Abstract

In agriculture, machinery is an important production asset. Many industries manage the life cycle of machines in use with the help of EAM information systems, which reduce unproductive downtime. This is relevant for agricultural production, since there is often a low utilization rate of machine change time, and in the structure of non-productive time costs, up to 30% is downtime due to technical reasons. However, EAM systems are not popular in the domestic agro-engineering field, while they can be used to automate a set of engineering management tasks, and digital solutions based on them contain additional opportunities for the implementation of production reserves. The purpose of the presented study is to improve the efficiency of life cycle management of agricultural machinery at the operational stage using the EAM information system. The object of research is the stages of development and functionality of modern information systems CMMS and EAM, integration of EAM with digital economy tools; ways of adapting and improving EAM to the specifics of domestic agricultural production; an indicator of the overall efficiency of the OEE of agricultural machinery. The subject of research is the impact of digitalization on the functionality of EAM systems; the impact of EAM on the indicator of the overall efficiency of agricultural machinery (on the example of combine harvesters). The main stages in the development of EAM are identified, where after 2011 their integration with the tools of the digital economy is observed, which translates EAM into the category of intelligent systems for managing the technical condition of machines. Recommendations for adapting EAM to the conditions of domestic agribusiness are proposed. The reference value of the OEE indicator for ACROS combine harvesters was determined, equal to 47%, and the value in real operation is 19.1%. It has been theoretically established that when using the EAM system, the shift time utilization ratio increases by 20%, and the OEE of combines increases by 1.41 times due to the reduction of machine downtime for technical reasons.

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