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People’s (and goods’) transport will fundamentally change due to autonomous driving and emission-reduced drives. This enables new mobility concepts, especially for inner-city transport of people. An example for such autonomous vehicles are so-called people-movers: small electrically powered busses carrying up to 15 passengers from individual departure points to individual destinations. Next to the research regarding autonomous driving and alternative types of drives, it is also necessary to study and research how future users are willing to use new types of inner-city transport. Such transport needs specific information platforms organizing the trips and routing the people mover. Those platforms do not yet exist.
As part of our research, we developed an exemplary people-mover platform architecture. We were using methods from agile software development to gather customer requirements, as well as an information logistics concept as a validated link between user requirements and the architecture. We designed the architecture using microservices to enable growth and adaptability at the same time. As the research is still going on, these characteristics are necessary in order to keep building a customer-focused platform for the inner-city mobility of the future.
The COVID-19 pandemic has shown companies that their on-premise infrastructures often reach their limits with a large number of remote accesses. The transition to cloud-based solutions could represent a more efficient alternative. However, many German companies, especially small and medium-sized enterprises (SME), are still hesitant to take this big step of transferring applications to the cloud. For this reason, this paper examines the question of whether existing migration approaches in the analysis phase fit the specific requirements of SMEs. Using a literature review methodology, we first identify and analyze determinant factors for cloud adoption in SMEs. On this basis, we analyze existing methods in the analysis phase for migrations from on-premise software to cloud solutions. We investigate whether these factors are considered in the analysis phase of the approaches and conclude their suitability for SMEs. Of the migration approaches we examined, none included all the factors we identified as relevant to SMEs. Fewer have considered all factors fully and in detail. We present the results of the literature search process in tabular form and conclude this paper with a discussion and synthesis of the literature as well as an outlook on further research fields.
For most industries, Artificial Intelligence (AI) holds substantial potentials. In the last decades, the extent of data created worldwide is exponentially increasing, and this trend is likely to continue. However, despite the prospects, many companies are not yet using AI at all or not generating added value. Often, an AI project does not exceed its pilot phase and is not scaled up. The problems to create value from AI applications in companies are manifold, especially since AI itself is diverse and there is no ‘one size fits all’ approach. One often stated obstacle, why many AI projects fail, is a missing AI strategy. This leads to isolated solutions, which do not consider synergies, scalability and seldom result in added value for the company. To create a company-specific AI strategy with a top-down approach, a generic but holistic framework is needed. This paper proposes a strategic AI procedure model that enables companies to define a specific AI strategy for successfully implementing AI solutions. In addition, we demonstrate in this paper how we apply the introduced strategic AI procedure model on an AI-based flexible monitoring and regulation system for power distribution grid operators in the context of an ongoing research project.