FUZZY MULTIPLE CRITERIA GROUP DECISION-MAKING IN PERFORMANCE EVALUATION OF MANUFACTURING COMPANIES
Sara SALEHI
Sara.salehi@rdu.edu.trRauf Denktas Universit, Faculty of Architecture and Engineering, Department of Software Engineering, Northern Cyprus (Turkey)
Abstract
Today's market competition requires constant improvement of manufacturing companies. The primary key to sustainable improvement is evaluating the efficiency of manufacturing processes, which inevitably demands access to thorough and comprehensive information. However, due to the multiple numbers of effective factors that are varied in nature and value, it is impossible to identify certain factors that ensure the efficiency of a manufacturing procedure. As a solution, this paper proposes a novel approach that applies fuzzy TOPSIS. This approach provides the flexibility of evaluating multiple and varied factors of different weights in scrutinizing the efficiency of a manufacturer. The proposed approach has been applied to three different manufacturers (i.e., alternatives) in three steps. In the first step, with reference to the related literature and comments of manufacturing experts, the valuable factors (i.e., the criteria) have been selected to which experts specified linguistic terms. Linguistic terms were then converted to fuzzy numbers. Fuzzy TOPSIS was applied to analyze the efficiency performance of manufacturers. In the last step, to determine the impact of criteria weights on the decision-making process, sensitivity analysis was carried out. The findings confirm the implacability of the proposed approach to manufacturing performances in a consolidated manner. The approach can be employed by marketing managers, senior administrators, and other authorities in the manufacturing and business sectors.
Keywords:
Fuzzy theory, Fuzzy TOPSIS, Decision-making, Manufacturing CompanyReferences
Abdullah, F. M., Al-Ahmari, A. M., & Anwar, S. (2023). An integrated fuzzy DEMATEL and fuzzy TOPSIS method for analyzing smart manufacturing technologies. Processes, 11(3), 906. https://doi.org/10.3390/pr11030906
DOI: https://doi.org/10.3390/pr11030906
Google Scholar
Ahmad, M. M., & Dhafr, N. (2002). Establishing and improving manufacturing performance measures. Robotics and Computer-Integrated Manufacturing, 18(3-4), 171–176. https://doi.org/10.1016/S0736- 5845(02)00007-8
DOI: https://doi.org/10.1016/S0736-5845(02)00007-8
Google Scholar
Alqahtani, A. Y., Gupta, S. M., & Nakashima, K. (2019). Warranty and maintenance analysis of sensor embedded products using internet of things in industry 4.0. International Journal of Production Economics, 208, 483–499. https://doi.org/10.1016/j.ijpe.2018.12.022
DOI: https://doi.org/10.1016/j.ijpe.2018.12.022
Google Scholar
Anderl, R., Haag, S., Schützer, K., & Zancul, E. (2018). Digital twin technology–an approach for industrie 4.0 vertical and horizontal lifecycle integration. it-Information Technology, 60(3), 125–132. https://doi.org/10.1515/itit-2017-0038
DOI: https://doi.org/10.1515/itit-2017-0038
Google Scholar
Attaran, M. (2017). The rise of 3-d printing: The advantages of additive manufacturing over traditional manufacturing. Business horizons, 60(5), 677–688. https://doi.org/10.1016/j.bushor.2017.05.011
DOI: https://doi.org/10.1016/j.bushor.2017.05.011
Google Scholar
Awodi, N. J., Liu, Y.-k., Ayo-Imoru, R. M., & Ayodeji, A. (2023). Fuzzy TOPSIS-based risk assessment model for effective nuclear decommissioning risk management. Progress in Nuclear Energy, 155, 104524. https://doi.org/10.1016/j.pnucene.2022.104524
DOI: https://doi.org/10.1016/j.pnucene.2022.104524
Google Scholar
Barlev, B., & Callen, J. L. (1986). Total factor productivity and cost variances: survey and analysis. Journal of Accounting Literature, 5, 35–56.
Google Scholar
Bartosik-Purgat, M., & Ratajczak-Mrożek, M. (2018). Big data analysis as a source of companies’ competitive advantage: A review. Entrepreneurial Business and Economics Review, 6(4), 197–215.
