Um modelo baseado em FMEA e Fuzzy TOPSIS para priorização de riscos em processos industriais

Magalhães, Wauires Ribeiro de; Lima Junior, Francisco Rodrigues

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Sumário

ORIGINAL ARTICLE • Gest. Prod. 28 (4) • 2021 • https://doi.org/10.1590/1806-9649-2020v28e5535 copiar

Um modelo baseado em FMEA e Fuzzy TOPSIS para priorização de riscos em processos industriais

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo:

O FMEA é um dos métodos mais utilizados para apoiar a análise de riscos em processos empresariais. Apesar disso, esse método apresenta algumas limitações, incluindo o uso de apenas três critérios de decisão, cujos pesos não são considerados. Com o objetivo de incrementar novos recursos ao FMEA, alguns estudos o combinam com métodos de decisão multicritério. Este estudo propõe um modelo baseado em FMEA e Fuzzy-TOPSIS para apoiar a priorização de riscos em processos de produção industrial. Uma aplicação piloto foi executada a fim de analisar e priorizar os riscos de falhas potenciais em um processo de fusão e vazamento de ferro nodular. Baseando-se na opinião de quatro especialistas da empresa, os modos de falhas potenciais foram definidos e avaliados. Os especialistas também escolheram os critérios e seus respectivos pesos. Os resultados da aplicação piloto sugerem que as falhas “tempo de fading excedido” e “composição química fora do especificado” sejam tratadas com maior prioridade. Os resultados dos testes de análise de sensibilidade ratificam a relevância destas falhas e evidenciam o efeito da variação dos pesos nos critérios. O modelo proposto é útil para apoiar a formulação de planos de ação focados na minimização ou eliminação das falhas prioritárias. Outras contribuições deste estudo consistem em: considerar os pesos dos critérios; permitir o uso de termos linguísticos para expressar os julgamentos dos decisores; considerar os custos referentes às falhas; e apoiar decisões em grupo.

Palavras-chave:
Avaliação de riscos; FMEA; Fuzzy-TOPSIS; Tomada de Decisão multicritério

Step I
a) Specify the system, design, or process under study;
b) Establish the team of experts;
c) Define process requirements or the functions of the product’s components;
d) Identify potential or known failure modes;
e) Analyze and describe the effects of each failure mode and assess its severity;
f) Investigate and define the probable causes of each failure mode and assess the occurrence of these causes;
g) Verify the existing controls and assess the failure mode detection capacity by these resources;
h) Calculate the RPN using Equation 1.
Step II
a) Sort RPN values in descending order. Failure modes with the highest RPN results are assumed as the most important and will have higher priority for taking actions.
b) Develop a prioritized corrective or preventive action plan.
Step III
a) Implement the action plan;
b) Assess the effectiveness of these actions, performing a new failure mode assessment considering the severity, occurrence, and detection criteria. If the actions have been effective, the reduction of the RPN value is expected to take place in relation to the initial result.

