Process Mining
Discover efficiency gains in your production and material flows through data-driven analysis and integration.
Utilise the hidden potential
of your data streams
In a world characterised by data, many industrial companies are faced with the challenge of gaining clear, actionable insights from the flood of data. It is no longer enough to simply collect data. The key lies in recognising hidden patterns and deriving concrete optimisations for your production processes.
Find out how process mining enables precise value stream mapping that is specifically tailored to the complex requirements of the manufacturing industry. This white paper will show you how targeted integration and analysis of your data can increase efficiency. Using a practical example from the industry, we illustrate how process mining can give you a competitive edge by revealing inefficiencies and enabling targeted process improvements so that you can optimise the use of your resources.
Why process mining?
Challenges in modern manufacturing:
Data isolation:
Production data often remains limited to individual machines or systems, leaving efficiency potential unutilised.
Complexity of data utilisation:
Different data formats make it difficult to analyse processes centrally.
Growing pressure to increase efficiency:
The competition never sleeps – data-based optimisation is increasingly becoming a decisive competitive advantage.
X2Log: The research project for analysing unstructured data
As part of the X2Log project, a comprehensive reference architecture was developed to apply process mining to unstructured data from industry. This project, funded by the Bavarian Research Foundation, brings together the expertise of leading partners: soffico GmbH as the provider of the Orchestra software, the Fraunhofer Institute for Applied Information Technology FIT, the RAPA Group and targenio GmbH.
Together, X2Log aims to break down fragmented data silos and create an integrated database that enables data from production, maintenance and logistics to be analysed holistically. Specific use cases will be used to show how data-based process optimisation works in practice and what efficiency potential it unleashes.
Markus Benndorff
