Failure Mode and Effect Analysis (FMEA) for identification of hazards and risk assessment in the malting process

Document Type : Complete scientific research article

Authors

1 Department of Food Science and Technology, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

2 Industrial Engineering, Faculty of Industrial Technologies, Urmia University of Technology, Urmia, Iran

Abstract

Abstract:
Background and purpose: Malt extract is widely used in the production of candy, malt, sauces and baking industries, confectionery, infant formula or baby food, chocolate, pharmaceuticals and dairy factories. Risk management in the malt production process plays an important role in improving product safety and factory efficiency. The failure mode and effect analysis method (FMEA) is a systematic and preventive tool in risk management that is used to define, identify, evaluate, prevent, eliminate or control the states, causes and effects of potential failures in a system. The aim of the current research is to use FMEA and Fuzzy Delphi as suitable tools to identify, refine and rank process failures in the malt production process from the receiving stage to packaging and provide preventive solutions to reduce important risks.
Materials and methods: First, unit operation in malt production were considered, which included: receiving and sifting barley, washing and steeping, germinating and clining, grinding and extracting, milling and mashing, filling and packaging. Then, with the aid of five experts of malt industry, as properly as the previous overall performance and documentation of the processes such as HSE and ISO information, the current situation and potential failures were determined separately for the production processes. Then, the causes of failures and the effects of these failures on the customer were determined.
Findings: In the malt production process, a total of 86 potential failures were identified and ranked in 6 main malting processes. The scores of severity (S), occurrence (O) and detection (D) were assigned by experts using the Fuzzy Delphi method. According to the given scores, RPN (Risk Priority Number) of each failure mode was calculated. According to the RPN number and the numbers of the three components, the failures were categorized into three levels: normal, semi- critical and critical. “ Receiving and cultivating” and “sprouting, drying and root separation: were the most risky steps and “milling and mashing” and “filling and packaging” low risk steps in the malt production process. Finally, preventive solutions were provided by experts for critical failures.
Conclusion: In order to reduce or eliminate critical failures that had a RPN number above 120 and 2 components above 6 (6 critical failures), practical solutions such as installing equipment in order to more accurately control pressure and humidity in processes, increasing control over equipment performance, sampling and checking out raw materials and semi-finished, were introduced.
Keywords: Malting process; Failure identification; Risk management; Severity; Occurrence; Detection

