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Overview

We are excited to announce updates to our standard analytics data model, which includes the addition of new dimensions and fields. These updates are aimed at providing more granular insights into your data, specifically focusing on sub-job level details.

The following new dimensions have been added to the standard analytics data model:

image-20240416-171048.png

Deployment and Data Refresh

When these updates are deployed:

  • Live Data Update: All live data (data within the last 60 days from the deployment date) will be updated to include the new dimensions.

  • Historical Data: Historical data will not automatically include the new dimensions. To update historical data, a history refresh must be requested from our support team.

New Dimensions and Fields

The following new dimensions and fields have been added:

  • Dim SubJobs

  • Fact Subjob Scrap

image-20240416-172735.pngimage-20240416-172817.png

New Formulas

Two new formulas have been introduced for accurate sub-job reporting:

  • SubJob Total Production

    sum([Total  Production])/(sum([Total Job Cycle Factor])/sum([Total sub_job_cycle_factor]))
  • SubJob Good Production

    sum([Total  Production])/(sum([Total Job Cycle Factor])/sum([Total Subjob Cycle Factor])) - [Total Subjob Scrap Amount]

Dedicated formulas and data fields are required to report sub-jobs in analytics. These formulas/fields must be used when reporting on sub-jobs. The existing formulas/values from fact core WILL NOT work.

Supported Use Cases

The following metrics and use cases are now supported:

  • Metrics:

    • Sub-Job Scrap Amount

    • Sub-Job Scrap Reason with Scrap Amounts

    • Sub-Job Total Production

    • Sub-Job Good Production

    • Sub-Job Cycle Factor

    • Other core data such as total hours, downtime hours, and uptime hours by sub-job

  • Use Cases:

    • Breakdown of sub-job level data by Job and/or Job Instance

image-20240403-234239.png

Notice above that the “Total Production” value is duplicated. That is why we must use the “SubJob Total Production” value, as it uses a formula to derive its value based on the sub-job cycle factor

  • Rollup/Summary by Sub-Job across multiple days or job instances

image-20240403-231925.png
  • Scrap reasons and amounts by sub-job, also at rollup level across multiple job instances

image-20240403-234422.png
  • Breakdown of sub-job level data by Shift

image-20240416-170818.png

Limitations

While the new dimensions and fields enhance the reporting capabilities, there are some limitations:

  • Sub-Job Total Production Reporting: Reporting on Sub-Job Total Production in the same widget that is pivoted by scrap reason is not supported.

image-20240329-165540.png

Notice that the second widget (without a scrap reason) has 2 extra job instance records that are missing in the first widget. If you add a scrap reason to a widget, it will filter out Fact Core records that do not have scrap assigned.

Scrap reasons should only be used in a widget that measures the actual sub-job scrap amount

  • Calculated Metrics Including Scrap (e.g., OEE): These metrics can only be accurately calculated at the parent job level.

image-20240403-232542.png

Notice above that the OeeProductive value (which is used to calculate OEE in Analytics) is the same for both sub-jobs even though one of them had scrap.
In order to accurately calculate OEE, it should be done at the parent job level, as this will ensure all sub-job level scrap is included in the calculation

By following these guidelines, you can ensure accurate and insightful reporting on your sub-job data. If you have any questions or need assistance with the updates, please contact our support team.

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