IFIP advances in information and communication technology
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663
Methods of machine learning (ML) are difficult for manufacturing companies to employ productively. Data science is not their core skill, and acquiring talent is expensive. Automated machine learning (Auto-ML) aims to alleviate this, democratizing machine learning by introducing elements such as low-code or no-code functionalities into its model creation process. Due to the dynamic vendor market of Auto-ML, it is difficult for manufacturing companies to successfully implement this technology. Different solutions as well as constantly changing requirements and functional scopes make a correct software selection difficult. This paper aims to alleviate said challenge by providing a longlist of requirements that companies should pay attention to when selecting a solution for their use case. The paper is part of a larger research effort, in which a structured selection process for Auto-ML solutions in manufacturing companies is designed. The longlist itself is the result of six case studies of different manufacturing companies, following the method of case study research by Eisenhardt. A total of 75 distinct requirements were identified, spanning the entire machine learning and modeling pipeline.
662
While digitization is a strategic advantage in numerous industries such as the automotive industry or mechanical engineering, other industries like the German quarrying industry have not yet established a transformation towards a digitized industry. This leads to inefficient work and inaccurate forecasting capabilities. To address these challenges, digital platforms can incentivize digitization
by supporting the capacity utilization and forecasting capability of these companies. In this paper, the quarrying industry is analyzed by a morphology and different types of companies are identified. Knowing the digital maturity of these companies and by determining the key factors to forecast demands and the capacity utilization, different operating models are derived. Combined with a morphology and the value creation system, different scenarios for the identification of platform services are examined. These scenarios are weighted in a utility analysis to get an operating model blueprint to develop and establish digital platforms in less digitized industries.
568
Numerous traditional, agile and hybrid development approaches have been proposed for the development of CPS. As the choice of development process is crucial to the success of development projects, it has become a major challenge to identify the best-suited process. This paper introduces a methodology for identifying the best-suited CPS development process, based on the individual boundary conditions for a certain development project within a company. The authors used a set of eight indicators to assess a CPS-development project. The results of the assessment were matched with CPS-development approaches. Based on the matching results a best-suited development process was selected. The application is shown for a use case in the German manufacturing industry. The developed method aims to reduce the risk of project failure due to the wrong choice of development process.