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Analysis of the correctness of determination of the effectiveness of maintenance service actions

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EN
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EN
This paper reports the results of an analysis of indicators describing the effectiveness of actions taken and repairs made by the maintenance services in a food industry company which had implemented a new manufacturing execution system (MES) 10 months prior to the study. The application of the above effectiveness indicators plays a significant role in the rationalization of functioning of maintenance services. Therefore, it is vital that they are calculated correctly and interpreted in a way that has a positive effect on the organization of maintenance works. The paper investigates four effectiveness indicators employed by the maintenance services of the company in question, i.e., mean time to failure (MTTF), mean time between failures (MTBF), mean time to repair (MTTR) and overall equipment effectiveness (OEE). The objective of the analysis was to verify the correctness of determination of the above indicators in the analysed company. In addition, the study was to determine whether the use of correctly determined indicators and results interpretation could lead to a higher effectiveness of the actions taken by the maintenance services department. Moreover, the paper presents a diagnosis of problems connected with incorrect determination and visualization of the above-mentioned indicators in the analysed company.
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autor
  • Lublin University of Technology Department of Production Engineering Nadbystrzycka 36, 20-618 Lublin, Poland
autor
  • Lublin University of Technology, Department of Production Engineering, Poland
  • Lublin University of Technology, Department of Production Engineering, Poland
Bibliografia
  • [1] Kosicka E., Kozłowski E., Mazurkiewicz D., Intelligent Systems of Forecasting the Failure of Machinery Park and Supporting Fulfilment of Orders of Spare Parts, Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017: Proceedings of the First International Conference on Intelligent Systems in Production Engineering and Maintenance ISPEM 2017, Switzerland: Springer, 2018.
  • [2] Kłosowski G., Gola A., Risk-based estimation of manufacturing order costs with artificial intelligence, [in:] Ganzha M., Maciaszek L., Paprzycki M. [Eds.], Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FEDCSIS), IEEE, pp. 729–732, 2016.
  • [3] Legutko S., Development trends in machines operation maintenance, Eksploatacja i Niezawodność – Maintenance and Reliability, 2, 42, 8–16, 2009.
  • [4] Mączyński W., Nahirny T., Efficiency of maintenance services as a component of the efficiency of an enterprise, Materiały Konferencji Innowacji w Zarządzaniu i Inżynierii Produkcji, Zakopane, 2012.
  • [5] Sobaszek Ł., Gola A., Świć A., Preditive scheduling as a part of intelligent job scheduling system, Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017: Proceedings of the First International Conference on Intelligent Systems in Production Engineering and Maintenance ISPEM 2017, Switzerland: Springer, 2018.
  • [6] Antosz K., Pacna A., Stadnicka D., Zielecki W., Lean Manufacturing Tools, Oficyna Wydawnicza Politechniki Rzeszowskiej, Rzeszów, 2013.
  • [7] Jasiulewicz-Kaczmarek M., The role and contribution of maintenance in sustainable manufacturing, IFAC Proceedings Volumes, 46, 9, 1146–1151, 2013.
  • [8] Antosz K., Stadnicka D., Evaluation measures of machine operation effectiveness in large enterprises: study results, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 17, 1, 107–117, 2015.
  • [9] Scheibelhofer P., Gleispach D., Hayderer G., Stadlober E., A methodology for predictive maintenance in semiconductor manufacturing, The Austrian Journal of Statistics, 41, 3, 161–173, 2012.
  • [10] Ozel T., Karpat Y., ¨ Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks, International Journal of Machine Tools and Manufacture, 45, 4–5, 467– 479, 2005.
  • [11] Lee H.H.Y., Scott D., Overview of maintenance strategy, acceptable maintenance standard and resources from a building maintenance operation perspective, Journal of Building Appraisal, 4, 4, 269– 278, 2009.
  • [12] Burnos T., Performance Indicators (Part 1). Selection and definition of indicators for the needs of the organisation, Służby Utrzymania Ruchu, 4, 62–65, 2016.
  • [13] Chin-Diew L., Gwo Dong L., Mean time to failure of systems with dependent components, Applied Mathematics and Computation, 246, 103–111, 2014.
  • [14] Francisko F., Implementation of project monitoring and evaluation to improve project effectiveness and efficiency, IJBC, 5, 7, 18–34, 2016.
  • [15] Gola A., Kosicka E., Mazurkiewicz D., Daniewski K., Analysis of errors in the evaluation of OEE using the bottling line as an example, Materiały Konferencji Innowacji w Zarządzaniu i Inżynierii Produkcji, Zakopane, 2016.
  • [16] Lirong C., Haijun L., Analytical method for reliability and MTTF assessment of coherent systems with dependent components, Reliability Engineering & System Safety, 92, 3, 300–307, 2007.
  • [17] Ranteshwar Singh, Dhaval B. Shah, Ashish M. Gohil, Milesh H. Shah, Overall equipment effectiveness (OEE) calculation – automation through hardware & software development, Procedia Engineering, 51, 579–584, 2013.
  • [18] Vijaya Kumar S., Mani V.G.S., Devraj N., Production planning and process improvement in an impeller manufacturing using scheduling and OEE techniques, Procedia Materials Science, 5, 1710– 1715, 2014.
  • [19] Kashif M., Otto T., Shevtshenko E., Karaulova T., Performance evaluation by using overall equipment effectiveness (OEE): an analyzing tool, International Conference on Innovative Technologies, INTECH, pp. 185–188, 2016.
  • [20] Shin D.G., Lee S.I., Son K.S., Countermeasure for construction machinery produced using 5Why technique, International Journal of Engineering and Technology (IJET), 7, 4, 1478–1484, 2015.
  • [21] The Japan Institute of Plant Maintenance, TPM for Every Operator, ProdPublishing.com, Wrocław, 2012.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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