Novi intuistički rasplinuti FMEA okvir sa Dombi agregacijom za kvalitet usluge u industriji 4.0 – primena u visokom obrazovanju
Sažetak
Квалитет услуга високог образовања (ХСК) је једна од истакнутих области редефинисаних у И4, која намеће многе изазове високошколским установама (ВШУ). У овом контексту, овај рад има за циљ да испуни два циља: а) да развије нови оквир за процену ризика за анализу начина и ефеката отказа (ФМЕА) користећи интуиционистички расплинути скуп (ИФС); б) да процени потенцијалне факторе ризика или неуспехе са којима се суочавају ВШУ у пружању супериорног ХСК у Индустрији 4.0 (И4). Овај рад користи модел доношења одлука са више критеријума (МЦДМ), као што је поређења између рангираних критеријума (ЦОБРАЦ), са Домби агрегацијом заснованом на ИФС-у за групно доношење одлука, да би се развило ново проширење ФМЕА оквира. Овај рад предлаже иновативан приступ уграђивањем додатне димензије у класични ФМЕА модел, као што је неухватљивост. Режими квара (ФМ) се идентификују са становишта ХСК атрибута. Након тога, овај рад испитује валидност исхода упоређивањем неколико МЦДМ модела и анализом осетљивости. На основу мишљења 23 стручњака, досадашњи рад открива доминацију фактора ризика, као што су етичка забринутост (ФМ-9), инфраструктурна ограничења (ФМ-2) и недостатак средстава (ФМ-6). Студија која је у току пружа неколико новина, као што је проширење ФМЕА са додатном димензијом и ИФС-Домби агрегација, користећи ЦОБРАЦ модел за ФМЕА, и иновативни приступ процени ризика за ХСК, који су корисни за доносиоце одлука и истраживаче. Kvalitet usluga visokog obrazovanja (HSK) je jedna od istaknutih oblasti redefinisanih u I4, koja nameće mnoge izazove visokoškolskim ustanovama (VŠU). U ovom kontekstu, ovaj rad ima za cilj da ispuni dva cilja: a) da razvije novi okvir za procenu rizika za analizu načina i efekata otkaza (FMEA) koristeći intuicionistički rasplinuti skup (IFS); b) da proceni potencijalne faktore rizika ili neuspehe sa kojima se suočavaju VŠU u pružanju superiornog HSK u Industriji 4.0 (I4). Ovaj rad koristi model donošenja odluka sa više kriterijuma (MCDM), kao što je poređenja između rangiranih kriterijuma (COBRAC), sa Dombi agregacijom zasnovanom na IFS-u za grupno donošenje odluka, da bi se razvilo novo proširenje FMEA okvira. Ovaj rad predlaže inovativan pristup ugrađivanjem dodatne dimenzije u klasični FMEA model, kao što je neuhvatljivost. Režimi kvara (FM) se identifikuju sa stanovišta HSK atributa. Nakon toga, ovaj rad ispituje validnost ishoda upoređivanjem nekoliko MCDM modela i analizom osetljivosti. Na osnovu mišljenja 23 stručnjaka, dosadašnji rad otkriva dominaciju faktora rizika, kao što su etička zabrinutost (FM-9), infrastrukturna ograničenja (FM-2) i nedostatak sredstava (FM-6). Studija koja je u toku pruža nekoliko novina, kao što je proširenje FMEA sa dodatnom dimenzijom i IFS-Dombi agregacija, koristeći COBRAC model za FMEA, i inovativni pristup proceni rizika za HSK, koji su korisni za donosioce odluka i istraživače.
