Failure Mode and Effect Analysis using Robust Data Envelopment Analysis (Case Study: Automobile Oil Filter)

Journal of Optimization in Industrial Engineering

مقاله 28، دوره 15، شماره 2 - شماره پیاپی 33، آذر 2022، صفحه 1-19   

نوع مقاله: Original Manuscript

شناسه دیجیتال (DOI): 10.22094/joie.2022.1938171.1888

نویسندگان

Nasser Safaie^* ؛ Seyed Amir Nasri

Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

چکیده

Risk management improves and increases the speed of development and optimal implementation of the company's strategy to achieve a competitive advantage. Risk identification and assessment are known as one of the main tools of safety management, which helps the safety manager better select risk reduction measures and standardization of automobile oil filter by creating a suitable information platform. In this regard, evaluating and analyzing failure modes and their effects is an appropriate tool for risk management and improving product quality. Due to the weaknesses of the traditional method the complexities of the fuzzy method, a new type of risk non-priority is presented by assigning different weights to each of the risk factors under uncertainty and the proposed method is compared with the traditional and fuzzy methods simultaneously. The purpose of this study is to analyze the failure mode and risks in operation and various stages of manufacturing automobile oil filter, then to prioritize and compare risks by applying the fuzzy theory method and robust data envelopment analysis. Oil filter is an essential part of the automobile that its standardization reduces fuel consumption, improves engine performance and consequently decreases environmental pollution. This research has used the combination of Failure Mode and Effect Analysis( FMEA) method for analyzing the reliability of the oil filter and fuzzy theory has been used to record experts' opinions on failure modes and calculate the risk priority of each subsystem under uncertainty. In order to eliminate the existing defects, a new method is introduced for calculating the risk priority number in the failure mode and effect analysis based on the data envelopment analysis method. In this research, the robust optimization method covers the results of the data envelopment analysis (DEA) and is less complex than the fuzzy method has been used. The results of the case study indicate that the proposed model is more effective and reliable than the traditional and fuzzy Risk Priority Number (RPN) and also the proposed method has less has complexity than the fuzzy method. This method provides a complete ranking and convincing prioritization of failure modes. After calculating the RPNs, the operations related to the spiral tube, fiber folding, ring bending and cutting, and fiber folding are the highest number of RPNs, respectively, and their corrective actions were also determined.

کلیدواژه‌ها

FMEA technique؛ Fuzzy theory؛ Risk management؛ Reliability؛ Failure modes and effects analysis؛ Robust Data Envelopment

مراجع

Charnes, A., Cooper, W.W., Rhodes, E., (1978). Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (6), 429–444.

Soyster, A.L.,(1973). Technical note—convex programming with set-inclusive constraints and applications to inexact linear programming. Operat. Res. 21 (5), 1154–1157.

Ho, C-H. & C-J. Liao (2011). “The use of failure mode and effects analysis to construct an effective disposal and prevention mechanism for infectious hospital waste”. Waste Management, 31(12), 2631-2637.

Zadeh Lotfi A. (1965) "Fuzzy sets", Information and control, 8 (3), pp. 338-353.

Liu, H-C.; Liu, L. Bian, Q-H. Lin, Q-L. Dong, N. & P-H. Xu (2011). “Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory”. Expert Systems with Applications, 38(4), 4403-4415.

jahangoshai Rezaee, M., Yousefi, S., Eshkevari, M. et al. Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA. Stoch Environ Res Risk Assess 34, 201–218 (2020)

Yazdi, M. Improving failure mode and effect analysis (FMEA) with consideration of uncertainty handling as an interactive approach. Int J Interact Des Manuf 13, 441–458. (2019)

Yang, S., Bian, C., Li, X. et al. Optimized fault diagnosis based on FMEA-style CBR and BN for embedded software system. Int J Adv Manuf Technol 94, 3441–3453.(2018)

Nazeri, A., Naderikia, R. A new fuzzy approach to identify the critical risk factors in maintenance management. Int J Adv Manuf Technol 92, 3749–3783. (2017)

Chang, D.S., Paul Sun, K.L., (2009). Applying DEA to enhance assessment capability of FMEA. Int. J. Qual. Reliab. Manage. 26 (6), 629–643.

