Purpose

The purpose of this document is to define the conversion approach to create Business Partners - Customer (Sales and Service) - FLCU01 in S/4 HANA.

In SAP ECC, the Customer Sales View is part of the Customer Master Data, which is used to store customer-related information for sales transactions. It includes details such as sales area, pricing, delivery preferences, and billing information. The setup typically involves maintaining customer records separately for different sales organizations, distribution channels, and divisions.
In SAP S/4HANA, the Customer Sales View is integrated into the Business Partner (BP) model, which replaces the traditional customer/vendor objects from ECC. The Business Partner serves as a central entity, allowing a single record to hold multiple roles (e.g., customer and vendor). The Customer Sales View in S/4HANA is represented under the BP role FLCU01, which contains sales-specific data such as sales area assignments, pricing conditions, and delivery preferences

Conversion Scope

The scope of this document covers the approach for converting active Customer Sales view from Legacy Source Systems into S/4HANA following the Business Partners - Customer (Sales and Service) - FLCU01 Master Data Design Standard.

The data from legacy system includes:

a. The BP(customer) general data is migrated under conversion spec CNV-3007
b. The sales area under which the sales view data is maintained for the customer is within the scope of S4 Hana 

The data from legacy system excludes:

Source Approx No. of Records

No. of Records

Additional Information

Multi-language Requirement

N/A

Document Management

N/A

Legal Requirement

N/A

Special Requirements

Due to compliance requirement, there will be one SAP instance for Rest of the World and one for China specifically. For entities in China, the data will be loaded into SAP China instance while the entire migration process will remain the same as rest of the world.

To identify the record is for SAP China Instance, it will use below logic. 

Customer Master Data - General Information

1. The customer has sales area data in below entities. 
2. If the customer is used in both China entities and ROW, then the sales data needs to be created in both SAP China and ROW instances.

Target Design

The technical design of the target for this conversion approach.

Data Cleansing

Conversion Process

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

The ETL (Extract, Transform, Load) process is a structured approach to data migration and management, ensuring high-quality data is seamlessly transferred across systems. Here’s a breakdown of its key components:

1. Extraction
The process begins with extracting metadata and raw data from source systems, such as Syensqo ECC system (i.e., WP2/PF2) periodically. The extracted data is then staged for transformation.

Image Removed

For CUI instance, the ETL process will be similar, but it will not use Syniti tool.

Image Removed

Data Privacy and Sensitivity

c. There is usage for the customer within 4 years within the sales org in scope.

The data from legacy system excludes:

1. The sales org for the sales view is out of scope, such as Oil & Gas and Aroma specific sales organizations.

Additional Information

Multi-language Requirement

N/A

Document Management

N/A

Legal Requirement

CMMC 2.0 is a mandatory DoD cybersecurity certification for contractors handling Controlled Unclassified Information (CUI) and Federal Contract Information (FCI). CUI includes sensitive technical data (e.g., design specs, system info) related to U.S. military and space applications. The Composites Business handles CUI and is therefore within CMMC scope. Without certification, the business risks disqualification from existing and future DoD programs.
It is mandatory to implement CMMC-compliant systems and processes to for all the organizations that are dealing with CUI. Therefore, there will be one SAP instance specifically for CUI related entities.  * 20260219 remove CUI

Special Requirements

A. Different SAP Instance Migration Approach

Due to compliance requirement, there will be one SAP instance for Rest of the World (ROW), one for China and one for CUI* 20260219 remove CUI. For BP general data, the same data will be created in all 3 SAP instances as it is Tier 1 object with central data governance and maintenance rule.

B. One Sales Organization per GBU 

As elaborated in KDD060 - Sales Enterprise Structure - Sales Organization, in the S4 Hana design, one sales organization will be mapped to one GBU. Therefore, it is possible that one sales organization in legacy system is mapped to multiple sales organization in the S4. When such scenario happens, one record of customer master data sales view will be split into multiple records based on the mapping.

C. Distribution Channel Transformation

In S4 Hana design, there will be 3 distribution channels defined.

• Domestic
• Export
• Intercompany

Domestic/Export distribution channel is used for external customers, and determined based on the Departure country (from Plant, or Sales Organization/Company code country if plant information is not applicable)/Destination Country (Ship-to Party country).

Intercompany distribution channel is determined based on the nature of the business partner, i.e. if it is Syensqo entity or affiliated companies, it will be defined as Intercompany Distribution Channel.

To identify Domestic/Export distribution channel, it will apply below logics. 

