Hi,
As focus and maturity of AI based solution is increasing, I here with share the AI Scenarios in each line of business in SAP S/4HANA.
The Realtime-Insights as available in each line-of-business (LOB) is underlined
The AI Scenarios in each line-of-business is highlighted in yellow.
The blogs-series will cover a few AI Scenarios (listed below)
- Inventory Analytics and Control (2.12.1.2)
- Predictive Slow Moving Material (Slow Mover / Non-Mover)
- Predictive Stock In Transit
- GR/IR Monitor
- RPA Scenarios
Do let me know if any other scenarios that you would be interested in.z
With release of SAP S/4HANA 2023, a quick check on the S/4HANA 2023 scope, innovation and Technical Highlights, with focus on Machine Learning process improvements :
Finance – Make better decisions with key details and feedback on the automatic matching of payments through machine learning.
With the Analyze Incoming Payments (F5588) app, we can monitor and analyze incoming payments to optimize clarification processes and team workload. we can use this app to regularly do the following:
- Analyze how many payments have been clarified manually within a given period.
- Analyze the effectiveness of the automated clarification process provided by the standard.
- If you have licensed and are using SAP Cash Application, add-on for contract accounting, validate the effectiveness of the applied machine learning to the clarification process.
- Use The New SAP Fiori app [Analyze Incoming Payments] to analyze incoming payments and provide an overview of the automatic process for payment matching:
- Know what happened with payments
- Identify what helped during the payment-clarification process
- See how well machine learning supported the automatic process for matching incoming payments
Collections Management
Machine-learning scenario for intelligent collections :
Intelligent Collections through Machine Learning
This innovative predictive scenario, designed for data scientists and machine learning experts, empowers users to anticipate the likelihood of delayed payments for invoices. Upon activation, the “Risk of Late Payment” field becomes a valuable tool for collection specialists in their day-to-day operations. The prediction of late payment risk is derived from historical data, utilizing the Intelligent Scenario Lifecycle Management.
Identify invoices in the accounts receivable workflow that pose a high risk of late payment. Utilize the identified high-risk invoices to:
- Generate prioritized worklists.
- Highlight invoices for prioritized attention in the accounts receivable process.
Benefits:
Prioritize attention to customers with invoices at a high risk of late payment. Motivate collection specialists to focus on documents with an elevated risk of late payment.
Information Credit & SAP help:
Boost purchasers’ efficiency through artificial intelligence and by guiding them to proactive recommendations toward more consistent requisition data.
Product Group Recommendations for Centrally Managed Purchase Requisitions
With this app, you can now assign or change the material group based on recommendations, using a machine learning service.
Useful Information about SAP S/4HANA 2023
The key changes are as follows:
- As of S/4HANA 2023 we switch to a two-year release cycle; that is, S/4HANA 2023 will be followed by S/4HANA 2025.
- An additional Feature Pack delivery is added (FPS3). The 3 Feature Packs for S/4HANA 2023 and subsequent releases will roughly have a half-yearly delivery schedule (the detailed release schedule planning for S/4HANA 2023 FPSs is in process).
- The general boundary conditions to ship with an FPS will be very similar to today’s FPS1 delivery (more details to come).
- All 3 Feature Packs will offer the possibility to deliver new or enhanced Fiori Apps. FPS1 remains the UI5 stable stack FPS.
- The mainstream maintenance for each release is extended from 5 to 7 years.
MP's S/4HANA 