Engineering Intelligence
Last updated
Last updated
Engineering Intelligence brings together all relevant data from engineering sub-systems, such as PLM, MES, and CI/CD systems, by leveraging modern approaches like a data mesh. By connecting this data and applying Generative AI (GenAI), engineering processes, as well as manufacturing and aftermarket operations, can be optimized. Engineering Intelligence addresses the need for consistency, efficiency, and actionable insights across increasingly complex systems.
Incumbent OEMs face unique challenges due to their highly complex product portfolios and organically grown, heterogeneous toolchains. Their systems span across multiple repositories, connecting requirements management, PLM, MES, CI/CD, ERP, CRM, and sales systems (as shown in the first image). While this setup has evolved over time to support specific needs, it introduces redundancy, inconsistency, and complexity across the engineering lifecycle. Managing this complexity is particularly challenging when dealing with the scale of variants seen in incumbent OEMs’ portfolios.
In contrast, EV start-ups operate with leaner product portfolios and much more stringent variant policies. They prioritize simplicity and standardization, minimizing the number of configurations and focusing on software-driven differentiation. Start-ups are often referred to as single-repo companies, where all engineering-related artifacts are managed within a single repository per domain (as shown in the second image). While they still maintain multiple repositories in practice, the stringent discipline of "one repo per domain" brings significant benefits, including reduced complexity, improved data consistency, and a single point of truth.
Engineering Intelligence must address the heterogeneity and redundancy inherent in complex engineering systems, particularly for incumbent OEMs. By leveraging a data mesh, data from disparate systems can be connected and made accessible across the organization, breaking down silos. Additionally, Generative AI can analyze this data to provide insights, automate routine tasks, and optimize engineering workflows. This allows companies to manage complexity, streamline processes, and ensure consistency across systems, from design to production and aftermarket support.
In summary, Engineering Intelligence, powered by data mesh and GenAI, is key to overcoming the challenges of heterogeneity and redundancy. It enables both incumbent OEMs and EV start-ups to optimize their engineering processes, reduce complexity, and achieve greater agility in the face of growing product and system demands.
Product Line Engineering (PLE) is a systematic approach to managing a portfolio of related products by identifying shared assets and features while accounting for differences. This approach is particularly valuable in managing vehicle variants efficiently, as it allows manufacturers to define a core architecture and customize features for specific configurations.
An emerging evolution of PLE is Type-Based Product Line Engineering (TLPE). TLPE introduces a more structured, modular approach to managing product lines by categorizing features and assets into distinct types. This allows for better reuse, standardization, and traceability across the engineering lifecycle.
Engineering Intelligence can both empower and benefit from TLPE. By integrating data from PLE systems, Engineering Intelligence can leverage AI-driven insights to identify opportunities for standardization, optimize feature reuse, and manage complexity effectively. Conversely, TLPE enhances the value of Engineering Intelligence by providing a clear, modular structure for data analysis, ensuring consistency across product lines.
In summary, Product Line Engineering, particularly with TLPE, offers a scalable approach to managing variants. Combined with Engineering Intelligence, it enables manufacturers to achieve greater efficiency, consistency, and innovation across complex product portfolios.
