Gain faster insights into your Order to Cash, Finance, and Inventory data with these Dashboards and Explores based on the SAP Cortex Data Foundation. Leverage or customize this Looker model to:
- Identify trends and patterns in your data
- Spot potential problems early on
- Make better decisions faster
- Orders Fulfillment - Monitor current delivery status, highlight late deliveries and compare pending deliveries with current stock.
- Order Snapshot - Monitor the health of the orders including product delivery efficiency.
- Order Details - See order details including status.
- Sales Performance - Review the sales performance of products, divisions, sales organizations and distribution channels.
- Billing and Pricing - Review price variations by customer and product.
- Accounts Receivable - Analyze total receivables, overdue receivables, days outstanding, and top companies with highest receivables.
- Accounts Payable - Find financial information such as accounts payable, accounts payable turnover, overdue payables, accounts payable aging, and cash discount utilization.
- Vendor Performance - Analyze vendor performance including delivery, lead time, price variance, purchase order status.
- Spend Analysis - Review Key Performance Indicators (KPIs) like total spend, active vendor count and cleared invoices. Breakdown spend by purchase organization, purchase group, vendor country, and material type.
- Inventory Management - Review inventory value over time (in total and by material type). Highlight other important KPIs including:
- Inventory Turn
- Days of Supply
- Obsolete Inventory Value
- Slow Moving Inventory Value
Get the required BigQuery datasets for this block by following the installation instructions for Google Cloud Cortex Framework.
Manually install this LookML Model following one of the options below.
Refer to the Looker documentation for Installing a Tool from Marketplace. Provide values for the required prompts as outlined in next section Required Parameters.
- Fork this GitHub repository
- Create a blank LookML project with any name (e.g., cortex_sap_operational)
- Connect the new LookML project to the forked repository
- Update the values of constants in the
manifest.lkml
file as described in the next section Required Parameters - Commit and deploy changes to production
With the Looker project based on your forked repository, you can customize the LookML to fit your unique business needs.
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Connection: Value of the BigQuery CONNECTION_NAME allowing Looker to query the Cortex REPORTING dataset.
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GCP Project ID: The GCP project where the SAP reporting dataset resides in BigQuery (i.e., GCP project ID). Identifying Project ID.
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Reporting Dataset: The deployed Cortex Data Foundation REPORTING dataset where the SAP views reside within the GCP BigQuery project.
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Client: The SAP Client number (mandt) to use for Reporting.
Dashboards require two Looker user attributes to work properly.
A Looker Admin should create the following user attributes and set their default values.
⚠️ Name each user attribute exactly as listed below:
Required User Attribute Name | Label | Data Type | User Access | Hide Value | Default Value |
---|---|---|---|---|---|
default_value_currency_required | SAP Default Currency to Display | String | Edit | No | USD or desired currency like EUR, CAD or JPY |
client_id_rep | SAP Client Id (mandt) for Reporting | String | Edit | No | Enter your SAP Client ID or 100 if using the provided test data |
Each dashboard user can personalize these values by following these instructions.
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Persistent Derived Tables: If using this block with production data, you may want to convert some derived tables to Persistent Derived Tables (PDTs) to improve query performance. Ensure your BigQuery Connection has enabled PDTs, then update any derived table syntax with the desired persistence strategy.
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Locale: The Looker user locale setting (as seen in account profile) maps to SAP language code for Materials_MD, Vendor Performance, and Inventory Metrics Overview views and determines material text language. See language_map for details.
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BI Engine Optimization: Some calculations perform better with BI Engine Optimization enabled in BigQuery.
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(Optional) Unhide additional dimensions and measures: Many dimensions and measures are hidden for simplicity. If you find anything valuable missing, update the field's
hidden
parameter value No in the relevant views.
To learn more about LookML and how to develop visit: