Machine learning (ML) is a branch of artificial intelligence that enables systems to learn from data and improve their performance without explicit programming. ML can help businesses to automate tasks, optimize processes, enhance customer experience, and gain insights from data. SAP Fiori is a user interface (UI) framework that provides a consistent, harmonized, and intelligent user experience across all SAP products. SAP Fiori apps can leverage ML capabilities to deliver more value and efficiency to users and businesses.
In this blog post, we will explore how ML capabilities are integrated into SAP Fiori apps, what benefits they bring, and what scenarios they can be applied to. We will also look at some examples and statistics of ML in SAP Fiori apps.
How ML Capabilities are Integrated into SAP Fiori Apps
SAP Fiori apps can use ML capabilities in two ways: embedded ML and side-by-side ML.
- Embedded ML is used for simple ML scenarios that require low CPU and RAM resources and no external data sources. Embedded ML is based on the SAP Analytics Cloud (SAC) and uses classic algorithms such as regression, clustering, classification, and time series. Embedded ML can be accessed through the Intelligent Scenario Lifecycle Management (ISLM) tool, which provides a common interface for creating, training, and publishing ML models within SAP Fiori apps. The models get better with more data accumulated over time.
- Side-by-side ML is used for complex ML scenarios that require high CPU and RAM resources and external data sources. Side-by-side ML is based on the SAP Business Technology Platform (BTP) and uses advanced algorithms such as deep learning, image processing, and natural language processing. Side-by-side ML can be accessed through the SAP Cloud Platform Machine Learning Foundation (SCP MLF) service, which provides a platform for developing, deploying, and managing ML models on the cloud. The models can be integrated with SAP Fiori apps via APIs.
What Benefits ML Capabilities Bring to SAP Fiori Apps
ML capabilities can bring various benefits to SAP Fiori apps, such as:
- Automation: ML can help automate repetitive or tedious tasks that would otherwise require human intervention or manual input. For example, ML can help automate sales order creation by extracting relevant information from scanned documents or images.
- Optimization: ML can help optimize business processes by providing predictions, recommendations, or guidance based on data analysis. For example, ML can help optimize inventory management by predicting individual lead time for stock transfer or material overdue.
- Personalization: ML can help personalize user experience by adapting to user preferences, behavior, or context. For example, ML can help personalize home pages by displaying relevant tiles or notifications based on user roles or activities.
- Intelligence: ML can help provide intelligence to users by generating insights, analytics, or feedback from data. For example, ML can help provide intelligence to managers by detecting anomalies, risks, or opportunities in business performance.
What Scenarios ML Capabilities Can Be Applied To
ML capabilities can be applied to various scenarios across different lines of business and industries. Here are some examples of scenarios that are delivered or possible with SAP Fiori apps:
- Asset and Service Management: Predict damage code or object part for maintenance requests based on historical data.
- Public Services: Predict late payment, receivership, or filing behavior of taxpayers based on behavioral insights.
- Sales: Automate sales order creation by extracting information from scanned documents or images.
- Finance: Reconcile intercompany transactions by matching invoices and payments. Predict bank reconciliation status by comparing bank statements and accounting entries.
- Manufacturing: Predict individual lead time for stock transfer based on demand-driven replenishment.
- Supply Chain: Detect material overdue in stock in transit based on shipment data. Identify blocked invoices with quantity variance based on purchase order data..
- Human Resources: Classify employee sentiment based on text analysis of feedback survey.. Recognize employee faces based on image processing of photos.
- Retail: Identify fruits based on image processing of photos3. Recommend products based on customer preferences or ratings.
Statistics of ML in SAP Fiori Apps
According to SAP (2020), there are more than 24 intelligent scenarios delivered via ISLM for embedded ML in SAP S/4HANA 2020 release. These scenarios cover various lines of business such as asset management, finance, manufacturing, sales, public services, and supply chain. According to Arai (2021), there are more than 40 ML algorithms available in S/4HANA embedded ML, including Automated Predictive Library (APL) and Predictive Analytics Library (PAL) These algorithms can be used for various ML tasks such as regression, classification, clustering, time-series, and anomaly detection. According to SAP (2019), there are more than 30 ML services available on SCP MLF for side-by-side ML, including image processing, natural language processing, speech processing, and data intelligence. These services can be used for various ML tasks such as object detection, text analysis, speech recognition, and data quality.
Conclusion
ML capabilities are a powerful and valuable addition to SAP Fiori apps. They can help enhance user experience, improve business efficiency, and provide insights from data. SAP Fiori apps can use embedded ML or side-by-side ML to integrate ML capabilities into their UI. There are many scenarios that can benefit from ML capabilities across different lines of business and industries. There are also many ML algorithms and services that can be used for different ML tasks. ML capabilities in SAP Fiori apps are a key component of the intelligent enterprise vision that SAP is pursuing.
