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Optimization the performance of a large industry production lines using BPMN 2.0,simulation, statistical methods & machine learning

Chairetis Lykourgos

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Summary

The aim of this paper is to showcase the process modeling and the simulation of business processes, the forecast of the factory productivity in the future months, as well as to cover the relation between the productivity of an industrial unit with the seasons of the year. More specifically, in the current paper one can find the processes in linear form according to the information collected through inquires, discussions with the engineers, the handlers, and the production manager and last but not least with the quality control officers. The process models have been depicted with the use of the BPMN 2.0 method, as well as the Camunda Modeler software. Moreover, data collection and analysis was performed and the data was used in the simulation processes, at the prediction with machine learning and in the statistical method. The data analysis and the machine learning were performed with the use of MS Excel and the Python software while the simulation was done with the use of the Bimp software.

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