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Identification and improvement of processes using selected quality tools: a case study

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EN
Abstrakty
EN
The aim of this work was to determine the effectiveness of using the FMEA method in the traditional and modern versions for a small company producing plastic toys. It also identified the production process of a plastic instrument toy selected for analysis by dividing operations into adding and not adding value, in accordance with the Lean concept. Through the FMEA sheets constructed and presented in this work in both versions of this method, the causes and effects of defects in the technological operations of the production process of the test subject were presented. A risk analysis was carried out using assessments available for the traditional and new versions, and preventive and detective actions were proposed. The result of the work was a comparison of risk indicators characteristic for both versions of the FMEA method and an assessment of the effectiveness of this method in the analysed enterprise. Emphasis was placed on the human factor in the research. Both the division of operations into adding and not adding value, as well as the FMEA method, were useful for further optimization and process improvement activities.
Rocznik
Strony
59--67
Opis fizyczny
Bibliogr. 34 poz., tab.
Twórcy
  • Czestochowa University of Technology, Faculty of Management, Department of Production Engineering and Safety 19b Armii Krajowej Ave., 42-218 Czestochowa, Poland
Bibliografia
  • 1. AIAG and VDA FMEA Handbook (2019) Design FMEA and process FMEA. Supplemental FMEA for monitoring and system response. 1. ed. USA: AIAG and VDA.
  • 2. Aires, C.F. & Pimenta, H.C.D. (2019) Environmental aspect and impact assessment across a physical-chemical laboratory through FMEA. Holos 35 (8), pp. 1–21, doi: 10.15628/ holos.2019.9189.
  • 3. Altuntas, S. & Kansu, S. (2019) An innovative and integrated approach based on SERVQUAL, QFD and FMEA for service quality improvement: A case study. Kybernetes 49(10), pp. 2419–2453, doi: 10.1108/K-04-2019-0269.
  • 4. Arunagiri, P. & Gnanavelbabu, A. (2014) Identifi cation of Major Lean Production Waste in Automobile Industries using Weighted Average Method. Procedia Engineering 97, pp. 2167–2175, doi: 10.1016/j.proeng.2014.12.460.
  • 5. Biesok, G. (2019) Zarządzanie procesami. In: Biesok, G. & Jakubiec, M. (eds) Współczesne koncepcje zarządzania. Wydawnictwo Naukowe Akademii Techniczno-Humanistycznej w Bielsku-Białej, pp. 25–46.
  • 6. Brajer-Marczak, R. (2018) Factors Determining Process Improvement – Findings from an Empirical Study. Przegląd Organizacji 8, pp. 25–33 (in Polish).
  • 7. Ciani, L., Guidi, G. & Patrizi, G. (2019) A Critical Comparison of Alternative Risk Priority Numbers in Failure Modes, Eff ects, and Criticality Analysis. IEEE Access, 7, pp. 92398–92409, doi: 10.1109/ACCESS.2019.2928120.
  • 8. Cui, J., Ren, Y., Yang, D. & Zeng, S. (2015) Model based FMEA for electronic products. First International Conference on Reliability Systems Engineering (ICRSE), Beijing, China, pp. 1–6, doi: 10.1109/ICRSE.2015.7366461.
  • 9. Czerwińska, K., Pacana, A. & Ulewicz, R. (2020) Analysis of the diagnostic process of castings used in automotive. Materials Research Proceedings 17, pp. 203–210, doi: 10.21741/9781644901038-30.
  • 10. Czerwińska, K. & Piwowarczyk, A. (2022) The use of combined quality management instruments to analyze the causes of non-conformities in the castings of the cover of the rail vehicle bearing housing. Production Engineering Archives 28 (3), pp. 289–294, doi: 10.30657/pea.2022.28.36.
  • 11. Dumas, M., La Rosa, M., Mendling, J. & Reijers, H.A. (2018) Process Identification. In: Fundamentals of Business Process Management, pp. 35–73. Springer, Barlin, Heilderberg, doi: 10.1007/978-3-662-56509-4_2.
  • 12. Furman, J. & Małysa, T. (2023) The role of visual management in the organization of safe work in production companies. Production Engineering Archives 29(2), pp. 195–200, doi: 10.30657/pea.2023.29.23.
  • 13. Gochel, A., Gegeyehu, S.G. & Abebe, M. (2022) Production lead time improvement through lean manufacturing. International Journal of Industrial and Systems Engineering 40 (2), pp. 147–156, doi: 10.1504/IJISE.2022.121045.
  • 14. Godina, R., Silva, B.G.R. & Espadinha-Cruz, P. (2021) A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry. Applied Sciences 11(8), 3726, doi: 10.3390/app11083726.
  • 15. Knop, K. & Ulewicz, R. (2022) Solving Critical Quality Problems by Detecting and Eliminating their Root Causes – Case-Study from the Automotive Industry. Materials Research Proceedings 24, pp. 181–188, doi: 10.21741/9781644902059-27.
