Industry 5.0 and women empowerment: a mixed-method analysis in supply chain and logistics sector

Purpose This research aims to explore the role of Industry 5.0 (I5.0) technologies in detecting and mitigating gender bias in workplace environments for building an inclusive workforce and reinforcing women’s empowerment within the supply chain and logistics sector. Design/methodology/approach A mixed-method analysis was used for this research. Qualitative data included an in-depth review of pertinent scholarly articles using the systematic literature review (SLR) method coupled with bibliometric analysis. Furthermore, machine learning was used to model the curated dataset from survey data responses to uncover deeper insights and patterns. This comprehensive approach integrates qualitative and quantitative data to propose a robust model to analyze the role of I5.0 technologies and unearths key factors impacting women’s participation in the workforce. Findings The findings of the study demonstrate that while the I5.0 workplace environment fosters an inclusive vision for the future workforce, persistent challenges, particularly around gender, continue to limit equitable participation. Insights from both the survey and semi-structured interviews point to recurrent issues such as gender bias incidents, underrepresentation of women in leadership roles and inconsistent access to training and upskilling opportunities. A Random Forest classification model trained on a balanced and label-encoded dataset (497 instances) achieved robust results with 84% accuracy, 81% recall, 73% precision and a 77% F1-score, highlighting critical predictors of perceived gender bias incidents. The top-ranked features include years of experience, age, training duration, productivity metrics and technology domain. Complementary K-Means clustering analysis further revealed three distinct clusters based on productivity and experience, capturing early-career, mid-career and senior professionals, with visible disparities in gender distribution, particularly at senior levels. These findings underscore the practical utility of predictive analytics to complement qualitative narratives and reinforce the relevance of the Women Empowerment Principles (WEPs) in guiding actionable policy interventions. Originality/value As far as we understand, this is a first-of-its-kind study providing a comprehensive and practical framework by offering a step-by-step guide to encourage gender diversity and inclusivity within the workforce, ensuring that organizations can implement effective strategies to create a more equitable and inclusive environment for women.