SupplyLogic

Medical device manufacturers have moved beyond asking whether generative models can draft text. The more consequential question is how these models will operate within design control, clinical evidence generation, regulatory affairs, quality systems, and post-market surveillance without weakening accountability. Over the next three to five years, the winners will not be the companies with the most pilots. They will be the manufacturers that connect artificial intelligence to governed product data, validated workflows, and named decision owners while preserving the traceability expected under ISO 13485, ISO 14971, 21 CFR Part 820, and regional market-authorization regimes. The emerging discipline of Generative AI in MedTech therefore needs to be understood as a lifecycle capability rather than a standalone writing assistant. Its value will come from reducing the friction between user needs, design outputs, verification evidence, clinical conclusions, submission content, manufacturing r...

In the fiercely competitive landscape of automotive manufacturing, the pressure to adopt advanced technologies has never been greater. As original equipment manufacturers (OEMs) and tier-one suppliers race to modernize their operations, many are investing heavily in automation technologies without fully understanding the strategic prerequisites for success. The difference between a transformative automation initiative and a costly misstep often lies not in the technology itself, but in how organizations approach implementation, change management, and integration with existing manufacturing execution systems (MES). Understanding these common pitfalls is essential for any manufacturer seeking to leverage automation to improve OEE, reduce waste, and maintain competitive advantage in an industry where margins are thin and quality standards are unforgiving. The journey toward Intelligent Production Automation in automotive manufacturing requires more than capital investment in robotics and...