Analysis of Effective Components In Assessing Readiness For The Deployment of The Industry 5.0 In SME Using The DANP Technique

Authors

DOI:

https://doi.org/10.31181/jidmgc21202631

Keywords:

Industry 5.0, Readiness Assessment, Effective Component Analysis, Successful Deployment, Fuzzy Delphi, Multi-Criteria Decision Making, DANP

Abstract

The main objective of the present study is to identify and rank the factors affecting the establishment of Industry 5.0 in small and medium-sized industries. To achieve this goal, a combined fuzzy Delphi approach and the DANP multi-criteria decision-making method were utilized. In the initial stage, indicators were gathered through a literature review, and then expert opinions were collected and validated using the fuzzy Delphi method. Subsequently, key components were identified in five main dimensions: technology, social factors, infrastructure, industry characteristics, and innovation. These components and their relevant sub-dimensions were prioritized using the DANP method based on expert opinions. The study's key findings reveal that sub-dimensions such as "market demand" and "product and technology development" are of high importance, followed by "providing a skilled workforce" and "human resource development." Additionally, innovation components like "investment in startups" and "development of green products" play a significant role in driving the transition to Industry 5.0. The research underscores that successful implementation of Industry 5.0 necessitates a comprehensive focus on market-driven factors, human capital development, and digital infrastructure enhancement. By presenting an analytical framework, this study can serve as a valuable resource for managers in strategic planning and decision-making during the transition to Industry 5.0.

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Published

2026-03-02

How to Cite

Hoseinpour, S., Hasani, A., & Sheikh, R. (2026). Analysis of Effective Components In Assessing Readiness For The Deployment of The Industry 5.0 In SME Using The DANP Technique. Journal of Intelligent Decision Making and Granular Computing, 2(1), 13-29. https://doi.org/10.31181/jidmgc21202631