A QFD‐Based Framework for AI‐Driven Quality Control in the Era of Industry 4.0

As manufacturing industries continue to advance through digital transformation, two major trends are reshaping the future of quality management: the integration of Artificial Intelligence (AI) into quality control processes and the widespread adoption of Quality 4.0 technologies. This study presents a structured framework designed to identify the key success factors for effective AI implementation in quality control. Recognizing the potential synergy between different AI applications, the framework accounts for how these applications can interact and reinforce one another across various stages of production. A methodology based on Quality Function Deployment (QFD) was developed to align targeted failure costs with the most appropriate AI applications, while also addressing the most significant barriers to implementation. The prioritization process utilized normalized improvement scores, calculated across two hierarchical levels ‐failure costs and AI applications, using the Mean Square Error (MSE) criterion. This study is primarily based on a comprehensive review and synthesis of existing literature in these domains, which serves as the foundation for the proposed framework. The methodology was applied in a food processing company, where reducing production defects emerged as the top‐quality priority. The most impactful AI solutions identified were AI‐driven Statistical Process Control (SPC) using IoT and edge computing, and automated visual inspection using AI and computer vision.