I’ve just returned from one of the biggest industrial artificial intelligence (AI) events of the year where Ultimo has premiered a solution to address one of the biggest challenges for our enterprise asset management (EAM) customers—mean time to repair (MTTR). This is a big deal. This solution puts the power of AI in the hands of the people that most need it. The scale of its potential positive impact on uptime, wrench time, people productivity, availability of assets and accuracy of data cannot be over-stated.
While AI is a super-hyped technology, as we blogged about in the summer, our philosophy around AI, and indeed all technical innovation, is that it must be customer-led. Everything we develop and do, serves as a force multiplier to save human time, and create organizational efficiencies.
Industry analysts agree. Gartner’s 2024 Hype Cycle for AI for example, reports that generative AI (GenAI) has passed the Peak of Inflated Expectations, and that this year, value would be derived from projects based on other AI techniques. The firm goes on to suggest that vertical use cases are likely to see the greatest traction through 2025 and help to set AI on the Slope of Enlightenment.
So, as we help our customers to cut through the hype and find brackets of opportunity to make money, save money, and/or reduce risk, we’re bringing AI into real-world production in the EAM market. These powerful AI features are available where it will matter most.
How it Works
This AI functionality helps industrial businesses in four important ways: –
Improving the productivity of the maintenance team
Downtime in manufacturing is a costly affair. An hour of downtime often exceeds $100,000 in costs. Resolving issues as fast as possible therefore comes with a real financial incentive. In the world of manufacturing, it is estimated that a whopping 80 percent of MTTR is spent simply diagnosing issues. The major cause of this inefficiency? Poor communication and a lack of detail in failure reports. Our AI will make a world of difference. Organizations can unlock significant value for every percent reduction in MTTR.
Increasing the availability of assets
Uptime and availability are closely related. An increase in uptime automatically means that relevant assets can perform their function for longer periods of time, i.e. being more available. I think the best approach angle would be from a value standpoint. An asset that is maintained properly, will have an increased uptime, will be more available and thus contribute more value to the organization. This AI feature directly impacts the MTTR – so getting to a resolution on critical issues faster means you also realize higher asset availability, thus increasing value the asset can add to your business.
Improving the quality of data and reporting
The jump in data quality here is vast. This is where AI helps organizations to accelerate their journey through the EAM maturity model – unlocking more advanced and beneficial strategies and capabilities. After all, database systems are only as good as the data held within them. Any sort of move towards business intelligence (BI) analytics, or even predictive and prescriptive maintenance needs a rock-solid base of data and data quality to be viable. So, to unlock more mature management strategies that add more value to the organization, data quality is the Number One driving enabler. This has an immediate effect on reporting, but also dashboarding, business intelligence (BI) integrations, tracking of key performance indicators (KPIs) and shifting from proactive to predictive maintenance. Our AI functionality progresses customers’ asset data from good to great.
Addressing the labor and skills shortage
By improving the quality of failure reporting, issues are resolved faster. This means that skilled employees can increase their valuable wrench time. Less administrative hassle further means that employees can focus on more value-added tasks instead.
For many of our customers, this might reduce eight steps of asset maintenance to just four. Gone are the days of a process that involves reading a failure report, contacting the operator, walking to the machine, observing the situation, getting to the warehouse, picking materials, going back to the machine and fixing the issue. Instead, the new paradigm goes from read the failure report, to pick materials, go to the machine and fix the issue.
This reduction in administrative time not only helps to focus skilled workers where they are most needed, but also improves employee satisfaction and collaboration between teams.
Incorporating AI into our EAM system will make it more intuitive, accessible, and predictive. This shift will unlock new levels of efficiency and effectiveness, enabling organizations to optimize their asset management strategies like never before.
Want to see our two-minute demonstration? Or learn more about the new AI functionality in Ultimo’s EAM solution? Book a conversation here.
Other blogs written by Chris van den Belt
