Purpose

The purpose of this document is to define the conversion approach to create Customer Hierarchy in S/4 HANA.

In SAP ECC, the customer hierarchy is a tree-like hierarchy where each node is a customer (including parent and child customers). The primarily purpose is used for pricing, rebates, and reporting across related customers. It will be maintained via transaction code VDH1N.

In SAP S/4HANA, customers are managed as Business Partners (BP), enabling a more flexible and integrated data model. Hierarchy nodes are created as BPs with sales area data. It is still maintained via VDH1N (or Fiori app Display/Maintain Customer Hierarchy).

Conversion Scope

The scope of this document covers the approach for converting active Customer Hierarchy from Legacy Source Systems into S/4HANA following the Customer Hierarchy Master Data Design Standard.

The data from legacy system includes:

1. For Parent, the sales area data has the sales organization in scope and the BP general data/sales view data is migrated.
2. For Child, the sales area data has the sales organization in scope and the sales view is still active.
3. Valid-to is after go live date
4. There is pricing or rebate indicator in the source system

The data from legacy system excludes:

1. The BP general is not in migration scope
2. or the sales view for Child customer is not in migration scope
3. Valid-to is before go live date
4. Both Parent customer and all the child customers have sales area deletion indicator

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. 

Special Requirements

Different SAP Instance Migration Approach

Please refer to the link for the entity mapping for each instance. SAP instance mapping based on company code

 - To identify the record is for which SAP instance, it will follow below logic. 

1. map the sales org to company code first, then map the company code to the SAP instance based on the mapping file.

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. 

Data Privacy and Sensitivity

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)

DCT Rules

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

Transformation Mapping

Transformation Dependencies

Pre-Load 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

LSMW Upload Template

Load Phase and Dependencies

The Customer Hierarchy will be loaded in the pre-cutover period.

Before loading, it will have dependency on the configuration. The configuration needs to be transported into the respective system first.

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

Completeness

Accuracy

Key Assumptions

• Master Data Standard is up to date as on the date of documenting this conversion approach and data load.
• Customer hierarchy is in scope based on data design and any exception requested by business.
• There will be 3 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. 

See also

Change log

Workflow history