Data-Driven Management of Workforce Dynamics under Automation and Digital Transformation in Textile Manufacturing Industries

Abstract

The increasing adoption of automation and digital transformation has fundamentally influenced workforce management in modern manufacturing industries. Organizations are increasingly integrating intelligent technologies to improve operational efficiency, optimize resource utilization, and enhance productivity while managing workforce transitions effectively. This study examines the impact of automation and digitalization on workforce dynamics, with particular emphasis on employment patterns and gender participation in manufacturing environments. Rather than considering technological change as a disruptive phenomenon, the study views automation as a gradual management-driven process in which advanced technologies are systematically incorporated into existing industrial operations. A quantitative research approach was employed to evaluate the relationship between automation adoption and workforce outcomes. Data were analyzed using the independent sample t-test through SPSS software to identify statistically significant differences among selected workforce variables. The findings indicate that effective management of digital transformation can improve organizational performance while reshaping job roles and workforce participation instead of completely replacing human labor. The study highlights the importance of strategic workforce management, evidence-based decision making, and inclusive employment policies for achieving sustainable industrial development. The proposed analytical perspective contributes to industrial information systems and management research by providing practical insights that assist industrial managers and policymakers in balancing technological advancement with workforce sustainability in digitally transforming manufacturing enterprises.