New Approach to the Analysis of Manufacturing Processes with the Support of Data Science

Abstract

This article introduces process mining as an innovative approach to enterprise data analysis, offering a systematic method for extracting, analyzing, and visualizing digital traces within information systems. The technique establishes connections within data, forming intricate process maps that serve as a foundation for the comprehensive analysis, interpretation, and enhancement of internal business processes. The article presents a methodical procedure designed to analyze processes using process mining. This methodology was validated through a case study conducted in the Fluxicon Disco software (version 3.6.7) application environment. The primary objective of this study was to propose and practically validate a methodical procedure applied to industrial practice data. Focusing on the evaluation and optimization of manufacturing processes, the study explored the integration of a software tool to enhance efficiency. The article highlights key trends in the field, providing valuable insights into process flows and identifying areas for improvement. The results contribute to the growing body of knowledge in process mining, emphasizing its applicability in fostering a more efficient and competitive manufacturing environment. In the model example, we successfully achieved a reduction in the time required for production cycles by 15% and improved resource utilization by 20%. This resulted in an increased process efficiency and a potential reduction in the required number of workers by up to 10%. These outcomes offer promising evidence of the advantages of our method and its application in an industrial setting.