Advances in Industrial Engineering

Advances in Industrial Engineering

A Hybrid Approach to Risk Prioritization Based on Failure Analysis and Fuzzy Cognitive Map: A Case Study of the Automotive Parts Industry

Document Type : Research Paper

Authors
Mustafa Jahangoshaye Rezaei
Samuel Youssefi
Majid Bagheri
Department of Industrial Engineering, Urmia University, Urmia, Iran
10.22059/jieng.2018.203846.1096
Abstract
The increase of competition, the extension of customer expectations and their frequent demand, and rapid technological changes have caused the rapid development of today’s manufacturer’s obligations. So that any deficiencies and deviations in the performance of the products lead to loss of manufacturer’s market share. In order to solve the problems and improve the quality of products, failures should be identified. In this study, potential failures are identified by implementing failure modes and effects analysis, by using the cross functional team in the car spare parts industry. Then, to achieve results according to the facts and to remove problem in risk priority number computation, and the identified interrelationships between failures are taken into consideration. Because the occurrence and control of any failure can affect other failures. In other words, the prioritization of failures based on the fuzzy cognitive map is done with regard to the three criteria including severity, occurrence and detection as well as interrelation between failures. A case study on auto parts manufacturing industry is used to show the abilities of integrating "failure modes and effects analysis-fuzzy cognitive map" for prioritizing failures.
Keywords
Automotive Parts
Failure Modes and Effects Analysis
Fuzzy Cognitive Map
Risk Prioritization
Subjects
  1. Rezaee, M. J., Salimi, A., and Yousefi, S. (2017). “Identifying and Managing Failures in Stone Processing Industry Using Costbased FMEA”, The International Journal of Advanced Manufacturing Technology, Vol. 88, No. 9-12, PP. 3329-3342.
  2. Nggada, S. H. (2012). “Software Failure Analysis at Architecture Level Using FMEA”, International Journal of Software Engineering and its Applications, Vol. 6, No. 1, PP. 61-74.
  3. Trafialek, J., and Kolanowski, W. (2014). “Application of Failure Mode and Effect Analysis (FMEA) for Audit of HACCP System”, Food Control, Vol. 44, PP. 35-44.
  4. Papadopoulos, Y., Parker, D., and Gran, C. (2004). “Automating the Failure Modes and Effects Analysis of Safety Critical Systems”, Proceedings of the Eighth IEEE International Symposium on High Assurance Systems Engineering, (HASE), IEEE Computer Society Press, PP. 310‒311.
  5. Yousefi, S., Alizadeh, A., Hayati, J., and Baghery, M. (2018). “HSE Risk Prioritization Using Robust DEA-FMEA Approach with Undesirable Outputs: A Study of Automotive Parts Industry in Iran”, Safety Science, Vol. 102, PP. 144-158.
  6. ArabianHoseynabadi, H., Oraee, H., and Tavner, P. J. (2010). “Failure Modes and Effects Analysis (FMEA) for Wind Turbines”, International Journal of Electrical Power and Energy Systems, Vol. 32, No. 7, PP. 817-824.
  7. Baghery, M., Yousefi, S., and 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”, Journal of Intelligent Manufacturing, Doi: 10. 1007/ S10845-016-1214-1.
  8. Hassan, A., Siadat, A., Dantan, J. Y., and Martin, P. (2010). “Conceptual Process Planning–an Improvement Approach Using QFD, FMEA, and ABC Methods”, Robotics and Computer-Integrated Manufacturing, Vol. 26, No. 4, PP. 392-401.
  9. Silva, M. M., De Gusmão, A. P. H., Poleto, T., E Silva, L. C. and Costa, A. P. C. S. (2014). “A Multidimensional Approach to Information Security Risk Management Using FMEA and Fuzzy Theory”, International Journal of Information Management, Vol. 34, No. 6, PP. 733-740.
  10. Chin, K. S., Wang, Y. M., Poon, G. K. K., and Yang, J. B. (2009). “Failure Mode and Effects Analysis by Data Envelopment Analysis”, Decision Support Systems, Vol. 48, No. 1, PP. 246-256.
  11. Asher, H. B. (1983). “Causal Modeling”. 2nd  Ed. Newbury Park, CA: Sage Publications.
  12. Kosko, B. (1986). “Fuzzy Cognitive Maps”, International Journal of Man-Machine Studies, Vol. 24, No. 1, PP. 65-75.
  13. RodriguezRepiso, L., Setchi, R., and Salmeron, J. L. (2007). “Modelling IT Projects Success with Fuzzy Cognitive Maps”, Expert Systems with Applications, Vol. 32, No. 2, PP. 543-559.
  14. Lee, K. C., Lee, H., Lee, N. and Lim, J. (2013). “An Agent-Based Fuzzy Cognitive Map Approach to the Strategic Marketing Planning for Industrial Firms”, Industrial Marketing Management, Vol. 42, No. 4, PP. 552-563.
  15. Kyriakarakos, G., Patlitzianas, K., Damasiotis, M., and Papastefanakis, D. (2014). “A Fuzzy Cognitive Maps Decision Support System for Renewables Local Planning”, Renewable and Sustainable Energy Reviews, Vol. 39, PP. 209-222.
  16. Mourhir, A., Rachidi, T., Papageorgiou, E. I., Karim, M., and Alaoui, F. S. (2016). “A Cognitive Map Framework to Support Integrated Environmental Assessment”, Environmental Modelling and Software, Vol. 77, PP. 81-94.
  17. Rezaee, M. J. and Yousefi, S. (2017). “An Intelligent Decision Making Approach for Identifying and Analyzing Airport Risks”, Journal of Air Transport Management, Doi: 10.1016/ J.Jairtraman. 2017. 06. 013.
  18. Rezaee, M. J., Yousefi, S., and Babaei, M. (2017). “Multi-stage Cognitive Map for Failures Assessment of Production Processes: An Extension in Structure and Algorithm”, Neurocomputing, Vol. 232, PP. 69-82.
  19. Papageorgiou, E. I., and Kannappan, A. (2012). “Fuzzy Cognitive Map Ensemble Learning Paradigm to Solve Classification Problems: Application to Autism Identification”, Applied Soft Computing, Vol. 12, No. 12, PP. 3798-3809.
  20. Papageorgiou, E. I., Stylios, C., and Groumpos, P. P. (2006). “Unsupervised Learning Techniques for FineTuning Fuzzy Cognitive Map Causal Links”, International Journal of HumanComputer Studies, Vol. 64, No. 8, PP. 727-743.
  21. Papageorgiou, E. I., and Salmeron, J. L. (2014). “Methods and Algorithms for Fuzzy Cognitive Map-Based Modeling”, In Fuzzy Cognitive Maps for Applied Sciences and Engineering, Springer Berlin Heidelberg, PP. 1-28.
Advances in Industrial Engineering
Volume 52, Issue 2
Summer 2018
Pages 193-205