Reference
Abbas, J. (2020). Service quality in higher education institutions: qualitative evidence from the students’ perspectives using Maslow hierarchy of needs. International Journal of Quality and Service Sciences, 12(3), 371-384. https://doi.org/10.1108/ijqss-02-2020-0016
Abdullah, F. (2005). HEdPERF versus SERVPERF: The quest for ideal measuring instrument of service quality in higher education sector. Quality Assurance in education, 13(4), 305-328. https://doi.org/10.1108/09684880510626584
Abulibdeh, A., Zaidan, E., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production, 437, 140527. https://doi.org/10.1016/j.jclepro.2023.140527
Akhmetshin, E. M., Barmuta, K., Vasilev, V. L., Okagbue, H. I., & Ijezie, O. A. (2020). Principal Directions of Digital Transformation of Higher Education System in Sustainable Education. E3s Web of Conferences, 208, 09042. https://doi.org/10.1051/e3sconf/202020809042
Alenezi, M. (2023). Digital learning and digital institution in higher education. Education Sciences, 13(1), 88. https://doi.org/10.3390/educsci13010088
Alenezi, M., & Akour, M. (2023). Digital Transformation Blueprint in Higher Education: A Case Study of PSU. Sustainability, 15(10), 8204. https://doi.org/10.3390/su15108204
Anastasiadou, S., & Zirinoglou, P. (2020). Failure modes and effects analysis (FMEA) application in Greek tertiary education. In ICERI2020 Proceedings (pp. 7767-7772). IATED. https://doi.org/10.21125/iceri.2020.1695
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
Benavides, L. M. C., Tamayo Arias, J. A., Arango Serna, M. D., Branch Bedoya, J. W., & Burgos, D. (2020). Digital transformation in higher education institutions: A systematic literature review. Sensors, 20(11), 3291. https://doi.org/10.3390/s20113291
Bhattacharjee, M., Bandyopadhyay, G., Guha, B., & Biswas, S. (2020). Determination and validation of the contributing factors towards the selection of a B-School-An Indian perspective. Decision Making: Applications in Management and Engineering, 3(1), 79-91. https://doi.org/10.31181/dmame2003001b
Biswas, S., Pamucar, D., Raj, A., & Kar, S. (2023a). A Proposed q-Rung Orthopair Fuzzy-Based Decision Support System for Comparing Marketing Automation Modules for Higher Education Admission. In Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022 (pp. 885-912). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8493-8_66
Biswas, S., Pamucar, D., & Simic, V. (2024). Technology adaptation in sugarcane supply chain based on a novel p, q Quasirung Orthopair Fuzzy decision making framework. Scientific Reports, 14(1), 26486. https://doi.org/10.1038/s41598-024-75528-5
Biswas, S., Sanyal, A., & Pamucar, D. (2025). Students’ Perceptions About the Webinars: An Intuitionistic Fuzzy Force Field Analysis. Spectrum of Operational Research, 2(1), 113-133. https://doi.org/10.31181/sor21202513
Biswas, S., Biswas, B., & Mitra, K. (2025). A novel group decision making model to compare online shopping platforms. Spectrum of decision making and applications, 2(1), 1-27. https://doi.org/10.31181/sdmap2120259
Biswas, S., & Pamučar, D. S. (2021). Combinative distance based assessment (CODAS) framework using logarithmic normalization for multi-criteria decision making. Serbian Journal of Management, 16(2), 321-340. https://doi.org/10.5937/sjm16-27758
Bonfield, C. A., Salter, M., Longmuir, A., Benson, M., & Adachi, C. (2020). Transformation or evolution?: Education 4.0, teaching and learning in the digital age. Higher education pedagogies, 5(1), 223-246. https://doi.org/10.1080/23752696.2020.1816847
Bowles, J. B., & Pel´aez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering & System Safety, 50(2), 203–213. https://doi.org/10.1016/0951-8320(95)00068-D
Ceylan, B. O., Akyar, D. A., & Celik, M. S. (2023). A novel FMEA approach for risk assessment of air pollution from ships. Marine Policy, 150, 105536. https://doi.org/10.1016/j.marpol.2023.105536
Chaabouni, S., & Mbarek, M. Ben. (2023). What Will Be the Impact of the COVID-19 Pandemic on the Human Capital and Economic Growth? Evidence From Eurozone. Journal of the Knowledge Economy, 15(1), 2482–2498. https://doi.org/10.1007/s13132-023-01328-3
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
Deb, P. P., Bhattacharya, D., Chatterjee, I., Chatterjee, P., & Zavadskas, E. K. (2023). An Intuitionistic Fuzzy Consensus WASPAS Method for Assessment of Open-Source Software Learning Management Systems. Informatica, 34(3), 529-556. https://doi.org/10.15388/23-INFOR523
Deb, P. P., Bhattacharya, D., Chatterjee, I., Saha, A., Mishra, A. R., & Ahammad, S. H. (2022). A decision-making model with intuitionistic fuzzy information for selection of enterprise resource planning systems. IEEE Transactions on Engineering Management, 71, 4820-4834. https://doi.org/10.1109/TEM.2022.3215608
Demir, A. T., & Moslem, S. (2024). A novel fuzzy multi-criteria decision-making for enhancing the management of medical waste generated during the coronavirus pandemic. Engineering Applications of Artificial Intelligence, 133, 108465. https://doi.org/10.1016/j.engappai.2024.108465
Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023, September). The impact of ChatGPT on higher education. In Frontiers in Education (Vol. 8, p. 1206936). Frontiers Media SA. https://doi.org/10.3389/feduc.2023.1206936
Dombi, J. (1982). A general class of fuzzy operators, the DeMorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy sets and systems, 8(2), 149-163.
