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Assessment of the modernized production system through selected TPM method indicators

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The subject of the studies is the evaluation of the operation of a production system after modernization. The analysed case concerns the modernization forced by the end of the product lifetime. The proposed methodology is that of a multicriterial evaluation of the system operation after modernization. The evaluation criteria are selected TPM indices: availability of machinery and equipment, production process capacity, product quality and overall equipment effectiveness (OEE). The additional criteria are reliability indices MTBF and MTTR of studied production lines and the MTTR of the most unreliable equipment in each analysed line. A yearly monitoring of production process was proposed for obtaining the statistical credibility of the evaluation results. Additionally, a fuzzy indicator of acceptability of the modernization assessment was proposed. The paper presents the results of studies of the system for production of zinc concentrate from post-production waste. The obtained values of OEE, MTBF and MTTR indicators for the three tested lines make it possible to state that the modernization carried out is acceptable.
Rocznik
Strony
677--686
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
  • AGH University of Science and Technology, Faculty Mechanical Engineering and Robotics, al. Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
  • 1. Albzeirat M.K., Hussain M.I., Ahmad R., Salahuddin A., Al-Saraireh F.M., Bin-Abdun N. Literature Review: Lean Manufacturing Assessment During the Time Period (2008-2017). International Journal of Engineering Management 2018; 2(2): 29-46. https://doi.org/10.11648/j.ijem.20180202.12
  • 2. Antosz K., Pasko L., Gola A. The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises. Appl. Sci. 2020, 10: 7922. https://doi.org/10.3390/app10217922
  • 3. Armstrong E. Productize: The Ultimate Guide to Turning Professional Services into Scalable Products. Vecteris: Cincinnati 2021.
  • 4. Broek M., Teunter R., de Jonge B., Veldman J. Joint condition-based maintenance and condition-based production optimization. Reliability Engineering and System Safety 2021; 214: 107743, https://doi.org/10.1016/j.ress.2021.107743
  • 5. Chen Ch., Wang C., Lu N., Jiang B., Xing Y. A data-driven predictive maintenance strategy based on accurate failure prognostics. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23 (2): 387-394, https://doi.org/10.17531/ein.2021.2.19
  • 6. Cheng G.Q., Zhou B.H., Li L. Integrated production, quality control and condition-based maintenance for imperfect production systems. Reliability Engineering and System Safety 2018; 175: 251-264, https://doi.org/10.1016/j.ress.2018.03.025
  • 7. Czerwinska K., Pacana A. Analysis of the implementation of the selected lean production method in the production company. Scientific Papers of Silesian University of Technology - Organization and Management Series 2019, 133: 43-54, https://doi.org/10.29119/1641-3466.2019.133.4
  • 8. Evseenko S., Kupriyanov Y. Modernization of Production Planning Methodology in the Context of Virtualization and Increasing Multi-Agent Meta-Environment. Advances in Social Science, Education and Humanities Research 2020; 392: 84-87, https://doi.org/10.2991/assehr.k.200113.018
  • 9. Gola A. Reliability analysis of reconfigurable manufacturing system structures using computer simulation methods. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21 (1): 90-102, https://doi.org/10.17531/ein.2019.1.11
  • 10. Han X, Wang Z, Xie M. et al. Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence. Reliability Engineering & System Safety 2021; 210: 107560, https://doi.org/10.1016/j.ress.2021.107560
  • 11. Hashemi M., Asadi M., Zarezadeh S.. Optimal maintenance policies for coherent systems with multi-type components. Reliability Engineering and System Safety 2020; 195: 106674, https://doi.org/10.1016/j.ress.2019.106674
  • 12. Jain A., Bhatti R., Singh H. OEE enhancement in SMEs through mobile maintenance: a TPM concept. International Journal of Quality & Reliability Management 2015; 32 (5): 503-516, https://doi.org/10.1108/IJQRM-05-2013-0088
  • 13. Jain A., Bhatti R., Singh H. Total productive maintenance (TPM) implementation practice: A literature review and directions. International Journal of Lean Six Sigma 2014; 5 (3): 293-323, https://doi.org/10.1108/IJLSS-06-2013-0032
  • 14. Koren Y., Gu X., Guo W. Reconfigurable manufacturing systems: Principles, design, and future trends. Frontiers of Mechanical Engineering 2018; 13(2): 121-136, https://doi.org/10.1007/s11465-018-0483-0
  • 15. Krolczyk J., Legutko S., Szczepańska A. Value Stream Mapping as a tool for the optimization of production - case study. MATEC Web of Conferences 2017; 121: 02006, https://doi.org/10.1051/matecconf/201712102006
  • 16. Levitin G, Xing L, Dai Y. Optimal operation and maintenance scheduling in m-out-n standby systems with reusable elements. Reliability Engineering & System Safety 2021; 211: 107582, https://doi.org/10.1016/j.ress.2021.107582
  • 17. Levitin G., Finkelstein M., Li Y-F. Balancing mission success probability and risk of system loss by allocating redundancy in systems operating with a rescue option. Reliability Engineering and System Safety 2020; 195: 106694, https://doi.org/10.1016/j.ress.2019.106694
  • 18. Li J, Wang Z, Ren Y, Yang D, Lv X. A novel reliability estimation method of multi-state system based on structure learning algorithm. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (1): 170-178, https://doi.org/10.17531/ein.2020.1.20
  • 19. Maganha I., Silva C., Ferreira L.M.D.F. Understanding reconfigurability of manfuacturing systems: An empirical analysis. Journal of Manufacturing Systems 2018; 48: 120-130, https://doi.org/10.1016/j.jmsy.2018.07.004
  • 20. Michlowicz E.: Logistics engineering and Industry 4.0 and Digital Factory. Archives of Transport 2021; 57(1): 59-72, http://doi.org/10.5604/01.3001.0014.7484.
