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Investigating the readiness of manufacturing industries to implement Industry 4.0 technologies: Machine learning-based approach
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DOI：10.1080/0951192X.2026.2622978.png)
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Abstract
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Transformations in business environments play a crucial role in driving the advent of Industry 4.0 (I4.0). This paradigm shift is marked by significant technological advancements, accelerated globalization, intensified competition, and the emergence of new challenges. The main objective of this research is to assess the readiness of the manufacturing industry in Bushehr province to adopt I4.0 technologies utilizing Machine Learning (ML) algorithms. To address this, the study (i) evaluates industrial readiness using the IMPULS model refined through expert input, (ii) applies a hybrid K-means-Particle Swarm Optimization (PSO) approach to cluster manufacturing industries based on their readiness levels, (iii) analyzes the performance of each cluster across I4.0 dimensions and identifies areas for improvement, and (iv) employs Neural Networks (NNs) to predict the readiness performance of industries. The results show two distinct clusters: industries in Cluster 1 perform poorly across all dimensions, while those in Cluster 2 exhibit higher readiness in ‘Smart Operations’, ‘Data-driven Services’, and ‘Employee Management’, moderate performance in ‘Smart Factory’ and ‘Strategy’, and weaker performance in ‘Cost’. These findings underscore the need to strengthen digital infrastructure and workforce capabilities to advance I4.0 implementation.
Keywords:
Industry 4.0 Readiness
manufacturing industries
Machine Learning
K-means-PSO
Neural Network
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