalahan pendugaan keragaman skor efisiensi DEA dengan regresi (Xue dan Harker, 1999; Casu dan Molineux, 1999). Pendekatan ini dilakukan oleh Xue dan Harker (1999): menitikberatkan bahwa skor efisiensi yang dihasilkan model DEA jelas bergantung

sama lain dalam analisis statistik.

Alasan dependensi ini sebenarnya merupakan fakta yang umum diketahui bahwa skor efisiensi DEA sendiri adalah indeks relatif efisiensi, bukan indeks efisiensi absolut. Dikarenakan keberadaan dependensi inheren di antara skor efisiensi, salah satu asumsi analisis regresi konvensional, independensi di dalam sampel (autokorelasi), dilanggar. Sehingga, prosedur regresi konvensional (uji asumsi klasik) menjadi tidak valid. Untuk langkah alternatifnya, Xue dan Harker (1999) serta Casu dan Molineux (1999) melakukan regresi bootstrap.

Kerangka "COOPER" dalam DEA

In large and complicated datasets, a standard process could facilitate performance assessment and help to (1) translate the aim of the performance measurement to a series of small tasks, (2) select homogeneous DMUs and suggest an appropriate input/output selection, (3) detect a suitable model, (4) provide means for evaluating the effectiveness of the results, and (5) suggest a proper solution to improve the efficiency and productivity of entities (also called Decision Making Units, DMUs). 

We suggest a framework which involves six interrelated phases: (1) Concepts and objectives, (2) On structuring data, (3) Operational models, (4) a Performance comparison model, (5) Evaluation, and (6) Results and deployment. Taking the first letter of each phase, we obtain the COOPER-framework (in honour of and in agreement with one of the founders of DEA). Figure 1 systemizes the six phases.