Master's Thesis from the year 2023 in the subject Engineering - Automotive Engineering, grade: 1,5, University of Applied Sciences Aalen, language: English, abstract: Digital transformation and disruptive business models of new market players create unprecedented uncertainty in the automotive industry. One of the main reasons is the lack of understanding for the need of adapting business models to a changing business environment due to new technologies and shifting customer wants and needs. Even the automotive industry is not spared from digital Darwinism, according to which it is not the strongest or most intelligent of a species that survives, but rather the one that adapts best to change. What opportunities do OEMs have to transform their business models? How can best practices be used to generate new value creation, and what influence does the transformation of the business model have on the ecosystem? What role will the dealer play in the future, and how can he still deliver added value to the OEM? This paper provides an in-depth insight into the world of automotive sales, as well as a detailed analysis of the status quo. Best practices based on successful business model transformations of the top tier OEMs are the basis for a deeper understanding and serve other companies as foundation for developing digital business models. The goal of this paper is to examine the impact of digital transformation on the dealer business model and how they can continue to deliver value to the OEM. For this purpose, guided expert interviews are conducted with 16 experts from various areas of the automotive industry. Subsequently, a qualitative content analysis according to Mayring will be used to assess the challenges and impacts of the agent business model and to develop improvement measures and key topics that offer added value for OEMs. The results of this thesis support dealers in understanding their future role in the automotive industry and show design recommendations for a future-proof business model.

Memoir with photos of a young girl who escaped Vienna, Austriain 1938.

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Wastewater-based epidemiology provides a conceptual framework for the evaluation of the prevalence of public health related biomarkers. In the context of the Coronavirus disease-2019, wastewater monitoring emerged as a complementary tool for epidemic management. In this study, we evaluated data from six wastewater treatment plants in the region of Saxony, Germany. The study period lasted from February to December 2021 and covered the third and fourth regional epidemic wave. We collected 1065 daily composite samples and analyzed SARS-CoV-2 RNA concentrations using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Regression models quantify the relation between RNA concentrations and disease prevalence. We demonstrated that the relation is site and time specific. Median loads per diagnosed case differed by a factor of 3-4 among sites during both waves and were on average 45% higher during the third wave. In most cases, log-log-transformed data achieved better regression performance than non-transformed data and local calibration outperformed global models for all sites. The inclusion of lag / lead time, discharge and detection probability improved model performance in all cases significantly but importance of these components was also site and time specific. In all cases, models with lag / lead time and log-log-transformed data obtained satisfactory goodness-of-fit with adjusted coefficients of determination higher than 0.5. Back-estimation of testing efficiency from wastewater data confirmed state-wide prevalence estimation from individual testing statistics but revealed pronounced differences throughout the epidemic waves and among the different sites.