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Mobilität in NRW neu denken
(2018)
Metropolitan Cities
(2019)
Durch die Vernetzung und Mobilisierung von geografisch verteilten Orten soll eine europäische Modellmetropole mit einzigartigem Charakter entstehen. Dies haben sich zahlreiche Unternehmen, Forschungseinrichtungen und die öffentliche Hand für die Entwicklung der fünftgrößten Metropolregion Europas, der Metropolregion Rhein-Ruhr, zum Ziel gesetzt und die Initiative METROPOLITAN CITIES ins Leben gerufen. Ausgehend vom Potenzial von über 12 Mio. Einwohnern, etwa 400.000 Studierenden,den meisten Start-up-Gründungen in der Republik,etablierten Konzernen und High-Tech-Start-ups, wurde eine einzigartige Vision für METROPOLITAN CITIES formuliert: die Abschaffung aller innovations-und mobilitäisbehindernder Barrieren. Die Region soll sich verhalten und entwickeln wie „ein großes Ganzes". Ökosysteme für Innovationen sollen forciert werden und Innovationen sollen unmittelbar in der Region umgesetzt und erprobt werden. Dass dies funktionieren kann, soll anhand der Bewerbung der Metropolregion Rhein-Ruhr um die Olympischen und Paralympischen Spiele 2032 demonstriert werden.
To stay competitive, the central challenge for many companies at present is to master the process of transformation in the sense of a fundamental redesign of central processes or even of the entire company. Digitization and the need to redefine and reposition oneself in a sustainable economy are just two examples of the main drivers of this transformation. In this context, the basic question of the fundamental necessity of a comprehensive transformation in the sense of a business transformation no longer even arises for most companies in the age of digital transformation. Instead, the focus is on the question of how companies can manage the complexity associated with the scope of a transformation and the necessary changes in terms of the business strategy and on how to ensure the efficiency and success of the transformation. The challenge is to simultaneously design new structures and systems on the substantive level and also to break up established patterns of behavior.
Metropolitan Cities
(2019)
Durch die Vernetzung und Mobilisierung von geografisch verteilten Orten soll eine europäische Modellmetropole mit einzigartigem Charakter entstehen. Dies haben sich zahlreiche Unternehmen, Forschungseinrichtungen und die öffentliche Hand für die Entwicklung der fünftgrößten Metropolregion Europas zum Ziel gesetzt.
Das Paradigma von Zweckgebäuden als Immobilie, deren Wert sich lediglich über die Lage, die Ausstattung, das Alter und die verfügbare Nutzungsfläche bemisst, wird zunehmend aufgelöst. Neubauten wie der cube in Berlin, The Edge oder auch Bestandsimmobilien wie das Spark, beide in Amsterdam, zeigen, dass der Wert einer Immobilie sich zunehmend über den Grad der Vernetzung, die Integration neuer datenbasierter Dienstleistungen und somit über eine deutlich gesteigerte Effizienz im Gebäudebetrieb definiert. Der folgende Beitrag systematisiert aus der Forschungsperspektive datenbasierte Dienstleistungen für das Smart Commercial Building (SCB) und liefert eine Übersicht existenter sowie zukünftiger datenbasierter Dienstleistungen. Die Ergebnisse dienen sowohl den Herstellern technischer Gebäudeausrüstung, Gebäudebetreibern, Objektbetreuern und Investoren gleichermaßen als Orientierung und Anhaltspunkt für zukünftige datenbasierte Dienstleistungen im SCB.
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.
The almost boundless possibilities of realizing saving potentials and innovations drive manufacturing companies to implement Business Analytics as part of the digitalization roadmap. The increasing research within the field of algorithm design and the wide range of user-friendly tools simplify generating first insights from data also for non-professionals. However, small and medium sized companies struggle implementing Business Analytics company-wide due to the lack of competencies. Especially the customization of a multitude of analytic methods in order to match a superordinate, business-relevant question is not done easily. This paper enables researchers as well as practitioners to close the gap between business relevant questions and algorithms. From a practical point of view, this paper helps shortening the search time for a suitable algorithm. Out of a research perspective, it aims to help positioning new algorithms within a structured framework in order to enhance the communication of algorithms’ capabilities.
Disruptive innovations confront companies with great challenges. Leading companies are losing their market position to disruptive competitors and are forced to react instantly to defend their position in the market. Companies not only lack knowledge of various strategic options that have been successfully used against disruptive attackers, they also do not know about the effects of these different strategic options on their own company. On the basis of a use case analysis, 30 companies were examined with regard to their strategic reaction on a disruptive attacker. In the evaluation of the use cases, the strategic options were grouped into clusters, from which seven master strategies could be identified. These seven master strategies were then transformed into a regulatory framework, which differentiates between reactive and proactive strategies and classifies them according to their intensity. With the help of the identified master strategies, companies will be able to identify options for action in competition with disruptive attackers, thus giving them greater chances of success in the defense of their market position. In addition, companies can use the master strategies to prepare an emergency strategy even before a disruptive attacker appears on the market, thus significantly minimizing the risk of customer loss.
Companies in the manufacturing industry are shifting towards a more service-oriented business model. One major challenge of this transformation is the information exchange between the different stages of the product-service-lifecycle.
We extend the existing body of knowledge by conducting an empirical study in the German manufacturing industry, addressing the cause-effect relationship between 1) information gathering over the product-service-lifecycle, 2) data analytics 3) interpretation and use of new information and 4) distribution of new product related information and the impact of these four aspects on performance.
The analysis reveals five different success factors with a significant impact on innovation and operation excellence. The implications from our research can help to develop new and more practical oriented Lifecycle-Product-Service-System approaches on the one hand. On the other hand it enables companies to focus on activities leading to higher service efficiency. Creating new stimuli will transform their existing business model to a more service-oriented one.
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.