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The design and development of smart products and services with data science enabled solutions forms a core topic of the current trend of digitalisation in industry. Enabling skilled staff, employees, and students to use data science in their daily work routine of designing such products and services is a key concern of higher education institutions, including universities, company workshop providers and in further education. The scope and usage scenario of this paper is to assess software modules (‘tools’) for integrated data and analytics as service (DAaaS). The tools are usually driven by machine learning, may be deployed in cloud infrastructures, and are specifically targeted at particular needs of the industrial manufacturing, production, or supply chain sector.
The paper describes existing theories and previous work, namely methods used in didactics, work done for visually designing and using machine learning algorithms (no-code / low- code tools), as well as combinations of these two topics. For tools available on the market, an extended assessment of their suitability for a set of learning scenarios and personas is discussed.
Smart services disrupt business models and have the potential to stimulate the circular economy transition of regions, enabling an environmentally friendly atmosphere for sustainable and innovation-driven growth of regions. Although smart services are powerful means for deploying circular economy goals in industrial practices, there is little systematic guidance on how the adoption of smart services could improve resource efficiency and stimulate smart regional innovation-driven growth, enabled through circular design. Implemented in the scope of Vorarlberg’s smart specialization strategy, this paper contributes to the literature on the circular economy and regional innovation-driven growth by assessing critical factors of the value creation and value capture implemented within the scope of the quadruple helix system. By identifying the main challenges and opportunities of collaborative value creation and value capture in setting-up smart circular economy strategies and by assessing the role of innovation actors within the quadruple helix innovation system, the study provides recommendations and set of guidelines for managers and public authorities in managing circular transition. Finally, based on the analysis of the role of actors in creating shared value and scaling-up smart circular economy practices in the quadruple helix innovation systems, the paper investigates the role of banks as enablers of circular economy innovation-driven regional growth and smart value creation.
Small and medium-sized enterprises often face resource deficits and there- fore depend on cooperating with other actors to stay innovative in a competitive environment. Establishing and maintaining actual co-creation and service inter- action strategies however is challenging. A reason for this is the complexity of finding methodologies and tools to create valuable outcome and the lack of knowledge of collaboration toolsets, also in virtual environments. This paper introduces an Innovation-Method-Framework consisting of innovation methods for increased service interaction and value co-creation among service stakeholders. Also, toolsets for the framework’s practical application are provided.
Recent developments in the area of Natural Language Processing (NLP) increasingly allow for the extension of such techniques to hitherto unidentified areas of application. This paper deals with the application of state-of-the-art NLP techniques to the domain of Product Safety Risk Assessment (PSRA). PSRA is concerned with the quantification of the risks a user is exposed to during product use. The use case arises from an important process of maintaining due diligence towards the customers of the company OMICRON electronics GmbH.
The paper proposes an approach to evaluate the consistency of human-made risk assessments that are proposed by potentially changing expert panels. Along the stages of this NLP-based approach, multiple insights into the PSRA process allow for an improved understanding of the related risk distribution within the product portfolio of the company. The findings aim at making the current process more transparent as well as at automating repetitive tasks. The results of this paper can be regarded as a first step to support domain experts in the risk assessment process.
To create a map of an unknown area, autonomous robots must follow a strategy to explore the area without knowing the optimal paths to reduce the time needed to map the whole area. To reduce the time to accomplish this task, multiple robots can work together to create a map in a more efficient way. However, without proper coordination, the time a team of autonomous robots needs to explore the unknown area can exceed the time needed by a single robot. To counteract the challenges, a shared infrastructure is needed which extracts useful information for the individual robots out of the shared information of all robots so the exploration can be coordinated. These measures introduce new challenges to the system, concerning the load of the communication infrastructure as well as the overall task of exploring and mapping becoming dependent on the correct communication and robustness of the shared team infrastructure. Therefore, the amount of communication and dependency of each individual robot of the rest of the other robots of the team must be reduced to ensure that the robots can continue working even if the communication with the shared infrastructure fails.