Chang, D.S., Chung, J.H., (2012). Risk evaluation of group package tour service failures that result in third-Party complaints. J. Travel Tourism Mark. 29 (8), 817–834.

Chin, K.S., Wang, Y.M., Poon, G.K.K., Yang, J.B., Failure mode and effects analysis by data envelopment analysis. Decis. Support Syst. 48 (1), 246–256. (2009)

Boral, S., Howard, I., Chaturvedi, S., McKee, K., Naikan, V., A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: An essential requirement for sustainable manufacturing., Sustainable Production and Consumption.,  21 (2020) 14–32

Wang L, Yan F, Wang F, Li Z, FMEA-CM based quantitative risk assessment for process industries—A case study of coal-to-methanol plant in China, Process Safety and Environmental Protection . (2020)

Safari, H., Faraji, Z. & Majidian, S. Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR. J Intell Manuf 27, 475–486 .(2016)

Liu, H., Xin You, J., Yue You., Shan, M. (2014). A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method,

Wan, C., Yan, X., Zhang, D., Qu, Z., Yang, Z., (2019). An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks., Transportation Research Part E., 125, 222-240.

Tu PY, Yam R, Tse P, Sun AO (2001) An integrated maintenance management system for an advanced manufacturing company. Int J Adv Manuf Technol 17(9):692–703.

Vinodh, S., Santhosh, D. (2011). Application of FMEA to an automotive leaf spring manufacturing organization, TQM Journal, Vol. 24, No. 3, 260-274

Baghery, M., Yousefi, S., Rezaee, M.J., 2016. Risk measurement and prioritization of auto parts manufacturing processes based on process failure analysis, interval data envelopment analysis and grey relational analysis. J. Intell. Manuf.

Yousefi, S., Alizadeh, A.,  Hayati,  J., Baghery, M., 2018. HSE risk prioritization using robust DEA-FMEA approach with undesirable outputs: A study of automotive parts industry in Iran. Safety Science. 102, 144-158.

Mhetre    R.S.,  Dhake,  R.J.  (2012),    Using    failure    mode  effect    analysis    in    precision    sheet    metal    parts  manufacturing    company,    International    Journal  of   Applied  Sciences  and  Engineering Research, 2012;  1(2), 302-311.

Ambekar,  S.B.  Edlabadkar  A.  &  Shrouty,  V.  (2013),  A  Review: Implementation of Failure Mode and Effect  Analysis,  International  Journal  of  Engineering  and  Innovative Technology (IJEIT,) 2 (8), February, 38  

Lo, HW., Shiue, W., Liou, J.J.H. et al. A hybrid MCDM-based FMEA model for identification of critical failure modes in manufacturing. Soft Comput 24, 15733–15745 (2020).

Wang, W., Liu, X., Chen, X., Qin, Y., (2019), Risk assessment based on hybrid FMEA framework by considering decision-maker r’s psychological behavior character, Computers& Industrial Engineering. 136, 516-527

Arash Geramian, Ajith Abraham & Mojtaba Ahmadi Nozari (2018): Fuzzy logic-based FMEA robust design: a quantitative approach for robustness against groupthink in group/team decision-making, International Journal of Production Research,

Qin, J., Xi, Y., Pedrycz, W., (2020), Failure mode and effects analysis (FMEA) for risk assessment based on

interval type-2 fuzzy evidential reasoning method, Applied Soft Computing Journal. 89, 106-134.

Li, H., Diaz, H., Soares, C., A failure analysis of floating offshore wind turbines using AHP-FMEA methodology, Ocean Engineering. 234, 109-126

Shaghaghia, M., Rezaie, K., (2012), Failure Mode and Effects Analysis Using Generalized Mixture Operators, Journal of Optimization in Industrial Engineering. 5, 1-10.

Dedimas, T., Gebeyehu, S., (2019), Application of Failure Mode Effect Analysis (FMEA) for Efficient and Cost-effective Manufacturing: A Case Study at Bahir Dar Textile Share Company, Ethiopia, Journal of Optimization in Industrial Engineering. 12, 23-29.