Proposed by:	Technique(s) Used	Application
Ekmekçioğlu & Kutlu (2012)Ekmekçioğlu, M., & Kutlu, A. C. (2012). A fuzzy hybrid approach for fuzzy process FMEA: an application to a spindle manufacturing process. International Journal of Computational Intelligence Systems, 5(4), 611-626. http://dx.doi.org/10.1080/18756891.2012.718104. http://dx.doi.org/10.1080/18756891.2012....	Fuzzy AHP (Analytic Hierarchy Process) and Fuzzy TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution)	Assembly process of an automotive industry
Kutlu & Ekmekçioğlu (2012)Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61-67. http://dx.doi.org/10.1016/j.eswa.2011.06.044. http://dx.doi.org/10.1016/j.eswa.2011.06...	Fuzzy AHP and Fuzzy TOPSIS	Manufacturing process of an automotive industry
Chang et al. (2013)Chang, K. H., Chang, Y. C., & Tsai, I. T. (2013). Enhancing FMEA assessment by integrating grey relational analysis and the decision making trial and evaluation laboratory approach. Engineering Failure Analysis, 31, 211-224. http://dx.doi.org/10.1016/j.engfailanal.2013.02.020. http://dx.doi.org/10.1016/j.engfailanal....	GRA (Grey Relational Analysis) and DEMATEL (Decision making and Trial Evaluation Laboratory)	Manufacturing and assembly process of electronic components
Maleki & Saadat (2013)Maleki, H., & Saadat, Y. (2013). Comparison of failure mode and effects analysis by using AHP vs. REMBRANDT system. International Journal of Industrial and Systems Engineering, 14(1), 5-19. http://dx.doi.org/10.1504/IJISE.2013.052918. http://dx.doi.org/10.1504/IJISE.2013.052...	AHP and REMBRANDT System	Manufacturing process of hydraulic pumps
Mahmoodi & Mirzazadeh (2014)Mahmoodi, A. M., & Mirzazadeh, A. (2014). A New Analysis of Failure Models and Effects by Fuzzy Todim with using Fuzzy Time Function. International Journal of Fuzzy Logic System, 4(2), 7-21. http://dx.doi.org/10.5121/ijfls.2014.4202. http://dx.doi.org/10.5121/ijfls.2014.420...	Fuzzy TODIM (Iterative Multicriteria Decision Making) and FTF (Fuzzy Time Function)	Process of an automotive industry
Bozdag et al. (2015)Bozdag, E., Asan, U., Soyer, A., & Serdarasan, S. (2015). Risk prioritization in Failure Mode and Effects Analysis using interval type-2 fuzzy sets. Expert Systems with Applications, 42(8), 4000-4015. http://dx.doi.org/10.1016/j.eswa.2015.01.015. http://dx.doi.org/10.1016/j.eswa.2015.01...	Interval Type 2 Fuzzy Sets	Assembly process
Haq et al. (2015)Haq, I. U., Izhar, K., Anwar, S., Khan, M. T., Ahmed, B., & Maqsood, S. (2015). Fuzzy logic based failure mode and effect analysis of automotive powertrain assembly systems. Technical Journal, 20, 57-64.	FST (Fuzzy Set Theory)	Assembly line at the Ford Motor Company
Ahmadi et al. (2017)Ahmadi, M., Molana, S. M. H., & Sajadi, S. M. (2017). A hybrid FMEA-TOPSIS method for risk management, case study: Esfahan Mobarakeh Steel Company. International Journal of Process Management and Benchmarking, 7(3), 397-408. http://dx.doi.org/10.1504/IJPMB.2017.084913. http://dx.doi.org/10.1504/IJPMB.2017.084...	TOPSIS	Steel Manufacturing Process
Certa et al. (2017)Certa, A., Enea, M., Galante, G. M., & La Fata, C. M. (2017). ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: an alternative to the risk priority number. Computers & Industrial Engineering, 108, 100-110. http://dx.doi.org/10.1016/j.cie.2017.04.018. http://dx.doi.org/10.1016/j.cie.2017.04....	ELECTRE TRI (Elimination and Choice Expressing Reality)	Processes at a dairy industry
Chen (2017)Chen, J. K. (2017). Prioritization of corrective actions from utility viewpoint in FMEA application. Quality and Reliability Engineering International, 33(4), 883-894. http://dx.doi.org/10.1002/qre.2064. http://dx.doi.org/10.1002/qre.2064...	Fuzzy ISM (Interpretive Structural Model), DEMATEL and ANP (Analytic Network Process)	Notebook manufacturing
Hajimolaali et al. (2017)Hajimolaali, M., Kebriaeezadeh, A., Abdollahiasl, A., Safari, H., & Yektadoost, A. (2017). A new approach in identifying and evaluating quality risks in the pharmaceutical industry. Journal of Pharmaco Economics and Pharmaceutical Management, 3(1-2), 17-20.	Fuzzy TOPSIS	Pharmaceutical industry manufacturing
Zhao et al. (2017)Zhao, H., You, J. X., & Liu, H. C. (2017). Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment. Soft Computing, 21(18), 5355-5367. http://dx.doi.org/10.1007/s00500-016-2118-x. http://dx.doi.org/10.1007/s00500-016-211...	IVIFS (Interval-valued Intuitionistic Fuzzy Set) and MULTIMOORA (Multi-Objective Optimization by Ratio Analysis)	Steel Manufacturing Process
Banduka et al. (2018)Banduka, N., Tadić, D., Mačužić, I., & Crnjac, M. (2018). Extended process failure mode and effect analysis (PFMEA) for the automotive industry: the FSQC-PFMEA. Advances in Production Engineering & Management, 13(2), 206-215. http://dx.doi.org/10.14743/apem2018.2.285. http://dx.doi.org/10.14743/apem2018.2.28...	FST	Manufacturing process of an automotive industry

