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masao_shimizu
Product and Topic Expert
Product and Topic Expert
1,350
Demand Planning Level in OBP

In the OBP (Oder-Based Planning), the demand key figures are defined at the day/product/location level in out-of-box standard, but you may face the requirement that needs additional attributes in the planning level. Customer group is one of the examples of the additional attributes. In this blog, I would like to explain how it can be set up in the RTI-based OBP planning area. The steps mentioned below do not include general settings to use the OBP planning area as well as the RTI integration between OBP and S/4(ECC). For the RTI general setup, please refer to the webinar recordings for the RTI integration (Introduction to IBP Real-time Integration (RTI) (on24.com)) and SAP Note 3110007 - IBP Real-time Integration: Information/Restrictions - SAP for Me.

What we can do by adding the customer group attributes:

  1. Set forecast, allocation at the product location customer group level.

  2. Perform forecast consumption at the product location customer group level.

  3. Display pegging information per product location customer group level in the Excel UI.


Steps to Enhance Demand Planning Level

  1. Create an RTI-based OBP planning area by copying the sample planning area SAP7F.

  2. Activate the planning area.

  3. Set up an integration profile for RTI.

  4. Add additional configurations required (i.e. planning profile, consumption profile).

  5. Load master data via RTI.

  6. Execute Job “Order-Based Planning: Generate Master Data for Sales Order Field Catalog”.

  7. Load transaction data via RTI.

  8. Execute the OBP planning run.

  9. Execute the job ‘Order-Based Planning: Generate Planning Objects for External Key Figures’ and generate the pegging data for the excel display.

  10. Check the data.


1.Create an RTI-based OBP planning area by copying the sample planning area SAP7F.


SAP7F is the sample planning area for RTI.


When creating planning area, select customer group master data type for additional demand attributes.


The “Customer Group” MDT is generated, and the “Customer Group” attribute is automatically added to the demand / pegging planning levels as key.

Customer Group Master Data Type


Demand Planning / Pegging Planning Level



2. Activate the planning area.

Once the OBP planning area is activated. The business meanings for OBP are activated.




Also, sales order field assignment for customer group is updated automatically.




Note: In the RTI-based OBP planning area, executing the job "Order-Based Planning: Update Versions" is not required anymore.

3. Set up an integration profile for RTI.

4. Add additional configurations required (i.e. planning profile, consumption profile).

Planning Profile

Include "ShipToCustomerGroup" in the sorting for demand prioritization.


Forecast Consumption Profile

Include "Customer Group" in the OBP planning level for consumption profile.


5. Load master data via RTI.

6. Execute Job “Order-Based Planning: Generate Master Data for Sales Order Field Catalog”.

No RTI enhancement and CI-DS task setup is needed to load data to customer group master data type. It can be generated by executing the operator “Order-Based Planning: Generate Master Data for Sales Order Field Catalog”.



7. Load transaction data via RTI.

No enhancement is needed to have customer group in sales order data.

8. Execute the OBP planning run.

9. Execute the job ‘Order-Based Planning: Generate Planning Objects for External Key Figures’ and generate the pegging data for the excel display.



13. Check the data.

Demand Planning Level / Pegging


Demand Prioritization

Demands are sorted by "Requested Date" / "Ship-to Customer Group".


Forecast Consumption

Forecasts are consumed considering open sales orders.


Conclusion

By having additional attribute like customer group in the demand planning level as well as the pegging planning level, we can consider the additional attribute in the followings.

  • Demand Prioritization

  • Forecast Consumption

  • Product Allocation

  • Demand Supply Matching using Pegging Data

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