DOI: https://doi.org/10.15678/EBER.2018.060411
Google Scholar
Bashir, Z., Rashid, T., Wątróbski, J., Sałabun, W., & Malik, A. (2018). Hesitant probabilistic multiplicative preference relations in group decision making. Applied Sciences, 8(3), 398. https://doi.org/10.3390/app8030398
DOI: https://doi.org/10.3390/app8030398
Google Scholar
Büchi, G., Cugno, M., & Castagnoli, R. (2020). Smart factory performance and industry 4.0. Technological Forecasting and Social Change, 150, 119790. https://doi.org/10.1016/j.techfore.2019.119790
DOI: https://doi.org/10.1016/j.techfore.2019.119790
Google Scholar
Chatterjee, P., & Stević, Ž. (2019). A two-phase fuzzy AHP-fuzzy TOPSIS model for supplier evaluation in manufacturing environment. Operational Research in Engineering Sciences: Theory and Applications, 2(1), 72–90. https://doi.org/10.31181/oresta1901060c
DOI: https://doi.org/10.31181/oresta1901060c
Google Scholar
Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1
DOI: https://doi.org/10.1016/S0165-0114(97)00377-1
Google Scholar
Choi, T.-M. (2018). A system of systems approach for global supply chain management in the big data era. IEEE Engineering Management Review, 46(1), 91– 97. https://doi.org/10.1109/EMR.2018.2810069
DOI: https://doi.org/10.1109/EMR.2018.2810069
Google Scholar
Chowdhury, P., & Paul, S. K. (2020). Applications of MCDM methods in research on corporate sustainability: A systematic literature review. Management of Environmental Quality: An International Journal, 31(2), 1477-7835. https://doi.org/10.1108/MEQ-12-2019-0284
DOI: https://doi.org/10.1108/MEQ-12-2019-0284
Google Scholar
National Research Council. (1979). Measurement and interpretation of productivity. National Academy of Sciences.
Google Scholar
Coxon, M., Kelly, N., & Page, S. (2016). Individual differences in virtual reality: Are spatial presence and spatial ability linked? Virtual Reality, 20, 203– 212. https://doi.org/10.1007/s10055-016-0292-x
DOI: https://doi.org/10.1007/s10055-016-0292-x
Google Scholar
Dos Santos, B. M., Godoy, L. P., & Campos, L. M. (2019). Performance evaluation of green suppliers using entropy TOPSIS-F. Journal of cleaner production, 207, 498–509. https://doi.org/10.1016/j.jclepro.2018.09.235
DOI: https://doi.org/10.1016/j.jclepro.2018.09.235
Google Scholar
Druehl, C., Carrillo, J., & Hsuan, J. (2018). Technological innovations: Impacts on supply chains. In: Moreira, A., Ferreira, L., Zimmermann, R. (eds) Innovation and Supply Chain Management, (pp. 259-281). Springer. https://doi.org/10.1007/978-3-319-74304-2_12
DOI: https://doi.org/10.1007/978-3-319-74304-2_12
Google Scholar
Eccles, R. G. (1991). The performance measurement manifesto. Harvard business review, 69(1), 131–137.
Google Scholar
Emovon, I., & Oghenenyerovwho, O. S. (2020). Application of MCDM method in material selection for optimal design: A review. Results in Materials, 7, 100115. https://doi.org/10.1016/j.rinma.2020.100115
DOI: https://doi.org/10.1016/j.rinma.2020.100115
Google Scholar
Guo, L., Yao, Z., Lin, M., & Xu, Z. (2023). Fuzzy TOPSIS-based privacy measurement in multiple online social networks. Complex & Intelligent Systems, 1–13. https://doi.org/10.1007/s40747-023-00991-y
DOI: https://doi.org/10.1007/s40747-023-00991-y
Google Scholar
Hajiaghaei-Keshteli, M., Cenk, Z., Erdebilli, B., Özdemir, Y. S., & Gholian-Jouybari, F. (2023). Pythagorean fuzzy TOPSIS method for green supplier selection in the food industry. Expert Systems with Applications, 224, 120036. https://doi.org/10.1016/j.eswa.2023.120036
DOI: https://doi.org/10.1016/j.eswa.2023.120036
Google Scholar
Hooshangi, N., Gharakhanlou, N. M., & Razin, S. R. G. (2023). Evaluation of potential sites in Iran to localize solar farms using a GIS-based Fermatean fuzzy TOPSIS. Journal of Cleaner Production, 384, 135481. https://doi.org/10.1016/j.jclepro.2022.135481
DOI: https://doi.org/10.1016/j.jclepro.2022.135481
