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Identification of the impact of the availability factor on the efficiency of production processes using the AHP and Fuzzy AHP

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Maintenance has a key impact on the efficiency of the production processes because the efficiency of the machines determines the ability of the system to produce in accordance with the assumed schedule. The key element of the system performance assessment remains the availability of technological equipment, which directly translates into the efficiency and effectiveness of the performed production tasks. Taking into account the dynamic nature of manufacturing processes, the proper selection of machinery and equipment for the implementation of specific production tasks becomes an issue of particular importance. The purpose of this research was to determine the impact of technical and non-technical factors on the material selection of machine tools for production tasks and to develop a method of supporting the selection of production resources using the AHP and Fuzzy AHP methods. The research was carried out in a manufacturing company from the automotive industry.
Słowa kluczowe
Rocznik
Strony
116--129
Opis fizyczny
Bibliogr. 26 poz., fig., tab.
Twórcy
  • Department of Management and Production Engineering, Faculty of Production Engineering and Logistics, Opole University of Technology, Poland
  • Department of Management and Production Engineering, Faculty of Production Engineering and Logistics, Opole University of Technology, Poland
  • School of Business and Entrepreneurship, D. Serikbayev East Kazakhstan Technical University, Kazakhstan
  • Department of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology, Poland
  • School of Business and Entrepreneurship, D. Serikbayev East Kazakhstan Technical University
Bibliografia
  • [1] Ahmad, R., & Kamaruddin, S. (2012). An Overview of Time-Based and Condition-Based Maintenance in Industrial Application. Computers & Industrial Engineering, 63(1), 135–149. https://doi.org/10.1016/j.cie.2012.02.002
  • [2] Al-Najjar, B. (2007). The lack of maintenance and not maintenance which cost: A model to describe and quantify the impact of vibration-based maintenance on company’s business. International Journal of Production Economics, 107, 260- 273. https://doi.org/10.1016/j.ijpe.2006.09.005
  • [3] Al-Najjar, B., & Algabroun, H. (2018). A Model for Increasing Effectiveness and Profitability of Maintenance Performance: A Case Study. In: M. Zuo, L. Ma, J. Mathe & H. Z. Huang (Eds.), Engineering Asset Management 2016. Lecture Notes in Mechanical Engineering (pp. 1–12). Springer. https://doi.org/10.1007/978-3-319-62274-3_1
  • [4] Aspinwall, E., & Elgharib, M. (2013). TPM Implementation in Large and Medium Size Organizations. Journal of Manufacturing Technology Management, 24(5), 688-710. https://doi.org/10.1108/17410381311327972
  • [5] Azizi, A. (2015). Evaluation Improvement of Production Productivity Performance Using Statistical Process Control, Overall Equipment Efficiency, and Autonomous Maintenance. Procedia Manufacturing, 2, 186–190. https://doi.org/10.1016/j.promfg.2015.07.032
  • [6] Bałdowska-Witos, P., Piotrowska, K., Kruszelnicka, W., Błaszczak, M., Tomporowski, A., Opielak, M., Kasner, R., & Flizikowski, J. (2020). Managing the uncertainty and accuracy of life cycle assessment results for the process of beverage bottle molding. Polymers, 12(6), 1320. http://doi.org/ 10.3390/polym12061320
  • [7] Bayazit, O. (2005). Use of AHP in decision-making for flexible manufacturing system. Journal of Manufacturing Technology Management, 16(7), 808–819. https://doi.org/10.1108/17410380510626204
  • [8] Blanchard, B. (2004). Logistic Engineering and Management. 5th Ed. Prentice Hall Inc.
  • [9] Burduk, A., Musiał, K., Kochańska, J., Górnicka, D., & Stetsenko, A. (2019). Tabu search and genetic algorithm for production process scheduling problem. Logforum, 15(2), 181–189. http://doi.org/10.17270/J.LOG.2019.315
  • [10] Crespo Márquez, A., Moreu de León, P., Gómez Fernández, J. F., Parra Márquez, C., & López Campos, M. (2009). The maintenance management Framework. A practical view to maintenance management, Journal of Quality in Maintenance Engineering, 15(2), 167–178. https://doi.org/10.1108/13552510910961110
  • [11] Das, S., & Chattopadhyay, A. B. (2003). Application of the analytic hierarchy process for estimating the state of tool wear. International Journal of Machine Tools & Manufacture, 43(1), 1–6. https://doi.org/10.1016/S0890-6955(02)00168-2
  • [12] Gola, A. (2014). Economic aspects of manufacturing system design. Actual Problems of Economics, 156(6), 205–212.
