The four Ps of closed-loop manufacturing: a hybrid SEM-ANN model for optimizing sustainable performance

Purpose Despite the growing interest in closed-loop manufacturing, there is a lack of comprehensive frameworks that integrate product development, production processes, people and policies (4Ps) to optimize sustainable manufacturing performance. This study investigates the influence of the four Ps of closed-loop manufacturing systems (product development, production processes, people and policies) on sustainable manufacturing performance (SMP). Design/methodology/approach To investigate the influence of the four Ps on SMP, a hybrid analytical model was employed, combining structural equation modeling (SEM) with artificial neural networks (ANN). Data were collected through a structured survey administered to 353 manufacturing firms in Malaysia. SEM was used to assess the relationships between the variables, while ANN was employed to capture nonlinear relationships and improve prediction accuracy. Findings The research findings demonstrate that product development practices, including eco-design, life cycle assessment and resource planning, exert the most significant influence on SMP. Furthermore, implementing green and lean manufacturing techniques, energy modeling and material utilization/toxicity planning significantly enhances sustainability outcomes. While the social setting (employee motivation, turnover and work–life quality) does not directly impact SMP, it plays a pivotal role in facilitating the implementation of internal environmental policies. Moreover, environmental management practices, both mandatory and voluntary, serve as intermediaries between the four Ps and SMP within closed-loop manufacturing systems. Practical implications The findings offer valuable insights for policymakers, industry leaders and manufacturing organizations. By prioritizing product development, implementing green and lean manufacturing practices and fostering a positive social setting, organizations can significantly enhance their sustainable performance. Additionally, the study highlights the importance of effective environmental management practices in mediating the relationship between other factors and SMP. Originality/value This study contributes to the literature by providing a comprehensive framework for understanding the factors that drive sustainable manufacturing performance. The hybrid SEM-ANN model offers a robust and innovative approach to analyzing the complex relationships between the four Ps and SMP.