Optimizing Production Engineering: Data Science And ML Solutions For Scalable Data Pipelines

Abstract

In the era of Industry 4.0, optimizing production engineering demands the integration of advanced data science and machine learning (ML) solutions within scalable, resilient data architectures. This study presents a comprehensive framework for enhancing manufacturing performance through intelligent, ML-driven data pipelines. By capturing high-velocity data from sensors, control systems, and machinery, and processing it through robust pipelines built on technologies like Apache Kafka, Spark, and Docker, the study achieved real-time analytics and adaptive decision-making capabilities. Supervised and unsupervised ML models, including XGBoost, Random Forest, and SVR, were deployed for use cases such as predictive maintenance, anomaly detection, quality forecasting, and throughput optimization. Results demonstrated statistically significant improvements across key performance indicators: cycle time was reduced by 20.2%, defect rate by 56.3%, and energy consumption by 23.6%. The pipeline achieved high throughput with minimal latency and near-zero data loss, even under simulated high-load conditions. Feature importance analysis and correlation heatmaps provided deep insights into process dynamics, enabling more informed operational strategies. This research validates the role of intelligent data infrastructure in transforming production engineering, offering a scalable, data-driven approach to achieving operational excellence, improved product quality, and increased sustainability in smart manufacturing environments.