Purpose

The purpose of this document is to define the conversion approach to create 1044 Quality Info Record in S/4 HANA.

Quality Info Records are used in SAP Quality Management (QM) to control the procurement and usage of materials from vendors or for customers. A QIR documents whether a supplier is approved to deliver a particular material, under what conditions, and with what quality agreements in place. It also supports restrictions such as blocking or requiring source inspections before delivery. This feature helps ensure supplier quality compliance and strengthens the linkage between procurement and quality processes.

In SAP S/4HANA, the structure and usage of Quality Info Records remain consistent with SAP ECC, generally defined by key combinations such as Material / Vendor / Plant or Material / Customer / Plant. QIRs can include attributes such as release status, validity dates, procurement blocks, source inspection requirements, and quality agreement indicators.

In SAP ECC, aside from the standard structures, Quality Info Records may also exist with extended combinations or enhancements, for example:

• Material / Vendor without plant reference,

• Material / Customer for sales-side QIRs,

• Additional custom fields capturing supplier quality ratings,

• Legacy blocks or obsolete release indicators that are no longer in use.
  Such cases must be reviewed carefully (pending MDS) to determine whether they remain relevant for migration.

This conversion aims to migrate active and relevant Quality Info Records from existing ECC systems into S/4HANA by applying the 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 BAPIs (e.g., BAPI_QM_QUALINFORECORD_CREATE), IDOCs, or direct table loads where applicable, ensuring compliance with procurement and quality integration processes.

Conversion Scope

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

The data from legacy system includes:

1. Data active and three years or less usage
2. Data without deletion flag
3. Data relevant for material/plant combination active according to the Latest QINF definition

The data from legacy system excludes:

1. Data older than three years
2. Data flagged for deletion
3. Data that belong to Inspection plans for materials not mapped in the latest version of the QINF table

Additional Information

Multi-language Requirement

Document Management

Not applicable

Legal Requirement

Not applicable

Special Requirements

Not applicable

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 S/4HANA system. 

Data Privacy and Sensitivity

Extraction

Extract data from a source into . There are 2 possibilities:

1. The data exists. connects to the source and loads the data into . 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 ; 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 . This is to be conducted using DCT (Data Collection Template) in

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)

Quality Info Record 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 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
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

Load Phase and Dependencies

Configuration

Conversion Objects

Error Handling

Post-Load 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.
• is in scope based on data design and any exception requested by business.

See also

Change log