TY - CHAP A1 - Schmitz, Manfred A1 - Dietze, Christian A1 - Czarnecki, Christian ED - Urbach, Nils ED - Röglinger, Maximilian T1 - Enabling digital transformation through robotic process automation at Deutsche Telekom T2 - Enabling digital transformation through robotic process automation at Deutsche Telekom N2 - Due to the high number of customer contacts, fault clearances, installations, and product provisioning per year, the automation level of operational processes has a significant impact on financial results, quality, and customer experience. Therefore, the telecommunications operator Deutsche Telekom (DT) has defined a digital strategy with the objectives of zero complexity and zero complaint, one touch, agility in service, and disruptive thinking. In this context, Robotic Process Automation (RPA) was identified as an enabling technology to formulate and realize DT’s digital strategy through automation of rule-based, routine, and predictable tasks in combination with structured and stable data. Y1 - 2019 SN - 978-3-319-95272-7 U6 - https://doi.org/10.1007/978-3-319-95273-4_2 SP - 15 EP - 33 PB - Springer CY - Cham ER - TY - CHAP A1 - Leise, Philipp A1 - Altherr, Lena A1 - Simon, Nicolai A1 - Pelz, Peter F. T1 - Finding global-optimal gearbox designs for battery electric vehicles T2 - Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019 N2 - In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements. KW - Powertrain KW - Gearbox KW - Optimization KW - BEV KW - WLTP Y1 - 2019 SN - 978-3-030-21802-7 U6 - https://doi.org/10.1007/978-3-030-21803-4_91 SP - 916 EP - 925 PB - Springer CY - Cham ER - TY - CHAP A1 - Stenger, David A1 - Altherr, Lena A1 - Abel, Dirk T1 - Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead T2 - Operations Research Proceedings 2018 N2 - In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead. KW - Product family optimization KW - Mixed-integer nonlinear black-box optimization KW - Engineering optimization KW - Machine learning Y1 - 2019 SN - 978-3-030-18499-5 (Print) SN - 978-3-030-18500-8 (Online) U6 - https://doi.org/10.1007/978-3-030-18500-8_47 SP - 379 EP - 385 PB - Springer CY - Cham ER - TY - CHAP A1 - Ibanez-Sanchez, Gema A1 - Wolf, Martin T1 - Interactive Process Mining-Induced Change Management Methodology for Healthcare T2 - Interactive Process Mining in Healthcare N2 - The adoption of the Digital Health Transformation is a tremendous paradigm change in health organizations, which is not a trivial process in reality. For that reason, in this chapter, it is proposed a methodology with the objective to generate a changing culture in healthcare organisations. Such a change culture is essential for the successful implementation of any supporting methods like Interactive Process Mining. It needs to incorporate (mostly) new ways of team-based and evidence-based approaches for solving structural problems in a digital healthcare environment. KW - Methodology KW - Change culture KW - Lean thinking KW - Interactive process mining KW - Objective data Y1 - 2020 SN - 978-3-030-53993-1 (Online) SN - 978-3-030-53992-4 (Print) U6 - https://doi.org/10.1007/978-3-030-53993-1_16 SP - 267 EP - 293 PB - Springer CY - Cham ER - TY - CHAP A1 - Schneider, Dominik A1 - Wisselink, Frank A1 - Nölle, Nikolai A1 - Czarnecki, Christian ED - Bruhn, Manfred ED - Hadwich, Karsten T1 - Influence of artificial intelligence on commercial interactions in the consumer market T2 - Automatisierung und Personalisierung von Dienstleistungen : Methoden – Potenziale – Einsatzfelder N2 - Recently, novel AI-based services have emerged in the consumer market. AI-based services can affect the way consumers take commercial decisions. Research on the influence of AI on commercial interactions is in its infancy. In this chapter, a framework creating a first overview of the influence of AI on commercial interactions is introduced. This framework summarizes the findings of comparing numerous customer journeys of novel AI-based services with corresponding non-AI equivalents. Y1 - 2020 SN - 978-3-658-30167-5 (Print) SN - 978-3-658-30168-2 (Online) U6 - https://doi.org/10.1007/978-3-658-30168-2_7 SP - 183 EP - 205 PB - Springer Gabler CY - Wiesbaden ER - TY - CHAP A1 - Altherr, Lena A1 - Leise, Philipp T1 - Resilience as a concept for mastering uncertainty T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78353-2 U6 - https://doi.org/10.1007/978-3-030-78354-9 N1 - Unterkapitel 6.3.1 des Kapitels "Strategies for Mastering Uncertainty" SP - 412 EP - 417 PB - Springer CY - Cham ER - TY - CHAP A1 - Altherr, Lena A1 - Leise, Philipp A1 - Pfetsch, Marc E. A1 - Schmitt, Andreas T1 - Optimal design of resilient technical systems on the example of water supply systems T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78356-3 N1 - Unterkapitel des Kapitels "Strategies for Mastering Uncertainty" SP - 429 EP - 433 PB - Springer CY - Cham ER - TY - CHAP A1 - Leise, Philipp A1 - Altherr, Lena T1 - Experimental evaluation of resilience metrics in a fluid system T2 - Mastering Uncertainty in Mechanical Engineering Y1 - 2021 SN - 978-3-030-78356-3 N1 - Unterkapitel des Kapitels "Strategies for Mastering Uncertainty" SP - 442 EP - 447 PB - Springer CY - Cham ER - TY - CHAP A1 - Czarnecki, Christian A1 - Hong, Chin-Gi A1 - Schmitz, Manfred A1 - Dietze, Christian ED - Urbach, Nils ED - Röglinger, Maximilian ED - Kautz, Karlheinz ED - Alias, Rose Alinda ED - Saunders, Carol ED - Wiener, Martin T1 - Enabling digital transformation through cognitive robotic process automation at Deutsche Telekom Services Europe T2 - Digitalization Cases Vol. 2 : Mastering digital transformation for global business N2 - Subject of this case is Deutsche Telekom Services Europe (DTSE), a service center for administrative processes. Due to the high volume of repetitive tasks (e.g., 100k manual uploads of offer documents into SAP per year), automation was identified as an important strategic target with a high management attention and commitment. DTSE has to work with various backend application systems without any possibility to change those systems. Furthermore, the complexity of administrative processes differed. When it comes to the transfer of unstructured data (e.g., offer documents) to structured data (e.g., MS Excel files), further cognitive technologies were needed. Y1 - 2021 SN - 978-3-030-80002-4 (Print) SN - 978-3-030-80003-1 (Online) U6 - https://doi.org/10.1007/978-3-030-80003-1 SP - 123 EP - 138 PB - Springer CY - Cham ER - TY - CHAP A1 - Czarnecki, Christian A1 - Fettke, Peter ED - Czarnecki, Christian ED - Fettke, Peter T1 - Robotic process automation : Positioning, structuring, and framing the work T2 - Robotic process automation : Management, technology, applications N2 - Robotic process automation (RPA) has attracted increasing attention in research and practice. This chapter positions, structures, and frames the topic as an introduction to this book. RPA is understood as a broad concept that comprises a variety of concrete solutions. From a management perspective RPA offers an innovative approach for realizing automation potentials, whereas from a technical perspective the implementation based on software products and the impact of artificial intelligence (AI) and machine learning (ML) are relevant. RPA is industry-independent and can be used, for example, in finance, telecommunications, and the public sector. With respect to RPA this chapter discusses definitions, related approaches, a structuring framework, a research framework, and an inside as well as outside architectural view. Furthermore, it provides an overview of the book combined with short summaries of each chapter. KW - Robotic process automation KW - management KW - technology KW - applications KW - research framework Y1 - 2021 SN - 978-3-11-067668-6 (Print) SN - 978-3-11-067669-3 (PDF) SN - 978-3-11-067677-8 (ePub) U6 - https://doi.org/10.1515/9783110676693-202 SP - 3 EP - 24 PB - De Gruyter CY - Oldenbourg ER -