Purpose

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

Legacy Plant Maintenance Characteristics is using a diverse range of formats across the different systems. There are instances where the same characteristics are duplicated within and across sources, with informal words, and linked to Plant Maintenance Classes but are not used by Plant Maintenance objects at all. This results in data inconsistency. As per SyWay design, EAM Characteristics will be standardized.

Conversion Scope

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

Characteristics define a single custom field used to capture specific properties, attributes or values of an object. In SAP, Classes and Characteristics together form the “Classification System”.
For Syensqo, Characteristics will be used to record key attributes supporting reliability categorization and various analyses. It will be primarily used for information and reporting of attribute data across a large set of technical objects (e.g. Functional Location Structure). These Characteristics will be created centrally and assigned to relevant Classes.

From the current source systems’ perspective, Class and Characteristic concepts already exist (but in slightly different structure). Characteristic data and their allowed 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.

While these systems serve as sources, extensive construction will be necessary to produce properly formatted data load sheets for S/4 HANA. 
The scope of this document covers the approach for converting active Characteristics from Legacy systems (PF2, WP2) and creation of New EAM Characteristics into S/4 HANA following the Characteristics Master Data Design Standard. 

The data from legacy system includes:

1. Characteristics assigned to Classes with Class Status = 1 (Released)
2. Characteristics assigned to 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
3. Valid from [Current Date]

The data from legacy system excludes:

1. Characteristics 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

Specify any special requirements or considerations that may impact the data conversion process based on specific locations, regulatory compliance or system limitations. Clearly outline any regional or localization requirements such as country-specific data formats, legal reporting obligations or industry standards that must be adhered to (e.g., localization rules for countries like China).

If the data conversion involves third-party systems or external data sources, such as Icertis, describe any additional requirements related to data mapping, transformation logic, validation rules or security measures that must be followed.

Target Design

With Functional input, document the technical design of the target fields that are in the scope of this document.

The technical design of the target for this conversion approach.

Data Cleansing

Not Applicable. Standardization activities (including deduplication, standardization and additions)  will be carried out within the DCT.

Conversion Process

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

Data Privacy and Sensitivity

Extraction

1. Syniti Migrate connects to the sources.
2. An initial extract of the relevant Characteristics will be loaded into a google sheet report to assist business with any standardization.

Extraction Run Sheet

Selection Screen

Data Collection Template (DCT)

Target Ready Data Collection Template will be created for EAM Characteristics  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.

Delta Data Management: Initial collection will be done via the report and one-time load to the DCT will be performed. Any delta after the initial collection within the DCT will require business to take due diligence to ensure any subsequent delta cleansing is verified and aligned within the DCT.

Characteristics DCT Rules

Characteristics 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 Syniti Migrate 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 Syniti Migrate
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

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 Object is in scope based on data design and any exception requested by business.

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