Criteria weight assessment				Alternative score assessment
Linguistic Term	Fuzzy Number			Linguistic Term	Fuzzy Number
Very Low Importance (VLI)	0.01	0.03	0.25	Very Low (VL)	0.10	0.10	2.50
Low Importance (LI)	0.01	0.25	0.50	Low (L)	0.10	2.50	5.00
Medium Importance (MI)	0.25	0.50	0.75	Moderate (M)	2.50	5.00	7.50
High Importance (HI)	0.50	0.75	1.00	High (H)	5.00	7.50	10.00
Very High Importance (VHI)	0.75	1.00	1.00	Very High (VH)	7.50	10.00	10.00

Process step	Failure Mode	Decision maker 1				Decision maker 2				Decision maker 3				Decision maker 4
Process step	Failure Mode	C	S	F	CO	C	S	F	CO	C	S	F	CO	C	S	F	CO
Melting	(F1) Metallic load composition error	VL	L	H	H	VL	L	M	M	L	L	M	M	VL	L	H	H
Metal treatment	(F2) Insufficient alloys	M	H	L	H	M	H	L	H	M	VH	L	M	M	H	L	H
Metal cleaning	(F3) Deficient bath cleanliness	M	M	M	M	M	M	M	M	M	M	L	L	M	M	M	H
Pouring	(F4) Bath weighing error	M	H	VL	VH	M	M	VL	H	M	M	M	L	M	M	VL	H
	(F5) Deficient inoculation	M	H	M	L	L	M	L	M	L	M	M	M	M	H	M	L
	(F6) Pouring start delay	M	L	L	H	L	M	L	H	L	M	M	M	L	M	L	H
	(F7) Furnace temperature below the specified	M	H	L	H	M	H	L	VH	M	M	L	M	M	H	L	H
	(F8) Deficient pot heating	L	L	L	H	L	L	L	M	VL	M	L	M	L	L	L	M
	(F9) Pouring time above the specified	M	VH	M	H	M	H	M	M	M	M	L	M	M	VH	M	H
	(F10) Incomplete pouring	M	H	L	H	M	H	L	H	M	M	M	L	M	H	L	H
	(F11) Interrupted pouring	M	M	L	L	M	M	L	L	M	M	L	L	M	M	L	L
	(F12) Chemical composition out of the specified	M	VH	M	M	M	H	M	M	M	VH	L	M	M	H	M	M
	(F13) Fading time exceeded	M	VH	L	L	M	VH	L	M	M	VH	L	M	M	VH	L	L
Criteria weights		VHI	HI	HI	VHI	HI	VHI	VHI	VHI	HI	HI	MI	HI	VHI	VHI	HI	VHI

	Cost (C)			Severity (S)			Frequency (F)			Control (CO)
	l	m	u	L	m	U	l	m	u	l	m	u
F1	0.10	0.70	3.13	0.10	2.50	5.00	3.75	6.25	8.75	3.75	6.25	8.75
F2	2.50	5.00	7.50	5.63	8.13	10.00	0.10	2.50	5.00	4.38	6.88	9.38
F3	2.50	5.00	7.50	2.50	5.00	7.50	1.90	4.38	6.88	2.53	5.00	7.50
F4	2.50	5.00	7.50	3.13	5.63	8.13	0.70	1.33	3.75	4.40	6.88	8.75
F5	1.30	3.75	6.25	3.75	6.25	8.75	1.90	4.38	6.88	1.30	3.75	6.25
F6	0.70	3.13	5.63	1.90	4.38	6.88	0.70	3.13	5.63	4.38	6.88	9.38
F7	2.50	5.00	7.50	4.38	6.88	9.38	0.10	2.50	5.00	5.00	7.50	9.38
F8	0.10	1.90	4.38	0.70	3.13	5.63	0.10	2.50	5.00	3.13	5.63	8.13
F9	2.50	5.00	7.50	5.63	8.13	9.38	1.90	4.38	6.88	3.75	6.25	8.75
F10	2.50	5.00	7.50	4.38	6.88	9.38	0.70	3.13	5.63	3.78	6.25	8.75
F11	2.50	5.00	7.50	2.50	5.00	7.50	0.10	2.50	5.00	0.10	2.50	5.00
F12	2.50	5.00	7.50	6.25	8.75	10.00	1.90	4.38	6.88	2.50	5.00	7.50
F13	2.50	5.00	7.50	7.50	10.00	10.00	0.10	2.50	5.00	1.30	3.75	6.25
Weight	0.63	0.88	1.00	0.63	0.88	1.00	0.50	0.75	0.94	0.69	0.94	1.00