Ervural, B., & Ayaz, H. I. (2023). A fully data-driven FMEA framework for risk assessment on manufacturing processes using a hybrid approach. Engineering Failure Analysis, 152, 107525. https://doi.org/10.1016/j.engfailanal.2023.107525
Fahim, A., Addae, B. A., Ofosu-Adarkwa, J., Qingmei, T., & Bhatti, U. A. (2021). Industry 4.0 and higher education: an evaluation of barriers affecting master’s in business administration enrolments using a grey incidence analysis. IEEE Access, 9, 76991-77008. https://doi.org/10.1109/ACCESS.2021.3082144
Feifei, L., Kian, M. W., & Adaikalam, J. (2022). A Review on Service Quality Dimensions in Higher Education Sector and Research Prospects after COVID-19 Pandemic. International Journal of Academic Research in Business and Social Sciences, 12(1), 328-337. http://dx.doi.org/10.6007/IJARBSS/v12-i1/12052
Fuchs, K., & Fangpong, K. (2021). Using the SERVQUAL framework to examine the service quality in higher education in Thailand. Education Quarterly Reviews, 4(2), 363-370. https://doi.org/10.31014/aior.1993.04.02.286
Görçün, Ö. F., Pamucar, D., & Biswas, S. (2023). The blockchain technology selection in the logistics industry using a novel MCDM framework based on Fermatean fuzzy sets and Dombi aggregation. Information Sciences, 635, 345-374. https://doi.org/10.1016/j.ins.2023.03.113
Hong, D. H., & Choi, C. H. (2000). Multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems, 114, 103–113.
Jamaludin, R., McKAY, E., & Ledger, S. (2020). Are we ready for Education 4.0 within ASEAN higher education institutions? Thriving for knowledge, industry and humanity in a dynamic higher education ecosystem?. Journal of Applied Research in Higher Education, 12(5), 1161-1173. https://doi.org/10.1108/JARHE-06-2019-0144
Khan, S., Zaman, S. I., & Rais, M. (2022). Measuring student satisfaction through overall quality at business schools: a structural equation modeling: student satisfaction and quality of education. South Asian Journal of Social Review, 1(2), 34-55. https://doi.org/10.57044/sajsr.2022.1.2.2210
Leow, A., Billett, S., & Le, A. H. (2023). Towards a continuing education and training eco system: A case study of Singapore. International Journal of Training Research, 21(3), 226-242. https://doi.org/10.1080/14480220.2023.2203944
Li, L. (2024). Reskilling and upskilling the future-ready workforce for industry 4.0 and beyond. Information Systems Frontiers, 26, 1697–1712. https://doi.org/10.1007/s10796-022-10308-y
Lin, S. W., & Lo, H. W. (2024). An FMEA model for risk assessment of university sustainability: using a combined ITARA with TOPSIS-AL approach based neutrosophic sets. Annals of Operations Research, 342, 2119–2145. https://doi.org/10.1007/s10479-023-05250-4
Liu, Y., & Shi, Y. (2023). Digital Transformation of College Teacher Education From the Perspective of Behaviorist Psychology: Initiatives, Effectiveness and Pitfalls. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns.2023.2.01598
Liu, P., Wu, Y., Li, Y., & Wu, X. (2024). An improved FMEA method based on the expert trust network for maritime transportation risk management. Expert Systems with Applications, 238, 121705. https://doi.org/10.1016/j.eswa.2023.121705
Liu, Z., Zhao, Y., & Liu, P. (2023). An integrated FMEA framework considering expert reliability for classification and its application in aircraft power supply system. Engineering Applications of Artificial Intelligence, 123, 106319. https://doi.org/10.1016/j.engappai.2023.106319
Liu, P., Shen, M., & Geng, Y. (2023a). Risk assessment based on failure mode and effects analysis (FMEA) and WASPAS methods under probabilistic double hierarchy linguistic term sets. Computers & Industrial Engineering, 186, 109758. https://doi.org/10.1016/j.cie.2023.109758
Mahyoob, M. (2020). Challenges of e-Learning during the COVID-19 Pandemic Experienced by EFL Learners. Arab World English Journal (AWEJ), 11(4), 351-362. https://dx.doi.org/10.24093/awej/vol11no4.23
Majid, M., Habib, S., Javed, A. R., Rizwan, M., Srivastava, G., Gadekallu, T. R., & Lin, J. C. W. (2022). Applications of wireless sensor networks and internet of things frameworks in the industry revolution 4.0: A systematic literature review. Sensors, 22(6), 2087. https://doi.org/10.3390/s22062087
MacCrimmon, K.R. (1968). Decision making among multiple-attribute alternatives: a survey and consolidated approach, Rand Corp Santa Monica Ca
