Activating the Scope items and how Predictive Intelligence is realized for SAP S/4HANA (Updated Dec 14th 2023)

[VenkataRaghu_Banda]

Part 5 of the blog series:

As of the latest update in Dec 2023, a few use cases have been deprecated and the corresponding tables below for both embedded and side-by-side scenarios shall reflect the same.The use cases that have been built around the different lines of business and industries leveraging the Machine Learning and Predictive Analytics algorithms. In this journey since 2018, a few AI use cases have been deprecated while a few have been updated and a few more new scenarios have been added. We are also building new use cases in the Generative AI space and connect back here in Q1 2024 to understand the new generative AI use cases that are added into the portfolio. 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. While this is the case, we are now moving away from this 3 letter scope item acronym but you will still understand and get a complete overview of all the AI use cases delivered.

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 Business Technology 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 are the steps to be followed.

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 "Analytics_Specialist" role is needed to first create the predictive model version, then train the model and finally activate the model.

   
   


3. 
   

   Preliminary steps - Creation of business data for the specified scope items and any pre-requisite scope items there-of!

   
   


4. 
   

   Select and train the model based on the data set provided or applied.

   
   


5. 
   

   Set a model version to active that will be used in the embedded application.

   
   


6. 
   

   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 the identification of any required scope items to be implemented and hence the 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.

You will be able to find the details of all the embedded ML scenarios that are already released with the names, user roles, the Fiori IDs and any other pre-requisites in the table of the below section.

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.

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/E2E processes as per the below table, let us now dive into the world of side-by-side ML scenarios.

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 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.

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 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.

You will be able to find the details of all the AI/ML scenarios that are already released with the names, user roles, the Fiori IDs, components/comm-scenarios and any other pre-requisites in the table below - this includes both the embedded as well as the side-by-side scenarios.

In future, another table shall be provided with the Gen AI scenarios as well.

AI use cases - SAP S/4HANA public Cloud, private cloud, DSC, Small & Medium Enterprises 

