FIR e. V. an der RWTH Aachen
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Electricity generated by wind turbines (WT) is a mainstay of the transition to renewable energy. In order to economically utilize WT is, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WT’s, are a focus of cost reduction activities. Implementing a data-driven prescriptive maintenance approach is one way to achieve this. So far, various approaches for prescriptive maintenance for onshore WT’s have been suggested.
However, little research has addressed the practical implementation considering sociotechnical aspects. The aim of this paper is therefore to identify success factors for the successful implementation of such a maintenance strategy with clear and holistic guidance on how existing knowledge on prescriptive maintenance from science can be transferred to business practice. These recommendations are developed through case study research and classified in the four structural areas of Acatech’s Industry 4.0 Maturity Index: Resources, Information Systems, Organizational Structure and Culture.
Industry 4.0 and smart factories have brought significant advancements in manufacturing processes, particularly in intralogistics. A key factor which forms the basis for creating smart intralogistics systems is data. However, there exist several data-related issues that hamper the efficiency of the intralogistics process such as data unavailability, poor data quality, inconsistent data, or underutilization of available data. The challenge is to identify, categorize, evaluate, and solve these issues. Overcoming this will help organizations understand the most impactful challenges.
By analysing real-world scenarios and interviewing industry experts, the problems present within the intralogistics process that are caused by the previously mentioned data-related factors are identified. The identified issues are clustered, and the clusters are characterized. A literature review explores the existing solutions or approaches to overcome these limitations. Subsequently finding out if the identified problems can be solved with current technologies and approaches or further research and development is needed. Next, a framework is developed which will act as a guide on the classification, evaluation and prioritization of the identified challenges. In the final part, the framework is validated on an industry specific use case and its limitations and future scope are discussed.
This master thesis emphasizes the significance of data in intralogistics processes by identifying and addressing data-related issues. The outcome on one hand is state-of-the-art solutions for the identified problems and on the other hand is a framework which will support businesses in determining how to tackle data-related issues to gain most benefit with respect to efficiency, productivity, flexibility and quality.
The advancements in Industry 4.0 technologies have provided unprecedented opportunities for optimizing material transportation through various use cases that are possible through rapid technological advance. An important driver for the use cases is data. However, the lack of understanding, which
specific data, from which sources and in what frequency, slows down the implementation of use cases or even reduces their potential benefits. Companies lack the ability to prepare themselves correctly for a use case integration, especially from the data perspective (e.g. data availability, quality, integration).
Therefore, the goal of this thesis is to create a framework for evaluation of Industry 4.0 use cases in the materials' transportation with regard to needed data. The scientific approach employed in this research involves research and analysis of existing frameworks for description or assessment of use cases in different fields and industries. Following, specific use cases related to material transportation in the context of Industry 4.0 will be identified in order to find similarities in the structure and requirements
regarding needed data, and thus identifying common characteristics and key parameters. These parameters will then serve as the foundation for developing a framework that enables companies to systematically analyse and assess potential use cases for material transportation, considering the data requirements and its integration challenges.
The expected result of this thesis is the development of a practical framework that empowers organizations to evaluate and implement Industry 4.0 use cases for material transportation effectively. By providing a structured methodology, this framework will facilitate decision-making processes and support companies in identifying the most suitable use cases based on their specific requirements and
data availability.