Status

Owner

LIU-ext, Ekawati 

StakeholdersThe business stakeholders involved in making, reviewing, and endorsing this decision. Type @ to mention people by name

Purpose

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

Cross selling is a sales technique where a customer is offered related or complementary products to what they have already purchased or are purchasing. The system can automatically suggest these items during sales order creation, potentially increasing sales volume and customer satisfaction. 


Conversion Scope

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

From the current system landscape, Cross Selling data exists separately in the legacy systems (PF2 and WP2), with potential discrepancies in both systems. Harmonization and validation are required to ensure accurate and consolidated data in S/4HANA. While PF2 and WP2 serve as source systems, extensive mapping and transformation logic will be necessary to produce properly formatted load templates in line with the target design.


The data from legacy system includes: Final relevancy rule pending MDS

  1. All active Cross Selling, where "valid from date up to 1 year after S/4HANA Go-Live date" AND "valid to date is after valid from date AND beyond S/4HANA Go-Live date" AND each of the below as per table combination:
    1. Sales organization in scope of S/4HANA
    2. Customer sales view migrated as per CNV-3003 Business Partners - Customer (Sales and Service) - FLCU01 (pending MDS as there are no data maintained in table 904)
    3. Material sales view migrated as per CNV-2003 Materials - Sales view with sales long text.

The data from legacy system excludes:

  1. Invalid Cross Selling; none of the above


List of source systems and approximate number of records
SourceScope

Source Approx No. of Records

Target SystemTarget Approx

No. of Records

WP2Cross Selling table: KOTD906150S/4HANA ROW150
PF2Cross SellingN/AS/4HANA ROWN/A
WP2Cross Selling table: KOTD906150S/4HANA China150
PF2Cross SellingN/AS/4HANA ChinaN/A
WP2Cross Selling table: KOTD90650S/4HANA CUI50
PF2Cross SellingN/AS/4HANA CUIN/A

Additional Information

Multi-language Requirement

N/A

Document Management

N/A

Legal Requirement

As per US Department of Defence (DOD) a Cybersecurity Maturity Model Certification/ CMMC 2.0 is a mandatory certification for all contractors handling Controlled Unclassified Information (CUI) and Federal Contract Information (FCI). CUI includes sensitive technical data (e.g., design specs, system info) related to U.S. military and space applications. The Composites Business handles CUI and is therefore within CMMC scope. Without certification, the business risks disqualification from existing and future DoD programs.
It is mandatory to implement CMMC-compliant systems and processes to for all the organizations that are dealing with CUI.

Therefore, there will be one SAP instance specifically for CUI related entities. The migration for CUI related entities will be covered by US based data consultant using separate tools.

Special Requirements

Cross Selling

A. Different SAP Instance Migration Approach

Due to compliance requirement, there will be one SAP instance for Rest of the World (ROW), one for China and one for CUI.

  1. For entities in China, the data will be loaded into SAP China instance while the entire migration process will remain the same as rest of the world.
  2. For entities which will reside in CUI, the migration will be handled by US based data consultant. 

Please refer to the link for the entity mapping for each instance. In case the data object is applicable for multiples instances, what business rule to follow?


 - To identify the record is for SAP ROW Instance, it will use below logic. 

  1. The Cross Selling has sales organization data in below entities. 

Source



PF23383SOLVAY SPOL KR
PF25686SOLVAY QUIMICA (AR)
PF25782SOLVAY SPOL US
PF25835SOLVAY SPOL IT
PF25846SOLVAY SPOL JP
PF25955SOLVAY SPECIALIT IN
PF25978SOLVAY SPOL BE
PF26080SOLVAY FRANCE FR
PF26327SLV SP CHEM ASIA PAC
WP2AE01AE Composite Matls
WP2AU01Technology Solutions
WP2AU02AU Novecare
WP2BR42Quimicos BR Novecare
WP2BR44Quimicos BR SpecPoly
WP2BR47Quimicos BR Tech Sol
WP2CA12Technology Solutions
WP2CA15Cytec CA Novecare
WP2CL01Cytec Chile Tech Sol
WP2FRAZSPOP Novecare
WP2GB34Solvay Solutions UK
WP2ID01CYID Tech Solutions
WP2IN04SSIPL IN Novecare
WP2IT07Novecare Italie
WP2JP02Solvay Japan, Ltd
WP2JP03Solvay Nicca, Ltd
WP2JP10Technology Solutions
WP2KR11CY KR Novecare
WP2KR12CY KR Tech Solutions
WP2MX01MX Tech Solutions
WP2MX02MX Novecare
WP2MX08MX Spec Polymers
WP2NL01Cytec Ind BV TS
WP2NL05Solvay Sol NL Noveca
WP2NZ01Solvay New Zealand
WP2PE01Technology Solutions
WP2SG03Solvay Sp Chem SG
WP2SG08SSCAP SG Tech Sol
WP2TH01Technology Solutions
WP2TH03Solvay (Bangpoo) SC
WP2US05Solvay US Novecare
WP2US23Technology Solutions
WP2US50Composite Mat (7008)


