Enterprise Asset Management (EAM) software provider IFS Ultimo has integrated AI functionality into its next-generation EAM platform to enhance troubleshooting, and enable organizations to reduce the mean time to repair (MTTR) of their assets. By leveraging AI, EAM toolsets will be more intuitive, accessible and predictive, therefore driving unprecedented efficiency and effectiveness in asset management practices. The new AI features were unveiled on today at IFS Unleashed, in Orlando, Florida, USA.
IFS Ultimo, an IFS company, energizes the financial resilience, regulatory compliance and operational excellence for healthcare, manufacturing, and logistics businesses through its innovative software-as-a-service (SaaS) enterprise asset management (EAM) solutions. Focused on maintenance, uptime, safety, cost control, and efficiency, the Company is known for rapid deployment, ease of use and an unparalleled time to value. Ultimo supports over 100,000 technicians who manage more than 15 million assets for 2400+ customers worldwide.
“AI will take EAM to the next level. Our vision for AI is to focus on real world use cases. When considering what AI to integrate into our platform, we are only embedding features which will add significant value for our customers and improve their user experience”, says Chris van den Belt, Head of Product Management, IFS Ultimo. “Infusing our EAM platform with AI functionalities will radically improve employee productivity and maximize asset availability.”
Explore related questions
Although the true impact of AI in EAM is yet to be seen, the present time offers an opportunity to explore and experiment with potential use cases. Predictive AI and generative AI are the two types of AI most relevant to asset management. While predictive AI uses historical data to predict future events or behaviors, generative AI creates new content such as text, audio, video, code, or images.
A predictive AI example use case could involve a visual asset inspection, where a mobile camera captures asset images that are automatically analyzed for defects or anomalies and triggers an alert or workflow in case of a defect. On the other hand, generative AI use cases could include a conversational front-end to maintenance documentation like manuals, operating procedures, and installation guides; or a maintenance virtual assistant or chatbot that guides maintenance personnel through work tasks. One could also explore developing pre-defined templates to report asset issues based on the equipment type.
Many AI technologies are geared towards predicting and preventing failures and incidents. However, for the majority of organizations, these technologies are more of a long-term goal than a short-term reality. Reactive maintenance will continue to feature prominently in most organization’s maintenance strategies. With this in mind, IFS Ultimo has made the conscious decision to harness the powers of AI to significantly reduce time spent on reactive maintenance. Realizing these short-term benefits starting today puts long-term objectives within arm’s reach.
It is estimated that 80 percent of time in MTTR is spent on diagnosing a problem. The biggest chunk of time wasted is due to a lack of communication and detail in failure reports. With Ultimo’s built-in AI capabilities, organizations can realize tremendous value with each percentage point reduction in MTTR. This is not chump change: the average cost of downtime in manufacturing often exceeds $100K per hour. Beyond the clear financial stakes, the productivity impact is also profound, especially in an industry where skilled labor is already hard to come by. Furthermore, the immense increase in overall data quality unlocks a wide array of new and exciting possibilities for achieving operational excellence.
The newly integrated AI functionality provides better quality of failure reporting. Having to spend less time on diagnosing a problem means skilled employees will benefit from increased wrench time, increased asset availability, reduced admin time, improved collaboration and improved employee satisfaction.
Front line workers spend the majority of their working day close to the assets they know so well. Any changes to the way these assets look, sound, smell or feel will not pass them by. Using a large language model (LLM), Ultimo detects the asset in question and provides a series of tailored suggestions that the reporter can easily add to the failure report without having to type. In doing so, all of the sensory observations are captured on the report accurately, providing maintenance teams with complete and accurate information to quickly solve the issue and increase asset availability and reliability.
This same approach will be used elsewhere in Ultimo to empower the faster resolution of diagnosed issues and enhance the accuracy of completed work activities registered in the system. The overall benefits include a substantial reduction of time spent on administration, severe improvements to data quality and a boost to employee satisfaction. Furthermore, Ultimo is working on integrated AI features that will greatly improve user experience, such as photo-based meter readings, auto-generated image and document captions and auto-translated multi-lingual data.
Chris concludes: “We are dedicated to developing our products to help users do their jobs more easily with our best-of-breed EAM software. AI has the capability to enhance EAM in future-ready and efficient ways – empowering employees, improving asset performance, and reducing costs. We are very excited to bring these new AI functionalities to our customers while making sure all relevant data protection is in place.”
What does this mean for ERP Insiders
AI can realize promise of predictive maintenance. AI-powered systems can analyze sensor data from equipment (e.g., temperature, vibration, and pressure) in real time to detect early signs of potential failure. By identifying patterns and anomalies, AI can predict when maintenance is needed, preventing costly unplanned downtime. Instead of following fixed maintenance schedules (preventive maintenance), AI can dynamically adjust maintenance intervals based on the actual condition of the assets (condition-based maintenance). This minimizes unnecessary maintenance while reducing the risk of equipment failure. Also, Machine learning models can predict the specific mode of failure for assets, helping maintenance teams to prepare the right tools and parts, thus reducing repair time and improving asset uptime.
Asset inspections stand to gain from AI. AI-powered computer vision can automate asset inspections by analyzing images and video from drones, cameras, or robots. This is especially useful for hard-to-reach or hazardous areas (e.g., wind turbines, pipelines). The AI system can detect cracks, corrosion, or other defects, reducing the need for manual inspections. Further, AI models can learn normal operating conditions for assets and detect deviations in real time. For example, AI can flag abnormal vibrations or temperature increases in machinery, which could indicate a potential issue. Also, AI can analyze data from IoT devices and sensors installed on assets to remotely monitor their health. This enables real-time decision-making, reducing the need for physical inspections and improving responsiveness to asset issues.
Asset lifecycle optimization gets boost from AI. AI can track asset performance over time and provide insights into asset efficiency and utilization. By analyzing historical data, AI helps organizations understand the ideal replacement or refurbishment time, extending the useful life of assets while minimizing operational costs. What is more, AI can aid in making data-driven decisions about capital investment by analyzing trends in asset performance and maintenance costs. It can recommend when to replace aging equipment and suggest cost-effective alternatives. Finally, AI can help determine the optimal time to retire or replace assets based on their historical performance, cost of maintenance, and current market conditions, ensuring maximum ROI from each asset.
