Analiza faktora koji utiču na unapređenje kvaliteta i dostizanje nivoa zrelosti PFMEA 4.0
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Jul 10, 2025
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Aleksandar Aleksić
http://orcid.org/0000-0002-7990-9123
Danijela Tadić
http://orcid.org/0000-0003-2236-967X
Dragan Marinković
http://orcid.org/0000-0002-3583-9434
Snežana Nestić
http://orcid.org/0000-0003-1461-9595
Nikola Komatina
http://orcid.org/0000-0001-6964-5673
http://orcid.org/0000-0002-7990-9123
Danijela Tadić
http://orcid.org/0000-0003-2236-967X
Dragan Marinković
http://orcid.org/0000-0002-3583-9434
Snežana Nestić
http://orcid.org/0000-0003-1461-9595
Nikola Komatina
http://orcid.org/0000-0001-6964-5673
Apstrakt
Procesna analiza efekata i načina otkaza (engl. Process Failure Mode and Effect Analysis, PFMEA) predstavlja izuzetno koristan analitički alat za upravljanje kvalitetom, koji omogućava procenu potencijalnih načina otkaza i rizika koji mogu nastati u proizvodnom procesu. U automobilskoj industriji, primena PFMEA je obavezna, a propisana je standardom IATF 16949:2016 i pratećim procedurama. Kada je reč o samom proizvodnom procesu, PFMEA ima ključnu ulogu u obezbeđivanju pouzdanosti procesa. Međutim, brojne ograničavajuće okolnosti, poput finansijskih ograničenja, raspoloživosti radne snage, tehničkih uslova, veština i znanja, mogu značajno uticati na efikasnost implementacije PFMEA. Iz tog razloga, savremene industrijske prakse teže ka većem stepenu automatizacije PFMEA, smanjujući ljudske greške i subjektivnost u procenama, što je u skladu sa principima Industrije 4.0. Cilj ovog istraživanja je evaluacija i rangiranje faktora koji utiču na unapređenje PFMEA i potencijalno dostizanje nivoa PFMEA 4.0, na osnovu relevantnih kriterijuma, kako bi se utvrdilo koji faktori predstavljaju najveće izazove i najzahtevniji su za rešavanje u praksi. U tu svrhu, korišćen je kombinovani, višeatributivni pristup odlučivanja (engl. Multi-Attribute Decision-Making, MADM). Istraživanje je sprovedeno u saradnji sa liderima PFMEA timova iz tri kompanije automobilske industrije koje posluju u Srbiji.
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Kako citirati
ALEKSIĆ, Aleksandar et al.
Analiza faktora koji utiču na unapređenje kvaliteta i dostizanje nivoa zrelosti PFMEA 4.0.
Zbornik Međunarodnog kongresa o procesnoj industriji – Procesing, [S.l.], v. 38, n. 1, p. 15-27, july 2025.
Dostupno na: <https://izdanja.smeits.rs/index.php/ptk/article/view/8201>. Datum pristupa: 15 mar. 2026
doi: https://doi.org/10.24094/ptk.025.1.015.
Sekcija
Menadžment kvaliteta i standardizacija u organizacijama
Reference
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[2] H. C. Liu, ‘FMEA Using Uncertainty Theories and MCDM Methods’, in FMEA Using Uncer-tainty Theories and MCDM Methods, Singapore: Springer Singapore, 2016, pp. 13–27. doi: 10.1007/978-981-10-1466-6_2.
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[7] A. En-Naaoui, M. Kaicer, and A. Aguezzoul, ‘Predicting risk criticalities of hospital steriliza-tion using fuzzy FMEA and artificial neural network’, in 2024 International Conference on In-telligent Systems and Computer Vision (ISCV), Fez, Morocco: IEEE, May 2024, pp. 1–6. doi: 10.1109/ISCV60512.2024.10620085.
