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 EAM Classes from Legacy Source Systems into S/4HANA following the EAM Class Master Data Design Standard. 

Grouping of objects with particular features can be achieved by creating Classes in the S/4 HANA system. A large number of objects can be managed and logically organized using this functionality. 

Syensqo will utilize SAP Classification system (class and characteristic functionality) to assign any additional data attributes to following objects:

• PM Assembly
• Equipment
• Functional Location
• Document Management (DIR)
• Task list
• Measuring Points
• Maintenance Plans

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.

From the current source systems’ perspective, Class and Characteristic concepts already exist (but in slightly different structure). Class data values are independently managed between PF2 and WP2. While these have been identified as source systems, a considerable amount of data construction will be required to support the production of data load sheets in the desired format.

The data from legacy system includes:

1. Classes with below Class Type
   • 001-Material Class
   • 002-Equipment Class
   • 003-Functional Location
   • 017-Document Management
   • 018-Task List Class
   • 037-Measuring Point  Class
   • 080-Maintenance Plans Class
2. Valid from [Current Date]
3. Status = 1 (Released)

The data from legacy system excludes:

1. Classes for Deletion (Deletion Indicator = X)

List of source systems and approximate number of records 

Additional Information

Multi-language Requirement

EAM Class description will be made available in English.
Multi language is supported for EAM Class. Login via a different language will have its description displayed in the logon language if the language key is maintained in the Class.

Document Management

Not Applicable

Legal Requirement

Not Applicable

Special Requirements

Not Applicable

Target Design

The technical design of the target for this conversion approach. 

1. Class

2. Class Descriptions

Note: Each Characteristic must have 1 entry for each language in scope: English, French, Italian, Mandarin, Brazilian, Portuguese, German and Spanish

3. Class Keywords

 Note: One Class should have at least one corresponding Class Keyword which is the same as Class Description.

4. Characteristic Assignment

Data Cleansing

No data cleansing is required for Classes in the legacy systems. If any cleansing is deemed necessary, it will be performed outside of the systems.

As a result, the cleansed Characteristics data will be populated directly in the Data Collection Template (DCT).

Conversion Process

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

Collection will be done manually in the Data Collection Template for the following scenarios:

• For sites not on SAP-PF2 or WP2 systems
• For new classes that need to be created to support the to-be design

Data Privacy and Sensitivity

Extraction

1. Extract data from a source into Syniti Migrate. There are 2 possibilities:

   1. The data exists. Syniti Migrate connects to the source and loads the data into Syniti Migrate. 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 Syniti Migrate; 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 Syniti Migrate. This is to be conducted using DCT (Data Collection Template) in Syniti Migrate

   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)

Target Ready Data Collection Template will be created for EAM Class data with exception of some fields which require transformation as mentioned in the transformation rule.

1. The extracted report will be loaded into the required structure using the DCT.
2. Standardization activities (including deduplication, standardization and additions)  will be carried out within the DCT.

1. Class DCT Rules

2. Class Description DCT Rules

3. Class Keyword DCT Rules

4. Characteristic Assignment DCT Rules

Extraction Dependencies

List the steps that need to occur before extraction can commence

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

There are no transformation rules all values will be copied from DCT.

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

Identify the phase as to “when” the load for this object will occur. <Pre-Cutover, Cutover, Post Cutover> and list the steps that need to occur before the load can commence

Configuration

List the Configurations required before loading can commence

Conversion Objects

Error Handling

The table below depicts some possible system errors for this data object during data load. All data load error is to be logged as defect and managed within the Defect Management

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. 
• Data entries in DCT are target-ready data unless a specific transformation rule is stated for that field in the transformation rules.

Any additional key assumptions.

See also

Insert links and references to other documents which are relevant when trying to understand this decision and its implications. Other decisions are often impacted, so it's good to list them here with links. Attachments are also possible but dangerous as they are static documents and not updated by their authors.

Change log

Workflow history