A MODEL FOR ASSESSING THE LEVEL OF AUTOMATION OF A MAINTENANCE DEPARTMENT USING ARTIFICIAL NEURAL NETWORK

Daniel HALIKOWSKI; Justyna PATALAS-MALISZEWSKA; Małgorzata SKRZESZEWSKA

doi:10.23743/acs-2018-30

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Published: Dec 30, 2018

DOI: https://doi.org/10.23743/acs-2018-30

DOI

https://doi.org/10.23743/acs-2018-30

Authors

Daniel HALIKOWSKI

daniel.halikowski@pwsz.nysa.pl

University of Applied Science in Nysa, Institute of Technical Science, ul. Armii Krajowej 7, 48-300 Nysa

Justyna PATALAS-MALISZEWSKA

j.patalas@iizp.uz.zgora.pl

University of Zielona Góra, Faculty of Mechanical Engineering, Institute of Computer Science and Production Management, Licealna 9 Street, 65-417 Zielona Góra

Małgorzata SKRZESZEWSKA

m.skrzeszewska@wm.uz.zgora.pl

* University of Zielona Góra, Faculty of Mechanical Engineering, Institute of Computer Science and Production Management, Licealna 9 Street, 65-417 Zielona Góra

Abstract

With regard to adapting enterprise to the Industry 4.0 concept, the first element should be the implementation and use of an information system within a manufacturing company. This article proposes a model, the use of which will allow the level of automation of a maintenance department to be forecast, depending on the effectivity of the use of the Manufacturing Executions System (MES) within a company. The model was built on the basis of the actual times of business processes completed which were supported by MES in the maintenance departments of two manufacturing enterprises using artificial neural network. As a result of research experiments, it was confirmed that the longer the time taken to complete business processes supported by MES, the higher is the degree of automation in a maintenance department.

Keywords:

Maintenance department, Artificial neural network, Manufacturing companies

References

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HALIKOWSKI, D., PATALAS-MALISZEWSKA, J., & SKRZESZEWSKA, M. (2018). A MODEL FOR ASSESSING THE LEVEL OF AUTOMATION OF A MAINTENANCE DEPARTMENT USING ARTIFICIAL NEURAL NETWORK. Applied Computer Science, 14(4), 70–80. https://doi.org/10.23743/acs-2018-30

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