Activating the Scope items and how Predictive Intelligence is realized for SAP S/4HANA (Updated July21st 2022)

Use Case	LoB	Scope item	User Role	Component	Fiori IDs	Status
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	Deprecated
Project cost forecast based on historical data	Idea	2Y7	SAP_BR_PROJECTMANAGER	PPM-FIO	F2513, F2538, F1837	Deprecated (new use case planned)
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	Deprecated
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
Process Implausible Meter Reading Results	Utilities
Process Outsorted Billing Documents	Utilities		SAP_BR_BILLING_SPECIALIST_ISU		F2186
Reactive Maintenance		4HH
Behavioral Insights	Public Sector
Business Rule Mining	MDM

Now that we understood the steps involved in implementing the embedded predictive scenarios with an example and also briefly highlighted all the embedded predictive scenarios released across the different LoBs, let us now dive into the world of side-by-side ML scenarios.

Let us now look into the Side-by-side ML scenarios that consume Machine Learning services from the SAP Business Technology Platform and how they are utilized by the SAP S/4HANA business processes. The following steps highlight the flow:

1. System access – The system is accessible via the Fiori LaunchPad. The system administrator provides the URL to access accordingly, the various apps assigned to your role.
2. Roles – The specific role for the ML service need to be assigned and should be used.
3. Preliminary steps – Creation of master data, organizational data and other data needed for the ML scenario.
4. Business Conditions – Any pre-requisite scope items need to be implemented first for the basic business conditions to be met.
5. Configuration – Configure the ML service.
6. Subscription – Subscribe to the corresponding application that uses the service, the scope item has the complete details of the service.
7. Communication – Create the comm system as the SAP_BR_ADMINISTRATOR. Then create the COMM scenario assigned for the specific ML service.
8. Training – Schedule the training job.
9. Infer the results from the prediction models by changing the role and login 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.

• 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 Business Technology Platform (SAP BTP).

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 BTP 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 Business Technology 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.

Use Case	LoB	Scope item	User Role	Comm Scenario	Status
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	Deprecated
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)	Deprecated (new use case planned)
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).	Deprecated (new use case planned)
Proposal of Material Group	Procure	2XV	SAP_BR_BUYER SAP_BR_PURCHASING_MANAGER SAP_BR_PURCHASER	SAP_COM_0253( Text Analysis)	Deprecated (new use case planned)
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).	Deprecated (new use case planned)
Image-Based Buying	Procure	3UH	SAP_BR_EMPLOYEE_PROCUREMENT SAP_BR_BPC_EXPERT	F1241 (app)	Deprecated (new use case planned)
Intelligent Approval Workflow	Procure	43E	SAP_BR_BUYER SAP_BR_PURCHASING_MANAGER SAP_BR_PURCHASER	SAP_COM_1054	Deprecated (new use case planned)
Create Sales Orders from Unstructured Data	Sales	4X9	SAP_BR_INTERNAL_SALES_REP	SAP_COM_1129
Intelligent Intercompany Reconciliation	Finance	4LG	SAP_BR_RECON_ACCOUNTANT	SAP_COM_0553

To help the customers and the partners, we also released a “Best Practices of doing Predictive Analytics and Machine Learning with SAP S/4HANA” in Q2 2021. You can find more information on the best practices at On-Premise (embedded and side-by-side), Cloud (embedded and side-by-side), explorative analytics. This would help to provide the documentation, technical guide set-ups and direct reference/access to all the released use cases – embedded ML, side-by-side ML and explorative predictive analytics.

We are also releasing a comprehensive book – “Implementing Machine Learning with SAP S/4HANA” by SAP-Press in the mid of September 2020.

• Resources and journey to machine learning with SAP S/4HANA
• Part 1 – Leveraging Predictive Intelligence with SAP S/4HANA
• Part 2 – Architecture and deep-dive of the different approaches around Predictive Intelligence
• Part 3 – Process flow leveraging Machine Learning and Predictive Analytics
• Part 4 – Scope and functionality in the context of an end-to-end process leveraging ML
• Part 5 – Activating machine learning functionality for SAP S/4HANA (this blog)
• Part 6 – Building ML into the digital core of SAP S/4HANA (Embedded ML)
• Part 7 – Enhancing the digital core with ML Services (Side-by-Side ML)
• Part 8 – Extending the digital core by leveraging ML with SAP Analytics Cloud
• Part 9 – ML Extensions to SAP S/4HANA processes
• Blog series – ISLM for machine learning with S/4
• Introducing the book – Implementing Machine Learning with SAP S/4HANA

Happy predicting the future!!