Bridging Classical Quality Tools and Industry 4.0: A Data-Driven Framework for Intelligent Process Control

This paper initiates a study that would help bridge the classical quality methodologies with the up-to-the-minute digital drift of Industry 4.0 technologies. To address this, the study proposes the development of a hybrid framework for implementing classical quality methodologies, namely Six Sigma and Total Quality Management, together with Quality 4.0 tools involving artificial intelligence, the Internet of Things, and big data analytics. The study implements an enriched AI-based Statistical Process Control system applicable to the real shop floor of an automotive manufacturer after conducting a systematic literature review to identify any existing models. The proposed system, over twelve months, brought about a 32% defect rate reduction. This study closes the loop of the constant feedback that is necessary for coupling heritage quality management with intelligent technologies to ensure the continuous, proactive, adaptive, data-driven control needed for a way towards smart, resilient manufacturing ecosystems.