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Institute
- Betriebswirtschaftliches Institut (110) (remove)
EU-Project number / Contract (GA) number
Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects.
Due to computational advances in the past decades, so-called intelligent systems can learn from increasingly complex data, analyze situations, and support users in their decision-making to address them. However, in practice, the complexity of these intelligent systems renders the user hardly able to comprehend the inherent decision logic of the underlying machine learning model. As a result, the adoption of this technology, especially for high-stake scenarios, is hampered. In this context, explainable artificial intelligence offers numerous starting points for making the inherent logic explainable to people. While research manifests the necessity for incorporating explainable artificial intelligence into intelligent systems, there is still a lack of knowledge about how to socio-technically design these systems to address acceptance barriers among different user groups. In response, we have derived and evaluated a nascent design theory for explainable intelligent systems based on a structured literature review, two qualitative expert studies, a real-world use case application, and quantitative research. Our design theory includes design requirements, design principles, and design features covering the topics of global explainability, local explainability, personalized interface design, as well as psychological/emotional factors.
We develop a purchasing portfolio method by integrating a company view, a market-based view and a process view, aggregated in a 3-dimensional portfolio cube. Top management typically takes another view on purchasing issues than purchasing itself. Furthermore, it seems crucial to include the process view, since strategies have to be executed and organisational design features to support these strategies have to be compatible with purchasing processes. This integrated approach seems more complete compared to single, 2-dimensional portfolio methods.
The global selection of production sites is a very complex task of great strategic importance for Original Equipment Manufacturers (OEMs), not only to ensure their sustained competitiveness, but also due to the sizeable long-term investment associated with a production site. With this in mind, this work develops a process model with which OEMs can select the most appropriate production site for their specific production activity in practice. Based on a literature analysis, the process model is developed by determining all necessary preparation, by defining the properties of the selection process model, providing all necessary instructions for choosing and evaluating location factors, and by laying out the procedure of the selection process model. Moreover, the selection process model includes a discussion of location factors which are possibly relevant for OEMs when selecting a production site. This discussion contains a description and, if relevant, a macroeconomic analysis of each location factor, an explanation of their relevance for constructing and operating a production site, additional information for choosing relevant location factors, and information and instructions on evaluating them in the selection process model. To be successfully applicable, the selection process model is developed based on the assumption that the production site must not be selected in isolation, but as part of the global production network and supply chain of the OEM and, additionally, to advance the OEM’s related strategic goals. Furthermore, the selection process model is developed on the premise that a purely quantitative model cannot realistically solve an OEM’s complex selection of a production site, that the realistic analysis of the conditions at potential production sites requires evaluating the changes of these conditions over the planning horizon of the production site and that the future development of many of these conditions can only be assessed with uncertainty.
Frequent acquisition activities in high-technology industries are due to the intense competition, driven by short product life cycles, more complex products/services and prevalent network effects. This dissertation theoretically analyzes the circumstances leading to technology-driven acquisitions and empirically tests these within a clearly defined market scenario.
Additive Fertigung – oftmals plakativ „3D-Druck“ genannt – bezeichnet eine Fertigungstechnologie, die die Herstellung physischer Gegenstände auf Basis digitaler, dreidimensionaler Modelle ermöglicht. Das grundlegende Funktionsprinzip und die Gemeinsamkeit aller additiven bzw. generativen Fertigungsverfahren ist die schichtweise Erzeugung des Objekts. Zu den wesentlichen Vorteilen der Technologie gehört die Designfreiheit, die die Integration komplexer Geometrien erlaubt.
Aufgrund der zunehmenden Verfügbarkeit kostengünstiger Geräte für den Heimgebrauch und der wachsenden Marktpräsenz von Druckdienstleistern steht die Technologie erstmals Endkunden in einer Art und Weise zur Verfügung wie es vormals, aufgrund hoher Kosten, lediglich großen Konzernen vorbehalten war. Infolgedessen ist die additive Fertigung vermehrt in den Fokus der breiten Öffentlichkeit geraten. Jedoch haben sich Wissenschaft und Forschung bisher vor allem mit Verfahrens- und Materialfragen befasst. Insbesondere Fragestellungen zu wirtschaftlichen und gesellschaftlichen Auswirkungen haben hingegen kaum Beachtung gefunden. Aus diesem Grund untersucht die vorliegende Dissertation die vielfältigen Implikationen und Auswirkungen der Technologie.
