System dynamics model for evaluating ESG-based strategies to reduce turnover in manufacturing

Purpose Employee turnover poses a significant challenge for human resources (HR), as it increases costs, reduces efficiency and impacts competitiveness. Companies are exploring new approaches to address this problem by applying environmental, social and governance (ESG) principles and HR analytics. This study aims to propose a prescriptive HR analytics model based on system dynamics simulation to evaluate social strategies based on ESG principles for reducing turnover and related costs in the manufacturing sector. Design/methodology/approach The proposed model integrates binary logistic regression to estimate turnover intention and simulates the employee turnover process using system dynamics. The model is tested using the IBM HR Analytics Employee Attrition and Performance data set and calibrated with parameters from the Mexican manufacturing industry. A sensitivity analysis supports the identification of key factors that influence turnover rates, with strategies designed by prioritizing the social component of ESG principles. Findings The results show that the annual turnover rate is highly sensitive to changes in key factors such as job involvement, age, job level, travel requirements and overtime. Strategies aimed at reducing overtime and improving job involvement significantly reduce turnover and associated costs. Turnover decreased from 29.7% to 8.4%, with annual cost savings of up to 70%. Research limitations/implications This research has some limitations that provide an opportunity to explore interesting studies in the future. By using a generated database, the generalization of the results to the different subsectors of manufacturing companies is reduced. Another limitation relates to the endogenous variables used in the human resources management model to represent the turnover process. Practical implications This paper offers several actionable insights and tools that can benefit organizations, particularly in the manufacturing industry, aiming to reduce employee turnover and associated costs, including the integration of a system dynamics model with binary logistic regression, which provides a prescriptive HR analytics tool that allows managers to simulate various scenarios and predict turnover rates. Social implications This paper highlights the importance of social ESGstrategies in workforce management. Focusing on improving working conditions (e.g. reducing overtime and fostering job engagement) aligns with global sustainability goals, particularly SDG 8: Decent Work and Economic Growth. While this study focuses on the manufacturing sector in Mexico, the methodologies and insights can be scaled or adapted to other industries or geographic regions with appropriate customization, providing broader relevance and applicability. Originality/value This study highlights the potential of system dynamics as a prescriptive HR analytic method for studying employee turnover. The proposed model supports the evaluation of ESG-based strategies to mitigate turnover’s impact on workforce management and related costs. It offers practical guidance for managers seeking to adopt sustainable retention strategies. Further studies could explore the integration of external variables and evaluate other ESG-driven interventions to strengthen future challenges in management.