Priya's Thursday, two ways

Priya, a manufacturing engineer at a mid-sized parts plant.

Priya starts her Thursday with a line stoppage on Assembly Line 3. The operator tells her the torque driver keeps faulting with error code E-47. She opens ChatGPT and uses the troubleshooting prompt: 'Operator says torque driver faults with E-47 on aluminum brackets. What are likely causes and steps to check?' In seconds, she gets a list: check calibration, inspect socket wear, verify air pressure. She checks the first two, finds a worn socket, and swaps it. The line is back up in 20 minutes.

Later, she needs to update the work instruction for that station. She pulls her handwritten notes from the stoppage and uses the process documentation prompt: 'Turn these notes into a clear work instruction for torque driver setup and fault recovery.' The AI drafts a step-by-step guide. Priya edits it, adds a photo, and posts it. Normally this would take an hour; now it takes 15 minutes.

After lunch, she reviews yesterday's production data. She uses the data analysis prompt: 'Here is OEE data for Line 3. Identify top downtime reasons and suggest improvements.' The AI points out that changeovers are taking longer than average. Priya had suspected this but now has numbers to back it up. She drafts a quick proposal using the improvement prompt and sends it to her manager.

With the time she saved, Priya walks the floor and talks to operators about their biggest frustrations. She notices a small fix that could reduce setup time even more. She takes a photo and uses the automation prompt to compare sensor options for a simple upgrade. By the end of the day, she has solved a problem, updated documentation, and started two improvement projects. She leaves on time, feeling ahead instead of behind.