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The 'Big Picture'

for Engineering Data

Engineering has lagged other business domains like finance or sales in benefiting from big data analytics. 

In engineering, a part's geometry isn't just highly correlated with part function, but also with business and performance metrics like cost, manufacturing process and part performance.  

CADseek Analytics fully automates the process of geometric comparison across entire datasets.  Any available meta-data can be used for report filtering so that aluminum parts don't get compared to those made of titanium. 

Projects such as consolidation, standardization, or cost variance discovery, which were previously prohibitive due to the tens-of-thousands of required hours, are made feasible with a report that takes less than an afternoon to run.  

Use Cases: 

Consolidation & Standardization

An Analytics reports groups models by a user defined level of similarity, providing a road map for eliminating duplication and discovering opportunities for standardization.    ​


Analytics can compare one dataset to another, allowing duplication and opportunities for standardization to be discovered.  

Price Variance Discovery

Often supplier parts are purchased from multiple vendors at different prices, but sometimes at too great a difference to be justified.

Performance Variance Discovery

CADseek allows meta-data from any source, so analysis can be run on performance metrics such as warranty or service life to find where parts with highly similar designs differ in performance.  

Data Cleanup 

Analytics can be used to find models that lack standard attributes.  

Pilot Study: 

Discovery of Unwarranted Price Variances

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