Purpose

The purpose of this document is to define the conversion approach to upload Purchasing Categories into S/4 HANA as part of the Procurement Data Migration.

In S/4HANA, Purchasing Categories are a new concept used to classify the goods and services a company procures from its suppliers.  One or more Material Groups can be assigned to a single Purchasing Category (the relationship between material group and Purchasing Category is n:1, not n:n).  Suppliers may also be linked to multiple Purchasing Categories.

For example, a Purchasing Category such as “IT Services” may include Material Groups like "Software Licenses" and "Consulting Services".  A supplier providing both software and consulting could therefore be connected to this Purchasing Category.

Supplier Segmentation and Preferred Supplier are captured per Purchasing Category, therefore they will be assigned in the purchasing category.

Supplier Segmentation, also referred to as supplier tiering, is the process of grouping suppliers into distinct categories. These categories are determined using a balanced assessment of how critical the supplier is to the organization, how much risk the relationship entails, and how the supplier has performed historically.

Creating and maintaining Purchasing Categories in S/4 HANA will be via the FIORI app "Manage Purchasing categories".

Link to MDS: DD-FUN- 050 Master Data Standard_1096-Category Strategies

Conversion Scope

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

Additional Information

Multi-language Requirement

The Purchasing Categories will be created in English, but multiple languages are supported.  The following languages are allowed:

• Core languages:  EN-English, FR-French, IT-Italian and ZH-Mandarin.
• Additional languages:  PT-Portuguese, DE-German and ES-Spanish.

Document Management

Not applicable.

Legal Requirement

Not applicable.

Special Requirements

Not applicable.

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:

Data Privacy and Sensitivity

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