Here is the last of the prediction articles I picked up in December but had too much news to pack in. Usually November and December are void of news. These thoughts are from Aaron Merkin, CTO, Fluke Reliability & Ankush Malhotra, President, Fluke Reliability.
The promise of AI to bridge the skilled labor gap
Traditional AI – This is artificial intelligence in its traditional form, learned data that powers predictive insights. We continue to see a shortage of skilled labor to support the industry overall as well as within reliability programs specifically. Active condition monitoring of assets is a prerequisite for a successful predictive maintenance program. Yet organizations wishing to begin, expand, or sustain condition monitoring programs frequently do not have access to the skilled labor necessary for them to execute these programs internally. We expect to see more organizations adopt applications that augment skilled users and enable them to make faster, more effective decisions. AI will supplant human expertise for analytics use cases, reducing barriers to entry for condition monitoring providers, lowering both skill level and the number of resources required to implement predictive maintenance programs. Solutions that involve AI-powered analytics with a significant amount of learned data already stored lower the barriers to widespread adoption of PdM and can increase the amount of assets measured in a condition monitoring strategy. With the availability of AI-powered analytics and remote condition monitoring services that provide expert analysis on a company’s behalf, even expertise-constrained operations can adopt a data-based maintenance strategy.
Adoption of Generative AI – The second category where we anticipate significant growth is the adoption of Generative AI co-pilots by operators, technicians and other plant floor or field personnel. Generative AI can act as a much-needed storehouse for institutional knowledge. As more and more skilled workers reach retirement age and leave the workforce, Generative AI takes on greater importance as a training and educational tool, passing knowledge along to new workers.
AI tools are at their best when they share workflows with human experts. In the year ahead, we expect to see generative AI:
- performing guided machine maintenance for new workers (and in the process, helping new workers to upskill).
- providing support to plant managers, especially those tasked with managing multiple worksites.
- working alongside human experts to provide support on the plant floor.
- facilitating the shift from experienced, highly skilled labor.
- replacing low-skilled, entry-level white-collar labor across the enterprise.
- We also anticipate greater outsourcing of narrow expert skillsets to augment staff generalists. The adoption of generative AI will assist in bridging communication gaps, sharing data and insights, and bringing far-flung teams together.
Increase in Predictive Maintenance for Sustainability Purposes
Whilst in many industry segments sustainability has been a debated topic for a while, in some ways the impact of a well-run maintenance strategy are either overlooked or left untracked. We expect to see an increased focus on using predictive maintenance to drive sustainability results for businesses, beyond just equipment reliability in the coming years. This includes using predictive maintenance tools for the availability of renewable energy assets, extending asset life to reduce the carbon footprint of industrial equipment, reducing pollution by maintaining efficient running machines, improving energy efficiency by managing engines correctly, and maintaining product quality in a production environment to prevent wastage.
Increased availability of IIoT
In recent years, economic pressures have increased the overall emphasis on efficiency and the threat of a downturn isn’t going away. This uncertainty also places emphasis on maintaining, rather than replacing machinery. We expect this trend to reach a new height in 2024, leading to greater adoption of IIoT tools and an increased demand for AI analytics.
In particular, we anticipate greater use of automation across sectors, and a far greater push to keep assets up and running for longer. This in turn will drive more organizations to shift to a condition monitoring / connected reliability approach. Advances in technology have dramatically lowered the cost of continuous measurement and monitoring tools, like wireless sensors. This is already resulting in increased coverage for the balance of plant assets and greater demand for analytic tools. The more assets measured, the more efficient a plant runs which impacts cost, inventory and OEE targets – it’s a win/win.
