Integrating Design Intelligence into Configuration Item Space for Robust Change Integrity

The development process of complex products is typically characterized by high integration across multiple domains and disciplines. Design changes are an integral part of this process, and effective management of frequent design changes is fundamental to ensuring product quality and safety. Ensuring the integrity of design changes is critical and has become a pressing issue in the development of complex products. Leveraging design intelligence, this paper proposes a method for modeling change integrity integrated with the Configuration Item (CI) space. Using the CI space dataset model, we establish rules for checking the integrity of design changes, providing a procedural paradigm for determining change integrity. By integrating design intelligence techniques such as predictive analytics and machine learning, our model enhances the accuracy and efficiency of identifying impacted documents and CIs. This approach improves configuration control capabilities for complex products and ensures quality and safety throughout the development process. Finally, through specific change examples, data analysis, and comparisons, we validate the effectiveness of the proposed model in maintaining design integrity and supporting informed decision-making.