  • 16. Mazur, M. & Momeni, H. (2019) Lean Production issues in the organization of the company – results. Production Engineering Archives 22(22), pp. 50–53, doi: 10.30657/ pea.2019.22.10.
  • 17. Memari, A., Fouladgaran, H.R.P., Rahim, R.A. & Ahmad, R. (2022) The Impact of Lean Production on Operational Performance: A Case Study. Asia-Pacific Journal of Business Administration, pp. 1–54, doi: 10.1108/APJBA-04-2022- 0190.
  • 18. Michalak, A. (2008) Zarządzanie procesami. Studium przypadku. Problemy Jakości 40(1), pp. 43–47.
  • 19. Nedeliaková, E., Hranický, M.P. & Valla, M. (2022) Risk identification methodology regarding the safety and quality of railway services. Production Engineering Archives 28(1), pp. 21–29, doi: 10.30657/pea.2022.28.03.
  • 20. Pacana, A. & Czerwińska, K. (2019) Analysis of the causes of control panel inconsistencies in the gravitational casting process by means of quality management instruments. Production Engineering Archives 25(25), pp. 12–16, doi: 10.30657/pea.2019.25.03.
  • 21. Pacana, A. & Czerwińska, K. (2020) Improving the quality level in the automotive industry. Production Engineering Archives 26(4), pp. 162–166, doi: 10.30657/pea.2020.26.29.
  • 22. Pandian, R., Sałek, R., Venkat, D. & Chruzik, K. (2020) Management of non-value-added activities to minimize lead time using value stream mapping in the steel industry. Acta Montanistica Slovaca 25(3), pp. 444–454, doi: 10.46544/ AMS.v25i3.15.
  • 23. Panyukov, D., Kozlovskii, V., Aidarov, D. & Shakurskii, M. (2022) FMEA Risk Analysis on the Basis of Action Priorities. Russian Engineering Research 42(10), pp. 1077– 1080, doi: 10.3103/S1068798X22100227.
  • 24. Plinta, D., Golińska, E. & Dulina, L. (2021) Practical application of the new approach to FMEA method according to AIAG and VDA reference manual. Communications – Scientific Letters of the University of Zilina 23 (4), pp. B325– B335, doi: 10.26552/com.C.2021.4.B325-B335.
  • 25. Sharma, K.D. & Srivastava, S. (2018) Failure Mode and Effect Analysis (FMEA) Implementation: A Literature Review. Journal of Advance Research in Aeronautics and Space Science 5(1&2), pp. 1–17.
  • 26. Shou, W., Wang, J., Wu, P. & Wang, X. (2020) Value adding and non-value adding activities in turnaround maintenance process: classification, validation, and benefits. Production Planning & Control 31(1), pp. 1–18, doi: 10.1080/ 09537287.2019.1629038.
  • 27. Shuyuan, J., Fuqiu, L, Jinjing, W. & Meinan, L. (2014) The effectiveness of the FMEA technology in the process of the aerospace product development. In: Nowakowski, T., Młynczyk, M., Jodejko-Pietruczuk, A. & Werbińska-Wojciechowska, S (Eds). Safety and Reliability: Methodology and Applications, CRC Press, pp. 151–155, doi: 10.1201/ b17399-28.
  • 28. Siwiec, D. & Pacana, A. (2019) The use of quality management techniques to analyse the cluster of porosities on the turbine outlet nozzle. Production Engineering Archives 24(24), pp. 33–36, doi: 10.30657/pea.2019.24.08.
  • 29. Siwiec, D. & Pacana, A. (2021) Method of improve the level of product quality. Production Engineering Archives 27(1), pp. 1–7, doi: 10.30657/pea.2021.27.1.
  • 30. Ulewicz, R. (2014) Practical application of quality tools in the cast iron foundry. Manufacturing Technology 14(1), pp. 104–111, doi: 10.21062/ujep/x.2014/a/1213-2489/ MT/14/1/104.
  • 31. Urbaniak, M. (2004) Zarządzanie jakością. Teoria i praktyka. Warszawa: Wydawnictwo Diffin.
  • 32. Urbaniak, M. (2007) Doskonalenie procesów (studium badań empirycznych). Problemy Jakości 9, pp. 9–12.
  • 33. Vanyi, G. (2016) Improving the effectiveness of FMEA analysis in automotive – a case study. Acta Universitatis Sapientiae Informatica 8 (1), pp. 82–95, doi: 10.1515/ausi2016-0005.
  • 34. Wolniak, R. (2019) Problems of use of FMEA method in industrial enterprise. Production Engineering Archives 23(23), pp. 12–17, doi: 10.30657/pea.2019.23.02.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a85669f9-b253-4256-8592-4dd10b7854e6
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