Developing an industry 4.0 readiness model using fuzzy cognitive maps approach

Monshizadeh, F, Moghadam, MRS, Mansouri, T ORCID: https://orcid.org/0000-0003-1539-5546 and Kumar, M 2022, 'Developing an industry 4.0 readiness model using fuzzy cognitive maps approach' , International Journal of Production Economics, 255 .

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Abstract

Industry 4.0, or the fourth industrial revolution, is a new paradigm in manufacturing digitalization, which provides various opportunities for enterprises. Industry 4.0 readiness models are worthy methods to aid manufacturing organizations in tracking the development of their businesses and operations. Nevertheless, there are different Industry 4.0 readiness models; no work has yet analyzed Industry 4.0 readiness degree and causal effects relationships using fuzzy cognitive maps. This paper proposes an Industry 4.0 readiness model that consists of readiness requirements obtained from the literature and validated through a mixed-method approach, including literature reviews and questionnaires. To validate the proposed Industry 4.0 readiness model, the exploratory factor analysis and confirmatory factor analysis methods are used. Fuzzy Cognitive Map is utilized to assess readiness, identify relevant concepts to improve readiness degree, implement Industry 4.0, and analyze causal relationships among concepts and dimensions. Through this model and the FCM method, managers can recognize relevant concepts and predict complicated cause-effect relationships among concepts in two states of static and dynamic analyses to increase readiness degree. The paper concludes by emphasizing managerial implications for successful applications in practice as well as future research suggestions on developing the Industry 4.0 readiness model.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: International Journal of Production Economics
Publisher: Elsevier
ISSN: 0925-5273
Depositing User: T Mansouri
Date Deposited: 26 Oct 2022 12:59
Last Modified: 26 Oct 2022 12:59
URI: https://usir.salford.ac.uk/id/eprint/65136

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