scrapifyer

Inspiration

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

in-depth polymer

What's next for scrapifyer

deploy it properly on predix.io + turn kaizen features into collaborative tools

Built With

• api
• html
• javascript
• polymer