Status
Owner	SUSANTO-ext, William
Stakeholders	GERVAIS, Pascal

Purpose

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

Sampling Procedures are master data in SAP Quality Management (QM) that determine how the inspection scope is defined, such as the number of units to be inspected from a lot or the percentage of the lot to be checked. They provide standardized rules for sample determination and ensure consistency across inspection lots, inspection plans, and inspection characteristics. Sampling procedures can be based on fixed sample sizes, percentage samples, or inspection severity levels defined by sampling schemes.

In SAP S/4HANA, the structure and usage of sampling procedures remain consistent with SAP ECC. Sampling procedures are typically defined at the plant level, with key attributes such as sampling type, sample size, code group assignment, validity dates, and indicator settings. They can be assigned to master inspection characteristics (MICs) or directly within inspection plans, ensuring harmonized inspection strategies across materials and processes.

In SAP ECC, aside from the standard structure of sampling procedure master data (procedure ID, plant, type, and parameters), there may be additional variants, such as procedures linked to specific inspection severity levels, schemes that determine dynamic modification rules, or customized procedures with client-specific enhancements. Some legacy systems may also include obsolete or unused sampling procedures, which will require cleansing and validation before migration (pending MDS).

This conversion aims to migrate active and relevant sampling procedure 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_INSPSAMPLINGPROCEDURE_CREATE), IDOCs, or direct table loads where applicable, ensuring data accuracy, compliance, and usability in the target system.

Conversion Scope

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

The data from legacy system includes:

1. Relevancy Criteria 1
2. Relevancy Criteria 2
3. Relevancy Criteria n

The data from legacy system excludes:

1. Exclusion Criteria 1
2. Exclusion Criteria 2
3. Exclusion Criteria n

List of source systems and approximate number of records

Source	Scope	Source Approx No. of Records	Target System	Target Approx No. of Records
PF2			S/4 HANA
WP2			S/4 HANA

Additional Information

Multi-language Requirement

Summarize Multi-language Requirement/s, if any

Document Management

N/A

Legal Requirement

N/A

Special Requirements

N/A

Target Design

With Functional input, document the technical design of the target fields that are in the scope of this document.

The technical design of the target for this conversion approach.

Table	Field	Data Element	Field Description	Data Type	Length	Requirement
QDSV	STICHPRVER	QSTPRVER	Sampling Procedure (ID)	CHAR	8	Mandatory
QDSV	STICHPRART	QSTPRART	Sampling Type (Fixed, %, With Scheme, 100%)	CHAR	3	Mandatory
QDSV	BEWERTMOD	QBEWMOD	Valuation Mode (e.g., nonconforming units)	CHAR	3	Optional
QDSV	KZOHI	QKZOHI	No Inspection Stage Change	CHAR	1	Optional
QDSV	KZUMFS	QKZUMFS	Multiple Samples (indep./dep.)	CHAR	1	Optional
QDSV	KZNOCUT	QKZNOCUT	Sample Size Not Reduced	CHAR	1	Optional
QDSV	STPRANZ	QSTPRANZ	# of Samples (if multiple samples)	INT1	3	Optional
QDSV	STPRUMF	QSTPRUMF	# Sampling Units in a Sample	INT4	10	Optional
QDSV	ANNAHMEZ	QANNAHMEZ	Acceptance Number (n-c/d, etc.)	INT2	5	Optional
QDSV	KFAKTOR	QKFAKTOR	K-Factor (variable inspection)	FLTP	16	Optional
QDSV	FBKEY	QFBKEY	Sample Determination Rule (FM key)	CHAR	2	Optional
QDSV	FBKEYMFS	QFBKEYMFS	Valuation Rule for Mult. Samples	CHAR	2	Optional
QDSV	STPRPLAN	QSTPRPLANV	Sampling Scheme (header ref.)	CHAR	3	Optional
QDSV	PRSCHAERFE	QPRSCHAERV	Inspection Severity (scheme)	NUMC	3	Optional
QDSV	AQLWERT	QAQLWERTV	AQL Value	DEC	7(3)	Optional
QDSV	PROZUMF	QPROZUMF	Sample Size as % of Lot	FLTP	16	Optional
QDSV	PROZAZL	QPROZAZL	Acceptance # as % of Sample	FLTP	16	Optional
QDSV	KZRAST	QKZRAST	For Inspection Points (type)	CHAR	1	Optional
QDSV	RASTER	QRASTER	Inspection Frequency (insp. points)	NUMC	3	Optional
QDSV	QRKART	QQRKART	Control Chart Type	CHAR	3	Optional
QDSV	ERSTELLDAT / AENDERDAT	QDATUMERST / QDATUMAEND	Created On / Changed On	DATS	8	Optional
QDSV	ERSTELLER / AENDERER	QERSTELLER / QAENDERER	Created By / Changed By	CHAR	12	Optional
QDSVT	SPRACHE	SPRAS	Language Key	LANG	1	Mandatory
QDSVT	STICHPRVER	QSTPRVER	Sampling Procedure (ID)	CHAR	8	Mandatory
QDSVT	KURZTEXT	QKURZTEXT	Short Text (Description)	CHAR	40	Mandatory

Data Cleansing

All data cleansing should take place in the data source system as defined in this document, unless system limitations prevent it.

If data cleansing is managed outside of the source system (e.g. Syniti Migrate, 3rd Party Vendor, DCT), the necessary documentation must be produced and appended to this deliverable for sign-off.

ID	Criticality	Error Message/Report Description	Rule	Output	Source System

Conversion Process

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

Summarize High-Level Process. Include diagrams, where applicable. Include information supporting details of Extract, Transform and Load specific to the Data Object

Data Privacy and Sensitivity

Summarize Data Privacy and Sensitivity Requirements, if any

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

Selection Screen

If applicable, this section will give the details on any selection screen parameters, including the parameter type, that are required to be entered to ensure consistent data extracts.

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

Data Collection Template (DCT)

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

<Object> DCT Rules

Field Name	Field Description	Rule

Extraction Dependencies

List the steps that need to occur before extraction can commence

Item #	Step Description	Team Responsible

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

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

Pre-Load Validation

Project Team

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

Completeness

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Accuracy

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Business

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

Completeness

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Accuracy

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

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

Load Phase and Dependencies

Identify the phase as to “when” the load for this object will occur. <Pre-Cutover, Cutover, Post Cutover> and list the steps that need to occur before the load can commence

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

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

Completeness

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Accuracy

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Business

The following post-load validations will be performed by the business.

Completeness

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Accuracy

Task	Action
title	specific details of what and how the task needs to be performed e.g. which reports are being used etc.

Key Assumptions

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

Any additional key assumptions.

See also

Insert links and references to other documents which are relevant when trying to understand this decision and its implications. Other decisions are often impacted, so it's good to list them here with links. Attachments are also possible but dangerous as they are static documents and not updated by their authors.

Change log