Digital transformation in the manufacturing industry : technologies and architectures
Creators: name: Olivotti, Daniel
Publisher: name: Hannover : Institutionelles Repositorium der Leibniz Universität Hannover
Year: 2020
Subjects: subject: Digital Transformation
subject: Manufacturing Industry
subject: Architectures
subject: Digital Twin
subject: Product-Service-Systems
subject: Predictive Maintenance
subject: Digitale Transformation
subject: Industrie
subject: Architekturen
subject: Vorausschauende Wartung
subject: Digitaler Zwilling
subject: Produkt-Service-Systeme
subject: Dewey Decimal Classification::300 | Sozialwissenschaften, Soziologie, Anthropologie::330 | Wirtschaft

description: This cumulative dissertation aims to contribute to the field of digital transformation in the manufacturing industry and is based on several scientific publications. Special focus is given to technologies and architectures and, in particular, to three main research topics that will contribute to this area. The first research topic addresses the maintenance of industrial machines. By enhancing static maintenance intervals and shifting to conditionbased maintenance or, further, to predictive maintenance, cost and time can be saved, and the likelihood of breakdown can be reduced. Different models help to calculated the optimal number of spare parts or optimize maintenance planning. To predict machine breakdowns, not only statistical methods but also advanced data analytic techniques are necessary. The field of industrial machines is very broad, and even a single company faces the issue of having its components or machines used in several different applications. The development of analysis models is therefore challenging. Concepts for enhancing data analytic techniques through combinations of domain knowledge experience are presented in this dissertation. The growing interest in predictive maintenance has led to various business models in the manufacturing industry. A taxonomy to classify these predictive maintenance business models is presented within this dissertation. Second, a detailed image of a machine or plant can provide valuable information to operators and managers. Therefore, this dissertation addresses the topic of installed base management and digital twins. Insights into the health status of individual components or plants are necessary for timely reactions to events and to support decision making. With the help of a digital representation of a component, machine or plant, new services can also be enabled. The third research topic addresses the increasing importance being place by industry on new services for manufacturing. Products are no longer sold independently but are offered along with services as product-service systems. Furthermore, so-called smart services offer the potential for digital transformations in the manufacturing industry. These services are customer-centric and are based on the usage of various data. In addition knowledge management for smart services is considered. By combining the features described in these topics, digital transformation in the manufacturing industry is driven and enabled. This digital transformation means changes for companies in terms of the technologies and IT architectures used as well as disruptive changes to current business models. However, with the help of digital transformation, customer demand can be satisfied, processes improved or accelerated and new value networks established.