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Disruptions regularly hit economies. Scholars and industry experts suggest many strategies to avoid disruptions or handle them effectively. Two things are repeatedly mentioned: Increasing resilience and deploying artificial intelligence (AI) technologies. In this dissertation, we1 look at both aspects and focus our efforts on production processes. To get a well-rounded view, we apply various research methods, i.e., surveys, case studies, and systematic literature reviews (SLRs). In our first paper (Chapter 2), we investigate the perceived organizational resilience of companies in the German manufacturing industry. We perform an SLR to analyze existing research on organizational resilience measures. We see that existing (qualitative) resilience measures are complex, challenging to interpret, and therefore, hard to scale and apply across multiple industries. Based on this, we develop a novel, low-threshold resilience measure consisting of six resilience items about the past perceived internal/external resilience, current perceived internal/external resilience, and anticipated need for internal/external resilience, called the Enterprise Resilience Index (ERI). Finally, we conduct an empirical study with ~200 German experts across various industries. Our survey shows that the German manufacturing industry perceives itself as relatively resilient, with significant differences between industries and company sizes. We also see that they anticipate a high need for external resilience across industries in the future. Most strikingly, the Machinery industry shows the lowest ERI levels while it anticipates a relatively high need for resilience, showing the development need for this industry in terms of resilience. To explore the aspect of AI, we focus on waste incineration plants (WIP) in Chapter 3. WIPs have various levels of automation, but they still rely on manual operations by human operators. Consequently, the combustion process is managed rather inefficiently, and steam outputs and emission levels are not optimal. Thus, we investigate how reinforcement learning (RL) can help enhance process automation and thus optimize the combustion process, e.g., by making more frequent and diverse interventions. An RL agent is trained via trial and error with a reward function that includes the optimization criteria. Since the actual equipment, i.e., the real WIP, cannot be used as the training environment, a digital twin is built using original plant data and a neural network. The RL agent is then trained in this offline environment with the deep Q-network algorithm (DQN). Our work demonstrates that a digital twin of a WIP can be built in a data-driven way. We show that the RL agent outperforms the human operator, increasing the steam output by 7.4% and reducing the oxygen level by 3.6%. Thus, applying RL might benefit the plant operator financially due to increased output and the environment in terms of reduced emission levels. Finally, we look at a practical aspect of AI: AI readiness and adoption (Chapter 4). Many companies across various sectors have adopted AI technologies. However, the supposedly high adoption rates are misleading since many applications are rather experimental and not applied in key business areas. We believe that this limited AI adoption arises from a lack of AI readiness. We conduct a case study in the waste incineration industry with over 160 clients and investigate which strategies facilitate AI adoption in not-AI-ready industries. Based on these interactions, we distill five strategies that counter typical AI readiness barriers, thus increasing AI readiness: education, trust, customer centricity, focus, and collaboration. These strategies focus on transforming businesses just as much as necessary to prepare them for the AI technology that is supposed to be implemented. With increased AI readiness, chances for AI adoption rise. We are convinced that these strategies can be applied in various environments. In summary, this dissertation gives empirical evidence and expands the literature on organizational resilience and benchmarking, reinforcement learning and digital twins, and AI readiness and adoption.
1 The term “we” refers to the authors of the respective chapters, as noted at the beginning of each chapter.
Future oriented libraries can make use of the current start-up trend. An orientation towards new and unorthodox target groups can lead to an enhanced extension of demand and can emphasize the status of libraries. The library of the WHU – Otto Beisheim School of Management is considering to involve a new target group, start-up founders amongst their alumni. To that end, a survey was carried out and evaluated in cooperation with the Institute of Information Science at the TH Köln – University of Applied Sciences in form of a bachelor thesis, which this article is based upon. Here, a structured pre-analysis tries to determine the demand of this specific target group (founders) and develops a concept to serve the demand of this target group specifically. The example of the case study illustrates a method for target groups specific information demand and also checks the consequences for libraries and their services who venture out of their regular clientele.
Supply chain complexity
(2019)
Supply chain management (SCM) is known as a competitive strategy in today’s business. Through increasing product variety, structures and processes, managers struggle with an increase in supply chain complexity (SCC), which leads to a negative influence on a firm’s efficiency in terms of lead times, flexibility and costs. In order to manage complexity, supply chain managers need to know its drivers or define appropriate management measures. However, managers can no longer manage SCC internally. In order to cope with this challenging issue and achieve overall benefits, it is necessary that the complete supply chain is involved in optimising supply chain processes. An aligned supply chain should ensure that supply chain partners benefit from cost savings. At the same time, supply chain processes should be agile to respond to market volatility, while also being cost-efficient to survive in the highly competitive world.
In this dissertation, three research cases are conducted with different focus areas whereby first drivers for internal and external SCC are explored in a single case study. In order to gain a representative overview of relevant drivers, not only interviews with top managers from a manufacturer are held but also with key managers from suppliers and customers. With the interviews held from the upstream to downstream supply chain, this part of the dissertation sheds light on topical SCC drivers and provides some hypotheses for managing detailed and dynamic SCC. With this case, an academic contribution is provided to better understand the relevance of SCC.
In a second step, another qualitative study addresses the requirements of a supply chain. The basis for this part is the triple-A supply chain of Hau Lee (2004), where he states from a theoretical perspective the prerequisites of a supply chain, namely agility, adaptability and alignment. In this dissertation, this approach is reviewed from a managerial perspective. It is analysed which key performance indicators of a firm are affected by the three aforementioned qualities. Finally, it is presented how agility, adaptability and alignment are linked together. In order to gain a common understanding, semi-structured interviews are conducted to extend literature through managerial experience.
In a third step, a quantitative simulation creating a discrete-event simulation model is provided to evaluate inventory and logistics costs’ effects in a company that faces significant SCC. The target is to align the supply chain by changing the existing distribution channel to direct delivery. The result shall demonstrate the financial benefit for the supply chain partners, as well as illustrating the conditions and risks when changing supply chain processes.
This dissertation shows that companies are increasingly faced by SCC. In order to respond flexibly to market volatility, supply chains must be agile to ensure that financial risk shall not alarm a company’s existence. Therefore, it is recommended to align the supply chain with a collaborative supply chain model.