Purchasers can predict delivery dates for purchase order items based on historical data, using Intelligent Scenario Lifecycle Management. Based on multiple parameters, the system can predict a date by which the supplier can deliver the material.

The feature suggests products and services based on users' past purchases. It collects purchasing history from your organization and uses artificial intelligence to recommend catalog items that might be of interest based on the individual user. Item recommendations display in a carousel at the top of the home page and in the item details page.

Enable category managers to populate content recommendations from large language models (LLM) integration based on contextual and dynamic questions for tools such as category segmentation, category market dynamics and category cost structure.

Enable category managers to populate content recommendations from large language models (LLM) integration based on contextual and dynamic questions for tools such as category segmentation, category market dynamics and category cost structure.

Automatically enrich and complete the following fields in draft invoices - G/L account, cost center and WBS (Work Breakdown Structure) elements - with the help of Data Attribute Recommendation capabilities embedded in SAP Ariba Central Invoice Management, a machine learning service trained with a set of invoices that you select. Once the service is trained and activated, SAP Ariba Central Invoice Management can automatically complete missing fields in draft invoices using the data learned during the training.

SAP Ariba Sourcing leverages machine learning to recommend the best suppliers for guided sourcing events based on historical data from similar events. This includes details such as item names, event types, departments, commodities, regions, material numbers, and past supplier performance, including invitations and contracts. The recommendation engine continuously improves: If you retrain the model, it learns from past user selections. For example, if 10 suppliers are suggested but only six are chosen, the model refines its future recommendations accordingly.

Enable category managers to populate content recommendations from large language models (LLM) integration based on contextual and dynamic questions for tools such as category segmentation, category market dynamics and category cost structure.​

Repair shops often deal with large volumes of paperwork. Manually entering this information into the SAP system is both time-consuming and prone to errors, especially when working under tight deadlines. These errors can lead to data loss and processing delays. AI-assisted in-house service initiation helps streamline this workflow. Repair staff can scan or photograph incoming paper documents, such as purchase orders. The system then automatically extracts the relevant data and generates a list of repair objects associated with the corresponding in-house service. Once the order is generated, staff can review the information and continue processing it through to completion. Minimum required version of SAP S/4HANA Cloud Private Edition is SAP S/4HANA 2023 FPS03​

Automate the Labor Demand Planning (LDP) now enables the prediction of planned durations for picking and packing processes, enhancing the efficiency of resource and workload planning. By utilizing historical workload data, LDP provides accurate predictions for task durations, eliminating the need for extensive preprocessing or reliance on Engineered Labor Standards (ELS). This feature allows warehouses to optimize labor allocation and reduce inefficiencies by predicting the time required for tasks such as picking, packing, and outbound deliveries. Minimum required version of SAP S/4HANA Cloud Private Edition is SAP S/4HANA 2023 FPS03.​

Equipment insights allows you to generate a summary based on previous performed activities for a piece of equipment. The purpose of the summary is to provide the technician with information about the piece of equipment, for example, used spare parts, the duration of the activity, performed checklists and their results. It provides dispatchers with intelligent service recommendations at an asset level by identifying patterns and trends based on equipment history and performance data.​

AI-assisted easy enterprise search helps SAP S/HANA Cloud Public Edition users to find relevant data of business objects in SAP S/4HANA Cloud Public Edition using natural language. They can formulate queries​ in the search field of the SAP Fiori Launchpad and get immediate answers.​

This feature helps process modelers to speed up process modelling and by converting text into BPMN diagrams. It streamlines workflows, simplifies complex processes, and enables faster collaboration cycles. They can save and refine generated diagrams directly in the tool, enabling continuous improvement. Companies can save time, enhance communication, and reduce costs.​

Situation Handling in SAP S/4HANA Cloud automatically alerts the responsible users about critical business issues, such as upcoming deadlines, pending confirmations, and expiring contracts. When an issue is detected by Situation Handling, SAP S/4HANA Cloud Public Edition, AI-assisted situation handling provides business users with targeted advice. Situation data and its related data context are captured for each business issue and can be used to design prompts. If business users choose AI-generated proposals, they will receive context-specific, actionable solution recommendations tailored to their responsibilities.​

Provide natural language filters simplifying the search process for service requests and allowing users to express queries in a more intuitive manner, overcoming the complexity of traditional search methods.​

Enable product managers to leverage generative AI when creating campaign descriptions. Enable submitters to obtain support from generative AI when creating idea descriptions. Allow users to enrich or simplify the suggested texts.​

Enable suppliers to generate the product summary and description of a network catalog product using AI. The existing product information such as product name, product description, product summary, manufacturer name, and product category as per UNSPSC category code will be used to generate the product summary and product description. In addition to the existing product information, suppliers can also add more details as prompts to generate AI-enhanced product summary and description.​

By analyzing and clustering existing sales patterns, the system automatically identifies similar sales curves, clusters them, and applies the most relevant pattern to generate accurate forecasts.​

Demand sensing is a valuable approach in supply chain management that helps organizations better understand and anticipate demand for their products and services. Demand sensing combines methodology and technology that predicts near-future demand based on short-term data.​

The conversational search feature with Joule provides faster, smoother, contextual access to SAP Help Portal content summarized in a clear answer to the question.​

By observing ERP execution history, the feature extracts historical weekly lead time data, useful for comparing observed execution values with those values used for planning. In this fashion, planning system parameters can remain synchronized with the real world, requiring less manual effort while providing better planning system results. The first implementation focuses on description of supply lead times and variances, by observing timing of Purchase Orders, Stock Transfers and Production Orders​. Prediction of future lead time, including use of external factors can be done using forecasting methods which require IBP for demand license.​

Custom alerts are used to find important or critical supply chain issues such as inventory shortages, an imbalance of supply and demand, or any unexpected changes in the supply chain. You can specify the threshold values that will be used to determine issues. For example, you can specify the threshold values for minimum stock levels in a particular location. Based on these threshold values, the system analyzes the data on the fly and finds where the threshold values are reached. This enables you and your colleagues to react quickly, before a supply chain situation becomes a problem. Custom alerts, with machine learning analyzes the supply chain data to determine where exception thresholds should be. By reacting to the changes in data, exception conditions that have changed or were previously unknown are identified.​

Discover hidden patterns in large sets of master data, review exceptions, and use the tailored recommendations to improve data quality.​

Using machine learning for batch jobs can help you detect anomalies in job duration and allow you to review your job schedules. The Outlier Jobs Detection job template uses a machine learning algorithm, namely the density-based clustering algorithm (DBSCAN), to determine the outlier jobs.​

Discover hidden patterns in large sets of master data, review exceptions, and use the tailored recommendations to improve data quality.​