IFS, the leading provider of enterprise cloud and Industrial AI, has officially announced the launch of its new IFS.ai-powered features that are designed to deliver maximum value at the disposal of asset and service-intensive industries, while simultaneously elevating the user experience to drive Industrial AI adoption at scale.
According to certain reports, the stated development marks the introduction of a revamped home page, which now offers a new, dynamic, and AI-powered experience for IFS Cloud, providing live project status visibility, as well as fuelling productivity and efficiency. Furthermore, the homepage comes decked up with an ability to automatically detect any anomalies and suggest relevant corrective actions. Such a facility, like you can guess, treads up a long distance in saving time and increasing accuracy of project analysis. Alongside that, the IFS Cloud’s home page also combines the new context aware IFS.ai Copilot uses cases with a growing ecosystem of interactive widgets that may help users plan, manage, build and service assets faster, smarter and safer.
We referred to the new context aware IFS.ai Copilot, it can now reach deeper than ever before, surfacing insights from all across the organization. You see, the Copilot’s new version is understood to have preconfigured industries capabilities that get even more powerful when integrated with customer data sources. For instance, in its current shape and form, the technology can seamlessly figure out where users are in IFS Cloud so to provide accurate insights related to it. A possible use case of the same would be how service leaders and dispatchers can now maximize field service delivery and future planning efficiency with AI-driven accelerated, accurate decision making, and obtain instant contextually relevant answers to questions.
“All new features and enhancements within IFS Cloud 24R2 are guided by IFS’s strategic themes. They are tailored to help customers unleash their full potential – both in terms of operational effectiveness and profitability – by leveraging IFS.ai to unlock and use the secret weapon from across supply chain and operations: their data. IFS is at the forefront and leading the charge with Industrial AI, which is turbocharging the asset and service-intensive industries that power our world. No one else is thinking this way for these hardcore industries,” said Christian Pedersen, Chief Product Officer at IFS.
Another innovation here arrives in regards to IFS.ai Copilot for FMECA (Failure Modes, Effects, Criticality Analysis), which now features the means to drive optimized asset availability, and the same time, cut down on maintenance costs. Furthermore, the technology can provide detailed analysis of how an asset might fail, the probability, and consequences of making or adjusting maintenance strategies.
Not just that, IFS.ai is now also well-equipped to ingest unstructured data and then flip it for something more relevant to a business process. For instance, it can take unstructured data from a new manufacturing customer PO and auto-create a new order, ensuring an accelerated production process. In case that wasn’t enough, then we ought to mention how the impact of this new order onto the shop floor can then be modeled and analyzed with the new Manufacturing Scheduling Optimization (MSO) Simulation capability.
All in all, such a technology makes it possible for production managers to improve capacity planning and meet customer demand, whereas on the other hand, it empowers asset managers to use its simulation capabilities for accurately predicting and planning essential asset maintenance based on different scenarios.
To expand upon its real-world benefits, IFS Cloud can be expected to, from here onwards, easily balance service demand with delivery across regions temporarily, or on a recurring basis, with support for roaming resources. It can be used to successfully deliver service while controlling costs with simplified crew, effective tool resource management, and specialist resources.
Beyond that, the technology can also be deployed to scale up forecasting accuracy for reducing safety stock and freeing up cash. Then, there is the prospect of enhancing asset operational efficiency and reducing downtime through identification of trends that may lead to failures.