- To identify the record is for SAP China Instance, it will use below logic. 

  1. The Cross Selling has sales organization data in below entities. 
SystemSales Organization CodeSales Organization Description
PF25876SOLVAY SHANGHAI LTD
PF25991SOLVAY SPEC POLYMERS
WP2CN15Solvay ZJ Novecare
WP2CN18Zhuhai SLV Novecare
WP2CN19Solvay ZJG Novecare
WP2CN27Solvay ZJ Tech Sol
WP2CN28CYIS Tech Solutions
WP2CN36CYIS Composites Matl
WP2CN41CEM (SH) Compos Mats


 - To identify the record is for SAP CUI Instance, it will use below logic. 

  1. The Cross Selling has sales organization data in below entities. 
SystemSales Organization CodeSales Organization Description
WP2DE13Composites Materials
WP2GB40Composites Materials
WP2US32Composite Materials
WP2US33CEM Defense Material

  





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.

TableFieldData ElementField DescriptionData TypeLengthRequirement
MARAMATNRMATNRMaterial NumberCHAR18Mandatory






















Data Cleansing

All data cleansing should take place in the data source system as defined in this document, unless system limitations prevent it.


IDCriticalityError Message/Report DescriptionRuleOutputSource System
1037-001
Change the 'Validity Period - TO' to before SyWay go live date.For main material, if it is in-active/ obsolete. 
PF2/ WP2
1037-002
Delete the alternative material.For alternative materials, if it is in-active/ obsolete. 
PF2/ WP2














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 S4 Hana system. 


For CUI instance, the ETL process will be similar, but it will not use Syniti tool.


Data Privacy and Sensitivity

For SAP CUI instances, the data will be processed by US Based consultants.


Extraction

Extract data from a source into Syniti Migrate for S/4HANA ROW and S/4HANA China relevant entities. 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

For SAP CUI related entities, it will be alternative extraction process and the data will be stored in approved tools.

Extraction Run Sheet

Req #Requirement DescriptionTeam Responsible
Extraction Scope Definition- Identify the source systems and databases involved.
- Define the data objects (tables, fields, records) to be extracted.
- Establish business rules for data selection.

Syniti / US Based Consultant for SAP CUI instance

Syniti / LTC Data team

Extraction Methodology- Specify the extraction approach (full, incremental, or delta extraction).
- Determine the tools and technologies used.
- Define data filtering criteria to exclude irrelevant records.		Syniti / US Based Consultant for SAP CUI instance
Extraction Execution Plan- Establish execution timelines and batch processing schedules.
- Assign responsibilities for extraction monitoring.
- Document dependencies on other migration tasks.		Syniti / US Based Consultant for SAP CUI instance
Data Quality and Validation- Define error handling mechanisms for extraction failures.Syniti / US Based Consultant for SAP CUI instance


Selection Screen

If applicable, this section will give the details on any selection screen parameters, including the parameter type, that are required to be entered to ensure consistent data extracts.
Selection Ref ScreenParameter NameSelection TypeRequirementValue to be entered/set
N/A



















Data Collection Template (DCT)

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

<Object> DCT Rules

Field NameField DescriptionRule












Extraction Dependencies

List the steps that need to occur before extraction can commence

Item #Step DescriptionTeam Responsible
1

Source System Availability

  • Ensure that the source database or application is accessible.
  • Confirm that necessary credentials and permissions are granted
Syensqo IT
2

Data Structure

  • Identify relationships between tables, views, and stored procedures.
Syniti / US Based Consultant for SAP CUI instance
3

