How SAP Machine Learning Can Benefit Businesses to Gain the Next Level

SAP solutions do not need any introduction in the corporate world. As a reliable and advanced ERP solution, SAP has significantly invested in emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML). SAP machine learning can enhance the accuracy and improve the decision-making process by giving actionable insights. In this post, we are going to discuss the impact of combining machine learning with SAP HANA Solutions. Let’s start from scratch.

Machine Learning Capabilities in SAP

Machine learning in SAP HANA gives two embedded libraries- the Predictive Analysis Library (PAL) and the Automated Predictive Library (APL). Here we give a brief account of machine learning features in SAP HANA-

1. PAL with SAP HANA Streaming Analytics

PAL has incremental ML capabilities for SAP HANA streaming analytics. It can keep updating the ML model on the fly, and clients can get a dynamic model.

2. PAL for Python Clients

It gives a set of Python algorithms and functions that enable clients to access the ML-based features in SAP HANA. The PAL Python has additional data frame management capabilities along with a few features related to visualization and utility.

3. PAL for R Clients

PAL provides clients with a set of client-side machine-learning algorithms for R. You can use the API from any R IDE like RStudio. As the actual ML process occurs in the SAP HANA database, there is no need to transfer the training data to either the R server or the client side.

4. APL for Python Clients

The APL package is available for clients as part of the SAP HANA Python Client API and it is useful for machine learning algorithms. It also provides users with functions to expose the data mining capabilities of SAP HANA’s automated analytics engine. The APL for Python clients package also includes algorithms for classification and regressor.

5. R Integration with SAP HANA Streaming Analytics

The data service enables the R script to be run as part of Continuous Computation Language (CCL) queries on the server. The query passes to the remote R server for streaming analysis. SAP developers can add an R data service in the streaming runtime tool and the cockpit of SAP HANA.

6. EML (External Machine Learning Library)

It supports Google TensorFlow integration. As an AFL (Application Function Library), an external machine learning library uses the gRPC remote procedure call package of Google to invoke predefined TensorFlow models remotely. EML also supports the use of external management functionality.

After going through the ML features of SAP HANA, we will dig deep into the machine learning technology’s capability for this powerful ERP solution.

Key Aspects of Machine Learning for SAP

Embedded ML

It denotes an integration of pre-trained algorithms directly into SAP applications like S/4HANA. It is useful for performing tasks like predicting demand, optimizing delivery routes, or detecting fraud. It requires minimal configuration and leverages existing data within SAP Business One solutions.

Side-by-Side ML

It enables companies to build custom ML models using external tools and data sources alongside SAP data. It offers greater flexibility for complex scenarios like sentiment analysis or image recognition, however, it requires more technical expertise and data preparation.

Focus on Outcomes

SAP's ML solutions target specific pain points across departments. For example, in finance, these solutions can predict cash flow and in the supply chain, ML can forecast inventory requirements. It ensures business users can readily apply ML without being data science experts.

Evolving ML Landscape

SAP continuously expands its ML capabilities, integrating new algorithms, pre-built models, and establishing partnerships with external AI providers. This ensures users have access to cutting-edge technology as ML technology evolves.

Overall, SAP and machine learning empower businesses to make data-driven decisions, automate tasks, and gain deeper insights. However, it is essential to address some challenges to leverage the benefits of the SAP-ML combination.

Challenges and Solutions

While SAP ML has immense potential, it is essential to address several challenges to ensure successful implementation. Here are some of the common challenges with their solutions.

People-based

The most common challenge in this category is the lack of skilled resources and ML expertise. Though ML is gaining popularity rapidly, many people are still unaware of its potential. Other challenges include internal resistance from employees and difficulty in aligning the SAP ML combination with business goals.

Companies can invest in training programs to upskill employees and establish open communication to explain the benefits of ML in the workplace. Moreover, it is necessary to define business goals that can get direct benefits from ML technology.

Data-based

Some major data-related challenges include the lack of high-quality data and integration issues. Other concerns are related to the security of data and privacy protection. Data silos can be another major issue.

You can address data-based challenges by implementing proper data governance practices and ensuring data accuracy consistently. Data cleansing tools and security features like encryption can help you safeguard corporate data. Advanced integration tools and platforms can break data silos effectively.

Technology-based

Selection of the right algorithm, model development, deployment, and continuous monitoring are some of the core technical challenges. A reputed SAP Partner can make a difference by addressing these aspects efficiently. Enterprises can opt for pre-built models of SAP and utilize visual deployment tools or cloud-based platforms. Furthermore, a recognized SAP solutions provider can handle model deployment and monitoring effectively.

SAP Machine Learning Use Cases

Machine learning can improve data-driven innovation in robust SAP ERP systems. This can help businesses make better decisions, improve efficiency, and reduce costs. Here are some SAP machine learning use cases across core industry sectors-

Manufacturing

The combination of machine learning with SAP can optimize production processes, predict equipment failures, and improve quality control.

Retail

From predicting market trends to personalizing campaigns, machine learning can offer many benefits to retailers with SAP solutions.

Healthcare

Machine learning can assist in diagnosing diseases, predicting patient outcomes, and developing new treatments. SAP helps maintain medical inventory.

Finance

SAP and machine learning can detect fraud, assess credit risk, and personalize financial products for customers with high accuracy.

Let's dig deep into the benefits of integrating ML into SAP solutions.

Benefits of Integrating Machine Learning with SAP

The SAP machine learning combination drives modern businesses towards informed decisions and streamlined operations.

Improved Decision-Making

ML analyzes vast amounts of data from various SAP modules to provide businesses with predictive insights. Companies can forecast demand with higher accuracy, optimize pricing strategies based on real-time customer behavior, or predict potential change. These actionable insights translate into data-driven decisions.

Automation and Efficiency

Repetitive tasks can frustrate employees and hinder productivity. ML can assist companies in getting rid of such tasks by automatically processing invoices, reconciling accounts, and generating reports. Machine learning in SAP also frees up your workforce for strategic tasks. Additionally, it optimizes processes by identifying bottlenecks and recommending improvements.

Simply put, integrating ML with SAP can be a game-changer for businesses as it gives deeper insights for better decisions and automates workflows for effortless efficiency.

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Future Trends in Machine Learning with SAP

The future of machine learning with SAP can bring the expectations of intelligent, interconnected operations. We can expect deeper integration with ML algorithms embedded directly within SAP applications. Be it automating tasks and predicting outcomes in real-time, ML integrations in SAP will make it possible.

Moreover, machine learning in SAP will bring a shift towards hyper-personalization for individual users and customers. ML optimizing processes can minimize environmental impact. Together, SAP and machine learning will empower businesses to operate smarter, faster, and more responsibly.

Conclusion

SAP Machine Learning solutions can be a game-changer for modern businesses as they can give the best of both worlds. Better decision-making, effective automation, and higher efficiency are some of the key benefits of combining SAP with ML technology. However, you need to consult a robust SAP partner to address various challenges and leverage the advantage of ML to drive growth for your business.

Silver Touch is an official SAP partner with an excellent track record of serving a global corporate clientele. Do you want to get a competitive edge with streamlined workflows and seamless customer services using SAP solutions? Simply send us an email at sap@silvertouch.com and we will get back to you in a short time.

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