Purpose

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

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

Conversion Scope

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

The data from legacy system includes:

• Reference Functional Location
• Functional Locations
• Equipment
• Work Centre
• Task Lists
• Measuring Points 
• Work to Permit
• Other Objects

Classes allow you to group objects based on specific criteria that you define.

• Classes are created for specific object types.
• The Class Type determines which object types can be classified within a given class.
• Characteristics are assigned to classes to describe the objects being classified.
• When assigning a characteristic to a class, its values can be customized or overwritten as needed.

The scope of this document covers the approach for converting active Classes from Legacy systems (PF1, WP1) and creation of New EAM CLasses into S/4 HANA following the <Project Name> Class Master Data Design Standard.

The data from legacy system excludes:

1. Classes with below Class Type
   • 002-Equipment class
   • 003-Functional location
   • 004-Ref. functional location
   • 007-Code groups
   • 009-Prod. resources/tools
   • 018-Task list class
   • 019-Work center class
   • 037-Measuring point
   • 039-Master warranty
   • 049-Permits
2. Valid from [Current Date]
3. Status = 1 (Released)

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