Fuzzy multiple criteria group decision-making in performance evaluation of manufacturing companies

• Autorzy: Salehi Sara

Identyfikatory

DOI

10.35784/acs-2023-23

• Języki publikacji: EN

Abstrakty

EN

In today's competitive industry landscape, it is crucial to assess manufacturing processes to enhance efficiency. However, identifying the critical factors that impact productivity can be a daunting task due to their intricate nature. To tackle this challenge, we propose a novel approach that combines fuzzy logic with TOPSIS to comprehensively evaluate manufacturing company efficiency. The method presented by the author treats this as a complex MCDM problem and accommodates diverse factors with distinct weights, which are crucial for a thorough efficiency analysis. This approach was applied to evaluate potential manufacturing entities in Cyprus through a three-step process. Firstly, relevant criteria were curated using literature and expert insights, endowing them with linguistic terms that were then translated into fuzzy values. Next, fuzzy TOPSIS evaluated efficiency, and sensitivity analysis gauged the criteria weight impact on decisions. This article introduces a new methodology for holistic manufacturing company evaluation. The synergy of fuzzy-set theory and TOPSIS proves effective amidst the ambiguity inherent in performance measurement. By uniting these methodologies, this study advances manufacturing performance evaluation, aiding informed decision-making. The research contributes a pioneering method to enhance manufacturing efficiency assessment while accommodating uncertainty through fuzzy logic integration.

Słowa kluczowe

EN

fuzzy theory; fuzzy TOPSIS; decision-making; manufacturing company

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2023
• Tom: Vol. 19, no 3
• Strony: 28--46
• Opis fizyczny: Bibliogr. 52 poz., fig., tab.

Twórcy

autor

Salehi Sara

• Rauf Denktas Universit, Faculty of Architecture and Engineering, Department of Software Engineering, Northern Cyprus

• https://orcid.org/0000-0003-1463-7304

Bibliografia

• [1] 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
• [2] 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
• [3] 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
• [4] 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
• [5] 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
• [6] 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
• [7] Barlev, B., & Callen, J. L. (1986). Total factor productivity and cost variances: survey and analysis. Journal of Accounting Literature, 5, 35–56.
• [8] 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.
• [9] 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
• [10] 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
• [11] 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
• [12] 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
• [13] 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
• [14] 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
• [15] 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
• [16] 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
• [17] 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
• [18] Eccles, R. G. (1991). The performance measurement manifesto. Harvard business review, 69(1), 131–137.
• [19] 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
• [20] Guo, L., Yao, Z., Lin, M., & Xu, Z. (2023). Fuzzy TOPSIS-based privacy measurement in multiple online social networks. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-023-00991-y
• [21] 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
• [22] 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
• [23] 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
• [24] 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
• [25] 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
• [26] 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
• [27] Kaplan, R. S., & Norton, D. P. (2005). The balanced scorecard: measures that drive performance. Harvard business review, 70, 71-79.
• [28] 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
• [29] 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
• [30] 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
• [31] 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
• [32] 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
• [33] 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 Communication, 14(10), 945–950. https://doi.org/10.12720/jcm.14.10.945-950
• [34] 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
• [35] Maddala, G., S. ( 1979). Measurement and interpretation of productivity. National Academy of Sciences.
• [36] 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
• [37] 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
• [38] Norman, R. G., & Bahiri, S. (1972). Productivity measurement and incentives. Transatlantic Arts.
• [39] 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
• [40] 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
• [41] 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, 1741- 0401. https://doi.org/10.1108/IJPPM-10-2022-0538
• [42] 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
• [43] 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
• [44] 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
• [45] 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
• [46] 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
• [47] 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
• [48] 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
• [49] 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
• [50] 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
• [51] 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
• [52] 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