[8] C. M. Lin and S.L. Chen, ‘FMEA-TSTM-NNGA: A Novel Optimization Framework Inte-grating Failure Mode and Effect Analysis, the Taguchi Method, a Neural Network, and a Genet-ic Algorithm for Improving the Resistance in Dynamic Random Access Memory Components’, Mathematics, vol. 12, no. 17, p. 2773, Sep. 2024, doi: 10.3390/math12172773.
[9] C. Duan, M. Zhu, and K. Wang, ‘Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning’, J. Intell. Fuzzy Syst., vol. 46, no. 4, pp. 10375–10392, Apr. 2024, doi: 10.3233/JIFS-232712.
[10] F. U. Hassan, T. Nguyen, T. Le, and C. Le, ‘Automated prioritization of construction project requirements using machine learning and fuzzy Failure Mode and Effects Analysis (FMEA)’, Autom. Constr., vol. 154, p. 105013, Oct. 2023, doi: 10.1016/j.autcon.2023.105013.
[11] J. E. Naranjo, J. S. Alban, M. S. Balseca, D. F. Bustamante Villagómez, M. G. Mancheno Falconi, and M. V. Garcia, ‘Enhancing Institutional Sustainability Through Process Optimi-zation: A Hybrid Approach Using FMEA and Machine Learning’, Sustainability, vol. 17, no. 4, p. 1357, Feb. 2025, doi: 10.3390/su17041357.
[12] S. Peddi, K. Lanka, and P. Gopal, ‘Modified FMEA using machine learning for food supply chain’, Mater. Today Proc., p. S2214785323022551, May 2023, doi: 10.1016/j.matpr.2023.04. 353.
[13] A. Aleksić, D. D. Milanović, N. Komatina, and D. Tadić, ‘Evaluation and ranking of failures in manufacturing process by combining best‐worst method and VIKOR under type‐2 fuzzy en-vironment’, Expert Syst., vol. 40, no. 2, p. e13148, Feb. 2023, doi: 10.1111/exsy.13148.
[14] B. Karatop, B. Taşkan, E. Adar, and C. Kubat, ‘Decision analysis related to the renewable energy investments in Turkey based on a Fuzzy AHP-EDAS-Fuzzy FMEA approach’, Comput. Ind. Eng., vol. 151, p. 106958, Jan. 2021, doi: 10.1016/j.cie.2020.106958.
[15] M. Khodadadi-Karimvand and H. Shirouyehzad, ‘Well drilling fuzzy risk assessment using fuzzy FMEA and fuzzy TOPSIS’, J. Fuzzy Ext. Appl., vol. 2, no. 2, Jun. 2021, doi: 10.22105/jfea.2021.275955.1086.
[16] N. Komatina, D. Tadić, A. Aleksić, and N. Banduka, ‘The integrated PFMEA approach with interval type-2 fuzzy sets and FBWM: A case study in the automotive industry’, Proc. Inst. Mech. Eng. Part J. Automob. Eng., vol. 236, no. 6, pp. 1201–1212, May 2022, doi: 10.1177/09544070211034799.
[17] D. K. Kushwaha, D. Panchal, and A. Sachdeva, ‘A modified FMEA approach based inte-grated decision framework for overcoming the problems of sudden failure and accidental haz-ards in turbine and alternator unit’, Facta Univ. Ser. Mech. Eng., p. Online First, 2023, doi: 10.22190/FUME221126010K.
[18] D. Tadić, N. Banduka, T. Petrović, and N. Komatina, ‘Application of FMEA in the automo-tive industry: Present situation and perspectives in the Republic of Serbia’, Trendovi U Posl., vol. 12, no. 1, pp. 127–135, 2024, doi: 10.5937/trendpos2401137T.
[19] T. L. Saaty, ‘The analytical hierarchy process, planning, priority’, Resour. Alloc. RWS Publ. USA, 1980.
[20] T. L. Saaty, ‘The Modern Science of Multicriteria Decision Making and Its Practical Applica-tions: The AHP/ANP Approach’, Oper. Res., vol. 61, no. 5, pp. 1101–1118, Oct. 2013, doi: 10.1287/opre.2013.1197.