Mian, S. H., Salah, B., Ameen, W., Moiduddin, K., & Alkhalefah, H. (2020). Adapting universities for sustainability education in industry 4.0: Channel of challenges and opportunities. Sustainability, 12(15), 6100. https://doi.org/10.3390/su12156100
Moraes, E. B., Kipper, L. M., Hackenhaar Kellermann, A. C., Austria, L., Leivas, P., Moraes, J. A. R., & Witczak, M. (2023). Integration of Industry 4.0 technologies with Education 4.0: advantages for improvements in learning. Interactive Technology and Smart Education, 20(2), 271-287. https://doi.org/10.1108/ITSE-11-2021-0201
Nasrallah, I., Sabbah, I., Haddad, C., Ismaiil, L., Kotaich, J., Salameh, P., ... & Bawab, W. (2023). Evaluating the academic scientific laboratories' safety by applying failure mode and effect analysis (FMEA) at the public university in Lebanon. Heliyon, 9(12), e21145. https://doi.org/10.1016/j.heliyon.2023.e21145
Nhleko, Y., & van der Westhuizen, T. (2022). The role of higher education institutions in introducing entrepreneurship education to meet the demands of industry 4.0. Academy of Entrepreneurship Journal, 28(1), 1-23.
Nguyen, H. V., Vu, T. D., Saleem, M., & Yaseen, A. (2024). The influence of service quality on student satisfaction and student loyalty in Vietnam: the moderating role of the university image. Journal of Trade Science, 12(1), 37-59. https://doi.org/10.1108/jts-12-2023-0032
Oliso, Z. Z., Alemu, D. D., & Jansen, J. D. (2024). The impact of educational service quality on student academic performance in Ethiopian public universities: a mediating role of students’ satisfaction. Journal of International Education in Business, 17(2), 340-370. https://doi.org/10.1108/jieb-07-2023-0042
Omar Ali, S. R., Md Shariff, N. A., Mohd Said, N. S., & Mat, K. A. (2020). The effects of service quality dimensions on students’ satisfaction: Hedperf model adoption. Jurnal Intelek, 15(1), 69-76. https://doi.org/10.24191/ji.v15i1.269
Pamucar, D., Simic, V., Görçün, Ö. F., & Küçükönder, H. (2024). Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals. Expert Systems with Applications, 239, 122312. https://doi.org/10.1016/j.eswa.2023.122312
Pamucar, D., Torkayesh, A. E., & Biswas, S. (2023). Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach. Annals of Operations Research, 328(1), 977-1019. https://doi.org/10.1007/s10479-022-04529-2
Pamučar, D., Stević, Ž., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of retailing, 64(1), 12-40.
Park, C., Kontovas, C., Yang, Z., & Chang, C. H. (2023). A BN driven FMEA approach to assess maritime cybersecurity risks. Ocean & Coastal Management, 235, 106480. https://doi.org/10.1016/j.ocecoaman.2023.106480
Peddi, S., Lanka, K., & Gopal, P. R. C. (2023). Modified FMEA using machine learning for food supply chain. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2023.04.353
Puška, A., Štilić, A., Nedeljković, M., Božanić, D., & Biswas, S. (2023). Integrating fuzzy rough sets with LMAW and MABAC for green supplier selection in agribusiness. Axioms, 12(8), 746. https://doi.org/10.3390/axioms12080746
Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., ... & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41-56. https://doi.org/10.37074/jalt.2023.6.1.29
Resende, B. A. D., Dedini, F. G., Eckert, J. J., Sigahi, T. F., Pinto, J. D. S., & Anholon, R. (2024). Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector. International Journal of Quality & Reliability Management, 41(4), 1063-1088. https://doi.org/10.1108/IJQRM-07-2023-0237
Salah, B., Alnahhal, M., & Ali, M. (2023). Risk prioritization using a modified FMEA analysis in industry 4.0. Journal of Engineering Research, 11(4), 460-468. https://doi.org/10.1016/j.jer.2023.07.001
Seikh, M. R., & Mandal, U. (2021). Intuitionistic fuzzy Dombi aggregation operators and their application to multiple attribute decision-making. Granular Computing, 6, 473-488. https://doi.org/10.1007/s41066-019-00209-y
Shenkoya, T., & Kim, E. (2023). Sustainability in Higher Education: Digital Transformation of the Fourth Industrial Revolution and Its Impact on Open Knowledge. Sustainability, 15(3), 2473. https://doi.org/10.3390/su15032473
Stanujkic, D., Karabasevic, D., Popovic, G., & Sava, C. (2021). Simplified pivot pairwise relative criteria importance assessment (PIPRECIA-S) method. Romanian Journal of Economic Forecasting, 24(4), 141-154.
