Evaluating SC 5.0 preparedness through human–AI collaboration and digital maturity in indian capital-intensive PSUS
DOI:
https://doi.org/10.14488/BJOPM.2826.2025Keywords:
Supply Chain 5.0, Indian PSUs, Technological Readiness, Human-AI Collaboration, SEM-MCDM-ANN, Readiness Index, Digital Transformation, Public Sector BenchmarkingAbstract
Purpose: This study aims to develop and empirically validate a hybrid multi-method framework to assess Supply Chain 5.0 (SC 5.0) preparedness in India’s capital-intensive engineering Public Sector Undertakings (PSUs). The framework evaluates readiness across five dimensions: Technological Readiness, Leadership & Change Management, Human–AI Collaboration Capability, Workforce Digital Skills and AI Literacy, and Organizational Learning & Innovation.
Methodology: A quantitative research design was employed using primary survey data from 485 professionals across six capital-intensive PSUs. The analysis was conducted in three phases: (i) Structural Equation Modeling (SEM) to test causal relationships and validate hypotheses, (ii) Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to construct a SC5.0 Readiness Index and rank PSUs, and (iii) Artificial Neural Networks (ANN) to predict and cross-validate the robustness of readiness drivers.
Findings: SEM results reveal that Technological Readiness (β = 0.331, p < 0.01), Workforce Skills (β = 0.298, p < 0.01), and Human–AI Collaboration Capability (β = 0.279, p < 0.01) significantly influence SC 5.0 readiness, with leadership commitment moderating the impact of digital infrastructure on transformation outcomes (p < 0.05). TOPSIS highlights BHEL (0.741), NTPC (0.703), and GAIL (0.689) as top-performing PSUs, while ANN validation achieved 91.48% accuracy, confirming model robustness.
Research Implications: The study advances theoretical understanding by integrating structural modelling with machine learning-based predictive analytics, offering a holistic approach to assessing SC 5.0 readiness. The high predictive accuracy of the ANN model (R² = 0.8841; equivalent to 91.48% accuracy) underscores the robustness of the framework, demonstrating that leadership, agility, and change-handling dynamics can be reliably forecast as critical enablers of SC 5.0. This establishes a methodological precedent for combining causal, prescriptive, and predictive approaches in future supply chain transformation research.
Practical & Social Implications: The findings provide actionable insights for PSU managers and policymakers to enhance digital transformation, workforce upskilling, and human–AI collaboration, thereby improving operational resilience and supporting sustainable industrial growth.
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