Fuzzy risk priority number assessment to detect midsole product defects

Abstract views: 205 , PDF downloads: 262
Keywords: Defect, Fuzzy risk priority number, Midsole, Pareto diagram, Quality


The quality of midsole products significantly impacts the quality, durability, and comfort of the users of shoe products manufactured by ABC company. The high percentage of the average number of product defects based on product yield data for 12 months is 3.1% which exceeds the average number of product defects required by the company by 2%. There are six types of defects in the midsole: yellowing, porous, bubbles, broken, over left material and trimming. Three types of midsole product defects from six types of defects are the focus of improvement based on the Pareto concept, the name of the type of defect is yellowing. Porous and over left material with a total defect percentage of 82.9%. This study aims to evaluate flaws in the midsole production process using a method that combines failure mode and effect analysis (FMEA), fuzzy logic, and Pareto diagram analysis. FMEA is used in shoe manufacturing to identify failure modes, their causes, and their effects. In contrast, fuzzy logic methods for input factors, such as occurrence (O), severity (S), and detection (D), are used to obtain a fuzzy risk priority number (FRPN). The assessment using rule-based FRPN provides strong evidence that the proposed methodology is logically useful for prioritizing the value of the RPN.


Download data is not yet available.


L. Ouyang, Y. Zhu, W. Zheng, and L. Yan, “An information fusion FMEA method to assess the risk of healthcare waste,” J. Manag. Sci. Eng., vol. 6, no. 1, pp. 111–124, 2021, doi: https://doi.org/10.1016/j.jmse.2021.01.001.

E. Krisnaningsih, A. T. Panudju, and I. Pratama, “Reducing defects in Mile line Of Rolling Plate Departement Using Six Sigma Method,” in Journal of Physics: Conference Series, 2020, vol. 1477, no. 2, p. 22021, doi: https://doi.org/10.1088/1742-6596/1477/2/022021

Q. Guo, K. Sheng, Z. Wang, X. Zhang, H. Yang, and R. Miao, “Research on Element Importance of Shafting Installation Based on QFD and FMEA,” Procedia Eng., vol. 174, pp. 677–685, 2017, doi: https://doi.org/10.1016/j.proeng.2017.01.205.

A. Geramian and A. Abraham, “Customer classification: A Mamdani fuzzy inference system standpoint for modifying the failure mode and effect analysis based three dimensional approach,” Expert Syst. Appl., vol. 186, no. April, p. 115753, 2021, doi: https://doi.org/10.1016/j.eswa.2021.115753.

J. Balaraju, M. Govinda Raj, and C. S. Murthy, “Fuzzy-FMEA risk evaluation approach for LHD machine-A case study,” J. Sustain. Min., vol. 18, no. 4, pp. 257–268, 2019, doi: https://doi.org/10.1016/j.jsm.2019.08.002.

R. Yahmadi, K. Brik, and F. ben Ammar, “Fuzzy risk priority number assessment for solar gel battery manufacturing defects,” Eng. Fail. Anal., vol. 124, no. October 2020, p. 105327, 2021, doi: https://doi.org/10.1016/j.engfailanal.2021.105327.

M. Kumru and P. Y. Kumru, “Fuzzy FMEA application to improve purchasing process in a public hospital,” Appl. Soft Comput. J., vol. 13, no. 1, pp. 721–733, 2013, doi: https://doi.org/10.1016/j.asoc.2012.08.007.

N. Chanamool and T. Naenna, “Fuzzy FMEA application to improve decision-making process in an emergency department,” Appl. Soft Comput. J., vol. 43, pp. 441–453, 2016, doi: https://doi.org/10.1016/j.asoc.2016.01.007.

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., no. November, 2020, doi: https://doi.org/10.1016/j.cie.2020.106958.

A. Arabsheybani, M. M. Paydar, and A. S. Safaei, “An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier’s risk,” J. Clean. Prod., vol. 190, pp. 577–591, 2018, doi: https://doi.org/10.1016/j.jclepro.2018.04.167.

C. Dağsuyu, E. Göçmen, M. Narlı, and A. Kokangül, “Classical and fuzzy FMEA risk analysis in a sterilization unit,” Comput. Ind. Eng., vol. 101, pp. 286–294, 2016, doi: https://doi.org/10.1016/j.cie.2016.09.015.

N. A. Wessiani and S. O. Sarwoko, “Risk Analysis of Poultry Feed Production Using Fuzzy FMEA,” Procedia Manuf., vol. 4, no. Iess, pp. 270–281, 2015, doi: https://doi.org/10.1016/j.promfg.2015.11.041.

J. R. Ribas, J. C. R. Severo, L. F. Guimarães, and K. P. C. Perpetuo, “A fuzzy FMEA assessment of hydroelectric earth dam failure modes: A case study in Central Brazil,” Energy Reports, vol. 7, pp. 4412–4424, 2021, doi: https://doi.org/10.1016/j.egyr.2021.07.012.

