Machine Learning and Predictive Analytics technologies have been incorporated into the SAP framework since the introduction of SAP’s flagship S/4 HANA product in 2015. It is used to optimize additional benefits for operational users and business users.
Now as the world is grappling with the ‘new normal’ in the pandemic, companies like Accely found themselves ahead of the curve as they were able to tackle the challenges of remote working and implementation. Accely has helped many companies drive their digital transformation journey through the implementation of SAP solutions. Accely provides SAP S/4 HANA modern ERP systems that leverage predictive analysis and machine learning to adequately equip the workforce in these challenging times.
In terms of deriving insights, delivering outcomes, or predicting results, you all know how important data is. Algorithms complete the puzzle by using data to make choices to help solve every business issue or situation. Whether shopping online, driving to a grocery store, handling your weekend schedule, or preparing for the next big event, these algorithms have become part and parcel of all we do on a daily basis.
The journey of SAP to develop intelligent applications began long before the launch of its current business suite, SAP S/4 HANA, and is now becoming even better by incorporating intelligent technology into the business processes of SAP S/4 HANA. Though there are numerous ways to build intelligence into SAP S/4 HANA, let’s look at some of the main aspects of how SAP S/4 HANA integrates smart technology, such as predictive analytics and machine learning.
Embedding Machine Learning(ML) in SAP S/4 HANA
Although within SAP S/4 HANA there are various ways of doing predictive analytics and machine learning, one of the main approaches is to incorporate machine learning techniques into the business processes themselves. SAP HANA machine learning libraries such as the Predictive Analysis Library (PAL) and the Automatic Predictive Library (APL) are used in this approach to create predictive models that are then incorporated into SAP S/4 HANA.
The Predictive Analytics Integrator (PAi) is the first version of the framework. A framework was created to help incorporate and control the life cycles of machine learning models into SAP S/4 HANA. The second version is called Integrated Scenario Lifecycle Management (ISLM), which is an improved version used in SAP S/4 HANA to deal with the implementation of machine learning.
Though PAi concentrated on specifically embedding the APL library of HANA without coding, the HANA PAL algorithms could be embedded into SAP S4HANA using a generic coding methodology. Now you could embed HANA PAL and HANA APL without any coding into the business applications of SAP S/4 HANA with ISLM technology. ISLM’s lifecycle management wonderfully manages this strategy.
Consuming Machine Learning(ML) from SAP Cloud Platform
While the embedded ML targets business logic and machine learning algorithms residing in SAP S/4 HANA, the side-by-side ML targets machine learning algorithms residing on the SAP Cloud Platform (usually in SAP Data Intelligence or the SAP AI foundation) and, depending on the application requirements, the business logic remains on either SCP or SAP S/4 HANA.
To ensure the load on SAP S/4 HANA systems is low and the runtime of the systems is appropriate, the side-by-side ML scenarios are scaled out and installed on the SAP Cloud Platform. You can use SAP HANA Machine Learning(ML) algorithms or non-SAP ML algorithms from other libraries, such as R programming, Tensor Flow, Sci-Kit Learn or Python, etc., with the latest paradigm of ISLM technology.
Using a mix of these machine learning libraries, ML resources can be constructed and made available for consumption by SAP S/4 HANA applications or SAP Cloud Platform applications. SAP S/4 HANA extension apps consume SAP Data Intelligence capabilities with SAP Cloud Platform business data (regardless of where the app resides, either SAP S/4 HANA or SAP Cloud Platform) and SAP Cloud Platform ML algorithms following the golden rule of taking the algorithms to the data!
SAP Data Intelligence is an essential component of cloud, hybrid or on-premise scenarios that are optimized for side-by-side ML situations. With the various toolsets, data scientists can use SAP Data Intelligence’s machine learning features to design, build, and train the models, along with managing the life cycle of the ML models.
Leveraging Predictive Analytics from SAP Analytics Cloud
There are cases where you do not need to incorporate machine learning into a business process, but based on a few scenarios, you still want to imagine predictions. This is where the technique of using predictive analytics for discovery comes into play. You could pick up whitelisted core data service (CDS) views in this strategy and build custom CDS views in SAP S/4 HANA.
These custom CDS views could then be linked to the SAP Analytics Cloud, and predictive models could be created with a live SAP S/4 HANA connection or by acquiring and loading SAP Analytics Cloud data set views from SAP S/4 HANA.
The ISLM architecture allows using of the PAL or APL algorithms to create the predictive models and use the Smart Predict features to visualize the results on SAP Analytics Cloud dashboards. This technique is primarily used by business users who do not need to stress about the underlying algorithms, but by working with local or acquired datasets, they need to construct fast simulations of forecasts or dashboards.
Conclusion
The inclusion of predictive analytics and machine learning in SAP S/4 HANA. Accely provides a seamless implementation of intelligent SAP S/4 HANA technologies in your existing SAP environments.
