Process Capability Analysis of Prediction Data of ML Algorithms

Abstract

This study integrates process capability analysis with Machine Learning (ML) methods to optimize business processes. ML, especially Random Forest (RF) and k-nearest neighbor (kNN) algorithms, has enabled the practical analysis of large data sets by using them together with process capability analysis. This integration enabled real-time monitoring and predictive analytics, enabling the proactive identification of process variations and the making of timely adjustments to maintain or increase process capability. Additionally, ML algorithms have helped optimize process parameters and identify critical factors affecting process performance, allowing for continuous improvement and achieving desired quality standards with greater efficiency. In conclusion, this study provides the basis for the synergy between process capability analysis and ML methods to enable businesses to achieve higher levels of quality control, productivity, and competitiveness in dynamic and complex production environments.