[21] J. Rezaei, ‘Best-worst multi-criteria decision-making method’, Omega, vol. 53, pp. 49–57, Jun. 2015, doi: 10.1016/j.omega.2014.11.009.
[22] D. Božanić, I. Epler, A. Puška, S. Biswas, D. Marinković, and S. Koprivica, ‘Application of the DIBR II – rough MABAC decision-making model for ranking methods and techniques of lean organization systems management in the process of technical maintenance’, Facta Univ. Ser. Mech. Eng., vol. 22, no. 1, p. 101, Apr. 2024, doi: 10.22190/FUME230614026B.
[23] M. Rakić, M. M. Žižović, B. Miljković, A. Njeguš, M. R. Žižović, and I. Đorđević, ‘Multi-criteria selection of standards for system analyst activities in organizations’, Facta Univ. Ser. Mech. Eng., vol. 21, no. 3, p. 433, Oct. 2023, doi: 10.22190/FUME230521023R.
[24] N. Komatina, D. Tadić, A. Aleksić, and A. D. Jovanović, ‘The assessment and selection of suppliers using AHP and MABAC with type-2 fuzzy numbers in automotive industry’, Proc. Inst. Mech. Eng. Part O J. Risk Reliab., vol. 237, no. 4, pp. 836–852, Aug. 2023, doi: 10.1177/1748006X221095359.
[25] M. Milanovic, M. Misita, and N. Komatina, ‘Determination of the optimal production plan by using fuzzy AHP and fuzzy linear programming’, J. Intell. Fuzzy Syst., vol. 38, no. 4, pp. 4315–4325, Apr. 2020, doi: 10.3233/JIFS-190913.
[26] C. L. Hwang and K. Yoon, ‘Methods for multiple attribute decision making. Multiple attrib-ute decision making: methods and applications a state-of-the-art survey’, 1981, pp. 58–191.
[27] R. Alkan, M. Yucesan, and M. Gul, ‘A Multi-attribute Decision-Making to Sustainable Con-struction Material Selection: A Bayesian BWM-SAW Hybrid Model’, in Advances in Best-Worst Method, J. Rezaei, M. Brunelli, and M. Mohammadi, Eds., in Lecture Notes in Opera-tions Research. , Cham: Springer International Publishing, 2022, pp. 67–78. doi: 10.1007/978-3-030-89795-6_6.
[28] F. S. Amalia, ‘Application of SAW Method in Decision Support System for Determination of Exemplary Students’, J. Inf. Technol. Softw. Eng. Comput. Sci. ITSECS, vol. 1, no. 1, pp. 14–21, Dec. 2022, doi: 10.58602/itsecs.v1i1.9.
[29] M. Keshavarz Ghorabaee, E. K. Zavadskas, L. Olfat, and Z. Turskis, ‘Multi-Criteria In-ventory Classification Using a New Method of Evaluation Based on Distance from Average So-lution (EDAS)’, Informatica, vol. 26, no. 3, pp. 435–451, Jan. 2015, doi: 10.15388/Informatica.2015.57.
[30] N. Komatina, ‘A compromise-based MADM approach for prioritizing failures: Integrating the RADAR method within the FMEA framework’, J. Sist. Dan Manaj. Ind., vol. 8, no. 2, pp. 73–88, Dec. 2024, doi: 10.30656/jsmi.v8i2.9283.
[31] N. Komatina, ‘A Novel BWM-RADAR Approach for Multi-Attribute Selection of Equipment in the Automotive Industry’, Spectr. Mech. Eng. Oper. Res., vol. 2, no. 1, pp. 104–120, Feb. 2025, doi: 10.31181/smeor21202531.
[32] T. L. Saaty, ‘Decision-making with the AHP: Why is the principal eigenvector necessary’, Eur. J. Oper. Res., vol. 145, no. 1, pp. 85–91, Feb. 2003, doi: 10.1016/S0377-2217(02)00227-8.
[33] Ž. Stević, M. Baydaş, M. Kavacık, E. Ayhan, and D. Marinković, ‘Selection of Data Con-version Technique via Sensitivity-Performance Matching: Ranking of Small E-Vans with PROBID Method’, Facta Univ. Ser. Mech. Eng., vol. 22, no. 4, pp. 643–671, Dec. 2024, doi: 10.22190/FUME240305023S.
[34] B. Kizielewicz and W. Sałabun, ‘SITW Method: A New Approach to Re-identifying Multi-criteria Weights in Complex Decision Analysis’, Spectr. Mech. Eng. Oper. Res., vol. 1, no. 1, pp. 215–226, Sep. 2024, doi: 10.31181/smeor11202419.
[2] H. C. Liu, ‘FMEA Using Uncertainty Theories and MCDM Methods’, in FMEA Using Uncer-tainty Theories and MCDM Methods, Singapore: Springer Singapore, 2016, pp. 13–27. doi: 10.1007/978-981-10-1466-6_2.
[3] H. C. Liu, L. Liu, and N. Liu, ‘Risk evaluation approaches in failure mode and effects analy-sis: A literature review’, Expert Syst. Appl., vol. 40, no. 2, pp. 828–838, Feb. 2013, doi: 10.1016/j.eswa.2012.08.010.
[4] N. Banduka, T. Petrović, and N. Komatina, ‘Model for the assessment of process failure mode and effect analysis maturity level in the automotive industry toward industry 4.0 trends’, Int. Rev., no. 3–4, pp. 219–228, 2024, doi: 10.5937/intrev2404219B.
[5] A. Kar and R. N. Rai, ‘A modified fuzzy PFMEA model for risk-centric Six Sigma assess-ment under the paradigm of Quality 4.0’, Int. J. Lean Six Sigma, vol. 16, no. 1, pp. 197–230, Jan. 2025, doi: 10.1108/IJLSS-08-2023-0131.
[6] L. Hezla, R. Gurina, M. Hezla, N. Rezaeian, M. Nohurov, and S. Aouati, ‘The Role of Arti-ficial Intelligence in Improving Failure Mode and Effects Analysis (FMEA) Efficiency in Con-struction Safety Management’, in AI Technologies and Virtual Reality, vol. 382, in Smart In-novation, Systems and Technologies, vol. 382. , Singapore: Springer Nature Singapore, 2024, pp. 397–411. doi: 10.1007/978-981-99-9018-4_29.
[7] A. En-Naaoui, M. Kaicer, and A. Aguezzoul, ‘Predicting risk criticalities of hospital steriliza-tion using fuzzy FMEA and artificial neural network’, in 2024 International Conference on In-telligent Systems and Computer Vision (ISCV), Fez, Morocco: IEEE, May 2024, pp. 1–6. doi: 10.1109/ISCV60512.2024.10620085.
[8] C. M. Lin and S.L. Chen, ‘FMEA-TSTM-NNGA: A Novel Optimization Framework Inte-grating Failure Mode and Effect Analysis, the Taguchi Method, a Neural Network, and a Genet-ic Algorithm for Improving the Resistance in Dynamic Random Access Memory Components’, Mathematics, vol. 12, no. 17, p. 2773, Sep. 2024, doi: 10.3390/math12172773.
[9] C. Duan, M. Zhu, and K. Wang, ‘Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning’, J. Intell. Fuzzy Syst., vol. 46, no. 4, pp. 10375–10392, Apr. 2024, doi: 10.3233/JIFS-232712.
[10] F. U. Hassan, T. Nguyen, T. Le, and C. Le, ‘Automated prioritization of construction project requirements using machine learning and fuzzy Failure Mode and Effects Analysis (FMEA)’, Autom. Constr., vol. 154, p. 105013, Oct. 2023, doi: 10.1016/j.autcon.2023.105013.
