Purpose

The purpose of this document is to define the conversion approach to create Open Sales Order in S/4 HANA.

Open sales orders are orders that are not fully delivered or billed and still require fulfillment or financial processing. This conversion ensures business continuity and data consistency for ongoing customer transactions. It should be a seamless migration, and business is able to continue working with open Sales Order in S/4 HANA.

Conversion Scope

The scope of this document covers the approach for converting Open Sales Orders, including relevant schedule lines and partner data, from legacy source systems into SAP S/4HANA.

From the current system landscape, open sales order data resides separately across legacy systems (WP2 and PF2), often with inconsistent formatting, incomplete references, and varying document structures. It is required to harmonize, cleanse, and validate the sales order data to ensure that only business-relevant, open orders are migrated to S/4HANA.

While WP2 and PF2 serve as the primary source systems, various transformation and mapping logic will be applied to generate load templates that conform to the target S/4HANA order structure, including header, item, schedule line, pricing, and partner functions. The conversion scope specifically includes orders that are not fully delivered or billed, with open quantities, future delivery dates, or billing schedules, and which remain critical to business continuity post go-live.

The data from legacy system includes:

1. Sales Orders with Open Quantities:
   1. Orders with undelivered items, i.e., delivery quantity < order quantity.
   2. Orders where the delivery status is not marked as complete.
   3. Orders with open billing quantity, meaning not fully invoiced.
2. Special Sales Documents Still in Processing: XXX to be decided
   1. Return orders or credit memo requests not yet fully processed.
   2. Partial deliveries or partial credit documents awaiting completion.
3. Orders Required for Business Continuity or Customer Service:
   1. Sales orders flagged by business or customer service for visibility after go-live.
   2. Orders still under negotiation or partial execution (e.g., changed or parked orders).

The data from legacy system excludes:

1. Fully Delivered and Billed Sales Orders:
   1. Orders where all items are delivered (delivery complete) and invoiced (billing complete).
   2. Orders archived in legacy system and no longer used operationally.
2. Rejected or Cancelled Orders:
   1. Sales orders where header or item status indicates cancellation or rejection.
   2. Orders marked with final rejection reason codes.
3. Out-of-Scope Sales Document Types or Sales Areas:
   1. Document types not in use or excluded by process design (e.g., internal orders, sample/test).
   2. Orders belonging to sales organizations, distribution channels, or divisions not part of S/4 scope.
4. Orders Without Business or Legal Justification:
   1. Orders with zero values, no meaningful transactional history, or created erroneously.
   2. Orders flagged by business for exclusion due to redundancy.

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:

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. 

Data Privacy and Sensitivity

Extraction

xtract 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)

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 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

Open Sales Orders will be loaded in the pre-cutover or cutover phase, depending on business cut-off.

Configuration

Conversion Objects

Error Handling

Post-Load Validation

Project Team

Completeness

Accuracy

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

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