Referential Integrity

  • Ensure dependent records are extracted together.
Syniti / US Based Consultant for SAP CUI instance
4

Extraction Methodology

  • Define whether extraction is full, incremental, or delta-based.
  • Establish batch processing schedules for large datasets.
Syniti / US Based Consultant for SAP CUI instance
5

Performance and Scalability Considerations

  • Optimize extraction queries to prevent system overload.
  • Ensure network bandwidth supports data transfer volumes.
Syniti / US Based Consultant for SAP CUI instance
6

Security and Compliance

  • Adhere to regulatory standards for sensitive information if applicable
Syniti / US Based Consultant for SAP CUI instance
7

Data cleansing of legacy Cross Selling data must be completed.

If standardization within the DCT begins using relevant data from PF2 and WP2 before the cleansing is finalized, it is understood that the business will take due diligence to ensure any subsequent delta cleansing is verified and aligned within the DCT.

Business


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

Item #Step DescriptionTeam Responsible
1Transformation Scope Definition
- Identify the source and target data structures.
- Define business rules for data standardization.
- Establish data cleansing requirements to remove inconsistencies.		Data Team
2Data Mapping and Standardization
- Align source fields with target fields.
- Ensure unit consistency (e.g., currency, measurement units)		Data Team
3Business Rule Application
- Implement data enrichment/collection if applicable
- Apply conditional transformations based on predefined logic/business rules		Data Team
4Transformation Execution Plan
- Define batch processing schedules.
- Assign responsibilities for monitoring execution.
- Establish error-handling mechanisms		Syniti / US Based Consultant for SAP CUI instance


Transformation Rules

Rule #Source systemSource TableSource FieldSource DescriptionTarget SystemTarget TableTarget FieldTarget DescriptionTransformation Logic









































Transformation Mapping

Use the exact name and reference this section in the “Transformation rules” above
Mapping Table NameMapping Table Description
MAP_VKORGSales Organization Mapping Table

MAP_VTWEG

Distribution Channel Mapping Table

SyWay - Sales Area.pptx --> All the actual distribution channels and divisions won't exist in the to-be solution

Check current Dist.Ch. and discuss with Functional team and how to do the mapping

MAP_KUNNR

Customer Mapping Table

MAP_MATNR

Material Mapping Table

Transformation Dependencies

List the steps that need to occur before transformation can commence
Item #Step DescriptionTeam Responsible
1Source Data Integrity
- Ensure extracted data is complete, accurate, and consistent.
- Validate that data types and formats align with transformation requirements.		Syniti / US Based Consultant for SAP CUI instance
2Referential Integrity
- Ensure dependent records are transformed together or in advance		Syniti / US Based Consultant for SAP CUI instance
3Transformation Logic and Mapping
- Define data mapping rules between source and target schemas.		Data Team
4Performance and Scalability Considerations
- Optimize transformation processes for large datasets.
- Ensure system resources can handle transformation workloads		Syniti / US Based Consultant for SAP CUI instance
5Logging and Error Handling
- Maintain detailed logs of transformation activities.
- Define error-handling procedures for failed transformations		Syniti / US Based Consultant for SAP CUI instance


Pre-Load Validation

Project Team

The following pre-load validations will be performed by the Project Team.

Completeness

TaskAction
Compare Data Counts
  1. Verify row counts between source and target databases.
  2. Identify missing or duplicated records.


Validate the mandatory fieldsValidate there is value for all the mandatory fields
Validate Primary Keys and Unique Constraints
  1. Check for duplicate or missing primary key values, i.e., if there is same BP number.
  2. Ensure unique constraints are maintained.


Test Referential IntegrityConfirm dependent records exist in related tables

Accuracy

TaskAction
Validate the transformationValidate the fields which require transformation have the value after transformation instead of the original field value
Check Data Consistency
  1. Compare field values across systems
  2. Validate data formats and structures



Business

The following pre-load validations will be performed by the business. 

Completeness

TaskAction
Compare Data Counts
  1. Verify row counts between source and target databases.
  2. Identify missing or duplicated records.


Review populated templates for missing or incorrect valuesUse checklists to verify completeness and correctness before submission



Accuracy

TaskAction

Conversion Accuracy

Business Data Owner/s to verify that all the data in the load table/file is accurate as per endorsed transformation/ mapping rules (and signed-off DCT data).






