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.

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

 

2001-2005

2001 HIRSCHBERG & LYE Clustering in A Data Envelopment Analysis using Bootstrapped Efficiency Scores

2001 COOPER ETAL Sensitivity and Stability Analysis in DEA: Some Recent Developments

2002 DESPOTIS & SMIRLIS Data Envelopment Analysis with Imprecise Data

2002 CINCA ETAL On Model Selection in Data Envelopment Analysis: A Multivariate Statistical Approach

2003 KUOSMANEN Modeling Blank Data Entries in Data Envelopment Analysis

2003 [EBOOK] RAMANATHAN An Introduction to Data Envelopment Analysis

2003 MANSSON How Can We Use The Result from A DEA Analysis? Identification of Firm-Relevant Reference Units

2003 HAAS Productive Efficiency of English Football Temas: A Data Envelopment Analysis Approach

2004 COOPER ETAL Data Envelopment Analysis: History, Models and Interpretations

2004 BANKER ETAL Returns to Scale in Different DEA Models

2004 ZHENG & PADMANABHAN Constructing Ensembles from Data Envelopment Analysis

2004 LINS ETAL A Multi-Objective Approach to Determine Alternative Targets in Data Envelopment Analysis

2005 BRONN & BRONN Reputation and Organizational Efficiency: A Data Envelopment Analysis Study

2006-2010

2006 KUOSMANEN & KORTELAINEN Valuing Environmental Factors in Cost-Benefit Analysis using Data Envelopment Analysis

2006 SHERMAN & ZHU Data Envelopment Analysis Explained

2006 STAUB Statistical Properties of DEA Estimators in Production Frontiers

2007 [EBOOK] MANZONI A New Approach to Performance Measurement using Data Envelopment Analysis

2008 KUOSMANEN & JOHNSON Data Envelopment Analysis as Nonparametric Least Squares Regression

2008 LANGROUDI ETAL Validity Examination of EFQM’s Results by DEA Models

2008 COELLI A Data Envelopment Analysis (Computer) Program

2008 McDONALD Using Least Squares and Tobit in Second Stage DEA Efficiency Analysis

2009 CHEN ETAL Addictive Efficiency Decomposition in Two-Stage DEA

2009 TONE & TSUTSUI Tuning Regression Results for Use in Multi-Stage Data Adjustment Approach of DEA
2009 PO ETAL A New Clustering Approach using Data Envelopment Analysis

2010 ASHRAFI ETAL Two-Stage Data Envelopment Analysis: An Enhanced Russell Measure Model

2010 EMROUZNEJAD & WITTE A Unified Process for Non-Parametric Projects

2010 AZIZI & MATIN  Two-Stage Production Systems under Variable Returns to Scale Technology: A DEA Approach

2011-2015

2011 ASHRAFI ETAL A Slacks-Based Measure of Efficiency in Two-Stage Data Envelopment Analysis

2011 SADIQ The Final Frontier: A SAS Approach to Data Envelopment Analysis

2012 JOHNSON & KUOSMANEN One-stage and Two-stages DEA Estimation of The Effects of Contextual Variables

2012 JABLONSKY Data Envelopment Analysis Models with Network Structure

2012 CHARLES & KUMAR Data Envelopment Analysis and Its Applications to Management

2012 TZIOGKIDIS Monte Carlo Experiments on Bootstrap DEA

2012 BERALDI & BRUNI Data Envelopment Analysis under Uncertainty and Risk

2012 TZIOGKIDIS Bootstrap DEA and Hypothesis Testing

2012 TZIOGKIDIS The Simar and Wilsons Bootstrap DEA Approach: A Critique

2013 JABLONSKY Two-Stages Data Envelopment Analysis Model with Interval Inputs and Outputs

2013 DEMERDASH ETAL Developing a Stochastic Input Oriented Data Envelopment Analysis (SIODEA) Model

2013 TONE & TSUTSUI An Epsilon-Based Measure of Efficiency in DEA

2013 [EBOOK] RUSYDIANA Mengukur Tingkat Efisiensi dengan Data Envelopment Analysis

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.

Regresi metode bootstrap adalah metode berbasis komputer untuk melakukan pengukuran akurasi terhadap pendugaan (estimasi) statistik, yang pertama kali diperkenalkan oleh Efron (1979), dan sejak masa itu menjadi alat statistik yang populer dan menyeluruh. Penelitian Simar (1992), kemungkinan merupakan penelitian pertama yang melakukan metode bootstrap untuk menghitung interval keyakinan atas skor efisiensi relatif yang dihasilkan oleh frontier non-parametrik.

