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S/4HANA and Machine Learning for Solution Architects

In my short blogs I try to provide an interesting overview on selected topics for SAP Solution Architects. This time it is Machine Learning (ML). I provide an overview of the SAP technology for Machine Learning, introduce some pre-built application-specific scenarios and show where to find more information. The blog does not cover implementation or technical details. I also do not cover conversational Artificial Intelligence or Robotic Process Automation (RPA).

Machine learning is a disruptive technology and is forecast to grow significantly and is one of the key innovations in digital transformation. It is a subset of Artificial Intelligence (AI) and predicts results using models that learn from large sets of sample data. In traditional solutions, humans create rules and solutions that work with clear requirements and structured data. Machine Learning can automate processes or decisions that are based on complex rules and structured data (e.g. database tables) or unstructured data (like natural language and images). An example is accurately classifying service tickets based on their text content. (Links to this below). The business benefit is to allow people to focus on tasks that add more value. The picture below shows possible ML scenarios in the Procure to Pay process.

There is a huge choice of software and solutions for ML. Whilst it is clear that incredibly complex problems can be solved by AI (e.g. self driving cars), the question for organisations using SAP enterprise software is can ML be applied to business scenarios with a clear return on investment. S/4HANA makes it easy to try simple ML scenarios and then allows you to expand into more ambitious solutions.

SAP provides two technologies for ML often referred to as “embedded ML” and “side-by-side ML”. Both approaches provide 1) pre-built application-specific business scenarios and 2) allow you to build your own machine learning solutions.

Embedded ML works within the S/4HANA instance and uses predictive analytics for simpler scenarios based mainly on structured data. It is used for use cases that demand low data, RAM and CPU. Examples of embedded SAP solutions include “early detection of slow or non-moving goods” and “payment block cash discount at risk”. Applications can access a machine learning framework through something called the Predictive Analysis Interface (PAI). The framework provides Fiori apps to train and manage machine learning models.

Side-by-side ML is the Leonardo solution for Machine Learning where the logic resides in the SAP Cloud Platform (SCP) and is accessed by S/4HANA through APIs. This provides “deep learning” that can work with vast quantities of structured and unstructured data from SAP and external sources. It provides support for open source frameworks and algorithms. SAP examples include “invoice and payment matching” and “service ticket intelligence”.

The ML scenarios available to you vary depending on whether you are using SAP S/4HANA on-premise, SAP Cloud Platform or SAP S/4HANA Cloud. Some e.g. SAP Cash Application require an additional license.  Here are some examples:

See this ASUG blog for lists of intelligent scenarios (many with links to more information):


If you are new to machine learning and would like to understand the principles and how it works you could look at the OpenSAP course “Enterprise Machine Learning in a Nutshell”.  This useful self-explanatory picture is taken from the course:


This blog provides information on how Predictive Analytics Interface can be used for embedded machine learning:

This blog provides more detail on how side-by-side ML fits into the overall SAP architecture:

I hope you found this blog informative. I would be happy to see your feedback on using SAP Machine Learning solutions.

Amin Hoque
Enterprise Architect at SAP Digital Business Services UK





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