J. Ahn, Y. Noh, S. H. Park, B. Il Choi, and D. Chang, “Fuzzy-based failure mode and effect analysis (FMEA) of a hybrid molten carbonate fuel cell (MCFC) and gas turbine system for marine propulsion,” J. Power Sources, vol. 364, pp. 226–233, 2017, doi: https://doi.org/10.1016/j.jpowsour.2017.08.028.

Q. Zhou and V. V. Thai, “Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction,” Saf. Sci., vol. 83, pp. 74–79, 2016, doi: https://doi.org/10.1016/j.ssci.2015.11.013.

N. G. Mutlu and S. Altuntas, “Risk analysis for occupational safety and health in the textile industry: Integration of FMEA, FTA, and BIFPET methods,” Int. J. Ind. Ergon., vol. 72, pp. 222–240, 2019, doi: https://doi.org/10.1016/j.ergon.2019.05.013.

L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, 1965, doi: https://doi.org/10.1016/S0019-9958(65)90241-X.

E. Krisnaningsih, T. Djatna, Y. Arkeman, M. Marimin, and E. Hambali, “Optimization of aggregate planning of rice husk charcoal production with Fuzzy Goal Programming approach,” SINERGI, vol. 26, no. 1, pp. 65–72, Jan. 2022, doi: https://doi.org/10.22441/sinergi.2022.1.009.

S. N. Sivanandam, S. Sumathi, and S. N. Deepa, Introduction to Fuzzy Logic using MATLAB. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, doi: https://doi.org/10.1007/978-3-540-35781-0

R. K. Sharma, D. Kumar, and P. Kumar, “Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling,” Int. J. Qual. Reliab. Manag., vol. 22, no. 9, pp. 986–1004, 2005, doi: https://doi.org/10.1108/02656710510625248.

G. Huang and L. Xiao, “Failure mode and effect analysis: An interval-valued intuitionistic fuzzy cloud theory-based method,” Appl. Soft Comput., vol. 98, p. 106834, 2021, doi: https://doi.org/10.1016/j.asoc.2020.106834.

M. J. Kalathil, V. R. Renjith, and N. R. Augustine, “Failure mode effect and criticality analysis using dempster shafer theory and its comparison with fuzzy failure mode effect and criticality analysis: A case study applied to LNG storage facility,” Process Saf. Environ. Prot., vol. 138, pp. 337–348, 2020, doi: https://doi.org/10.1016/j.psep.2020.03.042.

K. Jenab, R. M. Blecher, and S. Moslehpour, “SRB Field Joints Failure Analysis Using Fuzzy FMEA,” Int. J. Phys. Astron., vol. 3, no. 1, pp. 1–20, 2015, doi: https://doi.org/10.15640/ijpa.v3n1a1.

O. Olabanji and K. Mpofu, “Decision Science Letters,” vol. 9, pp. 21–36, 2020, doi: https://doi.org/10.5267/j.dsl.2019.9.001.

M. D. Ramere and O. T. Laseinde, “Optimization of condition-based maintenance strategy prediction for aging automotive industrial equipment using FMEA,” Procedia Comput. Sci., vol. 180, pp. 229–238, 2021, doi: https://doi.org/10.1016/j.procs.2021.01.160.

L. Wang, F. Yan, F. Wang, and Z. Li, “FMEA-CM based quantitative risk assessment for process industries—A case study of coal-to-methanol plant in China,” Process Saf. Environ. Prot., vol. 149, pp. 299–311, 2021, doi: https://doi.org/10.1016/j.psep.2020.10.052.

D. H. Stamatis, Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQ Quality Press, 2003. Available: https://books.google.co.id/books?id=TTxI8jbTkVwC

V. R. Renjith, M. Jose kalathil, P. H. Kumar, and D. Madhavan, “Fuzzy FMECA (failure mode effect and criticality analysis) of LNG storage facility,” J. Loss Prev. Process Ind., pp. 537–547, 2018, doi: https://doi.org/10.1016/j.jlp.2018.01.002.

W. B. Roush, T. L. Cravener, and H. Jordan, “Pareto Assessment of Quality Control in Poultry Processing Plants,” J. Appl. Poult. Res., vol. 2, no. 3, pp. 297–302, 1993, doi: https://doi.org/10.1093/japr/2.3.297.

S. Mukherjee, I. Pan, and T. Samanta, “Pareto optimization technique in actuation control for error minimization and reliability analysis in an operational pin-constrained digital microfluidic biochip,” Integr. VLSI J., vol. 59, no. October 2016, pp. 125–134, 2017, doi: https://doi.org/10.1016/j.vlsi.2017.07.004.

PlumX Metrics

How to Cite
E. Krisnaningsih, A. B. Sulistyo, A. Rahim, and S. Dwiyatno, “Fuzzy risk priority number assessment to detect midsole product defects ”, j. sist. manaj. ind., vol. 6, no. 1, pp. 77-88, Jun. 2022.
Research Article

Most read articles by the same author(s)

> >>