Zunächst werden Grundlagen der Fertigungstechnologie erläutert, die für das Verständnis der Arbeit eine zentrale Rolle spielen. Neben dem elementaren Funktionsprinzip der Technologie werden relevante Begrifflichkeiten aus dem Kontext der additiven Fertigung vorgestellt und zueinander in Beziehung gesetzt.
Im weiteren Verlauf werden dann Entwicklung und Akteure der Wertschöpfungskette der additiven Fertigung skizziert. Anschließend werden diverse Geschäftsmodelle im Kontext der additiven Fertigung systematisch visualisiert und erläutert. Ein weiterer wichtiger Aspekt sind die zu erwartenden wirtschaftlichen Potentiale, die sich aus einer Reihe technischer Charakteristika ableiten lassen. Festgehalten werden kann, dass der Gestaltungsspielraum von Fertigungssystemen hinsichtlich Komplexität, Effizienzsteigerung und Variantenvielfalt erweitert wird. Die gewonnenen Erkenntnisse werden außerdem genutzt, um zwei Vertreter der Branche exemplarisch mithilfe von Fallstudien zu analysieren.
Eines der untersuchten Fallbeispiele ist die populäre Online-Plattform und -Community Thingiverse, die das Veröffentlichen, Teilen und Remixen einer Vielzahl von druckbaren digitalen 3D-Modellen ermöglicht. Das Remixen, ursprünglich bekannt aus der Musikwelt, wird im Zuge des Aufkommens offener Online-Plattformen heute beim Entwurf beliebiger physischer Dinge eingesetzt. Trotz der unverkennbaren Bedeutung sowohl für die Quantität als auch für die Qualität der Innovationen auf diesen Plattformen, ist über den Prozess des Remixens und die Faktoren, die diese beeinflussen, wenig bekannt. Aus diesem Grund werden die Remix-Aktivitäten der Plattform explorativ analysiert. Auf Grundlage der Ergebnisse der Untersuchung werden fünf Thesen sowie praxisbezogene Empfehlungen bzw. Implikationen formuliert. Im Vordergrund der Analyse stehen die Rolle von Remixen in Design-Communities, verschiedene Muster im Prozess des Remixens, Funktionalitäten der Plattform, die das Remixen fördern und das Profil der remixenden Nutzerschaft.
Aufgrund enttäuschter Erwartungen an den 3D-Druck im Heimgebrauch wurde dieser demokratischen Form der Produktion kaum Beachtung geschenkt. Richtet man den Fokus jedoch nicht auf die Technik, sondern die Hobbyisten selbst, lassen sich neue Einblicke in die zugrunde liegenden Innovationsprozesse gewinnen. Die Ergebnisse einer qualitativen Studie mit über 75 Designern zeigen unter anderem, dass Designer das Konzept des Remixens bereits verinnerlicht haben und dieses über die Plattform hinaus in verschiedenen Kontexten einsetzen. Ein weiterer Beitrag, der die bisherige Theorie zu Innovationsprozessen erweitert, ist die Identifikation und Beschreibung von sechs unterschiedlichen Remix-Prozessen, die sich anhand der Merkmale Fähigkeiten, Auslöser und Motivation unterscheiden lassen.
Advanced Analytics in Operations Management and Information Systems: Methods and Applications
(2019)
The digital transformation of business and society presents enormous potentials for companies across all sectors. Fueled by massive advances in data generation, computing power, and connectivity, modern organizations have access to gigantic amounts of data. Companies seek to establish data-driven decision cultures to leverage competitive advantages in terms of efficiency and effectiveness. While most companies focus on descriptive tools such as reporting, dashboards, and advanced visualization, only a small fraction already leverages advanced analytics (i.e., predictive and prescriptive analytics) to foster data-driven decision-making today. Therefore, this thesis set out to investigate potential opportunities to leverage prescriptive analytics in four different independent parts.