Use Case	LoB/E2E Process	Scope item	User Role	Component / Comm Scenario	Fiori IDs (if applicable) / Technology	Status
S/4HC Public & Private Edition
Cash Application	Finance	1MV	SAP_BR_CASH_MANAGER	SAP_COM_1018	Traditional AI - ML
Cash Application (Receivables Line-Item Matching)	Finance (Invoice-to-cash)	1MV	SAP_BR_CASH_MANAGER (AR Accountant)	SAP_COM_1018	Traditional AI - ML	S4HC 1702, S4 1709
Payment Advice Extraction (old name: Remittance Advices)	Finance (Invoice-to-Cash)	1MV	SAP_BR_CASH_MANAGER (AR and AP Accountants)	SAP_COM_1018	Traditional AI - ML	S4HC 1808, S4 1809
Goods Receipt / Invoice Receipt Monitor ML Status Proposal	Finance (Record-to-report / Accounting and Financial Close)	2ZS	SAP_BR_ADMINISTRATOR (GL Accountant)	SAP_COM_0246	Traditional AI - ML	S4HC 1809, S4 1809
Digital Manufacturing Cloud - Visual Inspection with ML	DSCM (Produce-to-control)		Production Operator / Quality Inspector		Traditional AI - ML	SAP Digital Manufacturing Cloud 2005
Intelligent Slotting	Design-to-Operate		Warehouse Clerk / Supply Chain Planner		EML	OP - 2022 FP1, 2023
Intelligent Collections	Finance (Order-to-cash, Cash Collection)		AR Accountants, Customers using FI-AR and S/4 Receivables management		Traditional AI - ML	S4HC 2302
Intelligent Accrual Recommendation	Finance	3NF, 2VB	SAP_BR_ADMINISTRATOR	SAP_COM_0446	Traditional AI	Deprecated
Integrated Digital Content Processing for Content Mgt.	Idea	2YC	SAP_BR_ADMINISTRATOR	SAP_COM_0245	Traditional AI	Deprecated
Create Sales Orders from PDF	Sales (Order-to-cash/Order and contract management)	4X9	SAP_BR_INTERNAL_SALES_REP (Internal Sales Representative)	SAP_COM_1129	Traditional AI - ML	S4HC 2011, S4 2021
Intelligent Intercompany Reconciliation	Finance	4LG	SAP_BR_RECON_ACCOUNTANT	SAP_COM_0553	Traditional AI - ML	Deprecated
SAP Tax Compliance Smart Automation / GRC	Finance (GRC/Enterprise Risk and Compliance)		Compliance Risk Manager		Traditional AI - ML	S4HC 1610
Business Integrity Screening / GRC	Finance (GRC/Enterprise Risk Compliance)		SAP_BR_CASH_MANAGER / Compliance / Fraud Manager		Traditional AI - ML	S4HC 1709
Contract Consumption	Procure	1QR	SAP_BR_BUYER SAP_BR_PURCHASING_MANAGER SAP_BR_PURCHASER	MM-FIO-PUR-ANA	F2012, F1837 (EML)
Supplier Delivery Prediction	Procure (Buy-to-deliver/basic warehouse management)	3FY	SAP_BR_BUYER SAP_BR_PURCHASING_MANAGER SAP_BR_PURCHASER(purchaser/inventory specialist)	MM-FIO-PUR-ANA	F1837,F2358 (EML)	S4HC 1811, S/4 1909
Stock in Transit	Produce	20N	SAP_BR_INVENTORY_MANAGER	MM-FIO-IM-SGM	F2139, F1837 (EML)
Early detection of slow and non Moving stocks	Produce	20N	SAP_BR_INVENTORY_MANAGER	MM-FIO-IM-SGM	F2137 (EML)
Sales Forecast	Sales	2YJ	SAP_BR_SALES_MANAGER	SD-FIO-HBA	F3304 (EML)
Delilvery Performance / Delivery in Time	Sales (Sale-to-deliver/basic warehouse management & inventory)	2YJ	SAP_BR_SALES_MANAGER (Sales/inventory specialist)	SD-FIO-HBA	F3408, F1837 (EML)	S4HC 1905, S4 1909
Sales Performance Prediction (formerly Sales Forecast)	Sales (Order-to-cash)	2YJ	SAP_BR_SALES_MANAGER (sales manager)	SD-FIO-HBA	F3304, F1837 (EML)	S4HC 1811
Process Implausible Meter Reading Results	Utilities					Deprecated
Process Outsorted Billing Documents	Utilities		SAP_BR_BILLING_SPECIALIST_ISU		F2186	Deprecated
Reactive Maintenance		4HH				Deprecated
SAP S/4HANA for Behavioral Insights  (Intelligent Collections)	Public Sector / Finance (Order-to-cash)		AR Accountants, Collection Agents		EML	Add-on for SAP S/4HANA OP, Private Cloud Oct 2023: FICA Intelligent Dunning
Business Rule Mining	MDM				EML
Digital Supply Chain
IBP - Variable Impact Analysis for ARIMAX	Design-to-operate (Demand Planning)		Demand Planner		EML	SAP IBP 2302
IBP - Master data pattern detection	Design-to-operate (Config)		Configuration Expert		EML	SAP IBP 2305
Demand Forecasting	Design-to-operate (Demand planning)		Demand Planner		Traditional AI	IBP 2018
Master Data Anomaly Detection	Design-to-operate (Config)		Configuration Expert		Traditional AI	2111
Alert Threshold Detection	Design-to-operate (monitoring)		Demand Planner, Supply Planner		Traditional AI	SAP IBP 1808
Automated Determination of Segmentation Thresholds	Design-to-operate (demand planning)		Demand Planner, Supply Planner		Traditional AI	SAP IBP 2105
Outlier detection in batch jobs	Design-to-operate (administration)		IT Support		Traditional AI	SAP IBP 1902
Dynamic Lead time prediction (DSC planning)	Design-to-operate (Inventory and supply planning)		Configuration Expert		EML	SAP IBP 2205
Extended Service Parts Planning - Demand Planning	Plan-to-fulfill (baseline demand forecast)		Demand Planner		EML	S4HC 2022 Private Edition, S4HC OP 2022
Small & Midsized Enterprises (SME)
Business Card Scanning	Business Partners Management		Sales Representative		Traditional AI ( AI business services )	SAP Business One
Intelligent Invoice Scanning	Procure-to-Invoice (Payables and Invoice management)		Purchaser / AP Specialist		Traditional AI ( AI business services )	SAP Business One
Sales Forecast and Clustering for SAP Business One	Various Procurement Processes		Analytics user, Business user		EML	SAP Business One
SAP Best practices for Intelligent Automation for SAP Business One	Various Business processes		Various end users		AI business services and automation	SAP Business One
Business document extraction from e-mails	Procure-to-invoice / Order-to-cash		Various end users		Intelligent Automation + AI business services	SAP Business One
Sales Order Creation from a local purchase order	Order-to-cash		Sales department user		Intelligent Automation + AI business services	SAP Business One
Master data enrichment for business partner identification	Order-to-cash		Sales department user		Intelligent Automation + AI business services	SAP Business One
Proof of delivery note in the outbound delivery and invoice	Order-to-cash		Sales department user		Intelligent Automation + AI business services	SAP Business One
Supplier invoice upload for intelligent invoice scanning	Procure-to-invoice / Invoice and payables management		Purchaser / AP Specialist		Intelligent Automation + AI business services	SAP Business One
Activity Creation for Business Partners	Order-to-cash		Sales Representative		Intelligent Automation + AI busines services	SAP Business One
Automation for Return request process in SAP Business One	Return Material Authorization		Sales Representative, Warehouseman		Intelligent Automation	SAP Business One  FP 2208

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.

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.

• 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!!