Intelligent Asset Management has become a key focus for businesses looking to improve operational efficiency and reduce costs. The integration of artificial intelligence (AI) into asset management solutions is transforming the way businesses manage and maintain their assets. By leveraging advanced AI capabilities, SAP provides businesses with powerful insights and predictive analytics that enable proactive maintenance, improve asset performance, and optimize resource allocation. This integration of AI into asset management solutions represents a significant shift in the way businesses approach the management of their physical assets, offering a more intelligent, data-driven approach that can lead to significant operational improvements.
SAP has made significant strides in leveraging AI in Intelligent Asset Management across various focus areas. One of those is SAP Field Service Management, where AI is being utilized to optimize field service operations, improve technician productivity, and enhance customer satisfaction. In SAP Asset Performance Management, AI is being used to predict equipment failures, optimize maintenance schedules, and improve asset performance. SAP S/4HANA Cloud Asset Management leverages AI to ease maintenance processing by incorporating the Joule assistant. Lastly, SAP Mobile Execution and Dynamic Forms uses AI to facilitate voice recognition, text-to-speech functionality, and AI-driven maintenance execution, as well as to assist with browsing knowledge bases and generating equipment and activity summary reports.
To get a better understanding of the AI use cases in SAP Intelligent Asset Management, we are going to take a closer look at the already existing scenarios as well as the road map ahead. Please keep in mind that this is a rapidly evolving topic, and this training has been generated in Q2 & Q3 of 2024.
AI in SAP Field Service Management
In SAP Field Service Management, there are two available AI use cases: predictive routing and AI-based schedule optimization. First, let us dive into predictive routing, which helps optimize resources and routes for more efficient field service.
Now for AI-based schedule optimization. AI-based schedule optimization uses artificial intelligence to create and optimize schedules for field service management. It involves creating policies and objectives that prioritize certain jobs and resources, and then using AI algorithms to match technicians to specific jobs, rearrange schedules, and fill in gaps to make the schedules as optimal as possible. This can be done manually with the assistance of AI, or fully automated based on predefined rules and objectives, as well as internal or external events. The goal is to efficiently and effectively allocate resources and maximize productivity in field service operations.
Via the following link, you can access a video that demonstrates AI-based scheduling (created by SAP):
https://www.youtube.com/watch?v=rAH5KTP958o&list=PLWV533hWWvDnsYUyGMAsStp8J2Kn-wd-5&index=9AI in Asset Performance Management
One example of how AI is already used in SAP Asset Performance Management is the detection of anomalies and based on that the generation of a maintenance backlog. This leads to an increase in asset uptime and a reduction in maintenance costs. The solution addresses the challenge of unnoticed equipment anomalies and failures, which can result in potential downtime and higher maintenance expenses. By implementing an anomaly detection algorithm that leverages IoT (Internet of Things) sensor data, reliability engineers are alerted about potential machine failures and can take appropriate follow-up actions. The outcomes of this use case include a 35% increase in productivity for maintenance, travel, and diagnosis activities, an 11% reduction in production losses due to less equipment downtime, and a 10% reduction in carbon emissions, highlighting the significant impact of AI in asset performance management.
Road Map of Upcoming AI Capabilities in Intelligent Asset Management
In the future, there will be many more AI capabilities added into the following components of SAP Intelligent Asset Management: SAP Asset Performance Management, SAP S/4HANA Cloud, SAP Field Service Management, SAP Service and Asset Manager, and SAP Analytics Cloud. To gain an overview of these capabilities, see the following road map:
Since this training aims to give a high-level overview of Intelligent Asset Management, not all upcoming AI use cases will be covered in detail. The focus here is on two different use cases in SAP Asset Performance Management and SAP Service and Asset Manager.
One planned use case for AI in SAP Asset Performance Management is the utilization of AI-enabled visual inspection for condition monitoring, improving equipment health through computer vision and machine learning. Traditional planned maintenance methods often fail to predict equipment issues, leading to costly downtime and higher maintenance expenses. However, by using fixed installed cameras to capture images, computer vision can automate image processing, estimate remaining useful life, and detect anomalies. This allows for a shift from time-based to condition-based maintenance strategies, safeguarding critical equipment operations with less out-of-service time and overall maintenance program gains in efficiency and effectiveness.
In the context of SAP Service and Asset Manager, a relevant AI use case involves the implementation of voice recognition and voice to text functionality to improve technician productivity. The challenge faced by technicians is the time-consuming process of manual data entry when executing jobs and confirming operations. The solution addresses this challenge by allowing technicians to use their voice to give commands to the solution, thereby reducing the need for manual data entry when confirming operations and work orders. The outcomes of this AI implementation include increased technician productivity and wrench time, demonstrating the direct impact of faster execution and reduced errors through the elimination of manual data entry.