Status
Owner	SUSANTO-ext, William
Stakeholders

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. Active QIRs used within the last three (3) years in procurement or sales processes.

2. QIRs without deletion flag.

3. Plant-specific QIRs that will be migrated to the To-Be Plant Mapping (taking into consideration the To-Be definition of Plants).

4. QIRs referenced in active material-vendor (procurement) or material-customer (sales) combinations.

The data from legacy system excludes:

1. Inactive QIRs not used in more than three (3) years.

2. QIRs marked for deletion.

3. QIRs belonging to deleted plants (per To-Be Plant mapping).

4. Obsolete or duplicate QIRs that are no longer relevant (e.g., replaced by harmonized corporate standards).

List of source systems and approximate number of records

Source	Scope	Source Approx No. of Records	Target System	Target Approx No. of Records
PF2 and WP2	Quality Info Record will be extracted from PF2 and WP2 client	PF2: Raw Data   : 33650 records In-scope     : 8086 records WP2: Raw Data   : 8892 records In-scope     : 5168 records	S/4 HANA	In-scope data: 13254 records

Additional Information

Multi-language Requirement

Not applicable

Document Management

Not applicable

Legal Requirement

Not applicable

Special Requirements

Not applicable

Target Design

The technical design of the target for this conversion approach.

Table	Field	Data Element	Field Description	Data Type	Length	Requirement
QINF	MANDT	MANDT	The client field, identifies the SAP client for which record was created	CLNT	3	R
QINF	MATNR	MATNR	Material Number for which quality info record is to be created	CHAR	40	R
QINF	WERK	WERKS_D	Plant Code for which quality info record is to be created	CHAR	4	R
QINF	LIEFERANT	ELIFN	Vendor Code for which quality info record is to be created	CHAR	10	R
QINF	REVLV	REVLV	This field stores specific version of a material for a given vendor-plant combination in a quality info record	CHAR	2	R
QINF	FREI_DAT	QFREIDAT	Valid to date for the quality info record	DATS	8	R
QINF	FREI_MNG	QFREIMG	This field functions as a limit the controls how much of a product can be ordered from a specific vendor before further action is taken, such as needing a new quality inspection.	QUAN	13	C
QINF	SPERRGRUND	QSPERRGR	This field is a blocking reason code that specifies why a material-vendor combination is blocked or released for procurement transactions like quotations, purchase orders, or goods receipts.	CHAR	40	C
QINF	SPERRFKT	QSPERRFKT	This field can can block a supplier for purchase orders, goods receipts, or both, by setting the appropriate value.	CHAR	2	C
QINF	FREI_MGKZ	QFREIMGKZ	This indicator effectively blocks new purchase orders for the specified material from that vendor, as the "release quantity" has been exhausted or used up.	CHAR	1	C
QINF	NOINSP	QNOINSP	This field indicates whether a goods receipt inspection is to be skipped for a specific material, vendor, and plant combination. It allows users to skip inspections for materials that are already known to be of high quality or where no incoming inspection is required by the business process.	CHAR	1	C
QINF	QSSYSFAM	QSSYS_IST	Thi field indicates the specific quality management system used by a supplier for a particular material and vendor combination.	CHAR	4	C
QINF	QSSYSDAT	QQSSYSDAT	Valid to Date for Actual (Existing) QM system	DATS	8	C
QINF	VORLABN	QVORLABN	Lot Creation lead time for source inspection	INT1	3	C
QINF	VARIABNAHM	QINFVAR1	Inspection type for inspection lot origin (source inspection)	CHAR	8	C
QINF	CERTCONTROL	QCERTCONTROL	This field allows to control how quality certificates are handled for a material from a specific supplier, for example, to trigger inspections or block procurement until a certificate is received and verified.	CHAR	1	C
QINF	ZAEHL	Q_COUNT	Internal Counter for DB Objects	NUMC	6	S
QINF	ERSTELLER	QINFERST	Name of User who Created the Q-Info Record	CHAR	12	S
QINF	ERSTELLDAT	QINFERSTDT	Create Date of Q-Info Record	DATS	8	S
QINF	AENDERER	QINFAEND	Name of User who Most Recently Changed the Q-Info Record	CHAR	12	S
QINF	AENDERDAT	QINFAENDDT	Change Date of Q-Info Record	DATS	8	S
QINF	BEST_MG	QBSTMG	This field accumulates the total quantity of a specific material that has been ordered from a particular vendor for a given plant.	QUAN	13	C
QINF	ME	QBSTME	Base Unit of Measure for the Material	UNIT	3	C
QINF	DAT_RUECK	QRSDAT	The reset date clears the accumulated total ordered quantity, allowing for new orders to be placed again against the release quantity.	DATS	8	C
QINF	SPRACHE	SPRAS	This field indicates the language in which information, like text descriptions or status messages, is stored or displayed	LANG	1	C
QINF	QVVORH	QQSVVORH	If the Quality agreement exists ( is True) for this Material with this Supplier, certain aspects of the Quality Info Records will be determined by the Quality Agreement.	CHAR	1	S
QINF	LTEXTKZ	QLTEXTKZ	Inspection Characteristic Long Text Exists	CHAR	1	C
QINF	SPRAS	SPRAS	This field allows SAP to store and retrieve information in different languages for users and customers with varying language preferences.	LANG	1	C
QINF	PLOS	QPLOSQINF	This field provides the direct link between the material's procurement record and quality inspection process for that specific batch or quantity	NUMC	12	S
QINF	PLOS2	QPLOSQINF2	This field is used to link a info record to a subsequent inspection lot, to define conditions or actions that should be triggered based on the outcome of a previous inspection.	NUMC	12	S
QINF	STSMA	QSTSMAQINF	Status profile defines a set of statuses for a quality info record	CHAR	8	C
QINF	OBJNR	QINFOBJNR	System generated unique identifier for quality info record	CHAR	22	S
QINF	NOINSPABN	QNOWEABN	This field prevents goods receipt (GR) inspection from taking place if a source inspection lot has already been created for that material and vendor combination.	CHAR	1	C
QINF	LOEKZ	QLOEKZ	This field indicates that quality info record is flagged for deletion or has been archived, but it is not yet physically deleted from the database	CHAR	1	C

