This master's thesis aims to provide the operational decision-makers of a mechanical engineering company, specifically Erema Engineering GmbH, with a structured and comprehensible overview of the introduction of a future-proof knowledge database. The thesis combines theory and practice by first explaining the basic concepts of knowledge management, knowledge graphs and artificial intelligence on the basis of current literature and sources. The topic is then explored in greater depth using a case study. Using an online questionnaire, the expectations of employees with regard to a modern and sustainable knowledge database are surveyed. The quantitatively collected data is processed into qualitative statements. The aim is to use a new knowledge database to increase efficiency by creating a single source of truth and boost innovation within the company. The chapters Fundamentals and Explanation of Terms create a common basis for the topics of knowledge management and semantic networking through knowledge graphs. Future trends such as artificial intelligence, large language models (LLM) and chatbots are also discussed. The development and potential of knowledge management in mechanical engineering will be demonstrated using literature and best-practice examples. The case study analyses the introduction of new knowledge management software. Firstly, the current status is determined and the target status is defined. Market research was carried out on the basis of the resulting catalogue of requirements. The closest match between requirements and performance was found in the solution from Empolis, which is why their approach and solution are analysed in more detail. A number of examples are given, primarily from the customer service sector, as structuring the data enables more efficient customer interaction. The future-proof system should offer an intuitive user interface, a very good search function and a trustworthy AI solution. The implementation and possible evaluation will only be discussed briefly. Chapter 4.2 analyses opinions on the existing software and identifies expectations of the new knowledge database. Specific recommendations for action are derived from the literature and the insights gained, which should help to sustainably improve knowledge management in organisations through the support of AI. Future research could deal with the evaluation of the new software solution in order to check the fulfilment of the expected performance and the quality of the documents. Further studies could focus on the accompanying change management, the long-term use of the new software tool or the development of new business models based on knowledge graphs. It would also be interesting to see whether modern knowledge database systems are capable of fully capturing the knowledge of departing employees.
Date of Award | 2024 |
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Original language | German (Austria) |
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Supervisor | Manuel Brunner (Supervisor) |
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Zukunftsorientiertes Wissensmanagement im Maschinenbau: Die Zusammenführung von Informationen aus verschiedenen Fachbereichen -Handlungsempfehlungen basierend auf einer Fallstudie bei der Erema GmbH
Eilmsteiner, M. (Author). 2024
Student thesis: Master's Thesis