Smart Manufacturing Adoption and Its Effect on Operational Agility: Evidence from Post-Pandemic Industrial SMEs

• Abbas Ahsun

  Texas University

  Author

The post-pandemic period has compelled industrial small and medium-sized enterprises (SMEs) to reconfigure operations for heightened responsiveness, yet the extent to which smart manufacturing adoption translates into operational agility remains inadequately quantified. Existing studies have examined Industry 4.0 readiness or agility independently, but few have modelled the predictive relationship between specific smart manufacturing capabilities and agility outcomes using robust analytical approaches. This study addresses this gap by developing and validating a predictive framework linking smart manufacturing adoption dimensions—digital process integration, real-time data utilisation, and automated production control—to operational agility in post-pandemic industrial SMEs. Using survey data from 312 manufacturing SMEs and employing partial least squares structural equation modelling (PLS-SEM) complemented by machine learning-based feature importance analysis, the study identifies digital process integration as the strongest predictor of agility (β = 0.42, p < 0.001), followed by real-time data utilisation (β = 0.31, p < 0.01). The integrated model achieves 89.4% classification accuracy in predicting high versus low agility performers. Financial access significantly moderates the smart manufacturing–agility relationship, with resource-constrained firms exhibiting weaker conversion of adoption into agility. The findings suggest that SMEs should prioritise foundational data integration capabilities over advanced automation to achieve agility gains. Practically, the framework offers a diagnostic tool for SME managers to assess adoption priorities, while academically it extends dynamic capabilities theory by specifying the capability pathways through which smart manufacturing generates agility.

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Copyright (c) 2026 Abbas Ahsun (Author)

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