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With recent trends in artificial intelligence and data analytics, manufacturing companies are shifting their development processes to digital methodologies. This allows to manage the complexity of manufactured systems more efficiently and effectively. By this data from different lifecycle phases can be integrated into engineering models to optimize their properties. A promising approach that combines the model-based and data-driven worlds is data-driven model-based systems engineering (DDMBSE). DDMBSE focuses on a data-driven system model to continuously manage and update engineering artifacts with multiple data sources. But DDMBSE is difficult to practice due to a lack of guidance. Therefore, a DDMBSE maturity model has been developed. It structures an organization's DDMBSE maturity into five levels and organizes the criteria into model-based and data-driven categories. Each criterion includes a description and recommended actions to reach the next level. The maturity model was evaluated by two companies in the consumer electronics and electronic components industries.
Denis Tissen (Mon,) studied this question.