Purpose

This Data Conversion Specification outlines the scope, data requirements, extraction logic, transformation rules, validation steps, and load procedures for converting six months of historical sales order data into S/4HANA to support Net Realizable Value (NRV) calculations for inventory provisioning.

The converted data provides the basis for deriving the average selling price per material and plant at period-end. These values will be compared with material cost to identify potential inventory devaluation and post stock provisions accordingly.

Objective

• Enable NRV assessment by leveraging six months of historical sales prices.
• Ensure availability of average sales price (ASP) per material/plant for monthly financial reporting.
• Support Finance in executing accurate, compliant inventory provisioning.

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:

1. Extraction of 6 months historical sales order data from ECC Systems. Please refer to the relevancy criteria section for detailed data extraction logic / scope

2. Transformation and formatting according to S/4HANA NRV input structure.

3. One-time load of historical price records into S/4HANA.

4. Post–go-live: continued use of live S/4HANA sales order data for rolling history.

The data from legacy system excludes:

1. Any sales document created prior to six months of go-live. Please refer to the relevancy criteria section for detailed data extraction logic / scope and document types to be excluded

2. Sales documents created to sales area, customers and materials that are not in scope for migration to S/4HANA.

3. Sales document line items that are marked as rejected 

Additional Information

Multi-language Requirement

Not Applicable

Document Management

Not Applicable

Legal Requirement

Not Applicable

Special Requirements

Not Applicable

Target Design

The target design enables S/4HANA to perform Net Realizable Value (NRV) calculations using both historical and ongoing sales price data. The solution is designed to store, maintain, and utilize a rolling six-month average selling price (ASP) for each material and plant combination, ensuring accurate and timely monthly inventory provisioning.

1. Overall Design Concept

The target system (S/4HANA) will contain a dedicated repository to store historical sales price data extracted from ECC, as well as continuously updated sales price data generated from S/4HANA sales orders after go-live. This repository will serve as the foundational data source for computing NRV at period end.

The system will automatically derive the Average Selling Price (ASP) for every material–plant combination by aggregating monthly sales transactions for six months. This ASP will then be compared against the material’s current cost to determine whether a stock provision is required.

• ECC systems will supply six months of historical sales order data.

• Data will be cleansed, transformed, and loaded into ZZNRVDATA using LSMW.

 2. Target Data Structure

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 conversion process is designed to extract six months of historical sales price data from the ECC system, transform it into the required format for S/4HANA, and load it into the custom NRV history table (ZZNRVDATA). This ensures that the target system has the necessary baseline price history to perform NRV calculations from the first period-end after go-live.

Data Privacy and Sensitivity

Relevancy Criteria for Data Extraction

NRV Data Extraction Criteria (Monthly Average Material Price at Plant Level)

This data extraction is performed to generate NRV (Net Realizable Value) inputs based on historical sales order pricing from the ECC systems. The extraction focuses on determining the monthly average selling price at Material–Plant–Currency level.

Data Source Period

• Extract sales prices from sales documents (Document Category = C) created within the 6 calendar months preceding the Go-Live month.

Inclusion Criteria

• Only include sales orders and related billing-relevant items.
• Include only records where Customer, Material, and Plant are within project scope.
• Document types in scope:
  • Sales Documents
    • System: PF2 → KE, SB, TD, TDIV, TDR
    • System: WP2 → KE, OR, ZOR, ORB, RCM, ZEXP
  • Credit Memo Requests
    • System: PF2 → B1, B3, B4, CR, CRV, CRVA, ICR, RK, CC
    • System: WP2 → B1, B3, B4, G2, ZG2, CRB, ZCRB
  • Debit Memo Requests
    • System: PF2 → DR, DRV, DRVA, IDR, IDRV, CD
    • System: WP2 → L2, L3, ZCO, ZL2, ZL3, DRB
  • Scheduling Agreement
    • System: WP2 → LP

Exclusion Criteria

• Exclude the following:
  • Non-billing-relevant documents
  • Free-of-charge items
  • Spot sales
• Exclude following document types
  • Sales Documents
    • System: PF2 → KA, KB, KBCC, LA, M2, M3, T1, ZRKE, ZSE1, ZSER, ZTDL, ZPVA
    • System: WP2 → KA, KB, RCS, ZAGR
  • Credit Memo Requests
    • System: PF2 → CRVC, CRVS, ICRV
    • System: WP2 → ZG3
  • Debit Memo Requests
    • System: PF2 → DRVD, DRVS
    • System: WP2 → ZGV, ZLV, ZDRB

Data Aggregation Requirement

• Compute Monthly Average Selling Price per:
• Material
• Plant
• Sales Unit of Measure
• Currency

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