[11] J. E. Naranjo, J. S. Alban, M. S. Balseca, D. F. Bustamante Villagómez, M. G. Mancheno Falconi, and M. V. Garcia, ‘Enhancing Institutional Sustainability Through Process Optimi-zation: A Hybrid Approach Using FMEA and Machine Learning’, Sustainability, vol. 17, no. 4, p. 1357, Feb. 2025, doi: 10.3390/su17041357.
[12] S. Peddi, K. Lanka, and P. Gopal, ‘Modified FMEA using machine learning for food supply chain’, Mater. Today Proc., p. S2214785323022551, May 2023, doi: 10.1016/j.matpr.2023.04. 353.
[13] A. Aleksić, D. D. Milanović, N. Komatina, and D. Tadić, ‘Evaluation and ranking of failures in manufacturing process by combining best‐worst method and VIKOR under type‐2 fuzzy en-vironment’, Expert Syst., vol. 40, no. 2, p. e13148, Feb. 2023, doi: 10.1111/exsy.13148.
[14] B. Karatop, B. Taşkan, E. Adar, and C. Kubat, ‘Decision analysis related to the renewable energy investments in Turkey based on a Fuzzy AHP-EDAS-Fuzzy FMEA approach’, Comput. Ind. Eng., vol. 151, p. 106958, Jan. 2021, doi: 10.1016/j.cie.2020.106958.
[15] M. Khodadadi-Karimvand and H. Shirouyehzad, ‘Well drilling fuzzy risk assessment using fuzzy FMEA and fuzzy TOPSIS’, J. Fuzzy Ext. Appl., vol. 2, no. 2, Jun. 2021, doi: 10.22105/jfea.2021.275955.1086.
[16] N. Komatina, D. Tadić, A. Aleksić, and N. Banduka, ‘The integrated PFMEA approach with interval type-2 fuzzy sets and FBWM: A case study in the automotive industry’, Proc. Inst. Mech. Eng. Part J. Automob. Eng., vol. 236, no. 6, pp. 1201–1212, May 2022, doi: 10.1177/09544070211034799.
[17] D. K. Kushwaha, D. Panchal, and A. Sachdeva, ‘A modified FMEA approach based inte-grated decision framework for overcoming the problems of sudden failure and accidental haz-ards in turbine and alternator unit’, Facta Univ. Ser. Mech. Eng., p. Online First, 2023, doi: 10.22190/FUME221126010K.
[18] D. Tadić, N. Banduka, T. Petrović, and N. Komatina, ‘Application of FMEA in the automo-tive industry: Present situation and perspectives in the Republic of Serbia’, Trendovi U Posl., vol. 12, no. 1, pp. 127–135, 2024, doi: 10.5937/trendpos2401137T.
[19] T. L. Saaty, ‘The analytical hierarchy process, planning, priority’, Resour. Alloc. RWS Publ. USA, 1980.
[20] T. L. Saaty, ‘The Modern Science of Multicriteria Decision Making and Its Practical Applica-tions: The AHP/ANP Approach’, Oper. Res., vol. 61, no. 5, pp. 1101–1118, Oct. 2013, doi: 10.1287/opre.2013.1197.
[21] J. Rezaei, ‘Best-worst multi-criteria decision-making method’, Omega, vol. 53, pp. 49–57, Jun. 2015, doi: 10.1016/j.omega.2014.11.009.
[22] D. Božanić, I. Epler, A. Puška, S. Biswas, D. Marinković, and S. Koprivica, ‘Application of the DIBR II – rough MABAC decision-making model for ranking methods and techniques of lean organization systems management in the process of technical maintenance’, Facta Univ. Ser. Mech. Eng., vol. 22, no. 1, p. 101, Apr. 2024, doi: 10.22190/FUME230614026B.