  • [13] Kosicka, E., Gola, A., & Pawlak, J. (2019). Application-based support of machine maintenance. IFAC-PapersOnLine, 52(10), 131–135. http://doi.org/10.1016/j.ifacol.2019.10.033
  • [14] Kutlu, A. (2012). Fuzzy failure modes and effects analysis by fuzzy TOPSIS-based fuzzy AHP. Experts Systems with Application, 39(1), 61-67. https://doi.org/10.1016/j.eswa.2011.06.044
  • [15] Madu, C. (2000). Competing through maintenance strategies. International Journal of Quality and Reliability Management, 17(9), 937–948. https://doi.org/10.1108/02656710010378752
  • [16] Maletic, D., Maletic, C., Al-Najjar, B., & Gomiscek, B. (2014). The Role of Maintenance in Improving Company’s Competitiveness and Profitability: A Case Study in A Textile Company. Journal of Manufacturing Technology Management, 25(4), 441–456. https://doi.org/10.1108/JMTM-04-2013-0033
  • [17] Pizon, J., Kulisz, M., & Lipski, J. (2021). Matrix profile implementation perspective in Industrial Internet of Things production maintenance application. Journal of Physics: Conference Series, 1736(1), 012036. http://doi.org/10.1088/1742-6596/1736/1/012036
  • [18] Rakyta, M., Fusko, M., Haluska, M., & Grznar, P. (2015). Maintenance support system for Reconfigurable manufacturing systems. Annals of DAAAM and Proceedings of the International DAAAM Symposium (pp. 1102–1108). DAAAM International. http://doi.org/ 10.2507/26th.daaam.proceedings.155
  • [19] Relich, M., & Świć, A. (2020). Parametric estimation and constraint programming-based planning and simulation of production cost of a new product. Applied Sciences, 10(18), 6330. http://doi.org/10.3390/APP10186330
  • [20] Saaty, T. (1980). The analytic hierarchy process. McGraw–Hill.
  • [21] Sagar, M. K., & Singh, D. (2012). Supplier Selection Criteria: Study of Automobile Sector in India. International Journal of Engineering Research and Development, 4(4), 34–39.
  • [22] Swic, A., & Gola, A. (2013). Economic analysis of casing parts production in a flexible manufacturing system. Actual Problems of Economics, 141(3), 526–533.
  • [23] Szabelski, J., Karpiński, R., & Machrowska, A. (2022). Application of an artificial neural network in the modelling of heat cutting effects on the strength of adhesive joints at elevated temperature with imprecisive adhesive mix ratios. Materials, 15(3), 721. http://doi.org/10.3390/ma15030721
  • [24] Varela, M. L. R., Putnik, G. D., Manupati, V. K., Rajyalakshmi, G., Trojanowska, J., & Machado, J. (2018). Collaborative manufacturing based on cloud, and on other i4.0 oriented principles and technologies: a systematic literature review and reflections. Management and Production Engineering Review, 9(3), 90–99. http://doi.org/10.24425/119538
  • [25] Vollmann, T. E., Berry, W. L., Whybark, D. C., & Jacobs, F. R. (2005). Manufacturing Planning and Control for Supply Chain Management. 5th Ed. McGraw- Hill/Irwin.
  • [26] Zubrzycki, J., Świć, A., Sobaszek, Ł., Kovac, J., Kralikova, R., Jencik, R., Smidova, N., Arapi, P., Dulencin, P., & Homza, J. (2021). Cyber-physical systems technologies as a key factor in the process of Industry 4.0 and smart manufacturing development. Applied Computer Science, 17(4), 84–99. http://doi.org/10.23743/acs-2021-31
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c365ff76-5032-4772-806d-e18f795dcc0b
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