1. Fetch the Sales history of the customer within the sales organization in scope (VBAK/VBAP/VBPA).
2. Derive the matrix of Sales Organization/Plant/Plant country/Ship-to party/Ship-to country (CR0455 Derive the matrix of Sales Organization/Sales Org country/Ship-to party/Ship-to country)
3. If Plant country is same as Ship-to country, (CR0455 If Sales Org country is same as Ship-to country) then for key combination of Sales Organization/Ship-to party, the transformed distribution channel will be Domestic. 
4. If Plant country is different as Ship-to country, ((CR0455 If Sales Org country is different from Ship-to country)) then for key combination of Sales Organization/Ship-to party, the transformed distribution channel will be Export(there will be scenarios that the customer under the sales organization can be extended to both domestic and export distribution channel). 
5. Apply the result to rest of the customers in the same sales document, e.g., Sold-to customer, Payer, Bill to etc. It will then get a matrix of Sales Org/Customer/Distribution Channel(After transformation)

start of CR0455

To identify Domestic/Export distribution channel, it will further consider below scenarios on top of the sales document usage.

a. Delivery usage - fetch the delivery history of the customer within the sales org in scope (LIKP/LIPS/VBPA)

b. Billing usage -  fetch the billing history of the customer within the sales org in scope (VBRK/VBRP/VBPA)

The DC determination logic is the same as the sales document logic, using the Sales Org country to compare the ship-to customer country. 

With the enhanced logic considers sales document/delivery/billing, in alliance with the relevancy rule, the data will be based on 4 year usage ( VBAK/LIKP/VBRK-ERDAT field).

end of CR0455

If there is no sales history as this is a new customer, similar logic will be applied using Customer master data sales view partner data.

1. Fetch the partner information from customer master data sales view for sales organization in scope (KNVV/KNVP).
2. Derive the matrix of Sales Organization/Country/Ship-to party/Ship-to country
3. If Sales Organization country is same as Ship-to country, then for key combination of Sales Organization/Ship-to party, the transformed distribution channel will be Domestic. 
4. If Sales Organization country is different as Ship-to country, then for key combination of Sales Organization/Ship-to party, the transformed distribution channel will be Export. 
5. Apply the result to rest of the customers in the same customer master data, e.g., Sold-to customer, Payer, Bill to etc. It will then get a matrix of Sales Org/Customer/Distribution Channel(After transformation)

Consolidate all the entries and remove the duplicate records. As a result, there will be a matrix based on Sales Organization/Distribution Channel/Customer. This information will be the base to migrate the customer sales view data (such as KNVV/KNVP etc.). 

D. Intercompany Customer Sales Area Data

For Intercompany customer, it is in the migration scope. However, as the definition of the to-be Intercompany customer is different from the existing ECC Intercompany customer definition, instead of migrating the ECC Intercompany customer directly, a DCT will be utilized to collect the sales data. The DCT template will take reference from ECC sales data and business will need to validate and update the information.

Target Design

The technical design of the target for this conversion approach.

Data Cleansing

Conversion Process

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

The ETL (Extract, Transform, Load) process is a structured approach to data migration and management, ensuring high-quality data is seamlessly transferred across systems. Here’s a breakdown of its key components:

1. Extraction
The process begins with extracting metadata and raw data from source systems, such as Syensqo ECC system (i.e., WP2/PF2) periodically. The extracted data is then staged for transformation.

2. Transformation
Once extracted, the data undergoes cleansing, consolidation, and governance. This step ensures data integrity, consistency, and compliance with business rules. The transformation process includes:
- Data validation to remove inconsistencies.
- Standardization to align formats across datasets.
- Business rule application to refine data for operational use.

3. Loading
The transformed data is then loaded into the target S4 Hana system using migration cockpit. 

Image Added

Data Privacy and Sensitivity

N/A

Extraction

Extract data from a source into Syniti Migrate for SAP ROW and SAP China relevant entities. There are 2 possibilities:

1. The data exists. Syniti Migrate connects to the source and loads the data into Syniti Migrate. There are 3 methods:
   1. Perform full data extraction from relevant tables in the source system(s).
   2. Perform extraction through the application layer.
   3. Only if Syniti Migrate cannot connect to the source, data is loaded to the repository from the provided source system extract/report.
2. The data does not exist (or cannot be converted from its current state).  The data is manually collected by the business directly in Syniti Migrate. This is to be conducted using DCT (Data Collection Template) in Syniti Migrate

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)

Customer sales view DCT Rules

A. Sales Group

B. Customer Group 1

C. Incoterm *20260203 There is ongoing ECC project to clean this, so no need to use DCT any more.

D. Customer Sales View data collection - Sales Data (For intercompany and any new customer outside of ECC)

For Customer Sales View, the the intercompany part, the value will be prepopulated based on the enterprise structure and the configuration of sales area/plant assignment. 