Keywords

References

  1. Wang, X.C., and Wang, Q. 2015. Formulation and implementation of meat product HACCP plan based on FMEA. Advance Journal of Food Science and Technology, 7(8), 579-583
  2. Lee, J.C., Daraba, A., Voidarou, C., Rozos, G., Enshasy, H.A.E., and Varzakas, T. 2021. Implementation of food safety management systems along with other management tools (HAZOP, FMEA, Ishikawa, Pareto). The case study of listeria monocytogenes and correlation with microbiological criteria. Foods, 10(9), 2169.
  3. Aloini, D., Dulmin, R., and Mininno, V. 2007. Risk management in ERP project introduction: Review of the literature. Information & Management, 44(6), 547–567.
  4. Carlson, C.S. 2014. Understanding and applying the fundamentals of FMEAs,” in Annual Reliability and Maintainability Symposium. 10.1–35.
  5. Rezaee, M.J., Yousefi, S., Eshkevari, M., Valipour, and Saberi, M. 2020. Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA. Stoch. Environ. Res. Risk Assess. 34:1.201–218.
  6. Sharma, K.D., and Srivastava, S. 2018. Failure mode and effect analysis (FMEA) implementation: a literature review. J Adv Res Aeronaut Space Sci, 5, 1-17.
  7. Özilgen, S., and Özilgen, M. 2016. General template for the FMEA applications in primary food processing. Measurement, Modeling and Automation in Advanced Food Processing, 29-69.
  8. Shirani, M., and Demichela, M. 2015. Integration of FMEA and human factor in the food chain risk assessment. International Journal of Economics and Management Engineering, 9(12), 4247-4250.
  9. Wu, J.Y., and Hsiao, H.I. 2021. Food quality and safety risk diagnosis in the food cold chain through failure mode and effect analysis. Food Control, 120, 107501.
  10. Liu, H., & Liu, R., 2018. Risk analysis of cold-chain logistics distribution based on an improved FMEA method. Storage and Process, 18(4), 119-125.
  11. Fithri, P., Rafi, M., Pawenary, P., and Prabuwono, A.S. 2021. Risk analysis of production process for food SMEs using FMEA method: a case study. In E3S Web of Conferences (Vol. 331, p. 02010). EDP Sciences.
  12. Di Nardo, M., Murino, T., Osteria, G., and Santillo, L.C. 2022. A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in.
  13. Sutrisno, A., Kwon, H., Gunawan, I., Eldridge, S., and Lee, T. 2016. Integrating SWOT analysis into the FMEA methodology to improve corrective action decision making.
  14. Selim, H., Yunusoglu, M.G., and Yılmaz Balaman, Ş. 2016. A dynamic maintenance planning framework based on fuzzy TOPSIS and FMEA: application in an international food company. Quality and Reliability Engineering International, 32(3), 795-804.
  15. Scipioni, A., Saccarola, G., Centazzo, A., and Arena, F. 2002. FMEA methodology design, implementation and integration with HACCP system in a food company. Food control, 13(8), 495-501.
  16. Arvanitoyannis, I.S., and Varzakas, T.H. 2007. Application of failure mode and effect analysis (FMEA), cause and effect analysis and Pareto diagram in conjunction with HACCP to a potato chips manufacturing plant. International journal of food science & technology, 42(12), 1424-1442.
  17. Arvanitoyannis, I.S., and Varzakas, T.H. 2008. Application of ISO 22000 and failure mode and effect analysis (FMEA) for industrial processing of salmon: a case study. Critical reviews in food science and nutrition, 48(5), 411-429.
  18. Trafialek, J., and Kolanowski, W. 2014. Application of failure mode and effect analysis (FMEA) for audit of HACCP system. Food Control, 44, 35-44.
  19. Sabbaghi, H., Ziaiifar, A.M., and Kashaninejad, M. 2019. Design of fuzzy system for sensory evaluation of dried apple slices using infrared radiation. Iranian Journal of Biosystems Engineering, 50(1), 77-89.
  20. Sabbaghi, H., Ziaiifar, A.M., an Kashani-Nejad, M. 2021. Simulation of temperature fuzzy controller during infrared dry blanching and dehydration of apple slices by intermittent heating method. Iranian Food Science and Technology Research Journal, 16(6), 133-150.
  21. Garcia, P.A., and Schirru, R. 2005. A fuzzy data envelopment analysis approach for FMEA. Progress in Nuclear Energy, 46(3-4), 359-373.
  22. Sharma, R.K., Kumar, D., and Kumar, P. 2005. Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. International journal of quality & reliability management.
  23. Ivančan, J., and Lisjak, D. 2021. New FMEA Risks Ranking Approach Utilizing Four Fuzzy Logic Systems. Machines, 9(11), 292.
  24. Ebrahimi, S., Vachal, K., and Szmerekovsky, J. 2022. A Delphi-FMEA model to assess county-level speeding crash risk in North Dakota,” Transp. Res. Interdiscip. Perspect., vol. 16, p. 100688.
  25. Surono, I.S. 2016. "Ethnic fermented foods and beverages of Indonesia." Ethnic fermented foods and alcoholic beverages of Asia. Springer, New Delhi,. 341-382.
  26. Weerakkody, R.A. 2013. Surgical technology and operating-room safety failures: a systematic review of quantitative studies. BMJ Qual. Saf. 22:9.710–718.
  27. Kim, K.O. and Zuo, M.J. 2018. General model for the risk priority number in failure mode and effects analysis, Reliab. Eng. Syst. Saf., vol. 169, pp. 321–329.
  28. Azar, A. and Faraji, H. 2010. Fuzzy management science. Institute Mehraban book publisher, Tehran.
  29. Murray, T.J., Pipino, L.L. and Van Gigch, J.P. 1985. A pilot study of fuzzy set modification of Delphi, Hum. Syst. Manag., 5(1) 76–80.
  30. Kuo, Y.-F. and Chen, P.-C. 2008. Constructing performance appraisal indicators for mobility of the service industries using Fuzzy Delphi Method, Expert Syst. Appl., vol. 35, no. 4, pp. 1930–1939.
  31. Cheng, C.-H. and Lin, Y. 2002. Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation, Eur. J. Oper. Res., vol. 142, no. 1, pp. 174–186.
  32. Karlović, A., Jurić, A., Ćorić, N., Habschied, K., Krstanović, V. and Mastanjević, K. 2020. By-products in the malting and brewing industries—re-usage possibilities, Fermentation,. 6(3): 82.
  33. Neghab, A.P., Siadat, A., Tavakkoli-Moghaddam, R. and Jolai, F. 2011. An integrated approach for risk-assessment analysis in a manufacturing process using FMEA and DES, in 2011 IEEE International Conference on Quality and Reliability, pp. 366–370.
  34. Justé A. et al. 2011. Microflora during malting of barley: Overview and impact on malt quality, Brew. Sci, 64: 22–31.
Food Processing and Preservation Journal
Volume 14, Issue 4
January 2023
Pages 55-72

Files

Share

How to cite

Statistics