Information Modelling Framework for Addressing Information Fragmentation in Systems Engineering
Business processes in a broad range of industries along the value-chain and product life-cycle suffer from significant challenges pertaining to information fragmentation. These challenges include inconsistent information presentation, business units and datasets, heavy and chaotic manual information exchange, unknown data validity and tolerance ranges, loss of information provenance, data incompleteness, etc. To address these challenges, we need model-based standardised product data exchange between different business units along the value-chain and product life-cycle. Existing modelling languages such as OWL are expressive, but not sufficiently usable for non-semantic experts, while typically these experts (e.g., engineers) possess the essential knowledge for creating the data models. To this end, we develop an approach named as Information Modelling Framework (IMF) that aims at user-oriented and user-friendly information modelling for engineers, facilitating and allowing for a reliable and efficient use of AI and Digital Twin technologies in industrial applications. This talk presents our research of the IMF approach and exemplify its usage with real business cases towards digitalisation of industrial processes.
Lecture at NEMO2024
Date/Time: Thursday, July 18, 2024 at 09:00