With S4/HANA and the new version of SAP BTP and Cloud Foundry, SAP includes Machine Learning AI technologies to enable your business to adopt automated and accelerated processes.



8 minutes de lecture

How Machine Learning is Revolutionizing ERP?

Defined as a quality of the mind that understands and adapts easily, intelligence has for a long time been linked only to living human and animal species. However, since the 1950s, we have been talking about artificial intelligence (AI), a quality of mind “constructed” by human beings.

Today, when we talk about artificial intelligence, we think of the robots seen in some movies that are able to think, move and even act without any human intervention. The reality is a bit more complex and above all more vast than simple robotics.

In this article, we will review all these technologies before focusing on machine learning and its advantages to be used in your SAP environment.

What Is Artificial Intelligence?

Contrary to what one might think, this technology is not so recent. Indeed, it is estimated that the first artificial intelligence software was designed in the early 1950s. Turochamp was a computer program that “knew” how to play chess and had at least the same knowledge as an average player. 

It was only a few years later that the term “artificial intelligence” was first used. It referred to software that learned to speak English or that solved mathematical problems

Even so, there was some skepticism among experts about using the word “intelligence”.

The evolution was slow and the economic problems slowed down the innovations so much that we talk about the winter of artificial intelligence between 1973 and 1980, referring to many doubts about the positive evolution in the future for these technologies and thus to an intellectual and financial neglect.

Despite some interesting works in the 80’s, the interest for AI has strongly recovered in the early 2000’s with the emphasis on Deep Learning. Today, the future of AI seems promising. A report published by the International Data Corporation forecasts 18.8% growth in global revenues in this field this year.

Data at the Heart of AI

AI could not function without data. In computer science, data is defined as the representation of information in a program. 

A comment on a blog, a completed form or a subscription to a newsletter: everything is given today so much so that we talk about Big Data which are stored in a very large number of servers

This is the basic element of artificial intelligence. Indeed, in order to function, artificial intelligence will analyze a large amount of data to offer a result that will also materialize in the form of data.

A data can be either :

  • Structured: this is data that can be easily classified in a spreadsheet. They are usually in the form of numbers (date, phone number…) or letters (name, city…)
  • Unstructured: this is data that is neither organized nor formatted and that can hardly fit into boxes. In this category, we find in particular all files in video or photo format or comments on social networks.

What is Machine Learning?

Machine learning (ML) is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

As a sub-branch of artificial intelligence, Machine Learning is now booming. Yet, this technology is not as new as it seems.

Differences between the Design Principles

It was the computer scientist Arthur Samuel who first used this term to describe his 1952 program. This program was able to play checkers and above all to learn and improve after each game. It ended up beating the 4th best player in the United States. 

As for artificial intelligence, Machine Learning was to come to a halt until the beginning of the 1990s, thanks in particular to the arrival of much more powerful computers on the Internet. Scientists had access to more data and could share their results, which accelerated innovation.

IBM was one of the major players in the development of Machine Learning with the invention of a computer program still based on the game (chess this time). This program beat the world number one at the time in 1997. From that time on, it will develop in many fields, including business.

The Characteristics of Machine Learning

Textually, it is a learning machine that will learn automatically. The machine is actually an algorithm that will analyze data and produce results, including future predictions.

Machine Learning is a branch of intelligence that allows us to put forward data patterns in order to predict behaviors. To do this, the machine will learn by itself. Thus, like a human being who learns, it will be more and more efficient with time. There are several types of learning

Supervised learning: the machine has many examples that allow it to make the right decision. Human intervention is therefore essential at the beginning. For example, if you want to set up a Machine Learning technology that automatically identifies spam, you can give it examples of spam. 

It will identify the objects, signatures and even images to classify the emails it receives. More verification is required at the beginning but the more it is practiced, the more efficient it will be.

Semi-supervised learning: this is used with a large amount of mainly unstructured data. The idea is to facilitate the work of the machine by providing it with a small amount of structured and organized data that will allow it to better analyze the main data.

Unsupervised learning: in this case, there is no starting data that the artificial intelligence can use. It will therefore analyze the raw data directly. For example, this method can be used to analyze behaviors on social networks in order to propose targeted ads.