As predictive models are an essential prerequisite for prescriptive analytics, the first two parts of this work focus on predictive analytics. Building on state-of-the-art machine learning techniques, we showcase the development of a predictive model in the context of capacity planning and staffing at an IT consulting company. Subsequently, we focus on predictive analytics applications in the manufacturing sector. More specifically, we present a data science toolbox providing guidelines and best practices for modeling, feature engineering, and model interpretation to manufacturing decision-makers. We showcase the application of this toolbox on a large data-set from a German manufacturing company.
Merely using the improved forecasts provided by powerful predictive models enables decision-makers to generate additional business value in some situations. However, many complex tasks require elaborate operational planning procedures. Here, transforming additional information into valuable actions requires new planning algorithms. Therefore, the latter two parts of this thesis focus on prescriptive analytics. To this end, we analyze how prescriptive analytics can be utilized to determine policies for an optimal searcher path problem based on predictive models. While rapid advances in artificial intelligence research boost the predictive power of machine learning models, a model uncertainty remains in most settings. The last part of this work proposes a prescriptive approach that accounts for the fact that predictions are imperfect and that the arising uncertainty needs to be considered. More specifically, it presents a data-driven approach to sales-force scheduling. Based on a large data set, a model to predictive the benefit of additional sales effort is trained. Subsequently, the predictions, as well as the prediction quality, are embedded into the underlying team orienteering problem to determine optimized schedules.
Recent computing advances are driving the integration of artificial intelligence (AI)-based systems into nearly every facet of our daily lives. To this end, AI is becoming a frontier for enabling algorithmic decision-making by mimicking or even surpassing human intelligence. Thereupon, these AI-based systems can function as decision support systems (DSSs) that assist experts in high-stakes use cases where human lives are at risk. All that glitters is not gold, due to the accompanying complexity of the underlying machine learning (ML) models, which apply mathematical and statistical algorithms to autonomously derive nonlinear decision knowledge. One particular subclass of ML models, called deep learning models, accomplishes unsurpassed performance, with the drawback that these models are no longer explainable to humans. This divergence may result in an end-user’s unwillingness to utilize this type of AI-based DSS, thus diminishing the end-user’s system acceptance.
Hence, the explainable AI (XAI) research stream has gained momentum, as it develops techniques to unravel this black-box while maintaining system performance. Non-surprisingly, these XAI techniques become necessary for justifying, evaluating, improving, or managing the utilization of AI-based DSSs. This yields a plethora of explanation techniques, creating an XAI jungle from which end-users must choose. In turn, these techniques are preliminarily engineered by developers for developers without ensuring an actual end-user fit. Thus, it renders unknown how an end-user’s mental model behaves when encountering such explanation techniques.
For this purpose, this cumulative thesis seeks to address this research deficiency by investigating end-user perceptions when encountering intrinsic ML and post-hoc XAI explanations. Drawing on this, the findings are synthesized into design knowledge to enable the deployment of XAI-based DSSs in practice. To this end, this thesis comprises six research contributions that follow the iterative and alternating interplay between behavioral science and design science research employed in information systems (IS) research and thus contribute to the overall research objectives as follows: First, an in-depth study of the impact of transparency and (initial) trust on end-user acceptance is conducted by extending and validating the unified theory of acceptance and use of technology model. This study indicates both factors’ strong but indirect effects on system acceptance, validating further research incentives. In particular, this thesis focuses on the overarching concept of transparency. Herein, a systematization in the form of a taxonomy and pattern analysis of existing user-centered XAI studies is derived to structure and guide future research endeavors, which enables the empirical investigation of the theoretical trade-off between performance and explainability in intrinsic ML algorithms, yielding a less gradual trade-off, fragmented into three explainability groups. This includes an empirical investigation on end-users’ perceived explainability of post-hoc explanation types, with local explanation types performing best. Furthermore, an empirical investigation emphasizes the correlation between comprehensibility and explainability, indicating almost significant (with outliers) results for the assumed correlation. The final empirical investigation aims at researching XAI explanation types on end-user cognitive load and the effect of cognitive load on end-user task performance and task time, which also positions local explanation types as best and demonstrates the correlations between cognitive load and task performance and, moreover, between cognitive load and task time. Finally, the last research paper utilizes i.a. the obtained knowledge and derives a nascent design theory for XAI-based DSSs. This design theory encompasses (meta-) design requirements, design principles, and design features in a domain-independent and interdisciplinary fashion, including end-users and developers as potential user groups. This design theory is ultimately tested through a real-world instantiation in a high-stakes maintenance scenario.
