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

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.

Mapping the quantity, quality and structural indicators of Asian (48 countries and 3 territories) research productivity on cloud computing

Purpose

The purpose of this study was to map the quantity (frequency), quality (impact) and structural indicators (correlations) of research produced on cloud computing in 48 countries and 3 territories in the Asia continent.

Design/methodology/approach

To achieve the objectives of the study and scientifically map the indicators, data were extracted from the Scopus database. The extracted bibliographic data was first cleaned properly using Endnote and then analyzed using Biblioshiny and VosViewer application software. In the software, calculations include citations count; h, g and m indexes; Bradford's and Lotka's laws; and other scientific mappings.

Findings

Results of the study indicate that China remained the most productive, impactful and collaborative country in Asia. All the top 20 impactful authors were also from China. The other most researched areas associated with cloud computing were revealed to be mobile cloud computing and data security in clouds. The most prominent journal currently publishing research studies on cloud computing was “Advances in Intelligent Systems and Computing.”

Originality/value

The study is the first of its kind which identified the quantity (frequencies), quality (impact) and structural indicators (correlations) of Asian (48 countries and 3 territories) research productivity on cloud computing. The results are of great importance for researchers and countries interested in further exploring, publishing and increasing cross country collaborations related to the phenomenon of cloud computing.

Users’ perception of cybersecurity, trust and cloud computing providers’ performance

Purpose

This study aims to develop a scale to measure the cloud provider’s performance and it investigates the factors that impact that performance from the users’ perspective.

Design/methodology/approach

This paper proposes a research framework, develops hypotheses and conducts a survey to test the framework.

Findings

The results from both ordinary least square regression and structural equation modeling analyzes indicate that information technology complexity negatively and significantly affects users’ perception of the cloud computing providers’ performance. Additionally, the trust in the supervisor significantly enhances the otherwise insignificant positive relationship between providers’ cybersecurity capability and users’ perception of their providers’ performance.

Originality/value

The research makes important contributions to the cloud computing literature, as it measures users’ perception of the cloud computing provider’s performance and links it with cybersecurity, technical complexity and incorporates both the trust in the client firm’s supervisor and the strength of cybersecurity offered by cloud computing provider.

An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders' functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China.