Semenjak itu, bootstrap dipergunakan untuk membuktikan distribusi empiris atas skor efisiensi pada setiap kasus (pengamatan) dalam sampel penelitian; untuk memperoleh interval keyakinan dan mengukur bias (residu) dari skor efisiensi DEA; dan untuk menganalisa sensitivitas skor efisiensi atas keragaman sampel setelah skor diperoleh dari frontier non-parametrik (Simar dan Wilson, 1995).

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.

BUKU: Islamic Banking Efficiency: Efficiency Of Islamic Banks In Pakistan using Data Envelopment Analysis

Islamic banking is one of the most growing sectors of financial market and gaining popularity in Islamic world. With increasing competition and advances in banking systems Islamic banks must be efficient to reap the benefits of growing demand. 

This book investigates the efficiency of Islamic banks in Pakistan using non-parametric approach of Data Envelopment Analysis (DEA). The purpose is to look at the financial characteristics that make Islamic banks efficient. Keep in view the financial characteristics of performance, current study apart efficient Islamic banks from those that are found inefficient. 

The efficiency of Islamic banks is measured in specified input and output variables. Staff cost, fixed assets and total deposits are taken as input variables while total loans, income and liquid assets are taken as output variables.

BOOK: Microeconomics of Banking, FREIXAS & ROCHET

Over the last thirty years, a new paradigm in banking theory has overturned economists' traditional vision of the banking sector. The asymmetric information model, extremely powerful in many areas of economic theory, has proven useful in banking theory both for explaining the role of banks in the economy and for pointing out structural weaknesses in the banking sector that may justify government intervention. In the past, banking courses in most doctoral programs in economics, business, or finance focused either on management or monetary issues and their macroeconomic consequences; a microeconomic theory of banking did not exist because the Arrow-Debreu general equilibrium model of complete contingent markets (the standard reference at the time) was unable to explain the role of banks in the economy. 

This text provides students with a guide to the microeconomic theory of banking that has emerged since then, examining the main issues and offering the necessary tools for understanding how they have been modeled. This second edition covers the recent dramatic developments in academic research on the microeconomics of banking, with a focus on four important topics: the theory of two-sided markets and its implications for the payment card industry; "non-price competition" and its effect on the competition-stability tradeoff and the entry of new banks; the transmission of monetary policy and the effect on the functioning of the credit market of capital requirements for banks; and the theoretical foundations of banking regulation, which have been clarified, although recent developments in risk modeling have not yet led to a significant parallel development of economic modeling. This book also tells us about concept of banking efficiency.

Xavier Freixas is Dean of the Undergraduate School of Economics and Business Administration and Professor at the Universitat Pompeu Fabra, Barcelona. Jean-Charles Rochet is Professor of Mathematics and Economics at the University of Toulouse School of Economics.

BUKU: Strategic Performance Management and Measurement Using Data Envelopment Analysis

Organizations can use the valuable tool of data envelopment analysis (DEA) to make informed decisions on developing successful strategies, setting specific goals, and identifying underperforming activities to improve the output or outcome of performance measurement.

Pengantar Umum DEA

DEA (Data Envelopment Analysis) is the optimization method of mathematical programming to generalize the Farrell(1957) single-input/ single-output technical efficiency measure to the multiple-input/ multiple-output case by constructing a relative efficiency score as the ratio of a single virtual output to a single virtual input. Thus DEA become a new tool in operational research for measuring technical efficiency. It originally was developed by Charnes, Cooper, Rhodes(1978) with CRS and was extended by Banker, Charnes, Cooper(1984) to include variable returns to scale. So the basic DEA models are known as CCR and BCC. 

Since 1978 over 1000 articles, books and dissertation have been published and DEA has rapidly extended to returns to scale, dummy or categorical variables, discretionary and non-discretionary variables, incorporating value judgments, longitudinal analysis, weight restrictions, stochastic DEA, non-parametric Malmquist indices, technical change in DEA and many other topics. 

Up to now the DEA measure has been used to evaluate and compare educational departments (schools, colleges and universities), health care (hospitals, clinics) prisons, agricultural production, banking, armed forces, sports, market research, transportation (highway maintenance), courts, benchmarking, index number construction and many other applications.At the moment researchers follow wide ranges of DEA and related topics.

 

Here are some topics in DEA:
Returns to scale
Dummy or categorical variables
Discretionary and non-discretionary variables
Incorporating judgment
Longitudinal analysis
Weight restriction
Stochastic DEA
Fuzzy and imprecise DEA
Non-parametric Malmquist indices
Technical change in DEA
Dynamics of Data Envelopment Analysis
Sensitivity
and many more ….

