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.

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.

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.

BOOK: DEA, Theory and Techniques for Economics and Operations Research, SUBHASH C. RAY

• By Subhash C. Ray
  University of Connecticut
  
  
• Publisher: Cambridge University Press
  Print Publication Year:2004
  Online Publication Date:November 2009
  Online ISBN:9780511606731
  Hardback ISBN:9780521802567

BUKU: Data Envelopment Analysis: Returns-to-Scale Measurement

The paper provides an overview of the different approaches to measure returns-to-scale (RTS) in Data Envelopment Analysis (DEA). DEA is a promising approach allowing for analysis of efficiency and RTS in certain fields where other concepts like regression analysis are not applicable. Therefore, DEA literature and especially literature on RTS measurement in DEA is rapidly growing. 

Returns-to- scale and scale efficiency can lead to significant and long-lasting implications for management and politics. Furthermore, RTS classification can be used to decide on mergers and acquisitions. Following the description of the most common DEA models and their technology different approaches to measure RTS are described and advantages and disadvantages are elaborated. 

The approaches are subdivided in qualitative and quantitative approaches and RTS measurement in cost-based and non- radial models. Additionally, sensitivity analysis for RTS measurement is being dealt with. Finally, an empirical application is provided to illustrate RTS measurement approaches discussed in the paper.

Analisis Perbandingan Tingkat Efisiensi BMT Kota Tasikmalaya Periode 2008-2012 dengan Pendekatan Two Stage Data Envelopment Analysis

Oleh: Asri Prihastuti 

This study measures the comparative efficiency of BMT Tasikmalaya during the period 2008-2012. The method used is Two Stage DEA with the intermediation approach. The first stage of measuring the efficiency of each BMT using DEA. Input variables used were total deposit, equity and total labour. While the variables output is total financing and operating income. The second stage determines factors influencing the efficiency of BMT using Tobit Method. The variables used were BOPO, ROA (Return on Equity) and EQAS.

            The results show that the overall efficiency of BMT in the year 2008 reached 0.88 and the next year (2009) to 2012 increased significantly to reach 0.96. While the level of technical efficiency has increased by fluctuations in 2008-2012. Means that the management of the financial operations of BMT during 2008-2012 is relatively inefficient. The main cause of inefficiency in the output-oriented measure is operating income which can be increasing by 59.96%. Tobit results show that BOPO and ROE has statistically significant positive impact on overall efficiency of BMT Tasikmalaya. While the power of capital (CAR) has no significant positive impact on the efficiency of BMT Tasikmalaya

Keywords: Data Envelopment Analysis (DEA), Efficiency, Baitul Mal wa Tamwil

BUKU: Mining Data Envelopment Analysis using Clustering Approach: for Heterogeneous Decision Making Units

This book integrates two important fields of information technology, data mining and data envelopment analysis (DEA), to provide a new tool for measuring the performance of decision making units (DMU). Many investigations have dealt with the DEA models, but few have focused on heterogeneous DMUs, outlier detection, and scalability over large data sets. 

In this book, a comprehensive model is presented. A constraint based clustering method is introduced for early detection of outliers to evaluate the performance scores of non homogeneous DMUs. The book includes the different preprocessing stages used in applying different approaches of data mining. 

Along with the theory, an extensive analysis in assessing the transportation system funding for school districts in the state of North Dakota is provided. This book is originally a Ph.D. dissertation at NDSU,Fargo, ND, USA.

