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Engineering & Simulation · Engineering
Generative AI / BOM Automation
BOM-aware simulation and cost-weight trade-off support
Scaling Adjacent medium effect
Core capability
The system reduces manual BOM (Bill of Material) preparation work by extracting parts and quantities from documents and aligning them with enterprise systems used for procurement and production.
How it works
The system reads drawings, PDFs, and spreadsheets, extracts parts and quantities, standardizes inconsistent naming, and connects the results to enterprise records so BOM preparation requires far less manual work.
Application here
Part and material data from the BOM feeds engineering optimization so cost, weight, and performance trade-offs reflect real supply-chain inputs.
Business impact
This helps connect design decisions to actual cost and availability realities instead of treating them as purely technical trade-offs.
Limitations
It depends on reliable BOM cost and availability data, which is often incomplete. Optimization results still need engineering judgment.
In production
This is already used to cut manual BOM preparation effort where teams currently piece together parts data from multiple disconnected documents.
Research
The frontier is moving toward systems that not only read BOM data, but understand component relationships, detect inconsistencies, and connect the result more directly to sourcing and planning work.
Examples
Using RPA and AI to automate bill-of-materials in manufacturing
Description: European automotive manufacturer deployed AI and RPA to extract parts from engineering drawings, validate BOMs against standards, and update for supply disruptions; reduced creation time 70%, cut rework via inconsistency detection linking to production planning. (187 chars)


AI in PLM: Top Use Cases You Need To Know
Description: Airbus used generative AI for A320 cabin partition redesign, optimizing BOM for 45% weight reduction via topology algorithms tied to material constraints and simulation; integrated into PLM for production-ready cost-performance trade-offs. (198 chars)


Using RPA and AI to automate bill-of-materials in manufacturing
Description: Aerospace manufacturer applied AI to scan BOMs for missing parts, duplicates, and compliance issues against engineering data; fed validated BOM into optimization workflows, reducing annual rework costs by $2M before production. (192 chars)
https://bazucompany.com/blog/using-rpa-and-ai-to-automate-bill-of-materials-in-manufacturing/https://smartdev.com/ai-use-cases-in-plm/
Sources
Siemens Teamcenter — ; Siemens Simcenter —
https://plm.sw.siemens.com/en-US/teamcenter/https://plm.sw.siemens.com/en-US/simcenter/