scholarly journals ANALISIS KINERJA INDUSTRI MANUFAKTUR DI INDONESIA

2017 ◽  
Vol 17 (1) ◽  
pp. 183
Author(s):  
Etty Puji Lestari ◽  
Isnina WSU
Keyword(s):  
Data Envelopment Analysis ◽  
Market Share ◽  
Manufacturing Industry ◽  
Manufacturing Sector ◽  
Government Policies ◽  
Data Envelopment ◽  
Industry Development ◽  
Industry Sector ◽  
Using Data ◽  
Efficiency And Productivity

The problems on the manufacturing industry in Indonesia, among others, the disparity level of efficiency and productivity of each sub-sector of the manufacturing industry in Indonesian. This occurs due to the imbalance in the structure and dominant market share for some particular type of business in each sub-sector is in the manufacturing sector. The study will analyze the performance of the manufacturing industry in Indonesia using Data Envelopment Analysis. The study states that there is a difference of efficiency at every level of the industry. Therefore, government policies relating to industry development is absolutely necessary to improve the performance of the industry sector.

scholarly journals Evaluating 3D Printers Using Data Envelopment Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 4209
Author(s):  
Theodore Papatheodorou ◽  
John Giannatsis ◽  
Vassilis Dedoussis
Keyword(s):  
Data Envelopment Analysis ◽  
Fused Deposition Modeling ◽  
Production Systems ◽  
Manufacturing Sector ◽  
Data Envelopment ◽  
Programming Technique ◽  
Fused Deposition ◽  
Efficiency Performance ◽  
3D Printers ◽  
Using Data

Data Envelopment Analysis (DEA) is an established powerful mathematical programming technique, which has been employed quite extensively for assessing the efficiency/performance of various physical or virtual and simple or complex production systems, as well as of consumer and industrial products and technologies. The purpose of the present study is to investigate whether DEA may be employed for evaluating the technical efficiency/performance of 3D printers, an advanced manufacturing technology of increasing importance for the manufacturing sector. For this purpose, a representative sample of 3D printers based on Fused Deposition Modeling technology is examined. The technical factors/parameters of 3D printers, which are incorporated in the DEA, are investigated and discussed in detail. DEA evaluation results compare favorably with relevant benchmarks from experts, indicating that the suggested DEA technique in conjunction with technical and expert evaluation could be employed for evaluating the performance of a highly technological system, such as the 3D printer.

scholarly journals DINAMIKA TOTAL FACTOR PRODUCTIVITY INDUSTRI BESAR DAN SEDANG INDONESIA

2014 ◽  
Vol 16 (3) ◽  
pp. 277-308
Author(s):  
Ndari Surjaningsih ◽  
Bayu Panji Permono
Keyword(s):  
Data Envelopment Analysis ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Manufacturing Sector ◽  
Jel Classification ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Catching Up ◽  
Using Data

This paper calculates and decomposes the Total Factor Productivity (TFP) for large and medium scale industry in Indonesia covering the period of 2000-2009. By using Data Envelopment Analysis (DEA)  method, the result shows there is a shift of the supporting factors on the growth of TFP on manufacturing sector within the 2 (two) sample period. In the period of 2000-2004, efficiency change becomes the main contributor on the growth of TFP. Whereas in the period of 2005-2009, technical change becomes the main supporting factor of TFP,however it goes along with the growth of negative efficiency change or the decline of the company’s catching-up effect ability to adapt with the more advance technology. The grouping of the sample across subsectors, technical change and also efficiency change shows the declining amount of manufacture industry with superior productivity. Furthermore, the number of low and weakening catching-up industry is increasing.  Keywords: Indonesian manufacturing, total factor productivity, technical change, efficiency change, economic scale change, Data Envelopment Analysis JEL Classification: L6, M11

scholarly journals deaR-Shiny: An Interactive Web App for Data Envelopment Analysis

2021 ◽  
Vol 13 (12) ◽  
pp. 6774
Author(s):  
Rafael Benítez ◽  
Vicente Coll-Serrano ◽  
Vicente J. Bolós
Keyword(s):  
Data Envelopment Analysis ◽  
Web Application ◽  
Free Access ◽  
Data Envelopment ◽  
University Libraries ◽  
Dea Models ◽  
Web App ◽  
Using Data ◽  
Fuzzy Dea ◽  
Efficiency And Productivity

