Unlock BOM Optimization with AI

Each order required 6-7 engineers and 2-4 weeks to finalize the BOM

Engineers had to assess part availability, vendor lead times, and cost feasibility -- all manually

The process was labor-intensive, error-prone, and significantly impacted delivery timelines

Phase 01

AI Innovation Workshop

We kicked off the engagement with a cross-functional AI Innovation Workshop involving engineering, IT, and operations teams. The goal was to align business challenges with AI opportunities.

• Mapped the full lifecycle of BOM creation
• Identified inefficiencies, manual dependencies, and data gaps
• Evaluated AI readiness (data quality, systems, workflows)
• Prioritized BOM optimization as the most impactful and feasible use case

Phase 02

Data and Process Analysis

Our team conducted a deep dive into existing engineering workflows and data ecosystems to understand the full scope of BOM complexity.

• Collected historical BOMs, vendor catalogs, and procurement data
• Reviewed ERP integrations and engineering design tools
• Identified variables influencing BOM decisions like lead times, part specs, and costs

Phase 03

AI Model Design and Development

We selected a hybrid AI model architecture based on the complexity of the BOM structures, combining multiple AI techniques for optimal results.

• Used Transformer-based NLP models (GPT, BERT) for text and spec parsing
• Applied Graph Neural Networks (GNNs) to understand part dependencies
• Introduced Reinforcement Learning (RL) for adaptive optimization of parts selection
• Leveraged AutoML frameworks to accelerate model tuning and performance testing

Phase 04

Proof of Concept (PoC) Execution

A prototype AI system was developed and integrated into the existing BOM toolchain. The AI-generated BOMs were reviewed by a senior engineer for validation, enabling a human-in-the-loop workflow that balanced automation with quality assurance.

• Generated BOM drafts based on customer requirements and historical patterns
• Pulled live data from vendor systems to optimize selections
• Conducted multiple test cycles to validate speed, accuracy, and usability

Phase 05

Integration and Scalability Planning

Following PoC success, Captivix designed a plan to scale the solution across the organization. This phase ensures long-term sustainability and continuous improvement.

• Integration with broader ERP systems
• Expansion to multiple product lines
• Continuous learning loop to improve the AI engine with each order processed