AI in SAP S/4HANA
Use AI to streamline the creation and planning of maintenance orders.
Leverage historical data from maintenance orders and maintenance notifications to provide planners with templates for resolving new maintenance requests
Suggest potential orders and score based recommendations by analyzing and learning from past data that align with best practices with a higher likelihood of success
When you create a maintenance order for a notification within the Reactive Maintenance process, you can leverage machine learning capabilities of SAP AI Services to find suitable orders from which you can copy information based on historical data in your system.
By analyzing the description as well as other important attributes of the selected notification and its reference object, the machine learning algorithm is able to identify and recommend orders that have been created for similar notifications in the past.
As a maintenance planner, you can review the recommended orders and select the most suitable one as a reference template for the new maintenance order.
AI in SAP Asset Performance Management
Detect anomalies and generate maintenance backlog
An anomaly detection algorithm works in the background to automatically identify potential machine failures based on IoT sensor data.
Reliability Engineers are alerted about potential machine failures, assess the detected anomalies and can trigger suitable follow up actions.
Simulate policy modifications safely without impacting live-schedules.
AI enabled visual inspection for condition monitoring
Use of fixed installed cameras to capture images from component.
Computer vision automates processing of images and estimates remaining useful life and detects anomalies.
- Use qualified ML models for condition-based maintenance to trigger needed maintenance activities.
Simulate policy modifications safely without impacting live-schedules.
AI in SAP Field Service Management
AI-based Scheduling and AI Policy Designer
Create company specific policies to meet company scheduling objectives and outcomes.
Optimize schedule creation, and free up dispatchers to focus on high value added tasks.
Increase resource utilization and allocation by reducing travel times and improving job-technician assignment.
Simulate policy modifications safely without impacting live-schedules.
Prediction of Job Duration in Best Match Technician
Implementing AI-based assignment duration prediction in auto-scheduling.
Supported by all semi and fully-automated scheduling use cases.
A machine learning model analyzes historical data to forecast future assignment durations based on technician’s logged time efforts, equipment, skills, and other relevant factors.
Predictive Traffic Routing from Service Map
Improve prediction of travel times during assignment of activities to technicians
Reduce travel times and find most optimal routes based on predictive traffic AI patterns.
AI in SAP Joule
SAP Joule Copilot revolutionizes how you interact with your SAP business systems, making every touchpoint count and every task simpler. It helps you to get quick answers and smart insights on-demand, facilitating faster decision-making without bottlenecks.
Joule is available in
- SAP S/4HANA Cloud
- SAP Asset Performance Management (APM)
- SAP Field Service Management (FSM)