RELEASE NOTES
RELEASE NOTES
Jun 30, 2022
Here’s a quick wrap up to the last three months of improvements for April, May and June. We’re making Drishti easier to use with new language support, access to more education materials and updates for visual and analytic consistency.
As Drishti continues to expand globally, we want to ensure ease of use for all customers. Drishti now supports Japanese for all menus and screens displaying analytics and video. Depending on portal configuration, line, product and station descriptions, as well as tags, may be displayed in English.
We have polished the Drishti Portal to improve navigation. You will now see a new breadcrumb on top of every page to guide you to different sections and back. We have also improved consistency across pages for ease of use.
Just as our AI models need to remain reliable and consistently reflect current assembly processes, so do our analytics.
Drishti Analytics will now reflect your Work Definition—Drishti’s version of your standardized work. We use your line’s Work Definition to understand its configuration and provide charts with greater detail. You can now view cycle-breakdowns by human, machine, wait and walk times. You will also be able to understand per-piece cycle times when products are worked on in batch.
Accessing Drishti Education’s engaging and comprehensive learning library has never been easier.
On any page, clicking the round blue cap on the bottom right corner will display Drishti Education material relevant to that page. You can view video tutorials in a mini-player and navigate the Drishti Portal as you follow along to lean more about our many features.
The Drishti Tagged Anomalies chart was previously launched displaying only the frequency. Each anomaly is ranked by the number of occurrences within the selected time range.
While the number of occurrences are informative, sometimes the production time losses associated with each of them are more effective for prioritizing issues. We’re now including a duration toggle to visualize the ranking based on the total production losses per anomaly. Now you can better target the productivity issues Drishti uncovers.
