Elli Peterson's TechCrunch

A vehicle launch can look healthy in a program review and still be deteriorating underneath. Tooling milestones may be green, prototype builds may be complete, and suppliers may have submitted PPAP packages, yet the plant can remain exposed to unstable cycle times, configuration errors, and defects that escape into finished vehicles. I learned this during a launch in which daily firefighting obscured a deeper problem: engineering, supplier quality, material planning, and final assembly were each seeing different versions of the same risk. The experience changed how I evaluate AI in Automotive Manufacturing. The technology matters, but its value depends on whether it connects decisions across the concept-to-start-of-production chain. The practical scope of AI in Automotive Manufacturing extends far beyond installing computer vision at an inspection station. It includes finding weak signals in engineering changes, predicting supplier readiness, stabilizing JIT and JIS material flows, de...

My clearest lessons about AI in Electronics Manufacturing did not come from a polished strategy workshop. They came during prototype builds, yield reviews, supplier escalations, and late-night containment calls when a line was producing failures faster than engineering could explain them. In high-tech electronics, an algorithm creates value only when it respects the physical realities of solder paste, component variability, product configuration, test coverage, and production timing. The most successful initiatives I have seen began with a stubborn manufacturing problem, not with a desire to deploy an impressive model. A useful overview of AI in Electronics Manufacturing shows how widely the technology can be applied, but the factory-floor experience is more nuanced. A model must fit into NPI gates, BOM release controls, SMT engineering routines, quality escalation paths, and serialized traceability systems. It also needs owners who understand why an AOI call differs from an ICT failu...

The journey toward intelligent automation in manufacturing is rarely a straight line. After spending years working alongside production teams, engineers, and plant managers implementing automation strategies across multiple facilities, I've witnessed firsthand how transformative these technologies can be—and how challenging the path often proves. The stories from the factory floor reveal patterns that no white paper or vendor presentation can fully capture: the moments when legacy SCADA systems refuse to communicate with new IIoT sensors, the shift supervisor who becomes your greatest advocate after initial resistance, and the unexpected efficiency gains that emerge from places you never anticipated. These real experiences shape how we approach digital transformation in modern manufacturing environments. One of the most instructive experiences came during a brownfield implementation at a mid-sized automotive components facility. The plant had been operating for nearly four decades,...