Particularly for small and medium-sized enterprises (SME), implementing new digital solutions, such as AI systems, is often associated with challenges and, therefore, is not widespread. In practice, integrating AI systems still poses major challenges for manufacturing companies, especially in the fields of data quality, data processing, model selection, and cybersecurity. In theory, AI, which is often considered as automation of rational behavior, facilitates various use cases such as quality improvement, process control, demand planning, or logistics. AI techniques, especially machine learning (ML), are suitable for realizing intelligent systems, ensuring continuous process optimization, and transforming more sustainable energy usage in manufacturing. By enabling machines to extract, process, and send data, large quantities of datasets can be made available for applications based on artificial intelligence (AI). In this context, methods for analyzing large and heterogeneous datasets are vital competencies necessary for a more efficient production. Connecting machines with IoT technology and services connecting physical processes with digital services enable data processing and analytics. Ongoing digitalization and the implementation of CPPS in factories lead to the generation of large amounts of data. The integration of cyber-physical production systems (CPPS) allows for the extraction of process data and, consequently, lays the foundation for a smart, interconnected, and sustainable manufacturing ecosystem. The increasing connectivity is based on the Internet of Things (IoT), which is characterized by integrating technology-enabled physical objects into a cyber-physical network. The application in other practical use cases to support SMEs and simultaneously further development is advocated.Īs part of the fourth industrial revolution and the associated digitalization of production, data, things, and processes are becoming more and more interconnected. The paper provides an interdisciplinary, hands-on, and easy-to-understand decision support system that lowers the barriers to the adoption of ML cloud services and supports digital transformation in manufacturing SMEs. We identified 24 evaluation criteria for ML cloud services relevant for SMEs by merging knowledge from manufacturing, cloud computing, and ML with practical aspects. Following a design science research approach, including a literature review and qualitative expert interviews, as well as a case study of a German manufacturing SME, this paper presents a four-step process to select ML cloud services for SMEs based on an analytic hierarchy process. The purpose of this paper is to present a systematic selection process of ML cloud services for manufacturing SMEs. Although literature covers a variety of frameworks related to the adaptation of cloud solutions, cloud-based ML solutions in SMEs are not yet widespread, and an end-to-end process for ML cloud service selection is lacking. Small and medium-sized enterprises (SMEs) in manufacturing are increasingly facing challenges of digital transformation and a shift towards cloud-based solutions to leveraging artificial intelligence (AI) or, more specifically, machine learning (ML) services.
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