Analyzing the Performance of Listed Companies with Specific Data, an Application of Data Envelopment Analysis (DEA) with a Network Structure
الموضوعات : International Journal of Data Envelopment AnalysisM Matinfar 1 , Masomeh Taheri 2
1 - Science of Mathematics Faculty, Department of Mathematics, University of Mazandaran,P.O.Box 47416-95447, Babolsar, Iran
2 - Department of Mathematics, Faculty of Mathematical Sciences, University of Mazandaran, Babolsar, Iran
الکلمات المفتاحية: Data Envelopment Analysis, Efficiency, Network Structure,
ملخص المقالة :
This paper explores the efficiency of listed companies using data envelopment analysis (DEA) with network structure. The DEA method, as a non-parametric technique, provides the possibility to evaluate the efficiency of decision-making units (DMU) based on multiple input and output data. In this paper, DEA approach with a network structure presents a more complex model that allows considering the internal structure of production processes and the transmission of variables during different stages. In this regard, specific data related to listed companies for a specific period are collected and used. This data includes various inputs such as total assets, total equity of capital owners, allocable profit, registered capital, total assets, equity to asset ratio and capital to asset ratio. A two-stage network is addressed in this paper. First, a two-stage network according to the production feasibility set is modelled, and then, according to the modified SBM structure, the efficiency of the first and second stages and its total efficiency are obtained. The obtained results contribute to a deeper understanding of the factors affecting the efficiency of companies, their weaknesses and strengths, and strategies to improve efficiency. This research can provide valuable insights to company managers, investors, and other stakeholders to make better decisions.
1) Shoja Naghi, Fallah Jaloudar Mahdi, & Darvish Motavalli Mohammad Hossein. (2011). Determining the efficiency of units in District 12 of Islamic Azad University using the multi-component model in data envelopment analysis.
2) Bahrololoum Mohammad Mahdi, & Ghavami Pour Negar. (2018). Securitization of credit card receivables.
3) Asgharian Solmaz, Hosseinzadeh Lotfi Farhad, and Kazemipour Hamed. "Overall and two-stage efficiency of bank branches using a common set of weights with a fuzzy method." (2015): 89-108.
4) Kamaki, Fallahnejad, Hosseinzadeh Lotfi, & Rostami Mal Khalifeh. (2024). Evaluation and ranking of market risks in infrastructure investment projects using a hybrid DEA/AHP technique. Investment Knowledge, 13(51), 119-136.
5) Shoja, Hosseinzadeh Lotfi, Gholamaberi, & Rashidi Komijan. (2020). Four-stage supply chain efficiency in the presence of uncontrollable, undesirable, and negative factors using the SBM model in network DEA. Economic Modeling, 51(14), 73-98.
6) Hsu, C. W., & Hu, A. H. (2008). Green supply chain management in the electronic industry. International Journal of Environmental Science & Technology, 5(2), 205-216.
7) Jahani, A., Soofi, F., & Mennati, R. H. Rahimi Nezhad (2013).“Identifying the Ranking of the Companies Listed on the Tehran Stock Exchange Using Studied Variables and Analytical Hierarchy Process”. In The National Conference of Accounting and Management.
8) Cook, W. D., Zhu, J., Bi, G., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European journal of operational research, 207(2), 1122-1129.
9) Kao, C. (2013). Dynamic data envelopment analysis: A relational analysis. European Journal of Operational Research, 227(2), 325-330.
10) Badiezadeh, T., Saen, R. F., & Samavati, T. (2018). Assessing sustainability of supply chains by double frontier network DEA: A big data approach. Computers & operations research, 98, 284-290.
11) Carrillo, M., & Jorge, J. M. (2016). A multiobjective DEA approach to ranking alternatives. Expert systems with applications, 50, 130-139.
12) Zervopoulos, P. D., Brisimi, T. S., Emrouznejad, A., & Cheng, G. (2016). Performance measurement with multiple interrelated variables and threshold target levels: Evidence from retail firms in the US. European Journal of Operational Research, 250(1), 262-272.
13) Zha, Y., Liang, N., Wu, M., & Bian, Y. (2016). Efficiency evaluation of banks in China: A dynamic two-stage slacks-based measure approach. Omega, 60, 60-72.
14) Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
15) Emami, L., Hosseinzadeh Lotfi, F., & Rostamy Malkhalifeh, M. (2024). Fixed cost allocation in bank branches: A network DEA approach. International journal of finance & managerial accounting, 9(35), 15-30.
16) Eftekharian, S. E., Hashemi, S. F., Nemati, A., Mehrjoo, R., & Ahadzadeh Namin, M. (2024). Evaluating Relative and Integrated Efficiency of the Stock Market: NDEA Approach. Quarterly Journal of Fiscal and Economic Policies, 12(46), 191-225.