MACHINERY & MACHINE SAFETY
CAN INDUSTRY 5.0 BE ACHIEVED IN ONE YEAR?
By Aaron Merkin, chief technology officer, Fluke Reliability T
intelligence has largely focused on generative AI tools. But while ChatGPT grabs the headlines, manufacturers have been quietly funneling their resources into industrial AI applications. Already, the results show that these AI goals and ambitions. Today, the majority of manufacturers (61 per cent) expect to implement effective AI programs in less than a year. In the same time frame, they also expect to achieve their Industry 5.0 goals. These numbers, from a recent report conducted by Censuswide on behalf of Fluke Reliability, show that manufacturers are overwhelmingly bullish on AI and Industry 5.0. Censuswide surveyed 600 senior decision- makers and maintenance professionals across industries in the United Kingdom, United States, and Germany.
A clear consensus emerged from the research: organisations are investing heavily can deliver data-driven insights and cost- cutting automation.
93 per cent of those surveyed say AI will be a “high business priority” over the next 12 months.
Other top priorities include automation (42 per cent) and machine learning (40 per cent)
61 per cent of respondents expect to achieve their AI goals within 11 months. They expect to reach Industry 5.0 goals in 10 months.
On average, respondents are investing 44 per cent of their tech budgets on AI solutions.
30 per cent of those surveyed are going further, investing 51-75 per cent of their
We already knew that industry leaders were excited about AI, but this research indicates that Decision-makers now are putting their money where their mouth is by investing in it fully. As a result, we are on the verge of seeing full Industry 5.0 implementation.
Let us take a closer look at the data. We and challenges connected to the use of AI in manufacturing.
HOW ARE COMPANIES USING AI IN MANUFACTURING?
More than three-quarters (76.5 per cent) of the people surveyed said they are leveraging AI to build an effective predictive maintenance program.
Predictive maintenance allows teams to use condition monitoring data to detect emerging machine faults and make repairs long before defects lead to asset failure. This strategy extends asset lifespan, decreases downtime, and lowers operating costs.
It’s not a new approach, but until fairly recently, the barrier to access has been high. Only eight per cent of those surveyed are currently operating a predictive maintenance strategy, but 76.5 per cent want to shift to predictive maintenance in the near future. That’s where AI comes in. AI automates mundane tasks, like sifting through huge machine data sets and creating reports. This frees up employees to focus on more complex tasks. It also lowers costs and eliminates the risk of human error.
AI-powered diagnostic software can read and analyse huge quantities of condition monitoring data at high speed. It uses that data to diagnose machine faults quickly, with precise assessments. The best diagnostic engines, like Azima, have already been trained on billions of data points and have the expertise to pinpoint even the most subtle machine faults.
OTHER USE CASES FOR AI
Companies surveyed agreed that AI will increase maintenance programs. But the use cases for AI go beyond those overarching goals. Respondents also said they are leveraging AI to:
Optimise supply chains (a priority for 28 per cent of those surveyed)
Enable enhanced decision-making (a goal for 27 per cent of respondents)
Bridge the skills gap created by the ongoing skilled labor shortage (a focus for 31 per cent of respondents)
36 NOVEMBER 2024 | FACTORY&HANDLINGSOLUTIONS
IS THE AI TIME FRAME REALISTIC? The manufacturing leaders and maintenance managers surveyed overwhelmingly agreed on a time frame. The majority (61 per cent) expect to hit their AI and Industry 5.0 goals in under a year.
Given the urgency, it is worth questioning if reaching these goals in such a short time frame is too ambitious.
There are a few considerations here, starting with the obvious: every organisation will have different goals for its future AI programs. Likewise, organisations are at different points of readiness in terms of the data infrastructure they need to implement AI tools successfully. wide, all-encompassing time frame for the full implementation of AI.
What we can say is that AI tools are at a point where they can add value to an organisation immediately. Azima’s AI-powered diagnostic engine, for example, is ready to work right out of the box. It has been fully trained on decades worth of data, and it can deliver accurate, detailed assessments of machine health upon adoption. There is no lengthy implementation
Many manufacturers are already collecting huge quantities of data every day – enough data to drive powerful AI insights. It is only a short step from data collection to AI analysis. Given the resources that manufacturers are directing to AI, it is likely that we will see major industry transformations very quickly. So, with AI tools that are at the point of readiness, and worksites that are primed to deliver data, it is realistic to expect widespread AI penetration in under a year.
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58
