As manufacturing organizations pursue the promise of digital twins and digital threads, the ability to automate and link key lifecycle artifacts remains a critical challenge, especially at the intersection of engineering design and manufacturing execution. The Bill of Process (BoP), a central but often overlooked element of the digital thread, has traditionally required significant manual effort to compile from diverse data sources across the enterprise.

This presentation introduces a conceptual framework and prototype for an AI-enabled BoP generation system that leverages emergent technologies—including natural language processing, knowledge graphs, and multimodal AI—to integrate data from 3D CAD, PLM, and MES systems. This early-stage work explores how intelligent agents can interpret model-based engineering artifacts, infer manufacturing operations, and construct a coherent process plan that aligns with downstream execution systems.

Key discussion points include:

• Leveraging AI/ML for semantic understanding of engineering and manufacturing data
• Structuring linked data relationships across PLM, MES, and ERP using graph-based models
• Designing a system architecture that supports scalable and interoperable BoP generation
• A mockup demonstration showcasing how this concept can augment existing digital thread workflows

Presented as a work in progress, this session aims to foster dialogue on how the factory of the future can close the loop between design and execution using AI-native tooling and interoperable data models. The insights presented will be valuable to organizations seeking to deepen their digital manufacturing capabilities, streamline handoffs across domains, and accelerate their digital transformation journey.