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.
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