SMART LIBRARY: Koleksi 50 + Buku Metodologi Penelitian

Aplikasi Teknik Pengambilan Keputusan dalam Manajemen Rantai Pasok; Teori dan Aplikasi Sistem Pakar dalam Teknologi Manajerial; SEM dengan Lisrel 8.8, Konsep & Tutorial; Riset Pemasaran; Metode Penelitian Kombinasi (Mixed Methods); Analisis Ekonometrika & Statistika dengan Eviews (Ed 2), Riset Bisnis dengan Analisis Jalur SPSS; Pemodelan Sumber Daya Perikanan dan Kelautan; Konsep Dasar Riset Pemasaran & Perilaku Konsumen;

Analisis Sistem Dinamis, Lingkungan Hidup, Sosial, Ekonomi, Manajemen; Konsep dan Aplikasi SEM berbasis Varian dalam Penelitian Bisnis; Metode Riset Kualitatif dalam PR & Marketing Communication; Analisis SWOT Teknik Membedah Kasus Bisnis; An Introduction to DEA, A Tool for Performance Measurement; Statistika untuk Penelitian; Riset Pemasaran dan Konsumen; Analisis Input Output; Marketing Research;

Penelitian Bisnis Paradigma Kuantitatif; Teknik dan Aplikasi Pengambilan Keputusan Kriteria Majemuk; SPSS Statistik Parametrik; Riset Eksperimen dengan Excel 2007 dan Minitab 15; Buku Pintar Minitab 15; Olah Data Skripsi dan Penelitian dengan SPSS 19; Metodologi Penelitian Ekonomi Islam; Analytic Hierarchy Process; Buku Saku SPSS, Analisis Statistik Data; Decision Making with The ANP, Economic, Political, Social & Technological Application with BOCR;

Handbook on Data Envelopment Analysis, COOPER

This handbook covers DEA topics that are extensively used and solidly based. The purpose of the handbook is to (1) describe and elucidate the state of the field and (2), where appropriate, extend the frontier of DEA research. It defines the state-of-the-art of DEA methodology and its uses. This handbook is intended to represent a milestone in the progression of DEA. 

Written by experts, who are generally major contributors to the topics to be covered, it includes a comprehensive review and discussion of basic DEA models, which, in the present issue extensions to the basic DEA methods, and a collection of DEA applications in the areas of banking, engineering, health care, and services. 

The handbook's chapters are organized into two categories: (i) basic DEA models, concepts, and their extensions, and (ii) DEA applications. First edition contributors have returned to update their work.

The second edition includes updated versions of selected first edition chapters. New chapters have been added on: 

·         Different approaches with no need for a priori choices of weights (called “multipliers) that reflect meaningful trade-offs.

·         Construction of static and dynamic DEA technologies.

·         Slacks-based model and its extensions

·         DEA models for DMUs that have internal structures network DEA

·         Selection of DEA applications in the service sector with a focus on building a conceptual framework, research design and interpreting results.

 

William W. Cooper, Pakar Metode DEA

Professor Emeritus William W. Cooper, an academic giant widely considered to be a father of management science, died Wednesday, June 20, at the age of 97. A high school dropout and former boxing champ, he went on to revolutionize business education and research.

In a career that spanned nearly seven decades and included stints at the University of Chicago, Carnegie Mellon, and Harvard Business School, Cooper was a prolific researcher who was at the forefront of a new way of studying business, emphasizing scientific rigor and integrating disciplines. In the words of one of his star doctoral students, Andrew Whinston, professor at McCombs: “The models Bill pioneered fostered a huge transformation of worldwide company operations. He has, as they say in today’s jargon, a big footprint.”

He was also a fixture on campus, coming to work nearly every day until just a few weeks before his death.

BUKU DEA RAMANATHAN: AN INTRODUCTION TO DEA

'This book is an excellent tool for practitioners who are interested in the merits and pitfalls of the technique.... (The author's) research is an example of inventiveness, diligence and accuracy' - Freerk A. Lootsma, Delft Institute of Technology

Data envelopment Analysis is a Mathematical Programme for measuring performance efficiency of organizational units. The organizational units, termed as decision-making units (DMU) can be of any kind: manufacturing units, a set of schools, banks, hospitals, power plants, police stations, prisons, a set of firms etc.

DEA has been unsuccessfully applied to measure the performance efficiency of these different kinds of DMUs which share a common characteristic - that they are non-profit organization where measurement of performance efficiency is difficult.

DEA has been employed for assessing the relative performance of a set of firms that use a variety of identical inputs-say in the case of a school: quality of students, teachers, grants etc.,-to produce a variety of identical outputs-number of students who pass the final year, average grades obtained by the students in the final year etc.

