Leveraging Industry 4.0 to overcome supply chain challenges in Gauteng manufacturing small- and medium-sized enterprises: A Supply Chain Operations Reference model perspective

Background: Manufacturing small- and medium-sized enterprises (SMEs) in Gauteng, South Africa, face persistent supply chain challenges that hinder performance and sustainability. Although Industry 4.0 technologies offered opportunities to improve supply chain performance, limited research had linked these technologies to specific challenges across the Supply Chain Operations Reference (SCOR) model. Objectives: This study aimed to identify and validate supply chain challenges affecting manufacturing SMEs and recommend Industry 4.0 technologies to address them across SCOR processes. Method: The study was conducted among supply chain professionals employed in manufacturing SMEs in Gauteng, South Africa. This quantitative study, grounded in the positivist paradigm, included 252 supply chain professionals. Data were collected using a structured questionnaire and snowball sampling. IBM SPSS Statistics and SmartPLS 4 were used for descriptive analysis and measurement model evaluation. Results: Significant challenges were identified across all SCOR processes, including forecasting inaccuracies, supplier unreliability, inadequate visibility and traceability, inventory inaccuracies, poor returns monitoring, and miscommunication. The measurement model demonstrated strong reliability and validity, with Cronbach’s alphas above 0.86 and composite reliabilities exceeding 0.89. A SCOR Technology Selection Compass was developed to align Industry 4.0 technologies with identified challenges. Conclusion: The study concluded that Industry 4.0 technologies could be systematically aligned with SCOR challenges to support supply chain improvement in manufacturing SMEs. Contribution: The study provides a psychometrically supported assessment of supply chain challenges within South African manufacturing SMEs and introduces a SCOR-aligned conceptual framework for challenge-driven technology selection. The framework offers a basis for managerial prioritisation and future empirical validation.