Purpose

The purpose of this document is to define the functional requirements, methodology, and approach for the initial data loading and subsequent synchronization of Business Rule Framework Plus (BRF+) Output Management data in SAP S/4HANA.

This process will establish and maintain output determination records (e.g., email recipients, output channels, and conditions related to BP) in BRF+ based on the configuration and master data currently maintained in Classic Output Management (NAST framework) within the legacies from SAP ECC.

Conversion Scope

The conversion scope covers the extraction, transformation, and loading of output determination data from Classic Output Management (NAST-based framework) into BRF+ Output Parameter Determination (OPD) in S/4HANA, ensuring both initial migration and interim synchronization until full adoption of BRF+ is achieved.

The scope includes:

• Extraction of customer-related output condition records (e.g., for Sales Orders,  Quotations, Contracts, Scheduling Agreements, Invoice and Credit and Debit memos) from Classic Output Management, including associated communication details such as email addresses, language, and output channels;
• Cleansing and Harmonization of extracted data to remove obsolete, incomplete, or duplicated entries, ensuring only valid and active output records are migrated;
• Mapping of Classic condition record keys (application, condition type, key combination) to BRF+ OPD parameters (e.g., Customer, Sales Organization, Document Type, Output Type);
• Transformation of medium and dispatch settings from Classic (e.g., NAST medium codes) to BRF+ equivalent channels (e.g., Email, Print, EDI), ensuring compatibility with S/4HANA Output Management settings;
• Initial Load of the transformed dataset into BRF+ Output Parameter Determination using the OPD framework and/or BRF+ application data load methods;
• Ongoing Synchronization process to detect and apply changes made in Classic Output Management to BRF+, allowing a parallel run period until Classic Output Management is fully decommissioned;
• Validation to ensure that migrated BRF+ records produce identical output results as their Classic counterparts for equivalent sales scenarios.

Out of Scope:

• Migration of output determination for applications outside of sales (e.g., MM Purchasing outputs);
• Modifications to customer master data beyond the email and communication fields required for BRF+;
• Changes to output form templates (Smartforms/Adobe Forms) beyond what is required for enabling BRF+.

The data from legacy system includes:

1. Active output condition records for Sales (Application V1 and V3) linked to customers (Partner Function SP, BP,PY, CONTACTS) with maintained communication details (email address, fax, etc.)
   1. Output types relevant for Sales Orders, Quotations, Contracts, Invoices and memos and Scheduling Agreements that are in active use in the business process
2. Records with valid medium settings (e.g., Email, Print, EDI) compatible with S/4HANA BRF+ output channels
3. Language assignments that are valid in the target system

The data from legacy system excludes:

1. Obsolete, expired, or logically deleted output condition records
2. Condition records for customers that are blocked for sales or flagged for deletion in the customer master
3. Output types not used in the current or target S/4HANA sales processes
4. Records with incomplete or invalid communication data (e.g., missing email for email output medium)
5. Condition records belonging to applications other than sales (e.g., MM, FI outputs)

Data Profiling

List of source systems and approximate number of records for tables for

• Usage = B

• Application: = V1 and V3

• transmission mediums 5,6,7,8:

List of source systems and approximate number of records

Additional Information

Multi-language Requirement

Output texts and messages will be migrated for all relevant languages (EN, FR, NL, PT, etc.).

Document Management

All deliverables, SQL extractions, and DCT templates will be version-controlled in Confluence and Jira.

Legal Requirement

Summarize Legal Requirement/s, if any

Special Requirements

Specify any special requirements or considerations that may impact the data conversion process based on specific locations, regulatory compliance or system limitations. Clearly outline any regional or localization requirements such as country-specific data formats, legal reporting obligations or industry standards that must be adhered to (e.g., localization rules for countries like China).

If the data conversion involves third-party systems or external data sources, such as Icertis, describe any additional requirements related to data mapping, transformation logic, validation rules or security measures that must be followed.

Target Design

In S/4HANA Output Management (via BRF+), decision tables replace the condition technique. Each BRF+ decision table acts like a flexible rule set:

Input Parameters → Decision Logic → Output Parameters (Result)

They determine:

1. Output Type (e.g., ORDER_CONFIRMATION, BILLING_OUTPUT)
2. Output Channel (e.g., Email, Print, EDI)
3. Form Template (Adobe Form)
4. Receiver (Partner Function)
5. Language

Strategic Approach: BRF+ Output Simplification

• Merge multiple legacy ECC output types (e.g., ZC10,ZC11,ZARM...) into a single BRF+-driven output type, e.g. ORDER_CONFIRMATION to simplify maintenance and governance.

• Minimize the creation of outputs that are rarely triggered, focusing on high-value and frequently triggered scenarios.

