Systematic Review of DMAIC for Quality Monitoring and Control: A Case Study in the Mining Industry

Statistical Process Control (SPC) is widely recognized for its ability to monitor and address process variability through statistical techniques and graphical displays. However, its full potential remains underutilized in industrial applications, particularly in mining, where processes like trackless mobile machine assembly present unique challenges. This study explores a DMAIC-based intelligent quality control system designed to integrate systematic problem-solving with real-time quality monitoring. By incorporating SPC within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework, this approach enables data-driven decision-making and continuous improvement in manufacturing quality. A systematic literature review examines existing applications of DMAIC in manufacturing, with specific attention to its implementation in the mining sector and assembly of trackless mobile machines. Findings highlight the framework’s effectiveness in identifying and addressing quality issues, optimizing assembly processes, and enhancing operational efficiency. This research provides a foundation for advancing quality control systems in mining, ensuring reliability and performance in critical equipment assembly