Strategic Sourcing
Assembly Line
A federated learning approach to automated and secure supplier selection in cyber manufacturing as-a-service
The emergence of cyber or platform-based manufacturing as-a-service is rapidly disrupting the way discrete parts are sourced and manufactured. However, the centralized business model of cyber manufacturing as-a-service platforms raises concerns about data ownership and access control of independent manufacturing suppliers. Contrary to centralized platforms, cyber manufacturing as-a-service aims to connect designers with geographically distributed manufacturers by serving as a broker who matches the query part design requirements with the manufacturing capabilities of candidate suppliers in its network. One of the key challenges in realizing the vision of cyber manufacturing as-a-service is the lack of a computationally efficient method for manufacturing capability search while maintaining data security of the proprietary datasets of the suppliers in the network. In this paper, we propose a federated learning approach that utilizes a deep unsupervised part retrieval model (FL-DUPR) to learn a federated embedding of suppliers’ manufacturing capabilities without directly accessing their proprietary datasets. We demonstrate through two case studies that this approach yields a supplier selection accuracy of 89 % when the manufacturing capabilities of the suppliers do not overlap, and a multi-label supplier selection accuracy of 87 % when there are significant overlaps in the suppliers’ manufacturing capabilities. We also show that our unsupervised learning approach outperforms the baseline supervised learning classification model trained under the same federated learning framework. The results demonstrate the promise of the proposed federated embedding approach for automated identification of the required manufacturing capabilities offered by various suppliers without directly accessing their proprietary data, thus paving the way for a more secure cyber manufacturing as-a-service business model.
SnapEDA helps manufacturers find alternate chips in the semiconductor shortage
But Silicon Valley-based SnapEDA has the designs for entire catalogs of components — such as 75,000 components made by Panasonic — in its online catalog. More than 6.5 million parts are in the database.
“I created SnapEDA because I wanted to help product developers innovate faster,” SnapEDA CEO Natasha Baker said in an interview with VentureBeat. “Think of it like GitHub for electronics as a good analogy. But basically, it’s a place where product developers can go and get all the resources that they need to design electronics faster.”