What inspired this wish list request?
When developing dashboards for JMP Live that contain large numbers of products with significantly different run rates, the visualizations can quickly become noisy and difficult to interpret. This is especially true when multiple products share the same equipment or manufacturing track.
For example:
- Product A runs approximately 5 lots per week
- Product B runs approximately 79 lots per week
- Product C runs approximately 2 lots every 5 weeks
All three products run on the same equipment track. While the dashboard correctly displays the full population of data, the large differences in run frequency can cause the higher-volume products to dominate the visualizations. This makes it difficult for users to focus on lower-volume products or perform targeted outlier analysis.
Users often request the ability to isolate or filter specific product populations within the dashboard so they can investigate trends, anomalies, or performance issues without the noise from unrelated data.
What is the improvement you would like to see?
An enhancement that would allow users to interactively select or filter subsets of data directly within a JMP Live dashboard would significantly improve usability. Ideally, this would include the ability to:
- Select specific regions of a graph (such as lasso or box selection) to dynamically filter the dataset
- Filter by product, equipment, or other categorical variables directly from the visualization
- Update all linked graphs in the dashboard based on the selected subset
This type of interactive filtering would allow analysts to quickly narrow down to the most relevant population without needing to create multiple versions of the same dashboard.
Why is this idea important?
Interactive filtering would make dashboards more effective for both analysis and communication. Analysts could quickly isolate specific product populations for outlier investigations, while operational teams could view the data most relevant to their work.
This would improve usability, reduce the need to create multiple dashboards for different product groups, and make it easier to communicate insights during pass-down discussions and team reviews. Overall, it would allow teams to work more efficiently with large and diverse manufacturing datasets.