DEA assumes the performance of the DMUs by using the concepts of efficiency or productivity which is measured as the ratio of total outputs to total inputs. Also, the efficiencies estimated are relative to the best performing DMU or DMUs. The best performing DMU is given a score of 100% and the performance of other DMUs vary between 0 -100%.

BUKU TENTANG DEA: METODOLOGI PENELITIAN EKONOMI ISLAM

Buku ini mengkaji lebih dalam metodologi-metodologi aplikatif dan powerful yang dapat diaplikasikan bagi penelitian di bidang ekonomi Islam seperti zakat, lembaga keuangan syariah, manajemen dan bisnis syariah, ekonomi makro dan mikro. beberapa yang dikupas tuntas adalah metode Analytical Network Process (ANP), Data Envelopment Analysis (DEA), dan Vector Autoregression (VAR) model.

Dapatkan dan pesan bukunya sekarang juga.

BUKU: New Efficiency Theory: With Applications of Data Envelopment Analysis

New efficiency theory refers to the various parametric and semi-parametric methods of estimating production and cost frontiers, which include data envelopment analysis (DEA) with its diverse applications in management science and operations research. 

This monograph develops and generalizes the new efficiency theory by highlighting the interface between economic theory and operations research. Some of the outstanding features of this monograph are: (1) integrating the theory of firm efficiency and industry equilibrium, (2) emphasizing growth efficiency in a dynamic setting, (3) incorporating uncertainty of market demand and prices, and (4) the implications of group efficiency by sharing investments. 

Applications discuss in some detail the growth and decline of the US computer industry, and the relative performance of mutual fund portfolios.

Fuzzy DEA

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. 

 

However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. 

 

 In this study, we provide a taxonomy and review of the fuzzy DEA methods. We present a classification scheme with four primary categories, namely, the tolerance approach, the a-level based approach, the fuzzy ranking approach and the possibility approach. We discuss each classification scheme and group the fuzzy DEA papers published in the literature over the past 20 years.

Two Stages DEA (DEA Dua Tahap)

Selain metode yang lazim digunakan dalam pengukuran tingkat efisiensi dengan DEA, dikenal pula dengan Two Stages DEA atau DEA dua tahap. First Stage: Metode Data Envelopment Analysis (DEA). Metode DEA adalah sebuah metode frontier non parametric yang menggunakan model program linier untuk menghitung perbandingan rasio output dan input untuk semua unit yang dibandingkan dalam sebuah populasi. Tujuan dari metode DEA adalah untuk mengukur tingkat efisiensi dari decision-making unit (DMU ie.bank) relatif terhadap bank yang sejenis ketika semua unit-unit ini berada pada atau dibawah “kurva” efisien frontier-nya. Jadi metode ini digunakan untuk mengevaluasi efisiensi relatif dari beberapa objek (benchmarking kinerja).

Metode DEA menghitung efisiensi teknis untuk seluruh unit. Skor efisiensi untuk setiap unit adalah relatif, tergantung pada tingkat efisiensi dari unit-unit lainnya di dalam sampel. Setiap unit dalam sampel dianggap memiliki tingkat efisiensi yang tidak negatif, dan nilainya antara 0 dan 1 dengan ketentuan satu menunjukkan efisiensi yang sempurna. Selanjutnya, unit-unit yang memiliki nilai satu ini digunakan dalam membuat envelope untuk frontier efisiensi, sedangkan unit lainnya yang ada di dalam envelope menunjukkan tingkat inefisiensi.

Skor Efisiensi DEA = Output / Input

       Second Stage: Model Regresi Tobit. Metode Tobit mengasumsikan bahwa variabel-variabel bebas tidak terbatas nilainya (non-censured); hanya variabel tidak bebas yang censured; semua variabel (baik bebas maupun tidak bebas) diukur dengan benar; tidak ada autocorrelation; tidak ada heteroscedascity; tidak ada multikolinearitas yang sempurna; dan model matematis yang digunakan menjadi tepat. Dalam penggunaan metode analisis regresi untuk penelitian bidang sosial dan ekonomi, banyak ditemui struktur data dimana variabel responnya mempunyai nilai nol untuk sebagian observasi, sedangkan untuk sebagian observasi lainnya mempunyai nilai tertentu yang bervariasi. Struktur data seperti ini dinamakan data tersensor (censored data).

