Albrecht Fritzsche

Data-driven operations management: organisational implications of the digital transformation in industrial practice

Abstract The ongoing digital transformation on industry has so far mostly been studied from the perspective of cyber-physical systems solutions as drivers of change. In this paper, we turn the focus to the changes in data management resulting from the introduction of new digital technologies in industry. So far, data processing activities in operations management have usually been organised according to the existing business structures inside and in-between companies. With increasing importance of Big Data in the context of the digital transformation, the opposite will be the case: business structures will evolve based on the potential to develop value streams offered on the basis of new data processing solutions. Based on a review of the extant literature, we identify the general different fields of action for operations management related to data processing. In particular, we explore the impact of Big Data on industrial operations and its organisational implications.

An Application Map for Industrial Cyber-Physical Systems

The potential transformation cyber-physical systems can bring to a broad variety of domains is widely discussed in academia and industry. Despite the expected benefits in the industrial domain of further automatization of production processes and the possibility to produce “batch size one” at large-scale production costs, the majority of organizations hesitate in the implementation of cyber-physical systems. This can be attributed to uncertainty decision makers feel, about how to choose right applications of cyber-physical systems and if chosen how to implement these applications to the unique and specific needs of their organization. To address this problem this chapter introduces an application map which includes the spheres smart factory, industrial smart data, industrial smart services, smart products, product-related smart data and product-related smart services. Based on this model, the decision makers are provided a scheme of application fields for utilizing cyber-physical architectures adjusted to their unique business situation.

The Evolution of Strategic Options, Actors, Tools and Tensions in Open Innovation

Open innovation describes innovation processes that span across the boundaries of organizations or research and development (R&D) departments. It integrates different types of innovators—regardless of their institutional affiliations—to generate creative ideas, innovation concepts and novel solutions. The purpose of this chapter is to describe underlying mechanisms and strategic options for realizing open innovation. It presents three types of innovators and their functions in innovation endeavors and introduces five classes of tools that facilitate open innovation. Finally, challenges and core tensions are discussed as a basis for the successful management of strategic open innovation initiatives.

Corporate foresight in open laboratories – a translational approach

ABSTRACT Many companies have lately started to use open laboratories for innovation. This paper looks into the added value of these practices for corporate foresight. To differentiate corporate foresight in open laboratories from innovation, the paper uses the concept of translation from cultural theory, which addresses the treatment of semantic differences in the ways how different groups of people express meaning. Based on a multiple case study, the paper collects evidence about translation practices in open laboratories and investigates how they relate to corporate foresight. The results show that translation in open laboratories enables companies to gain insight into their own situation and engage in an ongoing re-negotiation of their relation with other stakeholders in commercial interactions. This is a distinctive aspect of corporate foresight in open laboratories which has so far been widely neglected in research. It creates various opportunities for further development of foresight practices.

Open Innovation and the Core of the Engineer’s Domain

Engineering as a professional domain is usually understood as the driving force of innovation. Recently, however, various forms of open innovation have become popular that do not necessarily require engineers as contributors. How does this affect the conception of the engineer? This paper assumes that open innovation proceeds as an evolutionary process. Computational models show that such processes need careful design. Even if engineers do not appear explicitly in open innovation, they can nevertheless be considered as parts of it, inasmuch as they provide the framework in which technical progress becomes possible. The engineer’s domain should therefore not be understood as a closed sphere containing innovation, but rather as a surface organizing the environment in ways that makes it possible to address change as innovation.

Spreading Innovations: Models, Designs and Research Directions

Diffusion models are often applied in business and management research to describe the spread of innovations. Regarding the mathematical representation of the process, these models usually remain rather simple—the main challenge is to find the appropriate object of reference, because innovations can be discussed on different levels of detail, from a general purpose technology down to a specific version of a product. Furthermore, contemporary innovations hardly remain static over time, but change their properties in the course of the diffusion process, due to technical updates and changing modes of application. This turns the attention to the question whether companies and public institutions can actually create conditions under which the spread of innovations can be framed as a diffusion process, or if there are other means to make it more predictable and controllable.

Cross-fertilization vs. Collaboration in Simulations of Open Innovation

Evolutionary models allow us to approach innovation by the means of computer simulation with genetic algorithms. Open innovation can be considered in these models in different ways. A popular model by David Goldberg connects re-combinations of elements during evolutionary processes with the exchange of information in cross-fertilization activities. Another possibility is to model the collaboration of contributors with specific skills and experiences through sophisticated change operators that work systematically on improvements with respect to certain aspects of the innovation context. A simulation of this procedure on an instance of the permutation flow shop scheduling problem shows that the usage of these operators can indeed increase the performance of the solution generation, if certain constraints are kept in consideration.

Translations of Technology and the Future of Engineering

Philosophers of technology, engineers, and other experts involved with the same subject matter look at technology in different ways. This paper explores what happens if conflicts and misunderstandings between them cannot be resolved. The exchange between the different expert groups on philosophical questions concerning technology is described as a continuous practice of coping with diversity. This practice can be described as translation, because it connects otherwise unrelated expressions of meaning. It lays the foundation for any further productive treatment of technology in society and future possibilities for the development of engineering. The chapters of this book are used as an illustration of the many different faces and levels of translation in the field.

Entwicklung digitaler Servicesysteme – Akteure, Ressourcen und Aktivitäten

Im Rahmen der digitalen Transformation entstehen vielerorts neue Wertschopfungssysteme, die durch Vernetzung von Akteuren und Ressourcen uber organisationale Grenzen hinweg hybride Angebote aus Produkten und Dienstleistungen auf den Markt bringen. Unter operativen Gesichtspunkten gilt die Aufmerksamkeit der Beteiligten dabei meist der Entwicklung spezieller Losungen fur konkrete Probleme. Weitere Entwicklungen hinsichtlich der Verteilung von Aufgaben und der Zuordnung von Verantwortlichkeiten erhalten dagegen wenig Aufmerksamkeit. Der hier vorliegende Beitrag nutzt Erkenntnisse aus verschiedenen Projekten im Kontext von Industrie 4.0, um eine ubergreifende Sicht auf die Dynamiken der Entstehung digitaler Servicesysteme zu entwickeln. Die Projekte werden dazu im Hinblick auf die involvierten Akteure, Ressourcen und Aktivitaten analysiert. Um das grose Ganze in komplexen Digitalisierungsprojekten betrachten zu konnen, werden folgende Losungsstrategien abgeleitet: a) die Nutzung von Interaktionsraumen, b) das systemische Management durch Regelungscockpits und c) die Einbeziehung des gesamten Lebenszyklus eines Servicesystems.

Manufacturing with a big M – The Grand Challenges of Engineering in Digital Societies from the Perspective of the Institute for Manufacturing at Cambridge University

Engineering used to be driven by a community of experts who set themselves apart from others by establishing clear boundaries of their profession. Today, however, these boundaries have become increasingly permeable, due to the increasing dynamic and complexity of technical and economic change. The manufacturing sector illustrates this process very well. Engineering is currently becoming much more deeply involved in the usage of technical artefacts and economic questions of value creation. Engineers are therefore facing the challenge of opening up their traditional domain to collaborate with other disciplines and integrate new knowledge in their theories, concepts and procedures. This contribution shows how the Institute for Manufacturing at Cambridge University copes with this challenge, expanding the scope of topics addressed in engineering and introducing new subjects in the curriculum of the students. All this seems to be a necessary prerequisite for engineers to uphold their claims of responsibility of technical development and their contribution to well-being in society.