Data Cleansing

ID	Criticality	Error Message/Report Description	Rule	Output	Source System
2020-001	C1	Invalid or inactive Material in QIR	QIR references a material that does not exist in MARA/MARC or is flagged for deletion/blocked at plant.	Material	PF2/WP2
1139-001 3018-001 3021-001 3024-001 3025-001 3028-001 3031-001	C1	Invalid or inactive Vendor in QIR (procurement side)	QIR (MATNR/LIFNR/WERKS) references vendor not in LFA1/LFM1 (or blocked for purchasing).	Vendor	PF2/WP2
1139-001 3018-001 3021-001 3024-001 3025-001 3028-001 3031-001	C1	Invalid or inactive Customer in QIR (sales side)	QIR (MATNR/KUNNR/WERKS) references customer not in KNA1/KNVV (or blocked).	Customer	PF2/WP2
2010-001 3027-001	C1	Invalid Plant	QIR plant (WERKS) does not exist or is obsolete.	Plant	PF2/WP2
1044-001	C1	Duplicate QIR key	Duplicate active QIRs with same combination (Material + Vendor/Customer + Plant) and overlapping validity.	QIR	PF2/WP2
1044-001	C1	Validity overlap within same key	Multiple QIR records for same key have overlapping GUELTIGAB/GUELTIGBIS dates.	QIR	PF2/WP2
1044-001	C1	QIR not released/approved	Release indicator/status not set to “Released/Approved” as per business rule.	QIR	PF2/WP2
1044-001	C1	QIR blocked	Procurement block indicator set (e.g., SPERRE = X) while record flagged as active for migration.	QIR	PF2/WP2
1044-001	C1	Expired QIR	Valid-to date is before cutover date; exclude unless explicitly required.	QIR	PF2/WP2
1044-001	C1	Future-dated QIR outside go-live horizon	Valid-from date is after the go-live window (e.g., > N days after cutover) and no business justification provided.	QIR	PF2/WP2
2020-001 1139-001 3018-001 3021-001 3024-001 3025-001 3028-001 3031-001	C1	Source Inspection required but no enabling setup	QIR requires source inspection (e.g., indicator set) but vendor/material lacks inspection type & control in S/4 target design.	Material/Vendor Setup	PF2/WP2
1044-001	C1	Missing text/description for key languages	Language-dependent short text missing in required languages for the QIR.	QIR Text	PF2/WP2
1044-001	C1	Conflicting status flags	Record simultaneously marked for deletion and released (inconsistent).	QIR	PF2/WP2
1139-001 3018-001 3021-001 3024-001 3025-001 3028-001 3031-001	C1	Vendor Subrange / Purchasing Org mismatch	QIR expects vendor subrange or purchasing org-specific data that doesn’t exist/align.	Vendor Master	PF2/WP2
2009-001	C1	Material quality view missing	Material lacks required QM view/settings where policy mandates QM integration with QIR.	Material	PF2/WP2
	C1	Customer-side QIR without usage	Customer QIR exists but material/plant not configured to use it (sales-side QM not in scope).	Scope Decision	PF2/WP2
	C1	Obsolete custom fields populated	Legacy custom fields used in ECC with no S/4 mapping; values must be cleared/mapped.	Mapping Issue	PF2/WP2
1044-001	C1	Audit fields inconsistent	Created/changed by/on are missing or malformed (optional clean-up for traceability).	QIR	PF2/WP2

