Purpose

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

Legacy Functional Locations is using a diverse range of formats across the different systems. There are instances where the same Functional Locations are duplicated within and across sources, with informal words. This results in data inconsistency. As per SyWay design, EAM Functional Locations will be standardized.

Conversion Scope

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

Functional Locations are technical objects that structure the maintainable objects of a company. They can represent either the location of a maintainable item, or the maintainable item itself. A functional location does not normally change and will record the history of all equipment that is installed at that location. 

Syensqo utilizes Functional Locations (FLOC) within the SAP S/4HANA system to define and manage its Asset Hierarchy. The asset hierarchy is structured across three main categories:

1. Organisational Level - This level represents the enterprise structure of Syensqo. It defines:

   • The Company

   • The Global Business Unit (GBU)

   • The Plant

2. Navigational Level - This level structures the hierarchy based on the functional or geographical layout of the plant. It defines:

   • Units within the Plant
   • Further sub-levels based on the physical location or logical grouping of assets, enabling effective representation and navigation within the hierarchy
3. Asset Level - This is the installation level where the actual Assets and Sub-assets are physically located. It includes:
   • Specific installation points for equipment
   • Maintainable assets themselves, serving as key points for maintenance planning and execution

The data from legacy system includes:

The data from legacy system excludes:

Additional Information

Multi-language Requirement

Document Management

Legal Requirement

Special Requirements

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:

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

Workflow history