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Performance evaluation using data envelopment analysis - stepwise modeling approach: A case study of construction industries in Indonesia Putri, Erni Puspanantasari; Parinov, Ivan A.; Plando, Almaceley S.
Jurnal Sistem dan Manajemen Industri Vol. 8 No. 2 (2024): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v8i2.8936

Abstract

The construction industries are inextricably linked to employment, investment, the quantity of infrastructure building projects, and other economic sectors in Indonesia. They serve as catalysts for the expansion of goods and service production. Apart from having a strategic role in the national economic, construction companies also experience various obstacles to developing their businesses. These obstacles include weakening the IDR exchange rate against the US dollar, regulatory and legal frameworks, labor and skills shortages, economic and financial instability, and environmental and sustainability concerns. In order for the construction industry to survive, develop, and remain competitive in the face of international competition, it is crucial to evaluate its performance constantly. This research aims to evaluate the construction industry's performance in Indonesia. There are 151,183 construction companies included in this study. Hence, these companies will continue to survive, grow, and compete in the face of global competition. The methods applied in this research are an input-oriented DEA envelopment model and a stepwise modeling approach. The research results indicated that 3% of the Indonesian construction industry is made up of efficient DMUs, and the remaining 97% are inefficient DMUs. DMUs are classified according to the distribution of efficiency scores. It is considered that for the classification of inefficient DMU, there exist four ranges, Rs: R1 (ES = 0.16-0.99), R2 (ES = 0.050-0.15), R3 (ES = 0.015-0.049), and R4 (ES = 0.000-0.014). The criteria for each classification, in terms of the level of effectiveness, are as follows: i) R0 Range (ES = 1]): Effective; ii) R1 Range (ES = 0.16-0.99): Relatively Low Ineffectiveness; iii) R2 Range (ES = 0.050-0.15): Moderate Ineffectiveness; iv) R3 Range (ES = 0.015-0.049): Significant Ineffectiveness; and v) R4 Range (ES = 0.000-0.014): Very High Ineffectiveness. The percentage of each classification is as follows: inefficient DMU-R1 0%, inefficient DMU-R2 30%, inefficient DMU-R3 37%, inefficient DMU-R4 30%.
Efficiency Evaluation in Indonesia's Quarrying Industry Using Variable Combinations DEA Puspanantasari Putri, Erni; Parinov, Ivan A.; Wongloucha, Chuleeporn
Spektrum Industri Vol. 22 No. 2 (2024): Spektrum Industri - October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/si.v22i2.227

Abstract

Data Envelopment Analysis (DEA) is a method considered to evaluate a company's performance. DEA applies multiplies the input and output variables for analyzing the efficiency but does not provide guidance in selecting those variables. As a rule, researchers use several methods. If the number of variables used is too many, it will affect the efficiency value. This will reduce the strength of the efficiency value, which can cause all DMU values to be efficient.   DEA and variable selection are important in performance evaluation because DEA aids in determining relative efficiency, whereas variable selection guarantees that the evaluation is based on the most relevant and significant aspects. The purpose of this study is to suggest the variable combination method for subtracting the number of variables that will be utilized in implementing the DEA. The method used in this study is the Average Input Variable Combinations (VCs)-Variable Returns-to-Scale (VRS) DEA.  The data were classified, defined, and processed with a view to computing efficiency scores and DMU classifications. The research result indicated that the proposed method (VCs-DEA) treats the variable reduction factor and the average calculation factor to obtain the final result of the efficiency score.  These two factors contribute to the accuracy of the efficiency value. Some real-world implications of these findings, such as making better use of resources, streamlining operations, and coming up with new plans, Furthermore, the evidence may be used to benchmark performance as well as help decision-makers in creating more effective policy. This study finds that only 1 out of 12 DMUs is efficient (8%), while the remaining 11 are inefficient (92%). Indonesia quarrying establishment can be classified into 3 categories such as Optimal Category (S-Sand); Middle Category (LS-Lime-Stone; F-Feldspars; Gr-Granite; SA-Stone and Andesite; K-Kaolin; Q-Quartz; and G-Gravel); and Less Category (So-Soil; C-Clay; M-Marble; and O-Others).