Learning by reinforcement: this is the principle of trial and error. Depending on the situation, the algorithm will make the decision it thinks is best. It will learn from its experience and adapt its answers more and more.

The choice of the type of learning will depend on the initial data and the capacities/objectives of those who use it. Indeed, supervised learning with structured data will not be very complex on the contrary of an unsupervised learning with unstructured data.

The final objective of Machine Learning is the prediction of information. Basically, there are different sources of data which are themselves different. 

The first step is to gather them into a single database. Usually, different companies process data in silos. We have on one side spatial data, on the other side numerical data…

How to Distinguish between Machine Learning and Deep Learning?

If you are familiar with the concept of Machine Learning, you have probably heard of Deep Learning, another branch of artificial intelligence. The basis is the same, i.e. algorithms, but they are based on the functioning of the human brain and neurons, which facilitates the analysis of unstructured data.

Unlike Machine Learning, there is no prior learning or training. Moreover, Deep Learning only works with a very large amount of data that we do not necessarily control. It is therefore necessary to have large data processing capacities and machines that are often costly in terms of energy and money.

Machine Learning at SAP

With S4/HANA, SAP has resolutely turned to artificial intelligence and Machine Learning to help you put your intelligent business in the forefront. SAP has owned many Machine Learning algorithms for several years. There are many applications that use artificial intelligence. We will review these.

SAP Data Intelligence

SAP Data Intelligence is a data processing solution that allows you to clean up data and, above all, to extract and relate it to each other in order to improve the productivity of your company. It is composed of a suite of applications that allows, among other things, the ingestion, cataloging or enrichment of data.

SAP Co Pilot

This is the digital assistant with voice or text commands. The principle is simple: you write or speak to the assistant who will interact with all the business applications. For example, you just have to ask how many days of vacation you have left to get the answer a few seconds later.

Thanks to Machine Learning technology, the assistant learns with each use and reduces its information processing time and the speed at which it communicates. Integrated with SAP BTP and SAP Fiori launchpad, it can be used even with the on premise version of SAP S4/HANA.

SAP Intelligent Robotic Process Automation

In our article on RPA, we mentioned IRPA, the combination of artificial intelligence and robotic process automation . With SAP, it is now possible to automate your workflow, to learn from past experiences to finally make better decisions. This hybrid solution is composed of three elements:

  • The Cloud Studio (or its on premise version, the Desktop Studio) to designate the automation process
  • The Cloud factory to orchestrate the automation
  • The Desktop Agent to run the application

This is possible thanks to the technology of the Contextor software. European leader of the RPA since the beginning of the year 2010, it has notably created an automaton dedicated to the transfer of refused credits for BNP Paribas. 

Today, more than 90% of the transfers are carried out automatically by the robot when 4 people were necessary before. Owned by SAP since 2018, it has been integrated into SAP Leonardo and SAP BTP, among others.

SAP Leonardo

SAP Leonardo is a cloud-based portfolio that brings together a set of solutions with which it is possible to develop any application. But these will not be fixed. Indeed, thanks to Machine Learning, each click, each transaction, each use of the applications induces learning and therefore a better performance in the short term.

In fact, SAP Leonardo involves many technologies in addition to machine learning: blockchain, IoT, design thinking…

It also allows you to host ready-to-use applications, some of which are based on Machine Learning. Among others, we can mention Service Ticketing for the sorting of service tickets or Customer Retention which allows the analysis of attrition.

SAP HANA Spatial Service

This is a set of services that runs on SAP BTP. These services provide simplified access through a single interface and will allow you to complement your business data with spatial information through different maps and geospatial tools.

It is possible to access the services:

  • Through the Spatial Service web application
  • Through many self-service applications that facilitate the creation of geolocalized applications

The idea here is to combine Machine Learning with spatial and geographical awareness.

SAP Predictive Analysis

Under this name is hidden a data mining and predictive modeling solution composed of four modules:

  • Data manager which prepares the various data to be analyzed
  • Modeler which allows workflow management and data manipulation through classification models
  • Social which extracts and uses the information and data put forward in the form of graphs or other.
  • Recommendations which allow the generation of personalized recommendations adapted to your sector of activity or your business process.

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