Knowledge Management · Artificial Intelligence · Digital Product Passport
Consulting for companies and organizations
I am offering another comprehensive (1080°) consulting service for businesses, titled "From Digital Obligation to True Competitive Advantage", in collaboration with two highly experienced partners.
360° Economic Viability & 360° Transformation & 360° Knowledge & Leadership
You can find details about this special offer in the flyer . Please feel free to contact us if you are interested or have any questions about our services.
This year, KMAIC has been supporting circulardata, an independent initiative for sharing and pooling sustainable activities, particularly in the context of the Digital Product Passport. Further details can be found at https://circular-data.org/o/f2df6055-7f34-4766-addd-ca8a6c171f25
The Digital Product Passport requires access to all relevant data as well as the "semantic interconnection" of this data within a company. Furthermore, connectivity with external data from suppliers and partners is also necessary. This interoperability—which also supports the exchange of Digital Twins—can be achieved through the use of the open standard of the Asset Administration Shell (IEC 63278-1). In addition, full interoperability requires seamless integration with existing business processes and models within the company, such as in Product Engineering / Model-Based Systems Engineering, as well as the orchestration of relationships at the data, model, and process levels. Model-based methods and descriptions (BPMN 2.0, SysML v2, AutoML, LLM, Predictive Maintenance) are already applied in many subareas of companies. The complete networking of these complex contexts and aspects can be realized using a semantic enterprise model based on World Wide Web Consortium (W3C) web standards, thereby contributing to further value creation within the company.
Note: I held this presentation in November 2025 at the tekom annual conference (Arena) in Stuttgart.
Presentation as part of tekom Deutschland e.V. events – current version 5.0 (May 2025).
This presentation from Semantics 2023 explains the key points of the European Green Deal, related sustainable product initiatives and standards, and—as a use case—concepts for implementing the Digital Product Passport (DPP) based on the latest technologies and frameworks, such as the application of the Asset Administration Shell (AAS) in combination with an Enterprise Knowledge Graph (EKG).
Download presentation (PDF, 2.5 MB)
In today's digital world, Large Language Models (LLMs) and related technologies play a central role in natural language processing and analysis. These models have the potential to fundamentally transform how companies process and utilize information. In this article, we explain the concepts of LLM, Retrieval-Augmented Generation (RAG), and GraphRAG, and how they can be applied in industry.
LLMs are powerful AI models trained to understand and generate human language. They are based on deep neural networks and trained on vast amounts of text data, enabling them to recognize complex patterns and relationships in language. LLMs are used in numerous areas, ranging from chatbots and virtual assistants to automated text analysis and generation.
Retrieval-Augmented Generation (RAG) combines the capabilities of LLMs with information retrieval systems. Instead of relying solely on the trained model, RAG accesses external data sources to provide more accurate and context-relevant information when generating answers. This method significantly improves the accuracy and relevance of generated content by incorporating up-to-date and specialized information into the process.
GraphRAG is an extension of the RAG principle that utilizes the structure of knowledge graphs to further optimize information retrieval and generation processes. Knowledge graphs represent data in a network-like structure that illustrates relationships between different pieces of information. By integrating knowledge graphs, GraphRAG can deliver deeper insights and well-founded answers by not only retrieving relevant data but also taking their interdependencies into account.
These AI technologies offer diverse application possibilities in industry. LLMs can be used to automate customer interactions or support decision-making processes. RAG and GraphRAG enable companies to utilize their data resources more efficiently and make well-founded, data-driven decisions. These technologies can provide significant competitive advantages, particularly in data-intensive sectors such as healthcare, finance, or logistics.
In summary, LLMs, RAG, and GraphRAG represent significant tools for the transformation of business processes. By integrating these technologies, companies can increase their efficiency, boost their innovative strength, and better adapt to market demands.
Digital transformation refers to a comprehensive transformation process based on the application of new digital technologies and agile approaches that affects all areas and processes of a company or institution.
In connection with the application and integration of digital technologies, one also speaks of "cognitive computing," which is based on solutions from the fields of Knowledge Discovery, Text Mining, Natural Language Processing, and Machine Learning.
The ability to adapt to changing conditions and information, along with the systematic learning capability of all components, are the most crucial capabilities of cognitive computing, in addition to considering the context of the application.
Digital transformation (as well as any knowledge management transformation) must encompass all domains within a company or institution; transforming technology alone is not sufficient (e.g., organization, employees, processes, strategy – see „Core Elements of Digital Transformation“).