Integrating data analytics and simulation for defect management in manufacturing environments

Defect management in manufacturing environments requires effective identification of the defects and finding proper solutions to resolve them. Predicting and preventing the defects before they can occur is the focus of quality risk management. To effectively manage defects, companies need to analyze historical data to identify the causes and solutions for defects as well as study the impact the defect can have on the processes, priorities, and operations. This study integrates data analytics and simulation modeling to develop a system for defect management in manufacturing environments. Simulation is used to analyze the behavior of the system whereas data analytics is used to develop prediction models for defect resolution. A case study from high-end server manufacturing environment, which is characterized by extensive test processes to ensure high quality and reliability of servers, is provided. The proposed approach helps decision makers analyze and manage defects and develop proactive means to prevent them.