Abstract
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. 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. The purpose of this paper is to present a systematic selection process of ML cloud services for manufacturing SMEs. 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. We identified 24 evaluation criteria for ML cloud services relevant for SMEs by merging knowledge from manufacturing, cloud computing, and ML with practical aspects. 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. The application in other practical use cases to support SMEs and simultaneously further development is advocated.
Highlights
As part of the fourth industrial revolution and the associated digitalization of production, data, things, and processes are becoming more and more interconnected [1]
On a higher level, represented by target dimensions, we observe that the selection of cloud-based machine learning (ML) services for this case study is mainly influenced by IT security aspects, suggesting that for Small and medium-sized enterprises (SMEs) in manufacturing, the protection of sensitive process data is of great importance
An important aspect is the reliability of ML services, which manifests itself in the reputation and transparency of the provider and the promised service reliability defined in service level agreements
Summary
As part of the fourth industrial revolution and the associated digitalization of production, data, things, and processes are becoming more and more interconnected [1]. 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 [2]. The integration of cyber-physical production systems (CPPS) allows for the extraction of process data and, lays the foundation for a smart, interconnected, and sustainable manufacturing ecosystem [3]. IoT technology and services connecting physical processes with digital services enable data processing and analytics. In this context, methods for analyzing large and heterogeneous datasets are vital competencies necessary for a more efficient production [6]. By enabling machines to extract, process, and send data, large quantities of datasets can be made available for applications based on artificial intelligence (AI) [7]. AI techniques, especially machine learning (ML), are suitable for realizing intelligent systems, ensuring
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