From an IS research perspective, this cumulative thesis addresses the lack of research on perception and design knowledge for an ensured utilization of XAI-based DSS. This lays the foundation for future research to obtain a holistic understanding of end-users’ heuristic behaviors during decision-making to facilitate the acceptance of XAI-based DSSs in operational practice.
This dissertation is divided into three studies by addressing the following constitutive research questions in the context of the biotechnology industry: (1) How do different types of inter-firm alliances influence a firm’s R&D activity? (2) How does an increasing number and diversity of alliances in a firm’s alliance portfolio affect its R&D activity? (3) What is the optimal balance between exploration and exploitation? (1) To answer these research questions the first main chapter analyzes the impact of different types of alliances on the R&D activities of successful firms in the biotechnology industry. Following the use of a new approach to measuring changes in research activities, the results show that alliances are used to specialize in a certain research field, rather than to enter a completely new market. This effect becomes smaller when the equity involvement of the partners in the alliance project increases. (2) The second main chapter analyzes the impact on innovation output of having heterogeneous partners in a biotechnology firm’s alliance portfolio. Previous literature has stressed that investment in the heterogeneity of partners in an alliance portfolio is more important than merely engaging in multiple collaborative agreements. The analysis of a unique panel dataset of 20 biotechnology firms and their 8,602 alliances suggests that engaging in many alliances generally has a positive influence on a firm’s innovation output. Furthermore, maintaining diverse alliance portfolios has an inverted U-shaped influence on a firm’s innovation output, as managerial costs and complexity levels become too high. (3) And the third main chapter investigates whether there is an optimal balance to be found between explorative and exploitative innovation strategies. Previous literature states that firms that are ambidextrous (i.e., able to focus on exploration and exploitation simultaneously) tend to be more successful. Using a unique panel dataset of 20 leading biotechnology firms and separating their explorative and exploitative research, the chapter suggests that firms seeking to increase their innovation output should avoid imbalances between their explorative and exploitative innovation strategies. Furthermore, an inverted U-shaped relationship between a firm’s relative research attention on exploration and its innovation output is found. This dissertation concludes with the results of the dissertation, combines the findings, gives managerial implications and proposes areas for potential further research.
Allocation planning describes the process of allocating scarce supply to individual customers in order to prioritize demands from more important customers, i.e. because they request a higher service-level target. A common assumption across publications is that allocation planning is performed by a single planner with the ability to decide on the allocations to all customers simultaneously. In many companies, however, there does not exist such a central planner and, instead, allocation planning is a decentral and iterative process aligned with the company's multi-level hierarchical sales organization.
This thesis provides a rigorous analytical and numerical analysis of allocation planning in such hierarchical settings. It studies allocation methods currently used in practice and shows that these approaches typically lead to suboptimal allocations associated with significant performance losses. Therefore, this thesis provides multiple new allocation approaches which show a much higher performance, but still are simple enough to lend themselves to practical application. The findings in this thesis can guide decision makers when to choose which allocation approach and what factors are decisive for their performance. In general, our research suggests that with a suitable hierarchical allocation approach, decision makers can expect a similar performance as under centralized planning.