Digital Value Networks


Effects of digital transformation

The Digital Value Networks research area examines the effects of digital transformation at the intra- and inter-organizational level and provides methods, approaches, and tools for planning, managing, and controlling digitalization activities through practice-oriented research. At the inter-organizational level, widely ramified digital value networks are emerging that are characterized by new types of business relationships, such as digital platform models. Climate change makes it increasingly urgent to think of value creation in climate-neutral terms and to create transformation processes in the direction of climate-neutral value creation networks. Challenges, such as the decentralization of energy generation (e.g. through renewable energies), and opportunities, such as new market opportunities, can be mastered and exploited through digital technologies. Particularly promising in this regard are energy flexibilization and the development of business models that incorporate climate neutrality of value networks. The systematization and control of these increasingly complex value networks is a central task for companies, which is reflected in the Digital Value Networks research area. At the intraorganizational level, the digital transformation is also bringing about far-reaching changes: At the business model level, digital technologies enable new types of data-driven revenue models (e.g., digital-enabled product-service systems in the industrial context). To accomplish this realignment at the business model level, far-reaching changes in the organization at the process, structural and cultural levels are required in addition to new types of technological-analytical capabilities. Management has the task of orchestrating and steering these transformative activities, which can only succeed through integrated opportunity and risk management. To view these perspectives in an integrated way, the competence area combines research on Digital Transformation Management, Digital Business Models, Opportunity & Risk Management, Energy Performance Management, and Data Analytics for Industrial Applications.

Digital Transformation Management

Digitalization opens up enormous previously unrealizable opportunities for companies, but also leads to a much more dynamic market environment that reduces or partially dissolves the previously clear boundaries between industries. To remain competitive in this competitive market environment in the long term, companies must continuously transform themselves digitally. This far-reaching, socio-technical transformation includes not only technological changes, for example through process automation or data analytics, but above all changes in organizational structures and ways of thinking. We are investigating the profound changes that organizations are undergoing as part of their digital transformation and how these changes can be actively managed and consciously shaped. The latest research makes it clear that digital transformation is not a one-off process, but should be understood as an ongoing process. With our research, we want to support companies in preparing for this “continuous change” and in defining and implementing the necessary structures, mindsets, and values. We place particular emphasis on the development of the necessary organizational capabilities in various areas of the company, so that companies can successfully undergo the digital transformation and are also well prepared for future challenges.

Digital Business Models

Increasing competitive pressure is impacting companies’ value propositions and business models. Companies must continuously adapt to continue to operate successfully in the future. Digital components are now also finding their way more and more into business models in practice, and the platform idea or data-driven services are also becoming more and more present. Companies must develop new business models in ever-faster cycles and thus innovate from the ground up. Even industrial companies that have primarily focused on the manufacture of physical products to date are not immune to this change. The value proposition is increasingly enriched by digital services, resulting in the close integration of products with digital services. Overall, a “servitization” of business models can be observed. An important challenge here is the adequate pricing of these additional digital services to be able to leverage the economic potential. The implementation of new revenue models such as “pay-per-result” poses major challenges for industrial companies and SMEs in particular. Within the scope of this topic area, challenges and potentials in the course of designing new, digital business models are researched and innovative solutions are developed and tested together with companies within the scope of practice or research projects.

Opportunity and Risk Management

While digitalization is often associated with efficiency benefits and the economic potential of hybrid value creation, this development also brings with it new risks and challenges for companies. For example, digitalization initiatives and measures at various organizational levels create new challenges (e.g., in the area of IT security) that companies must address. While, for example, the introduction of a new, automated production system promises high-efficiency gains, the effects on risk management must also be taken into account, which arise, for example, through increasingly networked and dependent processes. On a technical level, new systems can lead to changes in risk maps and new threat scenarios. Continuous consideration, introduction, and adaptation of adequate mitigation measures is thus essential in the context of holistic risk management. In addition, digitalization as a socio-technical phenomenon has an impact on the organization and the individual beyond technical domains. Management is called upon to use new approaches, methods, systems, and tools to guide organizations and individuals through digitalization in the course of far-reaching changes and to anticipate potential risks and actively counteract them. The Opportunity and Risk Management research area, therefore, focuses on a differentiated view of digitization that weighs up the opportunities of digitization against the accompanying challenges. The focus of the research is on the development of novel results and artifacts that support organizations in identifying and evaluating the potential opportunities and risks of digitalization.

Energy Performance Management

The effects of climate change are becoming increasingly apparent. In this context, reducing climate-impacting emissions such as CO2 is a crucial measure to mitigate climate change. A significant proportion of said emissions arise in the energy sector with the provision and use of energy. Whether heat, electricity, or motive power, in all these sectors it is crucial to switch from fossil fuels to renewable energy sources. For this transformation to succeed, it is not only important to understand how renewable energy is provided. Rather, the use of this energy in conjunction with the impact of technological innovations and information systems on energy demand is now a central aspect of research and practice. In addition, it is important to identify relevant factors that influence investment decisions in the energy sector, or what local and socioeconomic differences exist in energy use. The area of “Energy Performance Management” deals with these socially important issues. Based on the analysis of energy data, the application of methods of artificial intelligence and machine learning as well as the development of suitable simulations, it is attempted to better understand and comprehend decisions in connection with energy use (especially the change from fossil to renewable energy sources) of individuals. Based on this, for example, the effectiveness of currently discussed climate policy instruments can be evaluated. Another application of the data analyses is the development of various decision support systems that make the future consequences of switching to renewable energy sources transparent to the user and quantify risks. In addition, in “Energy Performance Management”, application-oriented research projects optimize energy management in microgrids as well as in industrial or residential quarters to ensure a sustainable and economical energy supply. For example, intelligent energy management platforms can be developed that not only ensure grid stability in real-time and under consideration of all physical boundary conditions, but at the same time optimize the provision of energy from various innovative generation and storage facilities under economic and ecological aspects. Similarly, in the industrial context, large savings potentials of climate-impacting emissions can be leveraged if energy-intensive processes are aligned with the volatile renewable energies in terms of their flexibility in terms of CO2 -adaptive control.

Data Analytics for Industrial Applications

Data analytics, artificial intelligence (AI), and machine learning (ML) form a supporting pillar in the course of Industry 4.0. In the industrial sector, in particular, the increase in sensors and actuators and the associated networking and possibility of scalable data storage and processing represent a highly relevant driver for innovation. A broad spectrum of research and industry partners provides access to extensive application-oriented data sets that can be evaluated with advanced algorithms. The applications under consideration range from real-time capable forecasting tools and autonomous decision support systems to predictive maintenance, customer segmentation and needs analysis, to semantic data processing and federated learning. During development, well-known frameworks and analysis models such as CRISP-DM serve as procedural model. The resulting applications and solutions span a wide range of maturity levels, starting with prototypes, through the Minimum Viable Product (MVP), to (partially) automated deployment. In addition to purely technical considerations, the focus is primarily on the translation of technical algorithms into suitable digital services and business models, as well as their structuring and presentation in the form of tangible and explainable (XAI) applications. This integrated approach increases efficiency in the industry and strengthens the competitiveness of manufacturing companies.

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Prof. Dr. Björn Häckel

Professorship for Digital Value Networks