This research paper investigates automated feature engineering techniques for predictive modeling tasks using native functionalities in popular computational environments. Automated feature engineering enhances model performance and reduces manual interference in data preprocessing tasks. This paper further explores feature engineering methodologies facilitated by native functionalities, focusing on their application and effectiveness in predictive modeling using neural network workflows. Through practical demonstrations and case studies, the paper aims to showcase the versatility and impact of automated feature engineering techniques on predictive modeling outcomes. The proposed approach integrates machine learning techniques, including neural networks, to accurately predict demand patterns and identify potential irregularities using popular Iris data. The significant difference is striking, with a remarkable 16% increase in accuracy observed after feature engineering. The study further explores the implementation of predictive analytics to recommend actionable strategies for mitigating risks and improving overall supply chain resilience. Through a comprehensive evaluation of the predictive models’ performance and real-world implementation, this case study demonstrates the significant impact of predictive analytics on enhancing decision-making processes and driving operational excellence in supply chain management. The findings emphasize the importance of adopting data-driven approaches and advanced analytics techniques to address the complexities and uncertainties inherent in modern supply chain operations. Ultimately, this research advances the decision sciences and data analytics field by providing practical insights and methodologies for optimizing business operations and achieving sustainable competitive advantage within the due time frame.