  • 21. Modrak, V., Soltysova Z. Development of operational complexity measure for selection of optimal layout design alternative. Int. Journal Production Research. 2018, 56: 7280-7295. https://doi.org/10.1080/00207543.2018.1456696
  • 22. Mostafa S.., Dumrak J., Soltan H. Lean maintenance roadmap. Procedia Manufacturing 2015; 2: 434 - 444, https://doi.org/10.1016/j.promfg.2015.07.076
  • 23. Mouzani I., Bouami D. The Integration of Lean Manufacturing and Lean Maintenance to Improve Production Efficiency. International Journal of Mechanical and Production Engineering Research and Development 2019; 9(1): 593-604. https://doi.org/10.24247/ijmperdfeb201957
  • 24. Narayanamurthy, G. and Gurumurthy, A. Systemic leanness: an index for facilitating continuous improvement of lean implementation. Journal of Manufacturing Technology Management 2016; 27 (8): 1014-1053. https://doi.org/10.1108/JMTM-04-2016-0047
  • 25. Narayanamurthy, G. and Gurumurthy, A. Leanness assessment: a literature review. International Journal of Operations and Production Management 2016; 36 (10): 1115-1160. https://doi.org/10.1108/IJOPM-01-2015-0003
  • 26. Nyhuis P., Wiendhal H-P. Fundamentals of production logistics. Theory, tools and applications. Springer - Verlag: Berlin Heidelberg 2009.
  • 27. Pascal D. Lean Production Simplified: A Plain-Language Guide to the World's Most Powerful Production System. CRC Press Taylor & Francis Group: Boca Raton 2015.
  • 28. Ruiz-Castro JE. A complex multi-state k-out-of-n: G system with preventive maintenance and loss of units. Reliability Engineering & System Safety 2020; 197: 106797, https://doi.org/10.1016/j.ress.2020.106797
  • 29. Sangwa, N.R. and Sangwan, K.S. Development of an integrated performance measurement framework for lean organizations. Journal of Manufacturing Technology Management 2018; 29 (1): 41-84. https://doi.org/10.1108/JMTM-06-2017-0098
  • 30. Sangwa, N.R. and Sangwan, K.S. Leanness assessment of organizational performance: a systematic literature review. Journal of Manufacturing Technology Management 2018; 29 (5): 768-788. https://doi.org/10.1108/JMTM-09-2017-0196
  • 31. Soltysova Z., Modrak V., Nazarejova J. A Multi-Criteria Assessment of Manufacturing Cell Performance Using the AHP Method. Appl. Sci. 2022, 12: 854, http:// doi.org/10.3390/app12020854. https://doi.org/10.3390/app12020854
  • 32. Syan C, Ramsoobag G. Maintenance applications of multi-criteria optimization: A review. Reliability Engineering & System Safety 2019; 190: 106520, https://doi.org/10.1016/j.ress.2019.106520
  • 33. Tekez E.,Tasdeviren G. Measuring the influence values of lean criteria on leanness. Journal of Manufacturing Technology Management 2020; 31(7): 1391-1416, https://doi.org/10.1108/JMTM-09-2019-0321
  • 34. Tiamaz Y., Souissi N. Classification of the lean implementation procedures for improving the business processes. 2018 International Conference on Intelligent Systems and Computer Vision (ISCV) 2018, pp. 1-6, https://doi.org/10.1109/ISACV.2018.8354019
  • 35. Varkova N.Y. Modernisation of production as a factor of influence on economic stability of the industrial enterprise. SHS Web of Conferences 2017; 35: 01146, https://doi.org/10.1051/shsconf/20173501146
  • 36. Werbińska-Wojciechowska S. Preventive Maintenance Models for Technical Systems. In: Technical System Maintenance: Delay-Time-Based Modelling. Cham: Springer International Publishing 2019, https://doi.org/10.1007/978-3-030-10788-8_2
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fe28a5d1-1879-4913-90f9-eb2725d256e3
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