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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.



Happy predicting the future!!

 



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