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Advancing digitalization is changing society and has far-reaching effects on people and companies. Fundamental to these changes are the new technological possibilities for processing data on an ever-increasing scale and for various purposes. The availability of large and high-quality data sets, especially those based on personal data, is crucial. They are used either to improve the productivity, quality, and individuality of products and services or to develop new types of services. Today, user behavior is tracked more actively and comprehensively than ever despite increasing legal requirements for protecting personal data worldwide. That increasingly raises ethical, moral, and social questions, which have moved to the forefront of the political debate, not least due to popular cases of data misuse. Given this discourse and the legal requirements, today's data management must fulfill three conditions: Legality or legal conformity of use and ethical legitimacy. Thirdly, the use of data should add value from a business perspective. Within the framework of these conditions, this cumulative dissertation pursues four research objectives with a focus on gaining a better understanding of
(1) the challenges of implementing privacy laws,
(2) the factors that influence customers' willingness to share personal data,
(3) the role of data protection for digital entrepreneurship, and
(4) the interdisciplinary scientific significance, its development, and its interrelationships.
Volatile supply and sales markets, coupled with increasing product individualization and complex production processes, present significant challenges for manufacturing companies. These must navigate and adapt to ever-shifting external and internal factors while ensuring robustness against process variabilities and unforeseen events. This has a pronounced impact on production control, which serves as the operational intersection between production planning and the shop- floor resources, and necessitates the capability to manage intricate process interdependencies effectively. Considering the increasing dynamics and product diversification, alongside the need to maintain constant production performances, the implementation of innovative control strategies becomes crucial.
In recent years, the integration of Industry 4.0 technologies and machine learning methods has gained prominence in addressing emerging challenges in production applications. Within this context, this cumulative thesis analyzes deep learning based production systems based on five publications. Particular attention is paid to the applications of deep reinforcement learning, aiming to explore its potential in dynamic control contexts. Analysis reveal that deep reinforcement learning excels in various applications, especially in dynamic production control tasks. Its efficacy can be attributed to its interactive learning and real-time operational model. However, despite its evident utility, there are notable structural, organizational, and algorithmic gaps in the prevailing research. A predominant portion of deep reinforcement learning based approaches is limited to specific job shop scenarios and often overlooks the potential synergies in combined resources. Furthermore, it highlights the rare implementation of multi-agent systems and semi-heterarchical systems in practical settings. A notable gap remains in the integration of deep reinforcement learning into a hyper-heuristic.
To bridge these research gaps, this thesis introduces a deep reinforcement learning based hyper- heuristic for the control of modular production systems, developed in accordance with the design science research methodology. Implemented within a semi-heterarchical multi-agent framework, this approach achieves a threefold reduction in control and optimisation complexity while ensuring high scalability, adaptability, and robustness of the system. In comparative benchmarks, this control methodology outperforms rule-based heuristics, reducing throughput times and tardiness, and effectively incorporates customer and order-centric metrics. The control artifact facilitates a rapid scenario generation, motivating for further research efforts and bridging the gap to real-world applications. The overarching goal is to foster a synergy between theoretical insights and practical solutions, thereby enriching scientific discourse and addressing current industrial challenges.
Die 2016 verabschiedeten Sustainable Development Goals (SDGs) der Vereinten Nationen sind Referenzrahmen von Nachhaltigkeitsstrategien auf Bundes- Landes- und kommunaler Ebene geworden. Städte rückten im Zuge der Agenda 2030 in den Mittelpunkt. Ihre Verwaltungen befinden sich dabei in einem herausfordernden Spannungsfeld: Einerseits haben die SDGs den holistischen Anspruch, vollständig in das Handeln der Kommunen integriert zu werden. Andererseits ist für eine effektive Umsetzung eine starke Anpassung der SDGs an den lokalen Kontext notwendig. Die vorliegende Arbeit betrachtet anhand einer Fallstudie die Frage, wie Kommunen die Nachhaltigkeitsziele der Vereinten Nationen in ihre Handlungsprogramme und Nachhaltigkeitsstrategien übersetzen, und welche Faktoren Einfluss auf diesen Prozess haben. Dabei wird ein translationstheoretischer Ansatz verwendet, der die Übertragung einer Idee in einen lokalen Kontext als aktiven Transfer versteht, bei dem das Handeln der beteiligten Akteure und deren Konstruktion der aufzunehmenden Idee im Fokus steht. Die Translation wird mit Hilfe von qualitativen Interviews nachvollzogen und analysiert. Die Ergebnisse zeigen, dass die SDGs zwar anhand ihrer Relevanz für die Kommune gefiltert werden, der normative Anspruch der SDGs aber erhalten bleibt und angesichts des als gering beurteilten Fortschritts der Kommune besonderes Gewicht erhält. Zentrale Einflussfaktoren für die Translation sind die verfügbaren personellen und finanziellen Ressourcen, die Akzeptanz für die SDGs in Verwaltung, Politik und Gesellschaft und nicht zuletzt das persönliche Engagement einzelner Verwaltungsmitarbeiter*innen.
Enterprise Resource Planning (ERP) system customization is often necessary because companies have unique processes that provide their competitive advantage. Despite new technological advances such as cloud computing or model-driven development, technical ERP customization options are either outdated or ambiguously formulated in the scientific literature. Using a systematic literature review (SLR) that analyzes 137 definitions from 26 papers, the result is an analysis and aggregation of technical customization types by providing clearance and aligning with future organizational needs. The results show a shift from ERP code modification in on-premises systems to interface and integration customization in cloud ERP systems, as well as emerging technological opportunities as a way for customers and key users to perform system customization. The study contributes by providing a clear understanding of given customization types and assisting ERP users and vendors in making customization decisions.