Subandi, S., & Hamid, M. S. (2021). Student satisfaction, loyalty, and motivation as observed from the service quality. Journal of Management and Islamic Finance, 1(1), 136-153. https://doi.org/10.22515/jmif.v1i1.3552
Sumrit, D., & Keeratibhubordee, J. (2025). Risk Assessment Framework for Reverse Logistics in Waste Plastic Recycle Industry: A Hybrid Approach Incorporating FMEA Decision Model with AHP-LOPCOW-ARAS Under Trapezoidal Fuzzy Set. Decision Making: Applications in Management and Engineering, 8(1), 42-81. https://doi.org/10.31181/dmame812025984
Sun, J., Liu, Y., Xu, J., Wang, N., & Zhu, F. (2023). A probabilistic uncertain linguistic FMEA model based on the extended ORESTE and regret theory. Computers & Industrial Engineering, 180, 109251. https://doi.org/10.1016/j.cie.2023.109251
Takalo, S. K., Abadi, A. R. N. S., Vesal, S. M., Mirzaei, A., & Nawaser, K. (2013). Fuzzy Failure Analysis: A New Approach to Service Quality Analysis in Higher Education Institutions (Case Study: Vali-e-asr University of Rafsanjan-Iran). International Education Studies, 6(9), 93-106.
Teeroovengadum, V., Nunkoo, R., Gronroos, C., Kamalanabhan, T. J., & Seebaluck, A. K. (2019). Higher education service quality, student satisfaction and loyalty: Validating the HESQUAL scale and testing an improved structural model. Quality assurance in education, 27(4), 427-445. https://doi.org/10.1108/QAE-01-2019-0003
Tooranloo, H. S., & sadat Ayatollah, A. (2016). A model for failure mode and effects analysis based on intuitionistic fuzzy approach. Applied soft computing, 49, 238-247.
Wang, K., Li, B., Tian, T., Zakuan, N., & Rani, P. (2023). Evaluate the drivers for digital transformation in higher education institutions in the era of industry 4.0 based on decision-making method. Journal of Innovation & Knowledge, 8(3), 100364.
Xu, Z., & Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International journal of general systems, 35(4), 417-433.
Xu, Z. S. (2007). Models for multiple attribute decision making with intuitionistic fuzzy information. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(03), 285-297.
Xue, Y., Zhang, J., Zhang, Y., & Yu, X. (2024). Barrier assessment of EV business model innovation in China: An MCDM-based FMEA. Transportation Research Part D: Transport and Environment, 136, 104404. https://doi.org/10.1016/j.trd.2024.104404
Yang, W. C., Chon, S. H., Choe, C. M., & Un-Ha, K. (2019). Materials selection method combined with different MADM methods. Journal of Artificial Intelligence, 1(2), 89-99. https://doi.org/10.32604/jai.2019.07885
Yener, Y., & Can, G. F. (2021). A FMEA based novel intuitionistic fuzzy approach proposal: Intuitionistic fuzzy advance MCDM and mathematical modeling integration. Expert Systems with Applications, 183, 115413.
Yu, Y., Yang, J., & Wu, S. (2023). A novel FMEA approach for submarine pipeline risk analysis based on IVIFRN and ExpTODIM-PROMETHEE-II. Applied Soft Computing, 136, 110065. https://doi.org/10.1016/j.asoc.2023.110065
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Žalėnienė, I., & Pereira, P. (2021). Higher education for sustainability: A global perspective. Geography and Sustainability, 2(2), 99-106. https://doi.org/10.1016/j.geosus.2021.05.001
Žižović, M., & Pamucar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126-137. https://doi.org/10.31181/dmame1902102z
Zulfiqar, M., Bhat, S., Sony, M., Salentijn, W., Swarnakar, V., Antony, J., ... & Foster, M. (2024). Unveiling the potential of FMEA in higher education: pathway to improved risk management and quality. The TQM Journal. https://doi.org/10.1108/TQM-01-2024-0017
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