I-PRAG-6: Data analysis and process mining for industrial systems
Experiment Description
The technological experiment aimed at enhancing the management of manufacturing operations has been successfully completed. It provided detailed insights into industrial processes and supported the meaningful optimization of their order and parameters. The focus was on delivering business intelligence support through a generic environment tailored for data logging and processing. This initiative significantly improved and optimized production processes from the user's perspective, particularly benefiting small- and medium-sized industrial production companies.
One of the key accomplishments of the experiment was making advanced data analysis accessible to companies that typically do not have data scientists on staff. By simplifying the use of data logging and processing tools, the experiment empowered these companies to enhance their manufacturing operations based on informed decisions, without requiring a detailed understanding of data science. This approach led to more efficient, optimized, and user-friendly management of manufacturing processes in the participating SMEs.
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Impact
The experiment involving data from industrial systems has successfully concluded, demonstrating the critical value of such data in assessing and optimizing the entire value chains of industrial companies. The use of more efficient data analytics significantly reduced the costs associated with human-based proprietary data analysis. As a result, a better analysis of the entire value chain led to more efficient operations, improvements in processes, and other benefits that crucially impacted the success factors of the industrial company.
A key achievement of the experiment was the application of process mining methods, which enhanced the insights of human engineers and operators into technology and production processes. These methods simplified and improved analytical results, presenting significant opportunities to optimize the operation of cyber-physical production systems. The enhanced insight into processes opened up numerous opportunities for optimization, including measures to operate industrial production systems more efficiently. This led to a notable impact on operational efficiency, particularly in terms of energy demand, the number of production steps, and overall smoother and better operations.