• Avoid using business partners or email addresses as decision table input parameters. Instead, leverage business partner attributes to determine the appropriate outputs and channels.

Implication:
There will be no direct one-to-one mapping from ECC condition records to BRF+ decision tables. The approach emphasizes a simplified, rules-driven model that covers the majority of business scenarios while reducing complexity.

Following Decision tables have been identified in the sandbox. This tables are for demonstration purposes and may change.

Data Cleansing

The cleansing activity involves validation of message types, partner functions, and language assignments. Records with missing partner data or invalid Output Types will be flagged for correction. Duplicate or inconsistent entries will be resolved before transformation. Currently none of those cases were encountered at Syensqo.

Conversion Process

The high-level process is represented by the diagram below:

Data Privacy and Sensitivity

All data extracted must be anonymized and handled according to data protection policies.

Extraction

Data will be extracted from ECC using SQLbased queries executed on the source database. The extraction will include relevant tables such as NACH, TNAPR, The extraction methods are as follows:

• Full extraction from relevant tables in ECC (preferred).
• Extraction through the application layer when database access is restricted.
• Manual extraction via reports or DCT if system access is not possible.

Relevancy criteria will be applied to include only active, valid, and inscope records.

Extraction Run Sheet: To be provided after the first mock cycle.

Source Table to be extracted: NACH Table

The agreed Relevancy criteria is applied to the extracted records to identify the records that are applicable for the Target loads

Extraction Run Sheet

Selection Screen

Data Collection Template (DCT)

BRF+ provides a builtin feature to download and upload decision tables as Excel files. 

• Steps:

  1. Go to your BRF+ application in transaction BRF+ (or BRFPLUS).

  2. Open the Decision Table object.

  3. From the Table Maintenance screen:

     • Click on “Download to Excel” → system exports the current content and structure.

     • Fill in or update the Excel file offline.

     • Use “Upload from Excel” to import it back.

  
  

Note: 

• Each table must be handled individually.
• The creation of Data collection template must be aligned with BRF+ configurations . For now, the following Template is prepared based on Configurations from S/4HANA Sandbox environment.  This will be replaced once the configuration is ready. The same applies to the contents of the DTs, they are here as examples and will be replaced by real configuration objects .

DT_OUTPUT_TYPE: 

DCT Rules:

DCT Template: 

DT_RECEIVER: 

DCT Rules:

DCT Template:

DT__CHANNEL:

DCT Rules:

DCT Template:

DT__EMAIL_RECEIVER:

DCT Rules:

DCT Template:

DT__EMAIL_SETTING:

DCT Rules:

DCT Template:

DT__OUTPUT_RELEVANCE: DCT Rules:

DT__OUTPUT_RELEVANCE: DCT Template:

Extraction Dependencies

Transformation

The Target fields are mapped to the applicable Legacy field that will be its source, this is a 3way activity involving the Business, Functional team and Data team. This identifies the transformation activity required to allow Syniti Migrate to make the data Target ready:

1. Perform value mapping and data transformation rules.
   1. Legacy values are mapped to the to-be values (this could include a default value)
   2. Values are transformed according to the rules defined in Syniti Migrate
2. Prepare target ready data in the structure and format that is required for loading via prescribed Load Tool. This step also produces the load data ready for business to perform Preload Data Validation

Transformation Run Sheet

Transformation Rules

Please note that there is no direct mapping between the condition types and the BRF+ output types. The table below will simply contain the list of legacy output types and how they would have been mapped if all conditions were migrated. Please note that all conditions no longer associated to V1have been excluded

Transformation Mapping

Transformation Dependencies

PreLoad Validation

Project Team

Completeness

Accuracy

Business

Completeness

Accuracy

Load

The load process includes:

1. Execute the automated data load into target system using load tool or product the load file if the load must be done manually
2. Once the data is loaded to the target system, it will be extracted and prepared for Post Load Data Validation

Load Run Sheet

Load Phase and Dependencies

Identify the phase as to “when” the load for this object will occur. <PreCutover, Cutover, Post Cutover> and list the steps that need to occur before the load can commence

Configuration

List the Configurations required before loading can commence

Conversion Objects

Error Handling

The table below depicts some possible system errors for this data object during data load. All data load error is to be logged as defect and managed within the Defect Management

PostLoad Validation

Project Team

Completeness

Accuracy

Business

Completeness

Accuracy

Key Assumptions

• Master Data Standard is up to date as on the date of documenting this conversion approach and data load.
• Data Object is in scope based on data design and any exception requested by business.

Any additional key assumptions.

See also

Insert links and references to other documents which are relevant when trying to understand this decision and its implications. Other decisions are often impacted, so it's good to list them here with links. Attachments are also possible but dangerous as they are static documents and not updated by their authors.

Change log