Google Scholar
Hosseinzadeh Lotfi, F., Allahviranloo, T., Shafiee, M., & Saleh, H. (2023). Supplier performance evaluation models. In Supply chain performance evaluation: Application of data envelopment analysis, (vol. 122, pp. 117–148). Springer. https://doi.org/10.1007/978-3-031-28247-8_4
DOI: https://doi.org/10.1007/978-3-031-28247-8_4
Google Scholar
Hwang, C.-L., & Yoon, K. (1981). Basic concepts and foundations. In multiple attribute decision making. Lecture notes in economics and mathematical systems ( vol. 186, pp. 16–57). Springer. https://doi.org/10.1007/978-3-642-48318-9_2
DOI: https://doi.org/10.1007/978-3-642-48318-9_2
Google Scholar
Hwang, C.-L., & Yoon, K. (1981). Methods for multiple attribute decision making. In: Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems, ( vol.186, pp. 58–191). Springer. https://doi.org/10.1007/978-3-642-48318-9_3
DOI: https://doi.org/10.1007/978-3-642-48318-9_3
Google Scholar
Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy multicriteria decision-making: a literature review. International journal of computational intelligence systems, 8(4), 637-666. https://doi.org/10.1080/18756891.2015.1046325
DOI: https://doi.org/10.1080/18756891.2015.1046325
Google Scholar
Kaplan, R. S., & Norton, D. P. (2005). The balanced scorecard: measures that drive performance. Harvard business review, 70, 71-79.
Google Scholar
Karczmarczyk, A., Jankowski, J., & Wątróbski, J. (2018). Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks. PloS one, 13(12), e0209372. https://doi.org/10.1371/journal.pone.0209372
DOI: https://doi.org/10.1371/journal.pone.0209372
Google Scholar
Khorram Niaki, M., & Nonino, F. (2017). Additive manufacturing management: a review and future research agenda. International Journal of Production Research, 55(5), 1419–1439. https://doi.org/10.1080/00207543.2016.1229064
DOI: https://doi.org/10.1080/00207543.2016.1229064
Google Scholar
Kuo, M.-S., Tzeng, G.-H., & Huang, W.-C. (2007). Group decision-making based on concepts of ideal and anti-ideal points in a fuzzy environment. Mathematical and Computer Modelling, 45(3-4), 324–339. https://doi.org/10.1016/j.mcm.2006.05.006
DOI: https://doi.org/10.1016/j.mcm.2006.05.006
Google Scholar
Leachman, C., Pegels, C. C., & Kyoon Shin, S. (2005). Manufacturing performance: evaluation and determinants. International Journal of Operations & Production Management, 25(9), 851–874. https://doi.org/10.1108/01443570510613938
DOI: https://doi.org/10.1108/01443570510613938
Google Scholar
Lee, J., Bagheri, B., & Jin, C. (2016). Introduction to cyber manufacturing. Manufacturing Letters, 8, 11–15. https://doi.org/10.1016/j.mfglet.2016.05.002
DOI: https://doi.org/10.1016/j.mfglet.2016.05.002
Google Scholar
Liu, Q., Kwong, C. F., Zhang, S., & Li, L. (2019). Fuzzy-TOPSIS based optimal handover decision-making algorithm for fifth-generation of mobile communications system. Journal of Communications., 14(10), 945–950. https://doi.org/10.12720/jcm.14.10.945-950
DOI: https://doi.org/10.12720/jcm.14.10.945-950
Google Scholar
Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of industrial information integration, 6, 1–10. https://doi.org/10.1016/j.jii.2017.04.005
DOI: https://doi.org/10.1016/j.jii.2017.04.005
Google Scholar
Markopoulos, P. M., & Hosanagar, K. (2018). A model of product design and information disclosure investments. Management Science, 64(2), 495-981. https://doi.org/10.1287/mnsc.2016.2634
DOI: https://doi.org/10.1287/mnsc.2016.2634
Google Scholar
Nila, B., & Roy, J. (2023). A new hybrid MCDM framework for third-party logistic provider selection under sustainability perspectives. Expert Systems with Applications, 234, 121009. https://doi.org/10.1016/j.eswa.2023.121009
DOI: https://doi.org/10.1016/j.eswa.2023.121009
Google Scholar
Norman, R. G., & Bahiri, S. (1972). Productivity measurement and incentives. Transatlantic Arts.
