Design of Experiments (DOE) has been widely used as a core tool for data-driven decision-making in various industrial fields such as process optimization, quality control, and product development. Recent advances in machine learning (ML) and artificial intelligence (AI) have enhanced the ability to process and predict large-scale complex data, suggesting new applicability of DOE in practice. This study discusses ways to apply traditional DOE methods in a more flexible environment and explores its possibilities through practical cases. In addition, by presenting specific strategies for integrating DOE and machine learning using JMP software, it provides a practical methodology for practitioners to effectively perform data-driven decision-making in real industrial environments. Through this, we aim to suggest the positive impact of integrated DOE and machine learning on process optimization, quality control, and product development.
