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Track record · Aerospace supplier

Finding BOM errors before they cost millions

For a leading aerospace-industry supplier (around 2,600 employees) an AI validation system for bills of materials was built: it learns from historical data what a plausible BOM looks like and flags deviations with a confidence score. Errors surface before they get expensive in manufacturing and purchasing.

The challenge

The problem

Product variants and machine complexity make BOMs explode. Manual quality assurance is expensive, error-prone and barely finds hidden errors across complete product variants.

Undetected errors cause high follow-up costs: scrap, rework, delays and expensive re-procurement of missing parts.

Document AI

The solution

How it was solved

Learning validation algorithm The validation model is built automatically from historical BOMs.
Continuous learning Every confirmed or corrected check improves the model.
Rules plus expert knowledge Engineering know-how feeds the validation as rules.
Errors with confidence Automatic error detection including a confidence estimate.

The results

95 % of previously undetected errors found (up to)
< 1 Min. validation time per product
> 1 Mio. parts lists processed
~10 % productivity gain in engineering

Technology

Methods used

Stack

  • Machine Learning
  • Pattern analysis
  • PLM/ERP
  • Confidence scoring

Per the project report: around 29 million euros less scrap cost and 11.5 million euros lower personnel cost.

Led by Beyonetix founders and senior engineers. Figures per the respective project report.

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