How does SAP integrate artificial intelligence (AI) into its solutions? Explore the various applications of AI in SAP, including supply chain management, data analytics, predictive maintenance, and much more. We will also discuss the benefits of using AI with SAP and how it can help your business stay competitive in an ever-changing market.
09/06/2022
ARTIFICIAL INTELLIGENCE AND SAP
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 can 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 intellectual and financial neglect.
Despite some interesting works in the 80s, the interest in AI strongly recovered in the early 2000s with an emphasis on Deep Learning. Today, the future of AI seems promising. A report published by the International Data Corporation forecasts an 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 is 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.
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 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 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 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 contrary to 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 at the forefront. SAP has owned many Machine Learning algorithms for several years. Many applications 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, 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 CoPilot
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-premises 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 and learn from past experiences to finally make better decisions. This hybrid solution is composed of three elements:
- The Cloud Studio (or the on-premises 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, and 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 that allow the generation of personalized recommendations adapted to your sector of activity or your business process.
Optimize your business processes with SAP and AI
All the solutions presented use machine learning algorithms, natural language processing techniques, and advanced analysis tools to help businesses manage their operations more efficiently and cost-effectively.
Here are some examples of AI applications in SAP:
Optimize your business processes with SAP and AI
Data Quality
Data quality is essential for AI as it determines the accuracy of results. Raw data can be unstructured, incomplete, inconsistent, or even false, which can negatively impact AI results.
Therefore, it is crucial for businesses to have a solid data management strategy to ensure that data is of high quality, structured, and cleaned through automated data cleaning, validation, and normalization processes.
System integration
AI often requires the integration of multiple systems and applications to function effectively. This can be challenging for businesses that have heterogeneous systems, disparate databases, and complex business processes.
To effectively integrate AI into SAP, businesses must develop a comprehensive information architecture strategy to ensure interoperability and synchronization of data between systems. This may include middleware technologies, API gateways, and standardized data exchange protocols to facilitate integration.
Costs
The costs associated with integrating AI into SAP can be high. This may be due to the costs of developing AI, employee training costs, and costs of maintaining and upgrading systems. Businesses must develop a solid financing plan to cover the costs associated with AI.
They may also look into third-party AI solutions or cloud services that can offer flexible payment options and reduced costs.
Data privacy and security
AI often requires access to sensitive data, such as personal, financial, or business information. This can lead to privacy and data security issues. Companies need to implement appropriate security measures to protect data and ensure that AI is used in compliance with data protection regulations.
This may include measures such as encryption, two-factor authentication, and monitoring for suspicious activity to prevent data security breaches.
Ultimately, to effectively integrate AI into SAP, companies must develop a solid strategy that takes into account all the challenges and characteristics of the business. Careful planning, technical expertise, and a data-centric approach are necessary to ensure the relevance, accuracy, and security of AI.
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