Searching and categorizing runs for auditability

Categorizing your runs effectively helps you manage, analyze, and compare different planning problems and scenarios within the platform.

Run names and tags

Our platform provides two ways to add metadata to runs:

  • Name: Each run can be assigned a descriptive name.

  • Tags: Runs can be labeled with one or more tags to facilitate filtering and organization.

Both the name and tags can be provided when starting a run (via the Platform UI or as part of the json file) or edited later on the Run Overview page in the Platform UI.

Searching and filtering runs

You can search for runs by name using the search bar in Run Overview and you can apply filters based on specific tags to focus on specific subsets of runs.

On the Run Overview page you can pick which columns to show for each Run - and pick any of the metrics defined by the model. This allows you to see the evolution of these metrics or spot anomalies for the filtered datasets.

Best practices for using tags

We recommend using tags to:

  • Segment your data: Assign different tags to represent distinct segments in your data, like regions or departments. For example, give each region you plan in a separate tag identifying that region. This allows you to later search for and compare all plans in a specific region efficiently.

  • Distinguish planning types: Use tags to differentiate between nightly planning, real-time planning and reference plans. This helps track how often real-time plan adjustments are necessary or to compare nightly planning with actual executions.

  • Separate simulations from operational plans: Clearly mark simulation runs (e.g. goal alignment experiments or test scenarios) separately from planning runs to be used in actual operations. This ensures that test results don’t interfere with live planning data.

To keep runs of your production systems distinct from development or staging environments, we recommend using different tenants and discourage using tags. This ensures a clear separation of environments, preventing test or experimental data from affecting production operations.

By consistently categorizing your runs using meaningful names and well-structured tags, you can streamline your workflow and make data-driven decisions more effectively.