IDENTIFICATION OF THE IMPACT OF THE AVAILABILITY FACTOR ON THE EFFICIENCY OF PRODUCTION PROCESSES USING THE AHP AND FUZZY AHP METHODS
Piotr WITTBRODT
p.wittbrodt@po.edu.plDepartment of Management and Production Engineering, Faculty of Production Engineering and Logistics, Opole University of Technology (Poland)
Iwona ŁAPUŃKA
Department of Management and Production Engineering, Faculty of Production Engineering and Logistics, Opole University of Technology (Poland)
Gulzhan BAYTIKENOVA
School of Business and Entrepreneurship, D. Serikbayev East Kazakhstan Technical University (Kazakhstan)
Arkadiusz GOLA
Department of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology (Poland)
Alfiya ZAKIMOVA
School of Business and Entrepreneurship, D. Serikbayev East Kazakhstan Technical University, (Kazakhstan)
Abstract
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.
Keywords:
accessability, efficiency, production process, maintenance, AHP, Fuzzy AHPReferences
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
DOI: https://doi.org/10.1016/j.cie.2012.02.002
Google Scholar
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
DOI: https://doi.org/10.1016/j.ijpe.2006.09.005
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-319-62274-3_1
Google Scholar
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
DOI: https://doi.org/10.1108/17410381311327972
Google Scholar
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
DOI: https://doi.org/10.1016/j.promfg.2015.07.032
Google Scholar
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
DOI: https://doi.org/10.3390/polym12061320
Google Scholar
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
DOI: https://doi.org/10.1108/17410380510626204
Google Scholar
Blanchard, B. (2004). Logistic Engineering and Management. 5th Ed. Prentice Hall Inc.
Google Scholar
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
DOI: https://doi.org/10.17270/J.LOG.2019.315
Google Scholar
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
DOI: https://doi.org/10.1108/13552510910961110
Google Scholar
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
DOI: https://doi.org/10.1016/S0890-6955(02)00168-2
Google Scholar
Gola, A. (2014). Economic aspects of manufacturing system design. Actual Problems of Economics, 156(6), 205–212.
Google Scholar
Kosicka, E., Gola, A., & Pawlak, J. (2019). Application-based support of machine maintenance. IFACPapersOnLine, 52(10), 131–135. http://doi.org/10.1016/j.ifacol.2019.10.033
DOI: https://doi.org/10.1016/j.ifacol.2019.10.033
Google Scholar
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
DOI: https://doi.org/10.1016/j.eswa.2011.06.044
Google Scholar
Madu, C. (2000). Competing through maintenance strategies. International Journal of Quality and Reliability Management, 17(9), 937–948. https://doi.org/10.1108/02656710010378752
DOI: https://doi.org/10.1108/02656710010378752
Google Scholar
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
DOI: https://doi.org/10.1108/JMTM-04-2013-0033
Google Scholar
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
DOI: https://doi.org/10.1088/1742-6596/1736/1/012036
Google Scholar
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
DOI: https://doi.org/10.2507/26th.daaam.proceedings.155
Google Scholar
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
DOI: https://doi.org/10.3390/app10186330
Google Scholar
Saaty, T. (1980). The analytic hierarchy process. McGraw–Hill.
DOI: https://doi.org/10.21236/ADA214804
Google Scholar
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.
Google Scholar
Swic, A., & Gola, A. (2013). Economic analysis of casing parts production in a flexible manufacturing system. Actual Problems of Economics, 141(3), 526–533.
Google Scholar
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
DOI: https://doi.org/10.3390/ma15030721
Google Scholar
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
Google Scholar
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.
Google Scholar
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/acs2021-31
DOI: https://doi.org/10.35784/acs-2021-31
Google Scholar
Authors
Piotr WITTBRODTp.wittbrodt@po.edu.pl
Department of Management and Production Engineering, Faculty of Production Engineering and Logistics, Opole University of Technology Poland
Authors
Iwona ŁAPUŃKADepartment of Management and Production Engineering, Faculty of Production Engineering and Logistics, Opole University of Technology Poland
Authors
Gulzhan BAYTIKENOVASchool of Business and Entrepreneurship, D. Serikbayev East Kazakhstan Technical University Kazakhstan
Authors
Arkadiusz GOLADepartment of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology Poland
Authors
Alfiya ZAKIMOVASchool of Business and Entrepreneurship, D. Serikbayev East Kazakhstan Technical University, Kazakhstan
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