[23] M. Rakić, M. M. Žižović, B. Miljković, A. Njeguš, M. R. Žižović, and I. Đorđević, ‘Multi-criteria selection of standards for system analyst activities in organizations’, Facta Univ. Ser. Mech. Eng., vol. 21, no. 3, p. 433, Oct. 2023, doi: 10.22190/FUME230521023R.
[24] N. Komatina, D. Tadić, A. Aleksić, and A. D. Jovanović, ‘The assessment and selection of suppliers using AHP and MABAC with type-2 fuzzy numbers in automotive industry’, Proc. Inst. Mech. Eng. Part O J. Risk Reliab., vol. 237, no. 4, pp. 836–852, Aug. 2023, doi: 10.1177/1748006X221095359.
[25] M. Milanovic, M. Misita, and N. Komatina, ‘Determination of the optimal production plan by using fuzzy AHP and fuzzy linear programming’, J. Intell. Fuzzy Syst., vol. 38, no. 4, pp. 4315–4325, Apr. 2020, doi: 10.3233/JIFS-190913.
[26] C. L. Hwang and K. Yoon, ‘Methods for multiple attribute decision making. Multiple attrib-ute decision making: methods and applications a state-of-the-art survey’, 1981, pp. 58–191.
[27] R. Alkan, M. Yucesan, and M. Gul, ‘A Multi-attribute Decision-Making to Sustainable Con-struction Material Selection: A Bayesian BWM-SAW Hybrid Model’, in Advances in Best-Worst Method, J. Rezaei, M. Brunelli, and M. Mohammadi, Eds., in Lecture Notes in Opera-tions Research. , Cham: Springer International Publishing, 2022, pp. 67–78. doi: 10.1007/978-3-030-89795-6_6.
[28] F. S. Amalia, ‘Application of SAW Method in Decision Support System for Determination of Exemplary Students’, J. Inf. Technol. Softw. Eng. Comput. Sci. ITSECS, vol. 1, no. 1, pp. 14–21, Dec. 2022, doi: 10.58602/itsecs.v1i1.9.
[29] M. Keshavarz Ghorabaee, E. K. Zavadskas, L. Olfat, and Z. Turskis, ‘Multi-Criteria In-ventory Classification Using a New Method of Evaluation Based on Distance from Average So-lution (EDAS)’, Informatica, vol. 26, no. 3, pp. 435–451, Jan. 2015, doi: 10.15388/Informatica.2015.57.
[30] N. Komatina, ‘A compromise-based MADM approach for prioritizing failures: Integrating the RADAR method within the FMEA framework’, J. Sist. Dan Manaj. Ind., vol. 8, no. 2, pp. 73–88, Dec. 2024, doi: 10.30656/jsmi.v8i2.9283.
[31] N. Komatina, ‘A Novel BWM-RADAR Approach for Multi-Attribute Selection of Equipment in the Automotive Industry’, Spectr. Mech. Eng. Oper. Res., vol. 2, no. 1, pp. 104–120, Feb. 2025, doi: 10.31181/smeor21202531.
[32] T. L. Saaty, ‘Decision-making with the AHP: Why is the principal eigenvector necessary’, Eur. J. Oper. Res., vol. 145, no. 1, pp. 85–91, Feb. 2003, doi: 10.1016/S0377-2217(02)00227-8.
[33] Ž. Stević, M. Baydaş, M. Kavacık, E. Ayhan, and D. Marinković, ‘Selection of Data Con-version Technique via Sensitivity-Performance Matching: Ranking of Small E-Vans with PROBID Method’, Facta Univ. Ser. Mech. Eng., vol. 22, no. 4, pp. 643–671, Dec. 2024, doi: 10.22190/FUME240305023S.
[34] B. Kizielewicz and W. Sałabun, ‘SITW Method: A New Approach to Re-identifying Multi-criteria Weights in Complex Decision Analysis’, Spectr. Mech. Eng. Oper. Res., vol. 1, no. 1, pp. 215–226, Sep. 2024, doi: 10.31181/smeor11202419.