Time lost in searching for actual defects root causes. Problem-solving process is usually reactive and not proactive enough. Besides, SMEs expertise is lost over time (mainly due to turnover or lack of efficient archieving).
What it does
Scrapify is built as a dashboard that, on one hand, provides a quick prediction model for checking whether a part is a defct or not. On the other hand, it goes over most of the main points used for problem solving: Kaizen, RCCA, etc.
How I built it
With polymer, JS and html. The smart app is built on azure studio.
Challenges I ran into
Deploy the webapp live on predix.io + decipher the APIs + push code to predix
Accomplishments that I'm proud of
Build a predictive model and deploy it live in less than a day.
What I learned
What's next for scrapifyer
deploy it properly on predix.io + turn kaizen features into collaborative tools