Purpose

The purpose of this document is to define the data conversion approach to create Customer Material Information Records (CMIR) in S/4 HANA.

Customer material info records (CMIRs) are used to store specific information about a material from a customer's perspective. This information helps in streamlining sales order processing and ensuring accurate order fulfilment. CMIRs link a customer's material number and description to the corresponding SAP material number, allowing for efficient order creation and data consistency. 

This conversion aims to migrate active and relevant customer material information records from existing ECC systems into S/4HANA by applying required transformation logic using Syniti as the data migration and transformation platform. The converted records will be loaded into the target S/4HANA system using standard SAP mechanisms such as IDOCs, BAPIs, or direct table loads where applicable.

Conversion Scope

The scope of this document covers the approach for converting active Customer Material Information Records (CMIR) from Legacy Source Systems into S/4HANA following the Customer Material Information Records (CMIR) Master Data Design Standard: DD-FUN-050 Master Data Standard_1036-Customer Material Information Records(CMIR).

From the current system landscape, Customer Material Information Records (CMIR) exist in the both legacy systems - PF2 and WP2. Harmonization and validation are required to ensure the accurate and consolidated data in S/4HANA. While PF2 and WP2 serve as source systems, a mapping and transformation logic will be necessary to produce properly formatted load templates in line with the target design.

20260114-Eka: MDS updated - Text Language updated from project Syway approved language to all existing CMIR language.

The data from legacy system includes: 

1. All active Customer Material Information Records (CMIR), where: 

   1. Customer sales view migrated as per CNV-3003 Business Partners - Customer (Sales and Service) - FLCU01
   2. AND Material sales view migrated as per CNV-2003 Materials - Sales view with sales long text.
   3. AND Sales organization is in scope of S/4HANA
   4. AND Distribution channel is in scope of S/4HANA
   5. AND Plant is in scope of S/4HANA OR the Plant is BLANK. 
2. For records selected in point #1 above; TEXT will be migrated for Text Object = KNMT and if the Text ID and the Text Language are included in the mapping table.

Additional Information

Multi-language Requirement

As per MDS, Text are only migrated for the valid/ approved language. Refer to MAP_LANGU - ECC (PF2/ WP2) to S/4AHANA Language Mapping Table. 

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 (ROW), one for China and one for CUI. Based on the sales organization the Customer Material Info Records (CMIR) records will be migrated to respective SAP instances.

Target Design

Customer Material Information Records (CMIR) Data strictly adheres to the Master Data Standard. The complete information of the tables and key fields that hold the Customer Material Information Records (CMIR) information follows the Master Data Standard document.

The technical design of the target for this conversion approach.

20251205-Eka: MDS updated - KNMT-RDPRF (Rounding Profile) and KNMT-MEGRU (Unit of Measure Grp) set to NOT USED.

Data Cleansing

All data cleansing should take place in the data source system as defined in this document, unless system limitations prevent it. Consider relevancy rule in the cleansing report except the Plant relevancy in 1036-001 to 1036-006.

How to clean: Customer invalid - Data cleanser to delete all materials in the CMIR, material invalid - Data cleanser to delete the invalid material only.

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, i.e. Syensqo ECC system WP2 and PF2. 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 S/4HANA system.

Data Privacy and Sensitivity

Extraction

Extract data from a SAP ECC WP2 and PF2 into Syniti Migrate for S/4HANA. Syniti Migrate connects to the source and loads the data into Syniti Migrate. 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)

Target Ready Data Collection Template will be created for data with exception of some fields which require transformation as mentioned in the transformation rule.

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

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

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 produce the load file as per format in Transformation Rule section 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 Customer Material Information Records (CMIR) will be loaded in the pre-cutover period.

Before loading, it will have dependency on the configuration and data objects in the S/4 HANA. 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

• Customer Material Information Records (CMIR) Master Data Standard is up to date as on the date of documenting this conversion approach and data load. 
• Customer Material Information Records (CMIR) 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.

See also

Change log

Workflow history