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.

This Conversion Specification does not include the WPX system (CUI Objects).

Conversion Scope

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

The data from legacy system includes:

1. Active Sampling Procedures that have been used in inspection plans or inspection lots in the last four (4) years. 
   a. Inspection plan
       i. QDSV-KZVWSVPL ='X'(Used in Inspection plan/Task list)
      ii. PLMK-STICHPRVER = QDSV-STICHPRVER, Refer Inspection plan relevancy for active inspection plans
   b. Inspection Lots(QALS-ERSTELDAT >= CURRENT DATE -4, QALS-STICHPRVER = QDSV-STICHPRVER)
      
2. Sampling procedure referenced in Material master inspection setup(QMAT-STICHPRVER). Relevancy rules for Material master QM view are applicable.
   QMAT-STICHPRVER = QDSV-STICHPRVER. Refer Material master QM view for active Material master QM data.
   
   
3. Sampling Procedures with valid sampling type, such as:
   • Fixed sample size,
   • 100% inspection,
   • Percentage-based sampling,
   • Sampling schemes (AQL, inspection severity levels) etc.

The data from legacy system excludes:

1. Inactive Sampling Procedures not used in inspection plans or inspection lots for more than four (4) years.
2. Sampling procedure not referenced in Material master inspection setup(QMAT-STICHPRVER). Relevancy rules for Material master QM view are applicable.
3. Sampling Procedures with invalid sampling type, not in below:
   • Fixed sample size,
   • 100% inspection,
   • Percentage-based sampling,
   • Sampling schemes (AQL, inspection severity levels) etc.

Additional Information

Multi-language Requirement

Sampling Procedure description will be maintained in English by default.

Since multi-language support is available for Sampling Procedure, users logging in with a different language will see the description displayed in their logon language, provided that the corresponding language key has been maintained in the Sampling Procedure.

Document Management

N/A

Legal Requirement

N/A

Special Requirements

N/A

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

Not applicable

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)

Extraction Dependencies

Before data extraction can commence, several prerequisite steps and conditions must be met to ensure a smooth and accurate extraction process. These dependencies involve confirming system readiness, validating data structures, and ensuring that appropriate access rights and credentials are in place.

Each step must be clearly defined, assigned to responsible teams, and completed prior to extraction activities. Proper coordination across stakeholders is required to mitigate risks and avoid delays in the migration timeline.

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

The Sampling procedure 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

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.
• Sampling procedure 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

CNV-1041 QM Inspection Plan

CNV-2009 Material master QM view

Change log