Purpose

The purpose of this document is to define the conversion approach to create 1038 Material BOM (Usage 1) in S/4 HANA.

Bills of Material (BOMs) are core master data objects in SAP that play a critical role across all manufacturing processes. They define the complete product structure by specifying the components—such as raw materials, semi-finished goods, and subassemblies—required to produce semi-finished and finished products. By providing a structured breakdown of materials, BOMs serve as the foundation for effective production planning, costing, and shop-floor execution.

BOMs take a central role in production master data, as they define the component structure required to manufacture semi-finished and finished goods. The migration of BOMs is therefore critical to ensure continuity of manufacturing activities in S/4HANA. Prior to migration, it is essential to perform exhaustive data cleansing, as some plants are currently experiencing issues with BOM consistency and validity.

In SAP, BOMs work in conjunction with the Master Recipe and Production Version to ensure consistent, controlled, and accurate manufacturing processes. Because of their central role in production master data, the migration of BOMs to S/4HANA is business-critical to safeguard continuity of operations.

Prior to migration, it is essential to perform comprehensive data cleansing. This includes validating that only active, relevant, and harmonized BOM records are carried forward. Special attention must be given to plants where inconsistencies or data quality issues have been identified, ensuring that obsolete, duplicate, or invalid BOMs are excluded. This preparation will secure a clean, reliable, and compliant BOM dataset in the target S/4HANA system.

All valid BOMS will be migrated to the S/4 HANA System, for all the relevant plants and for all the Semifinished and finished materials in scope (refer to CNV-9070).

Conversion Scope

The scope of this document covers the approach for converting active 1038 Data register for BOMS from Legacy Source Systems into S/4HANA following the1038 Master Data Design Standard.

The data The data from legacy system includes:

• Active BOMs in scope (> 3 years of use) that are linked to production orders, process orders, or costing runs
• BOMs without deletion flags, ensuring only valid and relevant records are migrated
• BOMs with active material components in active plants, ensuring alignment with migrated Material Master data
• BOMs assigned to in-scope plants, based on the To-Be Plant Mapping (considering new plant definitions)
• BOMs with valid usages (Usage 1 – Production) and valid alternative BOMs
• BOMs with valid units of measure (UoM), consistent with T006 conversion rules
• Semi-finished and finished materials covered under the migration scope (refer to CNV-9070)

The data from legacy system excludes

• Inactive BOMs not used in production or costing within the last three (3) years.

• BOMs marked for deletion in ECC.

• BOMs belonging to plants that are deleted or out of scope, based on To-Be Plant Mapping.

• BOMs containing obsolete or inactive material components that are not migrated to S/4HANA.

• Duplicate or redundant alternative BOMs, where only harmonized and validated BOMs will be retained.

• BOMs with invalid or inconsistent UoM assignments or without proper conversion maintained.

• Local or legacy-specific BOMs that are not aligned with harmonized corporate standards.

Additional Information

Multi-language Requirement

BOM header and component descriptions will be made available in English.
Multi-language is supported for BOM descriptions and item texts. When a user logs in with a different language, the description will be displayed in the logon language, provided that the language key is maintained in the Material Master or in the BOM long text.

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

All data cleansing activities must be performed in the source systems (e.g., PF2, WP2) wherever possible, following the rules and criteria defined in this document. The objective is to ensure that only valid, active, and relevant master data is migrated to S/4HANA, while obsolete, redundant, or inconsistent records are excluded.

If certain data cleansing activities cannot be executed directly in the source systems due to system limitations, they may be managed externally (e.g., using Syniti Migrate, 3rd Party Vendor tools, or DCT processes). In such cases, proper documentation of the cleansing activity must be maintained and appended to this deliverable to support review, validation, and sign-off by stakeholders.

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. 

For sites that are not on SAP-PF2 and WP2 systems, collection will be done manually in the data collection template.

The high-level process for DCT 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

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

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

Workflow history