An integrated Pythagorean fuzzy-based methodology for sectoral prioritization of industry 4.0 with lean supply chain perspective

ABSTRACT

Transition strategies to industry 4.0 (I4.0) have recently become crucial for an optimal human and technology interaction environment in business areas, which aims to reach a production system with minimum errors, scraps and reworks through computerized monitoring and traceability of out-of-control situations with high-quality products and minimum cost. Thus, the transition process of I4.0 can be considered a critical decision-making problem. Since the process consists of a wide range of impact areas, such as employees, workers, environment, and society, it should be considered in a multi-way perspective to obtain an acceptable roadmap. By minimizing the loss of information, this paper proposes a fuzzy methodology, DEMATEL, Cognitive Mapping (CM), and TOPSIS techniques, to analyse the sectoral prioritization of the I4.0 transition. The DEMATEL method is applied to identify the dependencies of the criteria. The criterion ‘Suitability of technological infrastructure’ is determined as the most effective one. The CM method is used to determine the weight of the criteria, in which ‘Technological Infrastructure Suitability’, and ‘Digitalization Level of Processes’ is the most important ones, respectively. Considered sectors are also prioritized by using the TOPSIS method. Finally, comparison and sensitivity analyses are performed for discussions on the transition process and possible outcomes.

KEYWORDS:

• Industry 4.0
• multi-criteria decision-making
• transition
• lean supply chain
• Pythagorean fuzzy sets

Acknowledgments

This study is supported by The Scientific and Technological Research Council of Türkiye (TÜBITAK) under Project Number 120K051.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Funding

This study is supported by The Scientific and Technological Research Council of Türkiye (TÜBITAK) under Project Number 120K051.