Purpose

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

In both legacy systems, batch characteristics (class type 023) are used to hold key information related to Logistics & Quality information. Currently, there are more than 1,000 characteristics where these are duplicated with identical descriptions and assigned to different classes. To enable effective global utilization and reporting purposes, these characteristics need to be standardized. SyWay program to harmonize and standardize these characteristics across the organization.

Conversion Scope

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

SAP Batch Characteristics are specific data fields used within the SAP system to describe and manage the properties of a batch of materials. They are part of the SAP Classification System and are typically associated with class type 023 (Batch Class).

Material Batches capture detailed information about each batch, such as production date, quality parameters, supplier details, or any other relevant property and enable traceability; allow track and trace batches throughout the supply chain with structured data for analysis and reporting.

The data from legacy system includes:

1. Characteristics assigned to Classes with below Class Type
• 023 – Batch Class (PF2, WP2)
2. Batch Characteristics assigned to Active Classes of 023.
3. Valid from [Current Date]
4. Status = 1 (Released)

The data from legacy system excludes:

1. Characteristics for Deletion (Deletion Indicator = X)

Additional Information

Multi-language Requirement

Characteristics descriptions are maintained in different languages.

Document Management

NA

Legal Requirement

NA

Special Requirements

NA

Target Design

The technical design of the target for this conversion approach.

Data Cleansing

No data cleansing is required within the source systems. Standardization and cleansing activities will be carried out outside of the Syniti tool, and the finalized characteristics will be populated in the DCT.

Conversion Process

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

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)

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