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

Item #Step DescriptionTeam Responsible
1Load Scope Definition
- Identify the target system and database structure.
- Define data objects (tables, fields, records) to be loaded.
- Establish business rules for data validation.		Data team
2Load Methodology
- Specify the loading tools and technologies (Migration Cockpit, LSMW, custom loading program).		Syniti / US Based Consultant for SAP CUI instance
3Data Quality and Validation
- Ensure data integrity checks (null values, duplicates, format validation).
- Perform pre-load validations to verify completeness.
- Define error handling mechanisms for load failures		Syniti / US Based Consultant for SAP CUI instance
4Load Execution Plan
- Establish execution timelines and batch processing schedules.
- Assign responsibilities for monitoring execution.
- Document dependencies on other migration tasks		Syniti / US Based Consultant for SAP CUI instance
5Logging and Reporting
- Maintain detailed logs of loading activities.
- Generate summary reports on loaded data volume and quality.
- Define escalation procedures for errors		Syniti / US Based Consultant for SAP CUI instance


Load Phase and Dependencies

The Cross Selling will be loaded in the pre-cutover period.

Before loading, it will have dependency on the configuration and data objects in the S/4HANA. The configuration needs to be transported into the respective system first.

Configuration


Item #Configuration Item
1Sales Organization
2Distribution Channel
3Cross Selling Condition Table
4Cross Selling Access Sequence
5Cross Selling Condition Type
6Cross Selling Procedure
7Maintain Customer/ Document Procedures for Cross Selling
8Define Cross Selling Profile
9Assign Cross Selling Profile

Conversion Objects

Object #Preceding Object Conversion Approach

3003

Business Partners - Customer (Sales and Service) - FLCU01

2003

Materials - Sales view with sales long text



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

Error TypeError DescriptionAction Taken

Configuration / Data Transformation

The value XXX for field XXX doesn't exist

  1. Check the mapping/ conversion is done properly in the loading file
  2. Validate the target value is configured/ transported in the target system
  3. Reach out to function team to validate the configuration

Configuration

There is mandatory field XXX missing

  1. Validate MDS if the fields are set as mandatory
  2. Validate if there is value in the pre-loading file
  3. Validate if the configuration for the mandatory fields are done properly




Post-Load Validation

Project Team

The following post-load validations will be performed by the Project Team.

Completeness

TaskAction

Validate Record count in the backend

Validate all tables has the same records as the loading file

Display Records

Pick up a few random Sales Organization and Customer, and run t-code: VD43 to validate the Cross Selling can be displayed without any error.

Perform Source-to-Target Comparisons

  1. Validate that migrated data matches source records.
  2. Check for discrepancies in numerical values, text fields, and timestamps

Accuracy

TaskAction
Execute Sample Queries and Reports
  1. Run queries to validate business logic.
  2. Generate reports to compare expected vs. actual results
Conduct Post-Migration ReconciliationGenerate reports comparing pre- and post-migration data.



Business

Post-load validation is a critical step in data migration, ensuring that transferred data is accurate, complete, and functional within the target system.

1. Ensuring Data Integrity
After migration, data must be consistent with its original structure. Post-load validation checks for missing records, incorrect mappings, and formatting errors to prevent discrepancies.
2. Business Continuity
Faulty data can disrupt operations, leading to financial losses and inefficiencies. Validating post-load data ensures that applications function as expected, preventing downtime.
3. Error Detection and Resolution
By validating data post-migration, businesses can detect anomalies early, reducing the cost and effort required for corrections


Completeness

TaskAction
Perform Source-to-Target Comparisons
  1. Validate that migrated data matches source records.
  2. Check for discrepancies in numerical values, text fields, and timestamps
Conduct Post-Migration ReconciliationGo through reports comparing pre- and post-migration data.



Accuracy

TaskAction
Perform Manual TestingConduct manual spot-checks for additional assurance.





Key Assumptions

  • Cross Selling Master Data Standard is up to date as on the date of documenting this conversion approach and data load. 
  • Cross Selling is in scope based on data design and any exception requested by business.
  • There will be 3 SAP instances, one for ROW, one for China and one for CUI only.
  • For SAP CUI instance, the migration activity will be handled by US based data consultant. 


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

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