FUZZY MULTIPLE CRITERIA GROUP DECISION-MAKING IN PERFORMANCE EVALUATION OF MANUFACTURING COMPANIES
Article Sidebar
Open full text
Main Article Content
DOI
Authors
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:
References
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
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
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
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
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
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
Barlev, B., & Callen, J. L. (1986). Total factor productivity and cost variances: survey and analysis. Journal of Accounting Literature, 5, 35–56.
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
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
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
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
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
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
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
National Research Council. (1979). Measurement and interpretation of productivity. National Academy of Sciences.
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
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
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
Eccles, R. G. (1991). The performance measurement manifesto. Harvard business review, 69(1), 131–137.
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
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
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
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
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
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
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
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
Kaplan, R. S., & Norton, D. P. (2005). The balanced scorecard: measures that drive performance. Harvard business review, 70, 71-79.
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
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
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
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
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
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
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
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
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
Norman, R. G., & Bahiri, S. (1972). Productivity measurement and incentives. Transatlantic Arts.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Article Details
Abstract views: 272
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.