Google Scholar
Palczewski, K., & Sałabun, W. (2019). The fuzzy TOPSIS applications in the last decade. Procedia Computer Science, 159, 2294–2303. https://doi.org/10.1016/j.procs.2019.09.404
DOI: https://doi.org/10.1016/j.procs.2019.09.404
Google Scholar
Pourjavad, E., & Mayorga, R. V. (2019). A comparative study and measuring performance of manufacturing systems with MAMDANI fuzzy inference system. Journal of Intelligent Manufacturing, 30(3), 1085– 1097. https://doi.org/10.1007/s10845-017-1307-5
DOI: https://doi.org/10.1007/s10845-017-1307-5
Google Scholar
Regragui, H., Sefiani, N., Azzouzi, H., & Cheikhrouhou, N. (2023). A hybrid multicriteria decision-making approach for hospitals’ sustainability performance evaluation under fuzzy environment. International Journal of Productivity and Performance Management. https://doi.org/10.1108/IJPPM-10-2022-0538
DOI: https://doi.org/10.1108/IJPPM-10-2022-0538
Google Scholar
Rezk, R., Singh Srai, J., & Williamson, P. J. (2016). The impact of product attributes and emerging technologies on firms’ international configuration. Journal of International Business Studies, 47, 610– 618. https://doi.org/10.1057/jibs.2016.9
DOI: https://doi.org/10.1057/jibs.2016.9
Google Scholar
Rouyendegh, B. D., Yildizbasi, A., & Üstünyer, P. (2020). Intuitionistic fuzzy TOPSIS method for green supplier selection problem. Soft Computing, 24, 2215– 2228. https://doi.org/10.1007/s00500-019- 04054-8
DOI: https://doi.org/10.1007/s00500-019-04054-8
Google Scholar
Rouyendegh, B. D., Yildizbasi, A., & Yilmaz, I. (2020). Evaluation of retail industry performance ability through integrated intuitionistic fuzzy TOPSIS and data envelopment analysis approach. Soft Computing, 24, 12255-12266. https://doi.org/10.1007/s00500-020-04669-2
DOI: https://doi.org/10.1007/s00500-020-04669-2
Google Scholar
Sakakibara, S., Flynn, B. B., Schroeder, R. G., & Morris, W. T. (1997). The impact of just-in-time manufacturing and its infrastructure on manufacturing performance. Management Science, 43(9), 1246–1257. https://doi.org/10.1287/mnsc.43.9.1246
DOI: https://doi.org/10.1287/mnsc.43.9.1246
Google Scholar
Salih, M. M., Zaidan, B.B., Zaidan, A. A., & Ahmed, M. A. (2019). Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017. Computers & Operations Research, 104, 207–227. https://doi.org/10.1016/j.cor.2018.12.019
DOI: https://doi.org/10.1016/j.cor.2018.12.019
Google Scholar
Solangi, Y. A., Tan, Q., Mirjat, N. H., & Ali, S. (2019). Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. Journal of Cleaner Production, 236, 117655. https://doi.org/10.1016/j.jclepro.2019.117655
DOI: https://doi.org/10.1016/j.jclepro.2019.117655
Google Scholar
Sotoudeh-Anvari, A. (2022). The applications of MCDM methods in covid-19 pandemic: A state of the art review. Applied Soft Computing, 126, 109238. https://doi.org/10.1016/j.asoc.2022.109238
DOI: https://doi.org/10.1016/j.asoc.2022.109238
Google Scholar
Stojčić, M., Zavadskas, E. K., Pamučar, D., Stević, Ž., & Mardani, A. (2019). Application of MCDM methods in sustainability engineering: A literature review 2008–2018. Symmetry, 11(3), 350. https://doi.org/10.3390/sym11030350
DOI: https://doi.org/10.3390/sym11030350
Google Scholar
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International journal of production research, 56(8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806
DOI: https://doi.org/10.1080/00207543.2018.1444806
Google Scholar
Yang, T., & Hung, C.-C. (2007). Multiple-attribute decision making methods for plant layout design problem. Robotics and computer-integrated manufacturing, 23(1), 126–137. https://doi.org/10.1016/j.rcim.2005.12.002
DOI: https://doi.org/10.1016/j.rcim.2005.12.002
Google Scholar
Zadeh, L. A. (1996). Fuzzy sets. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh, (pp. 394–432). World Scientific. https://doi.org/10.1142/2895
DOI: https://doi.org/10.1142/9789814261302_0021
Google Scholar
Authors
Sara SALEHISara.salehi@rdu.edu.tr
Rauf Denktas Universit, Faculty of Architecture and Engineering, Department of Software Engineering, Northern Cyprus Turkey
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