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The acquisition, processing and analysis of internal and external data is one of the key competitive factors for corporate innovation and competitive advantage. Many firms invest a significant amount of resources to take advantage of advanced analytics methods. Machine learning methods are used to identify patterns in structured and unstructured data and increase predictive capabilities. The related methods are of particular interest when previously undiscovered and unknown structures are discovered in comprehensive data sets in order to more accurately predict the outcome of manufacturing or production processes based on a multitude of parameter settings. So far, this knowledge is often part of the individual or collective knowledge of experts and expert teams, but rarely explicit and therefore not replicable for future applications. On the one hand, it is demonstrated in this paper how different machine learning algorithms have been applied to better predict the output quality in the process industry. On the other hand, it is explained how the application of machine learning methods could contribute to making previously not accessible process knowledge explicit. In order to increase the prognostic accuracy of the model diferrent methods were combined, later on compared and evaluated within an industrial case. In this paper a comprehensive approach to knowledge-based process engineering is being presented.
Patterns of Digitization
(2019)
This article describes the results of a survey designed to assess how companies are implementing digital transformation, including the various strategies they employ and the actions they take to achieve large-scale transformations. While a few companies seem to reach front-runner status, the majority seem to lag behind. This phenomenon is a top concern of boardrooms worldwide and motivated the development of this study. To help these organizations, we highlight differentiated strategic principles and characteristics of the companies' design processes digitally mature companies undertake to transform their businesses. These insights should help lagging companies understand what is involved in implementing a digital transformation and what they need to do to enforce this transformation.
In the course of the advancing digitalization, new business fields are characterized by a mixture of competition and cooperation of the actors involved. MOORE (1993) postulates that in analogy to natural ecosystems, long-term successful companies also operate in comparable network structures. In this context, there are pronounced controversies about the extent to which there are leading actors in such a business ecosystem and to what extent they can control the entire system. Similarly, it is largely unclear where the boundaries of a business ecosystem actually lie and how meaningful selective boundaries are. Especially the extent of the coopetition proves to be characteristic for the relationship between the involved actors. Therefore, the aim of this research approach is to develop a new approach for the analysis of corporate ecosystems. To ensure applicability, the developed approach was validated in a current case study in the telecommunications industry.