	Criteria				D⁺	Criteria				D^-
	C	S	F	CO	D⁺	C	S	F	CO	D^-
F1	0.49	0.45	0.30	0.57	1.82	0.14	0.14	0.16	0.18	0.62
F2	0.30	0.24	0.45	0.57	1.55	0.39	0.40	0.43	0.43	1.65
F3	0.30	0.35	0.37	0.56	1.58	0.39	0.39	0.37	0.29	1.44
F4	0.30	0.32	0.48	0.57	1.67	0.39	0.39	0.38	0.32	1.48
F5	0.36	0.30	0.37	0.56	1.58	0.32	0.32	0.34	0.35	1.33
F6	0.39	0.37	0.42	0.57	1.75	0.28	0.28	0.28	0.27	1.11
F7	0.30	0.28	0.45	0.57	1.59	0.39	0.40	0.40	0.38	1.57
F8	0.44	0.43	0.45	0.57	1.88	0.21	0.21	0.22	0.21	0.84
F9	0.30	0.24	0.37	0.57	1.48	0.39	0.40	0.43	0.41	1.63
F10	0.30	0.28	0.42	0.57	1.57	0.39	0.40	0.40	0.38	1.57
F11	0.30	0.35	0.45	0.46	1.56	0.39	0.39	0.37	0.29	1.44
F12	0.30	0.22	0.37	0.56	1.45	0.39	0.41	0.44	0.44	1.68
F13	0.30	0.18	0.45	0.56	1.49	0.39	0.42	0.47	0.47	1.75

Classification	CC_i	Failure Mode
1^st	0.540	F13	Fading time exceeded
2^nd	0.536	F12	Chemical composition out of the specified
3^rd	0.525	F9	Pouring time above the specified
4^th	0.514	F2	Insufficient alloys
5^th	0.501	F10	Incomplete pouring
6^th	0.496	F7	Furnace temperature below the specified
7^th	0.481	F11	Interrupted pouring
8^th	0.477	F3	Deficient bath cleanliness
9^th	0.471	F4	Bath weighing error
10^th	0.457	F5	Deficient inoculation
11^th	0.387	F6	Pouring start delay
12^th	0.308	F8	Deficient pot heating
13^th	0.255	F1	Metallic load composition error

Scenario	Criteria
Scenario	Cost	Severity	Frequency	Control
1	VHI	VLI	VLI	VLI
2	VLI	VHI	VLI	VLI
3	VLI	VLI	VHI	VLI
4	VLI	VLI	VLI	VHI

Classification	Pilot application		Scenario 1		Scenario 2		Scenario 3		Scenario 4
Classification	CC_i	Failure	CC_i	Failure	CC_i	Failure	CC_i	Failure	CC_i	Failure
1º	0.540	F13	0.3906	F12	0.3978	F13	0.1457	F12	0.1344	F13
2^o	0.536	F12	0.3897	F13	0.3700	F12	0.1435	F9	0.1343	F12
3^o	0.525	F9	0.3891	F9	0.3535	F2	0.1400	F13	0.1334	F2
4^o	0.514	F2	0.3891	F2	0.3513	F9	0.1395	F2	0.1321	F9
5^o	0.501	F10	0.3887	F11	0.3195	F10	0.1394	F10	0.1316	F10
6^o	0.496	F7	0.3881	F10	0.3190	F7	0.1374	F7	0.1313	F7
7^o	0.481	F11	0.3876	F7	0.2927	F5	0.1368	F3	0.1311	F11
8^o	0.477	F3	0.3848	F3	0.2782	F4	0.1335	F11	0.1270	F4
9^o	0.471	F4	0.3840	F4	0.2627	F11	0.1315	F4	0.1261	F3
10^o	0.457	F5	0.3354	F5	0.2596	F3	0.1249	F5	0.1153	F5
11^o	0.387	F6	0.3014	F6	0.2238	F6	0.1065	F6	0.1004	F6
12^o	0.308	F8	0.2397	F8	0.1712	F8	0.0840	F8	0.0802	F8
13^o	0.255	F1	0.1783	F1	0.1410	F1	0.0703	F1	0.0621	F1

Universidade Federal de São Carlos Departamento de Engenharia de Produção , Caixa Postal 676 , 13.565-905 São Carlos SP Brazil, Tel.: +55 16 3351 8471 - São Carlos - SP - Brazil
E-mail: gp@dep.ufscar.br

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[1] Source: The authors.

Databases	Number of resulting studies in each step
Databases	(1)	(2)	(3)	(4)	(5)	(6)
Science Direct	14	14	11	11	5	1
Scopus	5	5	5	4	0	0
Taylor & Francis	8	8	8	7	0	0
Google Scholar	270	263	205	188	13	12
Total number of selected studies						13