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

Not applicable

Extraction

Extract data from a source 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

Req #	Requirement Description	Team Responsible
Extraction Scope Definition	- Identify the source systems and databases involved. - Define the data objects (tables, fields, records) to be extracted. - Establish business rules for data selection.	Syniti / LTC Data team
Extraction Methodology	- Specify the extraction approach (full, incremental, or delta extraction). - Determine the tools and technologies used. - Define data filtering criteria to exclude irrelevant records.	Syniti
Extraction Execution Plan	- Establish execution timelines and batch processing schedules. - Assign responsibilities for extraction monitoring. - Document dependencies on other migration tasks.	Syniti
Data Quality and Validation	- Define error handling mechanisms for extraction failures.	Syniti

Selection Screen

Selection Ref Screen	Parameter Name	Selection Type	Requirement	Value to be entered/set

Data Collection Template (DCT)

The Data Collection Template (DCT) will not be applicable in this case. If there is a need to create a new Master Data (MD) for Inspection Plan object, the business must perform this activity in the source system. The newly created object will then be captured and migrated as part of the standard migration process.

Extraction Dependencies

Item #	Step Description	Team Responsible
1	Source System Availability Ensure that the source database or application is accessible. Confirm that necessary credentials and permissions are granted	Syensqo IT
2	Data Structure Identify relationships between tables, views, and stored procedures.	Syniti
3	Referential Integrity Ensure dependent records are extracted together.	Syniti
4	Extraction Methodology Define whether extraction is full, incremental, or delta-based. Establish batch processing schedules for large datasets.	Syniti
5	Performance and Scalability Considerations Optimize extraction queries to prevent system overload. Ensure network bandwidth supports data transfer volumes.	Syniti
6	Security and Compliance Adhere to regulatory standards for sensitive information if applicable	Syniti

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

Item #	Step Description	Team Responsible
1	Transformation Scope Definition - Identify the source and target data structures. - Define business rules for data standardization. - Establish data cleansing requirements to remove inconsistencies.	Data Team
2	Data Mapping and Standardization - Align source fields with target fields. - Ensure unit consistency (e.g., currency, measurement units)	Data Team
3	Business Rule Application - Implement data enrichment/collection if applicable - Apply conditional transformations based on predefined logic/business rules	Data Team
4	Transformation Execution Plan - Define batch processing schedules. - Assign responsibilities for monitoring execution. - Establish error-handling mechanisms	Syniti

Transformation Rules

Rule #	Source system	Source Table	Source Field	Source Description	Target System	Target Table	Target Field	Target Description	Transformation Logic

Transformation Mapping

Use the exact name and reference this section in the “Transformation rules” above

Mapping Table Name	Mapping Table Description

Transformation Dependencies

List the steps that need to occur before transformation can commence

Item #	Step Description	Team Responsible
1	Value Mappings are according to the latest design - <List of Value Mappings>	SyWay Data Team

Pre-Load Validation

Project Team

The following pre-load validations will be performed by the Project Team.

