Enhancing Sustainable Supply Chain Performance Prediction Using an Augmented Algorithm-Optimized XGBOOST in Industry 4.0 Contexts

Accurate prediction of supply chain performance, particularly profitability, as a key indicator of economic sustainability, is essential for data-driven decision-making in Industry 4.0-enabled sustainable supply chains. Traditional machine learning models often underperform due to suboptimal hyperparameter configurations, especially when dealing with high-dimensional, nonlinear operational data. To address the limitations of conventional machine learning models, which often exhibit instability and weak generalization in high-dimensional data, this study introduces a novel Salp Swarm Algorithm with Local Escaping Operator (SSALEO) to optimize XGBOOST for sustainable supply chain profit prediction. The theoretical innovation lies in the integration of LEO, which dynamically perturbs stagnant solutions to enhance convergence reliability, robustness, and interpretability compared with conventional metaheuristic–ML hybrids. This enhanced metaheuristic optimizer fine-tunes XGBOOST to deliver highly accurate predictions of supply chain profit, a critical dimension of economic sustainability. Evaluated on real-world supply chain datasets, SSALEO-XGBOOST achieves a coefficient of determination (R 2 of 0.985) and significantly outperforms benchmark models across error metrics Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Maximum Error (ME), and Relative Absolute Error (RAE). By leveraging this enhanced optimizer, the proposed SSALEO-XGBOOST framework achieves superior predictive accuracy and stability, enabling more consistent profit estimation and performance forecasting. For decision-makers in industry environments, the framework offers a practical tool to support data-driven sustainability assessment and digital transformation strategies, fostering intelligent and resilient industrial ecosystems.