E. Customer Sales View data collection - Partner Data (For intercompany and any new customer outside of ECC)

F. Customer Sales View data collection - Output Tax (For intercompany and any new customer outside of ECC)

Extraction Dependencies

Transformation

The Target fields are mapped to the applicable Legacy field that will be its source, this is a 3-way 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 Pre-load Data Validation

Transformation Run Sheet

Transformation Rules

List of Custom Target Reports for this object is maintained here: Conversion Specification - Custom Reports Register.       

Transformation Mapping

Extraction

Extract data from a source into Syniti Migrate for SAP ROW and SAP China relevant entities. There are 2 possibilities:

1. The data exists. Syniti Migrate connects to the source and loads the data into Syniti Migrate. There are 3 methods:
   1. Perform full data extraction from relevant tables in the source system(s).
   2. Perform extraction through the application layer.
   3. Only if Syniti Migrate cannot connect to the source, data is loaded to the repository from the provided source system extract/report.
2. The data does not exist (or cannot be converted from its current state).  The data is manually collected by the business directly in Syniti Migrate. This is to be conducted using DCT (Data Collection Template) in Syniti Migrate

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

For SAP CUI related entities, it will be alternative extraction process and the data will be stored in approved tools.

Extraction Run Sheet

Syniti / US Based Consultant for SAP CUI instance

Syniti / LTC Data team

Selection Screen

Data Collection Template (DCT)

DCT Rules

Extraction Dependencies

Source System Availability

• Ensure that the source database or application is accessible.
• Confirm that necessary credentials and permissions are granted

Data Structure

• Identify relationships between tables, views, and stored procedures.

Referential Integrity

• Ensure dependent records are extracted together.

Extraction Methodology

• Define whether extraction is full, incremental, or delta-based.
• Establish batch processing schedules for large datasets.

Performance and Scalability Considerations

• Optimize extraction queries to prevent system overload.
• Ensure network bandwidth supports data transfer volumes.

Security and Compliance

• Adhere to regulatory standards for sensitive information if applicable

Transformation

The Target fields are mapped to the applicable Legacy field that will be its source, this is a 3-way 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 Pre-load Data Validation

Transformation Run Sheet

Transformation Rules

Transformation Mapping

Transformation Dependencies

Pre-Load Validation

Project Team

Completeness

Accuracy

Business

Post-load validation is a critical step in data migration, ensuring that transferred data is accurate, complete, and functional within the target system.

1. Ensuring Data Integrity
After migration, data must be consistent with its original structure. Post-load validation checks for missing records, incorrect mappings, and formatting errors to prevent discrepancies.
2. Business Continuity
Faulty data can disrupt operations, leading to financial losses and inefficiencies. Validating post-load data ensures that applications function as expected, preventing downtime.
3. Error Detection and Resolution
By validating data post-migration, businesses can detect anomalies early, reducing the cost and effort required for corrections

and Resolution
By validating data post-migration, businesses can detect anomalies early, reducing the cost and effort required for corrections

Completeness

Accuracy

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

The Business Partner General will be loaded in the pre-cutover period.

Before loading, it will have dependency on the configuration. 

Configuration

Conversion Objects

Error Handling

Post-Load Validation

Project Team

Completeness

Accuracy

Business

Post-load validation is a critical step in data migration, ensuring that transferred data is accurate, complete, and functional within the target system.

1. Ensuring Data Integrity
After migration, data must be consistent with its original structure. Post-load validation checks for missing records, incorrect mappings, and formatting errors to prevent discrepancies.
2. Business Continuity
Faulty data can disrupt operations, leading to financial losses and inefficiencies. Validating post-load data ensures that applications function as expected, preventing downtime.
3. Error Detection and Resolution
By validating data post-migration, businesses can detect anomalies early, reducing the cost and effort required for corrections

.
3. Error Detection and Resolution
By validating data post-migration, businesses can detect anomalies early, reducing the cost and effort required for corrections

Completeness

Accuracy

Accuracy

Key Assumptions

• Master Data Standard is up to date as on the date of documenting this conversion approach and data load.
• BP Customer sales view is in scope based on data design and any exception requested by business.
• There will be 3 2 SAP instances, one for ROW, one for China and one for CUI only.For SAP CUI instance, the migration activity will be handled by US based data consultant. * 20260219 remove CUI
• One sales org will represent one GBU as captured in KDD060 - Sales Enterprise Structure.

See also

Change log

Workflow history