Problem
Design engineers currently get little to no feedback on life-cycle cost factors including manufacturing failures, obsolescence, or ergonomics/automation until manufacturing reviews are held late in design cycles.
Cognitive On-demand Design Advisor (CODA)
Leveraging artificial intelligence and machine learning (AI/ML) cloud models developed with manufacturing data, CODA will help design engineers reduce engineering change orders (ECOs).
Proposed Solution
CODA will leverage manufacturing data — using data engineering methodologies and Microsoft’s Azure ML environment — to help design engineers predict manufacturing or field defects. The team is focusing on electrical design and associated cost and obsolescence issues during the end product’s life cycle.
Impact
The goal is to reduce obsolescence-related ECOs by 20% with the opportunity to save more than $1 million per year by preventing obsolescence events that cause ECOs during product manufacturing or use. By using ML models trained with past events, CODA will be able to access decades of manufacturing and production expertise during the design phase, with that expertise growing as new data is captured and engineered.