Data-Driven Process Management for High-Quality Hip Joint Implants—A Case Study in Investment Casting

Quality 4.0 aims to make zero-defect manufacturing possible across industries through automating and digitizing quality functions. Casting companies suffer from the rejection of cast components in the post production quality checks, resulting in low profitability. While meeting the clients’ specifications is vital, casting companies should upgrade their production processes digitally to compete in global markets. This article proposes a Quality 4.0 deployment framework for investment casting companies. We adopt a case-based research methodology, and the case company makes metallic hip joint implants in an investment casting plant. We characterize each defect type and mechanical property, exploring various machine learning algorithms, and the best-fit models are those with high predictive performance (88% accurate in predicting defects and a root mean squared error of 0.09 in predicting mechanical properties). We perform within-case analysis to quantify the influence of potential causal variables and show the causal relationships among the implants’ quality characteristics. Furthermore, the proposed quality management system with real-time process sensing capability involves a repetitive quality inferencing scheme to predict the quality of the items being cast, which enables process regulation and further leads to the production of zero-defective castings. Hence, this case study fortifies the causal relationship of data-driven process management with the process outcomes of zero-defective casting of parts. The proposed Quality 4.0 theoretical framework, hence, emphasizes the feedback intervention and self-regulation capabilities of the casting companies for effective process management and encourages future researchers to investigate the role of data-driven process management in enhancing customer satisfaction and driving operational performance.