Skip to Content
Product Information

Scope items and how Predictive Intelligence is realized for SAP S/4HANA

As of this writing in the last week of January 2020, we have about 35 to 40 use cases that have been built around the different lines of business and industries leveraging the Machine Learning and Predictive Analytics algorithms. Connecting back to my earlier blog about the different approaches that could be leveraged for infusing intelligence into SAP S/4HANA, let us now review in detail how to realize the functionality.

Though you find complexity in SAP technology or SAP software, you will understand that there is a structured approach to dissect the information and understand how to access, implement and extend these functionalities. The beauty is encapsulated in the 3 letter acronym of the scope items. Any functionality can be activated or de-activated by choosing the corresponding scope item (3 letter scope item). You will also notice that some of these scope items would have pre-requisite scope items that have to be activated and implemented before you continue further.

Let us now dive into the scenarios and understand the mechanics behind the build and implementation! Here are the different approaches starting from embedding predictive models in SAP S/4HANA, followed by consuming ML services on the SAP Cloud Platform. We shall discuss “leveraging the predictive services from SAP Analytics Cloud” in a later blog while talking about extending the digital core with SAP Analytics Cloud Predictive services.

Embedded Predictive Scenarios:

In the context of embedding predictive models into the SAP S/4HANA business processes, there are a few steps that have to be followed. Here is the flow to be followed.

  • System access – The system is accessible via the Fiori LaunchPad. The system administrator provides the URL to access accordingly to access the various apps assigned to your role.
  • Roles – The “Analytics_Specialist” role is needed to first create the predictive model version, then train the model and finally activate the model.
  • Preliminary steps – Creation of business data for the specified scope items and any pre-requisite scope items there-of!
  • Select and train the model based on the data set provided or applied.
  • Set a model version to active that will be used in the embedded application.
  • Change Role – Login as the specific end user to access the app and run the functionality to see the predictions.

Now let us see an example of how a particular predictive functionality is enabled, applied and run in the context of a technical scope item. Eg., Quantity Contract Consumption – scope item 1QR.

a) Scope item and flow:

1QR – The purchaser can analyze a high-level overview of important information, such as expiring contracts, overdue purchase orders, or urgent purchase requisitions, as well as an overview of different procurement KPIs. That information can then be used to predict full consumption of a contract based on factors such as a historical data, other available influencing parameters, and so on. 

b) Roles: 

All of the following roles should to be assigned to be able to work with the Quantity Contract Consumption KPI.

Business Role Name

Business Role ID

Log On

Analytics Specialist

SAP_BR_ANALYTICS_SPECIALIST

Please ask your system administrator to assign the roles to the testers.

Buyer

SAP_BR_BUYER

Please ask your system administrator to assign the roles to the testers.

The predictive model training needs to be done by an analytics specialist.

The analytics specialist requires the business catalog SAP_BW_BC_UMM_PC.

To use the Quantity Contract Consumption app, the business catalog SAP_MM_BC_PUR_STRATEGY must have been assigned to end user (this business catalog is also included in the business roles SAP_BR_BUYER).

c) Business Data:

A few required scope items such as Purchase Contract BMD, Consumable Purchasing BNX, Procurement of direct materials J45 need to be run and the corresponding data to be available. The key step here is to identification of any required scope items to be implemented and data created accordingly.

d) Model training:

Finally train or re-train the model and activation of the required model version is to be done.

e) Access the app:

Now logon to the Fiori LaunchPad as the “Buyer” and access the app – Quantity Contract Consumption and follow the steps as specified in the scope item 1QR help documentation to run the scenario and see the predicted consumption results of the contracts to be expired.

The above 5 steps are to be done for any of the embedded predictive scenarios/use cases that were developed and released out-of-the-box with SAP S/4HANA functionality.

Let us now quickly review the embedded predictive scenarios that are released – with the scope item names, user roles required, Fiori IDs and any other pre-requisite scope items needed. With that understanding you would be more confident on how to proceed with your current implementation of embedding predictive functionality into SAP S/4HANA business processes.