Pendekatan Input Output Perbankan

Untuk menghitung efisiensi suatu lembaga keuangan dapat dilakukan dengan memperhatikan aktivitasnya. Ada tiga pendekatan di dalam menjelskan hubungan antara input dan output dari bank, yaitu :

1)      Pendekatan Produksi

Pendekatan ini melihat institusi finansial sebagai produser dari rekening tabungan dan kredit pinjaman. Pendekatan ini mendefinisikan output sebagai penjumlahan dari rekening-rekening tersebut atau rekening-rekening terkait.

2)      Pendekatan Intermediasi

Pendekatan ini menggambarkan kegiatan perbankan sebagai lembaga intermediasi yang mentrasformasi dana dari deposan (surplus spending unit) kepada peminjam (deficit spending unit). Dengan kata lain, dana pihak ketiga yang cendrung likuid, berjangka pendek, dengan resiko yang rendah ditransformasikan menjadi pembiayaan yang lebih berisiko, tidak likuid dan berjangka panjang. Oleh karena itu, pendekatan ini mendefinisikan input sebagai financial capital dan output sebagai volume pembiayaan atau investment outstanding.

3)    Pendekatan Modern. Pendekatan ini merupakan perbaikan dari kedua bentuk pendekatan di atas dengan memasukkan unsur manajemen risiko, proses informasi dan agency problems ke dalam teori perusahaan klasik. Pendekatan ini memperkenalkan perbedaan antara manajer bank dengan pemilik bank di dalam perilaku memaksimalkan keuntungan.

Tiga Macam Efisiensi

Dalam sudut pandang perusahaan dikenal tiga macam efisiensi, yaitu:

1)      Technical Efficiency yang merefleksikan kemampuan perusahaan untuk mencapai level output yang optimal dengan menggunakan tingkat input tertentu. Efisiensi ini mengukur proses produksi dalam menghasilkan sejumlah output tertentu dengan menggunakan input seminimal mungkin. Dengan kata lain, suatu proses produksi dikatakan efisien secara teknis apabila output dari suatu barang tidak dapat lagi ditingkatkan tanpa mengurangi output dari barang lain.

2)      Allocative Efficiency, merefleksikan kemampuan perusahaan dalam mengoptimalkan penggunaan inputnya dengan struktur harga dan tekhnologinya. Terminologi efisiensi Pareto sering disamakan dengan efisiensi alokatif untuk menghormati ekonom Italia Vilfredo Pareto yang mengembangkan konsep efficiency inexchange. Efisiensi Pareto mengatakan bahwa input produksi digunakan secara efisien apabila input tersebut tidak mungkin lagi digunakan untuk meningkatkan suatu usaha tanpa menyebabkan setidak-tidaknya keadaan suatu usaha yang lain menjadi lebih buruk. Dengan kata lain, apabila input dialokasikan untuk memproduksi output yang tidak dapat digunakan atau tidak diinginkan konsumen, hal ini berarti input tersebut tidak digunakan secara efisien. 

3) Economic Efficiency, yaitu kombinasi antara efisiensi teknikal dan efisiensi alokatif. Efisiensi ekonomis secara implisit merupakan konsep least cost production. Untuk tingkat output tertentu, suatu perusahaan produksinya dikatakan efisien secara ekonomi jika perusahaan tersebut menggunakan biaya dimana biaya per unit dari output adalah yang paling minimal. Dengan kata lain, untuk tingkat output tertentu, suatu proses produksi dikatakan efisien secara ekonomi jika tidak ada proses lainnya yang dapat digunakan untuk memproduksi tingkat output tersebut pada biaya per unit yang paling kecil.