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Monitoring the performance of petrochemical organizations in Saudi Arabia using data envelopment analysis

Alidrisi, Hisham; Aydin, Mehmet Emin; Bafail, Abdullah Omer; Abdulal, Reda; Karuvatt, Shoukath Ali

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Authors

Hisham Alidrisi

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Dr Mehmet Aydin Mehmet.Aydin@uwe.ac.uk
Senior Lecturer in Networks and Mobile Computing

Abdullah Omer Bafail

Reda Abdulal

Shoukath Ali Karuvatt



Abstract

The petrochemical industry plays a crucial role in the economy of the Kingdom of Saudi Arabia. Therefore, the effectiveness and efficiency of this industry is of high importance. Data envelopment analysis (DEA) is found to be more acceptable in measuring the effectiveness of various industries when used in conjunction with non-parametric methods such as multiple regression, analytical hierarchy process (AHP), multidimensional scaling (MDS), and other multiple criteria decision making (MCDM) approaches. In this study, ten petrochemical companies in the Kingdom of Saudi Arabia are evaluated using Banker, Charnes and Cooper (BCC)/Charnes, Cooper, and Rhodes (CCR) models of DEA to compute the technical and super-efficiencies for ranking according to their relative performances. Data were collected from the Saudi Stock Exchange on key financial performance measures, five of which were chosen as inputs and five as outputs. Five DEA models were developed using different input–output combinations. The efficiency plots obtained from DEA were compared with the Euclidean distance scatter plot obtained from MDS. The dimensionality of MDS plots was derived from the DEA output. It was found that the two-dimensional positioning of the companies was congruent in both plots, thus validating the DEA results.

Citation

Alidrisi, H., Aydin, M. E., Bafail, A. O., Abdulal, R., & Karuvatt, S. A. (2019). Monitoring the performance of petrochemical organizations in Saudi Arabia using data envelopment analysis. Mathematics, 7(6), 519. https://doi.org/10.3390/math7060519

Journal Article Type Article
Acceptance Date Jun 4, 2019
Online Publication Date Jun 6, 2019
Publication Date Jun 6, 2019
Deposit Date Jun 10, 2019
Publicly Available Date Mar 29, 2024
Journal Mathematics
Electronic ISSN 2227-7390
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 7
Issue 6
Pages 519
DOI https://doi.org/10.3390/math7060519
Keywords data envelopment analysis, benchmarking, petrochemical industries, technical and super-efficiencies, multidimensional scaling, efficiency and scatter plots
Public URL https://uwe-repository.worktribe.com/output/1493192
Publisher URL http://doi.org/10.3390/math7060519

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