Completeness

Task	Action
Compare Data Counts	Verify row counts between source and target databases. Identify missing or duplicated records.
Validate the mandatory fields	Validate there is value for all the mandatory fields
Validate Primary Keys and Unique Constraints	Check for duplicate or missing primary key values. Ensure unique constraints are maintained.
Test Referential Integrity	Confirm dependent records exist in related tables

Accuracy

Task	Action
Validate the transformation	Validate the fields which require transformation have the value after transformation instead of the original field value
Check Data Consistency	Compare field values across systems Validate data formats and structures

Business

Completeness

Task	Action
Compare Data Count	Verify row counts between source and target databases. Identify missing or duplicated records.
Review populated templates for missing or incorrect values	Use checklists to verify completeness and correctness before submission

Accuracy

Task	Action
Conversion Accuracy	Business Data Owner/s to verify that all the data in the load table/file is accurate as per endorsed transformation/ mapping rules (and signed-off DCT data).

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

Item #	Step Description	Team Responsible
1	Load Scope Definition - Identify the target system and database structure. - Define data objects (tables, fields, records) to be loaded. - Establish business rules for data validation.	Data team
2	Load Methodology - Specify the loading tools and technologies (Migration Cockpit, LSMW, custom loading program).	Syniti
3	Data Quality and Validation - Ensure data integrity checks (null values, duplicates, format validation). - Perform pre-load validations to verify completeness. - Define error handling mechanisms for load failures	Syniti
4	Load Execution Plan - Establish execution timelines and batch processing schedules. - Assign responsibilities for monitoring execution. - Document dependencies on other migration tasks	Syniti
5	Logging and Reporting - Maintain detailed logs of loading activities. - Generate summary reports on loaded data volume and quality. - Define escalation procedures for errors	Syniti

Load Phase and Dependencies

The Quality Info records will be loaded in the pre-cutover (PreCutover 4 phase) period.

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

Configuration

List the Configurations required before loading can commence

Item #	Configuration Item

Conversion Objects

Object #	Preceding Object Conversion Approach
	list the exact title of the conversion object of only the immediate predecessor – this will then confirm the DDD (Data Dependency Diagram)

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

Error Type	Error Description	Action Taken

Post-Load Validation

Project Team

Completeness

Task	Action
Validate Record count in the backend	Validate all tables with prefix “KOTG” has the same records as the loading file
Display Records	Pick up a few random Material Listing or Material Exclusions, and run t-code: VB03 to validate the Material Listing and Exclusions can be displayed without any error.
Perform Source-to-Target Comparisons	Validate that migrated data matches source records. Check for discrepancies in numerical values, text fields, and timestamps

Accuracy

Task	Action
Execute Sample Queries and Reports	Run queries to validate business logic. Generate reports to compare expected vs. actual results
Conduct Post-Migration Reconciliation	Generate reports comparing pre- and post-migration data.

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

Task	Action
Perform Source-to-Target Comparisons	Validate that migrated data matches source records. Check for discrepancies in numerical values, text fields, and timestamps
Conduct Post-Migration Reconciliation	Go through reports comparing pre- and post-migration data.

Accuracy

Task	Action
Perform Manual Testing	Conduct manual spot-checks for additional assurance.

Key Assumptions

• Master Data Standard is up to date as on the date of documenting this conversion approach and data load.
• Inspection Plan is in scope based on data design and any exception requested by business.
• Data cleansing has met the required percentage threshold for the specified mock cycle and all preparation activities have been completed.
• Data entries in DCT are target-ready data unless a specific transformation rule is stated for that field in the transformation rules.

See also

Change log