Scope items in the embedded predictive scenarios

Use Case LoB Scope item User Role Component Fiori IDs
SAP Tax Compliance Smart Automation / GRC Finance
Business Integrity Screening / GRC Finance SAP_BR_CASH_MANAGER
Detect Abnormal Liquidity Items (formerly: Machine Learning in Cash and Liquidity Management) Finance 30K SAP_BR_CASH_MANAGER F1837
Project cost forecast based on historical data Idea 2Y7 SAP_BR_PROJECTMANAGER PPM-FIO F2513, F2538, F1837
Contract Consumption Procure 1QR SAP_BR_BUYER
SAP_BR_PURCHASING_MANAGER
SAP_BR_PURCHASER
MM-FIO-PUR-ANA F2012, F1837
Propose resolution for invoice payment block Procure 2XX SAP_BR_BUYER
SAP_BR_PURCHASING_MANAGER
SAP_BR_PURCHASER
MM-FIO-PUR-ANA F0593, F1060A, F1837
Supplier Delivery Prediction Procure 3FY SAP_BR_BUYER
SAP_BR_PURCHASING_MANAGER
SAP_BR_PURCHASER
MM-FIO-PUR-ANA F1837,F2358
Stock in Transit Produce 20N SAP_BR_INVENTORY_MANAGER MM-FIO-IM-SGM F2139, F1837
Demand-Driven Replenishment: Dynamic Buffer Level Adjustment (using stock transfer) Produce 20N SAP_BR_PRPDN_PLNR PP-DD F2831, F1837
Defect Code Proposal (incl. Text Recognition) Produce 20N SAP_BR_QUALITY_TECHNICIAN
SAP_BR_QUALITY_ENGINEER
F2649,F2868
Early detection of slow and non Moving stocks Produce 20N SAP_BR_INVENTORY_MANAGER MM-FIO-IM-SGM F2137
Quotation Conversion Probability Rate Sales 2YJ SAP_BR_SALES_MANAGER SD-FIO-HBA F1904, F1871, F1837
Sales Forecast Sales 2YJ SAP_BR_SALES_MANAGER SD-FIO-HBA F3304
Delilvery Performance / Delivery in Time Sales 2YJ SAP_BR_SALES_MANAGER SD-FIO-HBA F3408, F1837
Sales Performance Prediction (formerly Sales Forecast) Sales 2YJ SAP_BR_SALES_MANAGER SD-FIO-HBA F3304, F1837
Side-by-side Machine Learning Scenarios:
Let us now look into the Side-by-side ML scenarios that consume Machine Learning services from the SAP Cloud Platform and is utilized by the SAP S/4HANA business processes.
  • System access – The system is accessible via the Fiori LaunchPad. The system administrator provides the URL to access accordingly to access the various apps assigned to your role.
  • Roles – The specific role for the ML service need to be assigned and should be used. The
  • Preliminary steps – Creation of master data, organizational data and other data needed.
  • Business Conditions – Any pre-requisite scope items need to be implemented first for the basic business conditions to be met.
  • Configuration – Configure the ML service.
  • Subscription – Subscribe to the corresponding application that uses the service, here the scope item has the details.
  • Communication – Create the comm system as the SAP_BR_ADMINISTRATOR. Then create the COMM scenario assigned for the specific ML service.
  • Training – Schedule the training job.
  • Infer the results from the prediction models by changing the role and loggin as the specific end user to access the app and run the functionality to see the predictions.
Let us now take the example of a scope item 3NF – Machine Learning for Accruals Management.This also requires a pre-requisite scope item 2VB – Purchase Order Accruals. Here the accruals management provides recommendations during the accrual review process.

a) Scope item and flow:

3NF – The machine learning service used for accruals management is a Cloud service that uses machine learning technology to observe your accruals management and provide recommendations during the accrual review process. To support the process, the machine learning service can learn from decisions taken in the past, and apply learned knowledge to the new business situation. For accrual amounts that need manual review, the system adopts the machine learning service and then provides recommendations for reliable accruals for each purchase order. You can also review all the reliable accruals or only the reliable accruals that are above a certain confidence level in one go by using the mass review function.

b) Roles: You will need to start with the SAP_BR_ADMINISTRATOR role to do the required configuration.

c) Business Data and Pre-requisites for configuring the ML Service:

  • The scope items 2VB (Purchase Order Accruals) and XX_3NF (Machine Learning for Accruals Management (Cloud only)) are both active.

You can check this in the app Manage Your Solution under View Solution Scope.

If the scope item is not active, please request the activation via a BCP ticket on component: XX-S4C-OPR-SRV.

  • The Accruals Recommendation service is active in your account on SAP Cloud Platform (SCP).

You can request the activation via a BCP ticket on component: CA-ML-OPS.

After the service activation you should be able to see the Accruals Recommendation service in the Cloud Foundry service marketplace, under any space in your SCP account.

To create a space, you can go to the activated subaccount, select Spaces and click New Space.

d) Subscribe to the Accruals Application:

  1. Open the space in SAP Cloud Platform.
  2. Under Services, open Service Marketplace.
  3. Choose the service Accruals Recommendation tile.
  4. To create a new service instance, choose New Instance.
  5. Under Service Keys, choose Create Service Key. The system generates and displays the oAuth credentials.

e) Create the communication system:

  1. Log on to the SAP Fiori launchpad as an Administrator.
  2. Select the Communication Systems tile.
  3. On the Communication Systems screen, choose New.
  4. Make the following entries:
Field User Action or Values Example
System ID system ID ACCRUALS_ML_INTEGRATION
System Name system name ACCRUALS ML COMMUNICATION SCENARIO
  1. Choose Create.
  2. Under Technical Data, fill in the following fields:
Name Description
Host Name The host name for target system.
OAuth 2.0 Endpoint The endpoint of oAuth authentication server.
OAuth 2.0 Token Endpoint The token endpoint of oAuth authentication server.
  1. Under User for Outbound Communication, create a new user with the following information:
Name Description
Authentication Method OAuth 2.0
OAuth 2.0 Client ID The client ID of oAuth authentication server user.
Client Secret The client password of oAuth authentication server user.
  1. Choose Create.
  2. Choose Save.

