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. Both consistent and inconsistent Saaty matrices were used for comparison.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. Based on them the consistency index was computed and compared. Authors calculated the values of random index needed for calculation of the consistency index in AHP for all concerned scales. However, the consistency varies among applied judgment scales. Results suggest that judgment scales have a profound impact on criteria priorities but not on ranking of criteria. Thus the focus of the paper is to analyze the impact of using different judgment scales on the resulting priorities and consistency to default scale as proposed by Saaty. The goal of this paper is to compare and discuss the application of various judgment scales on the results in particular practical example that has been used in previous paper by Saaty (2003). There has been studies that have been concerned with the comparison of judgment scales but there were no studies concerned with consistency measures that are needed. In this paper the author reviews and discusses effects of utilization of various judgment scales on priority estimation in AHP. In the recent literature many authors used different judgment scales which influenced the results and decisions. The Analytic Hierarchy Process (AHP) is widely used method in multiple-attribute decision making.
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