In this paper, we describe an interactive web application (deaR-shiny) to measure efficiency and productivity using data envelopment analysis (DEA). deaR-shiny aims to fill the gap that currently exists in the availability of online DEA software offering practitioners and researchers free access to a very wide variety of DEA models (both conventional and fuzzy models). We illustrate how to use the web app by replicating the main results obtained by Carlucci, Cirà and Coccorese in 2018, who investigate the efficiency and economic sustainability of Italian regional airport by using two conventional DEA models, and the results given by Kao and Liu in their papers published in 2000 and 2003, who calculate the efficiency scores of university libraries in Taiwan by using a fuzzy DEA model because they treat missing data as fuzzy numbers.

scholarly journals Measurement of Efficiency and Productivity of Zakat Institutions in Indonesia using Data Envelopment Analysis and the Malmquist Productivity Index

2018 ◽  
Vol 3 (3) ◽  
pp. 69-82 ◽  
Author(s):  
Selvia Rustyani ◽  
Suherman Rosyidi
Keyword(s):  
Data Envelopment Analysis ◽  
The Other ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
First Year ◽  
Annual Data ◽  
Data Envelopment ◽  
Input Variables ◽  
Using Data ◽  
Efficiency And Productivity

This study aims to measure the level of efficiency and productivity of zakat institutions in Indonesia.Quantitative research using the methods of Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). There are six Amil Zakat (LAZ) institutions in Indonesia, namely Yayasan Dana SosialAl-Falah (YDSF), Al-Azhar Peduli Ummat, Aksi Cepat Tanggap (ACT), Yayasan Rumah Yatim Arrohman Indonesia,Pos Kemanusiaan Peduli Ummat (PKPU), and Rumah Zakat Indonesia, with 2014–2016 annual data as the number of DMUs (decision making units). This study uses an intermediation approach in determining the variables.The input variables of this research are Collected Funds (X1), Total Costs (X2), and Amil Acceptance (X3), while the Output variables are Funds Distributed (Y1) and Total Assets (Y2).There were two LAZs that experienced inefficiencies in 2014 and 2015, namely LAZ YDSF and ACT. Meanwhile, in 2016, all LAZs achieved optimal levels of efficiency. The results of the MPI analysis show that in the first year two LAZs experienced a decline in productivity, namely LAZ Al-Azhar and PKPU. The other four LAZs sawan increase in productivity, namely LAZ YDSF, ACT, Rumah Yatim, and Rumah Zakat. In the second year, three LAZsexperienced a rise in productivity, namely LAZ Al-Azhar, PKPU, and Rumah Zakat, while the other three LAZs experienced a decrease in productivity, namely LAZ YDSF, ACT, and Rumah Yatim.

Data Envelopment Analysis (DEA) in the Psychological Sciences

2019 ◽  
Author(s):  
Jeffrey A. Shero ◽  
Sara Ann Hart
Keyword(s):  
Data Envelopment Analysis ◽  
Aggressive Behavior ◽  
Personality Traits ◽  
Educational Administration ◽  
Latent Variable ◽  
Organizational Management ◽  
Data Envelopment ◽  
Targeted Interventions ◽  
Component Skills ◽  
Using Data

Using methods like linear regression or latent variable models, researchers are often interested in maximizing explained variance and identifying the importance of specific variables within their models. These models are useful for understanding general ideas and trends, but often give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method with roots in organizational management that make such insights possible. Unlike models mentioned above, DEA does not explain variance. Instead, it explains how efficiently an individual utilizes their inputs to produce outputs, and identifies which input is not being utilized optimally. This paper provides readers with a brief history and past usages of DEA from organizational management, public health, and educational administration fields, while also describing the underlying math and processes behind said model. This paper then extends the usage of this method into the psychology field using two separate studies. First, using data from the Project KIDS dataset, DEA is demonstrated using a simple view of reading framework identifying individual efficiency levels in using reading-based skills to achieve reading comprehension, determining which skills are being underutilized, and classifying and comparing new subsets of readers. Three new subsets of readers were identified using this method, with direct implications leading to more targeted interventions. Second, DEA was used to measure individuals’ efficiency in regulating aggressive behavior given specific personality traits or related skills. This study found that despite comparable levels of component skills and personality traits, significant differences were found in efficiency to regulate aggressive behavior on the basis of gender and feelings of provocation.

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