Motorola's traditional approach to finding critical manufacturing issues required sending engineering teams to walk the hundred-meter assembly line for hours hoping to catch new issues at the right moment. Once an issue was found, resolution involved late night phone conferences, email requests, factory trips, extensive experiments, and executive meetings. This was costly, inefficient, and did not scale across their multi-product portfolio.
Motorola deployed Instrumental inspection stations at key assembly stages across all mobile phone programs. Setup and calibration took only two days. Machine-learning algorithms automatically find new defects by searching all units in seconds rather than relying on human observation. Engineers can set up enduring monitors with a few clicks, and the Intercept feature provides real-time pass/fail judgements on the line in under two seconds using edge computing.
Instrumental has inspected every development and pre-production unit on seven Motorola mobile phone products. ML algorithms regularly identify dozens of unique new issues on each program, resulting in tens of live Intercept tests per program. Programs that used Instrumental during development ramped faster than those that did not. The system reduced the number of experimental builds needed to validate products and enabled faster time to stability in ramp. Immediate feedback helped operators get up to speed faster.
Have a similar implementation?
Share your customer's AI results and link it to your vendor profile.
Submit a case study →
Your brand here — Reach manufacturing decision-makers researching AI adoption.