f) Create the communication Arrangement:

  1. Log on to the SAP Fiori launchpad as an Administrator.
  2. Under Communication Management, select the Communication Arrangements tile.
  3. On the Communication Arrangements screen, choose New.
  4. In the New Communication Arrangement dialog box, in the Scenario field, enter SAP_COM_0446.
  5. Choose Create.
  6. The Communication Arrangements displays.
  7. In the Common Data section, in the Communication System field, select the communication system that you created in the previous step: Create Communication System.
  8. Choose Save.

g) Schedule the training job:

  1. Log on to the Web UI for your SAP S/4HANA system using the user you received.
  2. In the Accruals Management business group, open Schedule Accruals Job.
  3. Choose New.
  4. As a job template, choose Train Accruals Prediction Model on Historical Data.
  5. Under Scheduling Options, set the running schedule according to your requirement. The default frequency is set to one week.
  6. Choose Back and monitor the background job.

h) Train the Accruals Prediction Model based on historical data:

  1. This functionality is available in the Schedule Accruals Jobs app. Select the Train Accruals Prediction Model on Historical Data template.
  2. A machine learning service which is a feature of the Review Purchase Order Accruals – For Cost Accountant app predicts whether user will adjust the proposed periodic planned costs. This job takes data from the table that contains the history of the previous interactions of the cost accountants and trains the prediction model using these data.

i) Infer Accruals from the prediction model:

  1. This functionality is available in the Schedule Accruals Jobs app. Select the Infer Accruals from Prediction Model template.
  2. A machine learning service, as a feature of the Review Purchase Order Accruals – For Cost Accountant app, helps to predict whether you need to adjust the proposed periodic planned costs.
  3. You run this job best outside of business hours after the Train Accruals Prediction Model on Historical Data job is finished.
Let us now quickly review the side-by-side ML scenarios that are released – with the scope item names, user roles required, Comm Scenarios and any other pre-requisite scope items needed. With that understanding you would be more confident on how to proceed with your current implementation of ML functionality for the SAP S/4HANA business processes.
Scope items in the side-by-side ML scenarios
Use Case LoB Scope item User Role Comm Scenario
Cash Application Finance 1MV SAP_BR_CASH_MANAGER SAP_COM_1018
Remittance Advice Finance 1MV SAP_BR_CASH_MANAGER SAP_COM_1018
Cash Application (Feature Release) Finance 1MV SAP_BR_CASH_MANAGER SAP_COM_1018
SAP Cash Application (Feature Release II) Finance 1MV SAP_BR_CASH_MANAGER SAP_COM_1018
Payment Advice Extraction (old name: Remittance Advices) Finance 1MV SAP_BR_CASH_MANAGER SAP_COM_1018
Goods Receipt / Invoice Receipt Monitor ML Status Proposal Finance 2ZS SAP_BR_ADMINISTRATOR SAP_COM_0246
Payables Line Item Matching Finance 1MV SAP_BR_CASH_MANAGER SAP_COM_1018
Intelligent Accrual Recommendation Finance 3NF, 2VB SAP_BR_ADMINISTRATOR SAP_COM_0446
Integrated Digital Content Processing for Content Mgt. Idea 2YC SAP_BR_ADMINISTRATOR SAP_COM_0245
Proposal of new catalog item Procure 2XW SAP_BR_BUYER
SAP_BR_PURCHASING_MANAGER
SAP_BR_PURCHASER
SAP_COM_0253( Text Analysis)
Proposal of options for Materials without Purchase Contract Procure 30W SAP_BR_BUYER
SAP_BR_PURCHASING_MANAGER
SAP_BR_PURCHASER
SAP_COM_0298 (Contract Proposal Integration).
Proposal of Material Group Procure 2XV SAP_BR_BUYER
SAP_BR_PURCHASING_MANAGER
SAP_BR_PURCHASER
SAP_COM_0253( Text Analysis)
Proposal of options for Materials without Purchase Contract v2(formerly: Optimized Purchase Requisition Processing: Propose Creation of RFQs) Procure 30W SAP_BR_BUYER
SAP_BR_PURCHASING_MANAGER
SAP_BR_PURCHASER
SAP_COM_0298 (Contract Proposal Integration).
Image-Based Buying Procure 3UH SAP_BR_EMPLOYEE_PROCUREMENT
SAP_BR_BPC_EXPERT
F1241 (app)
Intelligent Approval Workflow Procure 43E SAP_BR_BUYER
SAP_BR_PURCHASING_MANAGER
SAP_BR_PURCHASER
SAP_COM_1054
Here are some quick links to the blogs in this series to give you a complete understanding of how Predictive Intelligence is infused into SAP S/4HANA.
Happy Predicting the future!!
Be the first to leave a comment
You must be Logged on to comment or reply to a post.