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Prozessorientierte Messung der Customer Experience am Beispiel der Telekommunikationsindustrie
(2018)
Hohe Wettbewerbsintensität und gestiegene Kundenanforderungen erfordern bei Telekommunikationsunternehmen eine aktive Gestaltung der Customer Experience (CX). Ein wichtiger Aspekt dabei ist die CX-Messung. Traditionelle Zufriedenheitsmessungen sind oft nicht ausreichend, um die Kundenerfahrung in komplexen Prozessen vollständig zu erfassen. Daher wird in diesem Kapitel eine prozessübergreifende Referenzlösung zur CX-Messung am Beispiel der Telekommunikationsindustrie vorgeschlagen. Ausgangspunkt ist ein industriespezifisches Prozessmodell, das sich an dem Referenzmodell eTOM orientiert. Dieses wird um Messpunkte erweitert, die Schwachstellen in Bezug auf die CX identifizieren. Für die erkannten Schwachstellen werden über eine Referenzmatrix mögliche Auslöser abgeleitet und anhand von typischen Geschäftsfallmengen bewertet. Somit ist eine direkte Zuordnung und Erfolgsmessung konkreter Maßnahmen zur Behebung der Schwachstellen möglich. Die so entwickelte Referenzlösung wurde im Projekt K1 bei der Deutschen Telekom erfolgreich umgesetzt. Details zur Umsetzung werden als Fallstudien dargestellt.
Resilience as a concept has found its way into different disciplines to describe the ability of an individual or system to withstand and adapt to changes in its environment. In this paper, we provide an overview of the concept in different communities and extend it to the area of mechanical engineering. Furthermore, we present metrics to measure resilience in technical systems and illustrate them by applying them to load-carrying structures. By giving application examples from the Collaborative Research Centre (CRC) 805, we show how the concept of resilience can be used to control uncertainty during different stages of product life.
The overall energy efficiency of ventilation systems can be improved by considering not only single components, but by considering as well the interplay between every part of the system. With the help of the method "TOR" ("Technical Operations Research"), which was developed at the Chair of Fluid Systems at TU Darmstadt, it is possible to improve the energy efficiency of the whole system by considering all possible design choices programmatically. We show the ability of this systematic design approach with a ventilation system for buildings as a use case example.
Based on a Mixed-Integer Nonlinear Program (MINLP) we model the ventilation system. We use binary variables to model the selection of different pipe diameters. Multiple fans are model with the help of scaling laws. The whole system is represented by a graph, where the edges represent the pipes and fans and the nodes represents the source of air for cooling and the sinks, that have to be cooled. At the beginning, the human designer chooses a construction kit of different suitable fans and pipes of different diameters and different load cases. These boundary conditions define a variety of different possible system topologies. It is not possible to consider all topologies by hand. With the help of state of the art solvers, on the other side, it is possible to solve this MINLP.
Next to this, we also consider the effects of malfunctions in different components. Therefore, we show a first approach to measure the resilience of the shown example use case. Further, we compare the conventional approach with designs that are more resilient. These more resilient designs are derived by extending the before mentioned model with further constraints, that consider explicitly the resilience of the overall system. We show that it is possible to design resilient systems with this method already in the early design stage and compare the energy efficiency and resilience of these different system designs.
The Carologistics team participates in the RoboCup Logistics League for the seventh year. The RCLL requires precise vision,
manipulation and path planning, as well as complex high-level decision
making and multi-robot coordination. We outline our approach with an
emphasis on recent modifications to those components.
The team members in 2018 are David Bosen, Christoph Gollok, Mostafa
Gomaa, Daniel Habering, Till Hofmann, Nicolas Limpert, Sebastian Schönitz,
Morian Sonnet, Carsten Stoffels, and Tarik Viehmann.
This paper is based on the last year’s team description.
During the development of a Competence Developing Game’s (CDG) story it is indispensable to understand the target audience. Thereby, CDGs stories represent more than just the plot. The Story is about the
Setting, the Characters and the Plot. As a toolkit to support the
development of such a story, this paper introduces the UserFocused Storybuilding (short UFoS) Framework for CDGs. The Framework and its utilization will be explained, followed by a description of its development and derivation, including an empirical study. In addition, to simplify the Framework use regarding the CDG’s target audience, a new concept of Nine Psychographic Player Types will be explained. This concept of Player Types provides an approach to handle the differences in between players during the UFoS Framework use. Thereby,
this article presents a unique approach to the development of
target group-differentiated CDGs stories.
The continuing growth of scientific publications raises the question how research processes can be digitalized and thus realized more productively. Especially in information technology fields, research practice is characterized by a rapidly growing volume of publications. For the search process various information systems exist. However, the analysis of the published content is still a highly manual task. Therefore, we propose a text analytics system that allows a fully digitalized analysis of literature sources. We have realized a prototype by using EBSCO Discovery Service in combination with IBM Watson Explorer and demonstrated the results in real-life research projects. Potential addressees are research institutions, consulting firms, and decision-makers in politics and business practice.
Angesichts des anhaltenden Wachstums wissenschaftlicher Veröffentlichungen werden Instrumente benötigt, um Literaturanalysen durch Digitalisierung produktiver zu gestalten. Dieser Beitrag stellt einen Ansatz vor, der bibliographische Daten aus der Literaturdatenbank EBSCO Discovery Service mithilfe von Text-Analytics-Methoden erschließt. Die Lösung basiert auf dem Textanalysesystem IBM Watson Explorer und eignet sich für explorative Literaturanalysen, um beispielsweise den Status quo emergierender Technologiefelder in der Literatur zu reflektieren. Die generierten Ergebnisse sind in den Kontext der zunehmenden Werkzeugunterstützung des Literaturrechercheprozesses einzuordnen und können für intra- sowie interinstitutionelle Wissenstransferprozesse in Forschungs- und Beratungskontexten genutzt werden.