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Assessing telecommunication contractor firms using a hybrid DEA-BWM method

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Telecommunication companies have an important role in technology development, so evaluating the performance of these companies has been an interest of managers. This article uses a hybrid method using data envelopment analysis (DEA) and the best-worst method (BWM) to measure the performance of communication companies. The hybrid DEA-BWM method is used for the weight determination and performance assessment of 17 telecommunication contractor firms in the Khorsan Razavi province of Iran. We considered four inputs: gross losses, sales cost, legal reserve, and fixed assets. On the other side, three outputs including operation income, operation profit, and retained earnings are considered as outputs. Considering the input-output parameters and using the hybrid method by seven selected criteria, we rank all contractor firms. We found that the BPM firm has the best performance while and GKS firm is found as the firm with the weakest performance. Compared with the classical DEA methods, we found more reliable results with higher discrimination power, using the hybrid DEA-BWM.
Rocznik
Strony
189--200
Opis fizyczny
Bibliogr. 25 poz., rys.
Twórcy
  • Faculty of Industrial Engineering and Management Science, Shahrood University of Technology, Shahrood, Iran
  • Faculty of Industrial Engineering and Management Science, Shahrood University of Technology, Shahrood, Iran
Bibliografia
  • [1] Babaee, S., Toloo, M., Hermans, E., and Shen, Y. A new approach for index construction: The case of the road user behavior index. Computers & Industrial Engineering 152 (2021), 106993.
  • [2] Charnes, A., Cooper, W. W., and Rhodes, E. Measuring the efficiency of decision-making units. European Journal of Operational Research 2, 6 (1978), 429–444.
  • [3] Cheaitou, A., Larbi, R., and Al Housani, B. Decision-making framework for tender evaluation and contractor selection in public organizations with risk considerations. Socio-Economic Planning Sciences 68 (2019), 100620.
  • [4] Dahooie, J. H., Qorbani, A. R., and Daim, T. Providing a framework for selecting the appropriate method of technology acquisition considering uncertainty in hierarchical group decision-making: Case study: Interactive television technology. Technological Forecasting and Social Change 168 (2021), 120760.
  • [5] Dehshiri, S. J. H., Emamat, M. S. M. M., and Amiri, M. A novel group BWM approach to evaluate the implementation criteria of blockchain technology in the automotive industry supply chain. Expert Systems with Applications 198, (2022), 116826.
  • [6] Dellnitz, A., Tavana, M., and Banker, R. A novel median-based optimization model for eco-efficiency assessment in data envelopment analysis. Annals of Operations Research 322, 2 (2023), 661–690.
  • [7] Gharedaghi, G., and Omidvari, M. A contractor selection model for gas and oil industries in safety approach using ANPDEMATEL in grey environment. Safety and Reliability 37, 1 (2017), 25–47.
  • [8] Heydarpour, S., Seyyed Esfahani, S. H., and Khorshidvand, B. Providing a DEA and AHP hybrid model to evaluate contractors’ performance (Case study: Zarand Iranian Steel Co. (ZISCO)). Journal of Industrial Engineering and Management Studies 9, 1 (2022), 1–10.
  • [9] Jain, V., Kumar, A., Kumar, S., and Chandra, C. Weight restrictions in Data Envelopment Analysis: A comprehensive Genetic Algorithm based approach for incorporating value judgments. Expert Systems with Applications 42, 3 (2015), 1503–1512.
  • [10] Kao, C., and Hung, H.-T. Data envelopment analysis with common weights: the compromise solution approach. Journal of the Operational Research Society 56, 10 (2005), 1196–1203.
  • [11] Khalili, M., Camanho, A. S., Portela, M. C. A. S., and Alirezaee, M. R. The measurement of relative efficiency using data envelopment analysis with assurance regions that link inputs and outputs. European Journal of Operational Research 203, 3 (2010), 761–770.
  • [12] Krishna Rao, M. V., Kumar, V. S. S., and Rathish Kumar, P. Optimal contractor selection in construction industry: The fuzzy way. Journal of The Institution of Engineers (India): Series A 99, 1 (2018), 67–78.
  • [13] Liu, S.-T. Restricting weight flexibility in fuzzy two-stage DEA. Computers & Industrial Engineering 74 (2014), 149–160.
  • [14] Marović, I., Perić, M., and Hanak, T. A multi-criteria decision support concept for selecting the optimal contractor. Applied Sciences 11, 4 (2021), 1660.
  • [15] Morkunaite, Z., Podvezko, V., Zavadskas, E. K., and Bausys, R. Contractor selection for renovation of cultural heritage buildings by PROMETHEE method. Archives of Civil and Mechanical Engineering 19, 4 (2019), 1056–1071.
  • [16] Omrani, H., Alizadeh, A., Emrouznejad, A., and Oveysi, Z. A novel best-worst-method two-stage data envelopment analysis model considering decision-makers’ preferences: An application in bank branches evaluation. International Journal of Finance & Economics 28, 4 (2023), 3593-3610.
  • [17] Omrani, H., Amini, M., and Alizadeh, A. An integrated group best-worst method – Data envelopment analysis approach for evaluating road safety: A case of Iran. Measurement 152 (2020), 107330.
  • [18] Rezaei, J. Best-worst multi-criteria decision-making method. Omega 53 (2015), 49–57.
  • [19] Saaty, T. L. The Analytic Hierarchy Process, McGraw-Hill, New York, 1980.
  • [20] Tajik, M., Makui, A., and Mansouri, N. Suppliers’ evaluation and ranking in telecommunication infrastructure company using the TOPSIS method in an uncertain environment. In Logistics and Supply Chain Management: 7th International Conference, LSCM 2020, Tehran, Iran, December 23-24, 2020, Revised Selected Papers , (Cham, 2021), Z. Molamohamadi, E. Babaee Tirkolaee, A. Mirzazadeh and G. W. Weber, Eds., vol. 1458 of Communications in Computer and Information Science book series, Springer, pp. 84–99.
  • [21] Vaidya, O. S., and Kumar, S. Analytic hierarchy process: An overview of applications. European Journal of Operational Research 169, 1 (2006), 1–29.
  • [22] Wen, Y., An, Q., Xu, X., and Chen, Y. Selection of Six Sigma project with interval data: common weight DEA model. Kybernetes 47, 7 (2018), 1307–1324.
  • [23] Yang, H., Kim, S. Y., and Yim, S. A case study of the Korean government’s preparation for the fourth industrial revolution: Public program to support business model innovation. Journal of Open Innovation: Technology, Market, and Complexity 5, 2 (2019), 35. 200 O. Valizadeh and M. Ghiyasi
  • [24] Zhao, Z.-Y., Tang, C., Zhang, X., and Skitmore, M. Agglomeration and competitive position of contractors in the international construction sector. Journal of Construction Engineering and Management 143, 6 (2017), 04017004.
  • [25] Zohrehbandian, M., Makui, A., and Alinezhad, A. A compromise solution approach for finding common weights in DEA: An improvement to Kao and Hung’s approach. Journal of the Operational Research Society 61, 4 (2010), 604–610.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-68428bb9-87f0-4a95-b2c3-d53a1ef25dbe
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