A Performance Evaluation Model for the Logistics Service Companies of the Ministry of Oil Using an Integrated Approach of Data Envelopment Analysis and the Markowitz Method
Keywords:
Performance evaluation, ata envelopment analysis, Markowitz modelAbstract
The oil industry is one of the most influential and largest industries in the country, which in recent years has been significantly affected by the unjust U.S. sanctions. Therefore, the performance of the companies operating in this sector is of great importance. The logistics companies active in the industry play a supporting role, and their weak performance can cause substantial damage to the national economy. Hence, it is essential that they are continuously evaluated. This study was conducted with the aim of evaluating the performance of the logistics companies operating in the oil industry. In this research, to evaluate the performance of logistics companies in the oil industry, performance evaluation indicators were identified by reviewing the literature and consulting with experts and professionals. Subsequently, a two-stage network data envelopment analysis approach was employed to measure the performance of these companies. The results showed that the average efficiency in 2020 was 80% in the first stage, 74% in the second stage, and 60% in total efficiency. In that year, units four and six were on the efficiency frontier with the highest efficiency, while unit eighteen had the lowest efficiency at 28%. In 2021, the average efficiency was 79% in the first stage, 85% in the second stage, and 68% in total. In this year, units four and eight were on the efficiency frontier with the highest efficiency, and unit twelve had the lowest efficiency at 50%. A comparison of efficiency across the two years indicates that total efficiency increased by 22%. Furthermore, this study, by employing an integrated approach of data envelopment analysis and the Markowitz model, aimed to simultaneously maximize returns, minimize risk, and maximize efficiency.
Downloads
References
Bajec, P., & Tuljak-Suban, D. (2019). An Integrated Analytic Hierarchy Process-Slack Based Measure-Data Envelopment Analysis Model for Evaluating the Efficiency of Logistics Service Providers Considering Undesirable Performance Criteria. Sustainability, 11(8), 2330. https://doi.org/10.3390/su11082330
Chang, T. Y., & Chung, P. H. (2012). Two-stage performance model for evaluating the managerial efficiency of higher education: Application by the Taiwanese tourism and leisure department. Journal of Hospitality, Leisure, Sport & Tourism Education, 11, 168-177. https://doi.org/10.1016/j.jhlste.2012.04.003
Chen, C., & Yan, H. (2011). Network DEA model for supply chain performance evaluation. European Journal of Operational Research, 213, 147-155. https://doi.org/10.1016/j.ejor.2011.03.010
Estadi, B., & Ebrahimi Sadrabadi, H. Z. K. S. (2021). Presenting a model for optimal allocation of human resources to operational processes using the Markowitz model: A case study in the urology department of a specialized kidney center. Scientific-Research Journal of Engineering and Quality Management, 11(1), 77-87. https://www.pqprc.ir/article_136306.html
Faridi, S., Madanchi Zaj, M., Daneshvar, A., Shahverdiani, S., & Rahnamai Roud Poshti, F. (2022). Portfolio Optimization Based on a Combined Omega Ratio and Markowitz Mean-Variance Model Using Two-Level Ensemble Machine Learning. Financial Knowledge and Security Analysis (Financial Studies), 14(55), 33-54. https://www.sid.ir/paper/1063401/en
Gan, W. H., Cheng, C. H., Zhong, R., & Li, X. K. (2019). Comprehensive Evaluation of the Efficiency of China's Highway Freight Transport Platform Based on AHP-DEA. In S. Springer (Ed.), Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018 (pp. 346-354). https://doi.org/10.1007/978-981-13-3402-3_37
Guchhait, R., & Sarkar, B. (2024). A decision-making problem for product outsourcing with flexible production under a global supply chain management. International Journal of Production Economics, 272, 109230. https://doi.org/10.1016/j.ijpe.2024.109230
Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347. https://doi.org/10.1016/j.ijpe.2003.08.003
Ho, C. B. (2010). Measuring dot com efficiency using a combined DEA and GRA approach. Journal of the Operational Research Society, 62(4), 776-783. https://doi.org/10.1057/jors.2010.3
Hong, S., & Kim, J. (2004). Architectural criteria for website evaluation - conceptual framework and empirical validation. Behaviour & Information Technology, 23(5), 337-357. https://doi.org/10.1080/01449290410001712753
Hopp, W. J., & Spearman, M. L. (2004). To will or not to pull: what is the questioned? Manufacturing & Service Operations Management, 6(2), 133-148. https://doi.org/10.1287/msom.1030.0028
Jahangiri, S., & Shokouhyar, S. (2024). An integrated FBWM-FCM-DEMATEL model to assess and manage the sustaina bility in the supply chain: A three-stage model based on the consumers ’ point of view. Applied Soft Computing, 157, 111281. https://doi.org/10.1016/j.asoc.2024.111281
Ji, C. Y., Tan, Z. K., Chen, B. J., Zhou, D. C., & Qian, W. Y. (2024). The impact of environmental policies on renewable energy investment decisions in the power supply chain. Energy Policy, 186, 113987. https://doi.org/10.1016/j.enpol.2024.113987
Kai, A. H., Chang, H. J., & Lin, C. Y. (2009). An evaluation model of buyer-supplier relationships in high-tech industry—the case of an electronic components manufacturer in Taiwan. Computers & Industrial Engineering, 57(4), 1417-1430. https://doi.org/10.1016/j.cie.2009.07.012
Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949-962. https://doi.org/10.1016/j.ejor.2007.10.008
Liu, S. T. (2011). A note on efficiency decomposition in two-stage data envelopment analysis. European Journal of Operational Research, 212(3), 606-608. https://doi.org/10.1016/j.ejor.2011.03.001
Liu, S. T., & Wang, R. T. (2009). Efficiency measures of PCB manufacturing firms using relational two-stage data envelopment analysis. Expert Systems with Applications, 36(3), 4935-4939. https://doi.org/10.1016/j.eswa.2008.06.014
Lo Storto, C. (2013). Evaluating ecommerce websites cognitive efficiency: An integrative framework based on data envelopment analysis. Applied Ergonomics, 44(6), 1004-1014. https://doi.org/10.1016/j.apergo.2013.03.031
Mehregan, M. (2012). DEA: Quantitative Models to Assess Organizational Performance. Academic Book Publishing. https://www.researchgate.net/publication/351821026_astfadh_az_rwykrd_trkyby_chndmrhlhay_thlyl_pwshshy_dadhha_DEA_w_kart_amtyazy_mtwazn_BSC_bray_bhbwd_arzyaby_mlkrd
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial neural networks in supply chain management, a review. Journal of Economy and Technology, 1, 179-196. https://doi.org/10.1016/j.ject.2023.11.002
Tavana, M., Sorooshian, S., & Mina, H. (2023). An integrated group fuzzy inference and best–worst method for supplier selection in intelligent circular supply chains. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05680-0
Wang, W., Chen, Y., Wang, Y., Deveci, M., Cheng, S., & Brito-Parada, P. R. (2024). A decision support framework for humanitarian supply chain management – Analysing enablers of AI-HI integration using a complex spherical fu zzy DEMATEL-MARCOS method. Technological Forecasting and Social Change, 206, 123556. https://doi.org/10.1016/J.TECHFORE.2024.123556
Xia, T., Wang, Y., Lv, L., Shen, L., & Cheng, T. C. E. (2023). Financing decisions of low-carbon supply chain under chain-to-chain competition. International Journal of Production Research, 61(18), 6153-6176. https://doi.org/10.1080/00207543.2021.2023833
Xu, J., Meng, Q., Chen, Y., & Jia, Z. (2023). Dual-Channel Pricing Decisions for Product Recycling in Green Supply Chain Operations: Considering the Impact of Consumer Loss Aversion. International journal of environmental research and public health, 20(3), 1792. https://doi.org/10.3390/ijerph20031792
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 1404 سيدحبيب اله بحرالعلوم (نویسنده); قنبر عباس پور اسفدن; کیامرث فتحی هفشجانی, محمود مدیری (نویسنده)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.