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.

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.