IN-HOUSE TRAINING DAN KONSULTASI DEA UNTUK KORPORASI

PENDAHULUAN

Data Envelopment Analysis pertama kali diperkenalkan oleh Charnes, Cooper dan Rhodes pada tahun 1978 dan 1979. Semenjak itu pendekatan dengan menggunakan DEA ini banyak digunakan di dalam riset-riset operasional dan ilmu manajemen. Pendekatan DEA ini lebih menekankan kepada pendekatan yang berorientasi kepada tugas dan lebih difokuskan kepada tugas yang penting, yaitu mengevaluasi kinerja dari unit pembuat keputusan/UPK (decision making units). Semenjak tahun 1980an, pendekatan ini banyak digunakan untuk mengukur tingkat efisiensi dari industri perbankan secara nasional.

DEA merupakan suatu teknik program linier yang digunakan untuk mengevaluasi bagaimana suatu proses pengambilan keputusan dalam suatu unit beroperasi secara relatif dengan unit lain dalam sampel. Selanjutnya proses tersebut akan membentuk suatu garis frontier yang terbentuk dari unit-unit yang efisien yang kemudian dibandingkan dengan unit yang tidak efisien untuk menghasilkan nilai efisiensinya masing-masing.

Karena pentingnya metode riset ini, maka SMART CONSULTING bekerjasama dengan pihak manapun untuk mengadakan pelatihan selama 2 hari dalam rangka memenuhi kebutuhan para akademisi maupun praktisi yang hendak menggunakan metode DEA.

 

MATERI TRAINING DEA:

1.      Konsep Dasar Efisiensi

2.      Perbedaan SFA, DFA dan DEA

3.      Metode Parametrik dan Non-Parametrik

4.      Kelebihan dan Kekurangan beberapa Metode Pengukuran Efisiensi

5.      Efisiensi Teknis

6.      Efisiensi Alokatif

7.      Mengenal Konsep Constant Return to Scale (CRS)

8.      Konsep Variable Return to Scale (VRS)

9.      Input-Oriented Measures

10.  Output-Oriented Measures

11.  Karakteristik DEA

12.  Dua Model DEA

13.  Materi Praktik dengan Software

14.  Contoh Penelitian dengan DEA

15.  Diskusi dan Sharing

PROFIL TRAINER

Ascarya, Ir. MBA., M.Sc  (Peneliti Bank Indonesia pada Pusat Pendidikan Studi Kebanksentralan/PPSK, Dosen Sekolah Tinggi Ekonomi Islam (STEI) Tazkia, Dosen Pasca Universitas Trisakti, Pembicara Konferensi dan Forum Nasional dan Internasional Ekonomi-Keuangan Islam, menyelesaikan Master pada Pittsburg University, USA).

Aam Slamet Rusydiana, SEI (Konsultan dan peneliti pada lembaga riset ekonomi Islam SMART Consulting, Penulis buku, Pembicara pada training-training metodologi di beberapa kampus negeri dan swasta, Pembicara pada seminar dan konferensi ekonomi Islam, baik nasional maupun tingkat internasional, Kandidat Master Universitas Indonesia)

Abrista Devi, MEI (Konsultan pada lembaga riset ekonomi Islam SMART Consulting, aktifis Ikatan Ahli Ekonomi Islam, Penulis buku, Dosen pada beberapa kampus swasta)

SASARAN PESERTA

Praktisi Perbankan dan Lembaga Keuangan Lain, terutama Divisi Riset Development, dan Pemimpin Cabang; Para penentu kebijakan pada perusahaan, baik lembaga keuangan seperti perbankan maupun korporasi lain, serta kalangan akademisi: Dosen dan Peneliti.

PENYELENGGARAAN

Durasi Waktu Training dan Konsultasi Data Envelopment Analysis (DEA) memerlukan waktu 2 (dua) hari efektif. Biaya dan Peserta Total biaya satu paket adalah Rp. 25.00.000,- (dua puluh lima juta rupiah). Transportasi dari Jakarta ke lokasi (PP), dan akomodasi 2 (dua) trainer-konsultan selama kegiatan menjadi beban penyelenggara. Jumlah maksimum peserta setiap kegiatan maksimal 20 orang. Setiap peserta akan memperoleh copy program belajar Software Data Envelopment Analysis, hand out, training kits, asesoris, dan sertifikat. Sarana-Prasarana Penyelenggara bertanggung jawab menyediakan sarana-prasarana training seperti komputer untuk setiap peserta, ruang pembelajaran dan sebagainya.

Website: www.konsultan-smart.com