A Systematic Selection Process of Machine Learning Cloud Services for Manufacturing SMEs

Title data

Kaymakci, Can ; Wenninger, Simon ; Pelger, Philipp ; Sauer, Alexander:
A Systematic Selection Process of Machine Learning Cloud Services for Manufacturing SMEs.
In: Computers. Vol. 11 (2022) Issue 1 . - 14.
ISSN 2073-431X
DOI: https://doi.org/10.3390/computers11010014

Project information

Abstract in another language

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.