Everyone is grappling with new technologies: futurologists, consultancy bureaus, asteroid miners, Oreo tasters… What will 2025 look like for them? How will it look for us? Read all about it in our regular round-up of manufacturing-related news. 

Fun fact: the name Augury comes from the Greco-Roman practice of observing bird behavior to receive omens that could offer insights into the future. According to Plato, this method was later superseded by haruspices, inspecting a sacrificed animal’s liver, since this was considered much more prestigious. 

So, in terms of hygiene alone, it’s easy to understand why crystal balls ended up cornering the soothsaying market.

The Long History of Futurology

“In A Century of Tomorrows, Glenn Adamson offers a hurtling history of the art, science, and big business of looking ahead,” according to ‘From Tarot Cards to Streamlined Design, We Can’t Stop Predicting the Future’.

“The act of probing into the future need not be predictive to be useful,” writes historian Adamson. “Instead, considering what the future might look like can focus attention on the good and bad of the present. Adamson opens with a fascinating, albeit brief, account of weather forecasting, which became more reliable with the advent of the telegraph: as he notes, ‘a lot of tomorrow’s weather is already here today; it’s just somewhere else, usually a little farther west.’”

In more recent weather news: ‘DeepMind AI Weather Forecaster Beats World-Class System’.

The Future Is Futuristic 

According to ‘Dreams Of Asteroid Mining, Orbital Manufacturing And Much More’, an army of “space cadets who see the world beyond Earth as something not just to be explored but conquered” is arising.

These cosmo-peneurs are confident they will soon be able to piggyback on asteroids to mine platinum and other precious metals, make optical fiber in Zero-G far superior to the conventional sort, or improve drug efficacy by crystallizing their ingredients in orbit. 

But it won’t be easy. “The obstacles would be formidable, even to the less ambitious goal of setting up a nursing home for retired billionaires. But formidable is not insurmountable.”

Go, space cadets, go!

Future Past

“A technology pioneered by Benjamin Franklin is being revived to build more efficient electric motors,” according to ‘Electric Motors Are About to Get a Major Upgrade Thanks to Benjamin Franklin’.

It’s said Franklin used his invention, an electrostatic motor, on a picnic to power a turkey rotisserie. However, enabling technologies only matured enough recently to make the motor efficient enough to go whole-hog. .

“It’s reminiscent of the early 1990s, when Sony began to produce and sell the first rechargeable lithium-ion batteries, a breakthrough that’s now ubiquitous. […] These motors could lead to more efficient air-conditioning systems, factories, logistics hubs, and data centers, and – since they can double as generators – better ways of generating renewable energy. They might even show up in tiny surveillance drones.”

Major players such as FedEx and Rockwell Automation are already testing the motors. 

Are Even the Fact-Based Soothsayers Threatened?

“The golden age for CEO whisperers may be coming to an end,” according to ‘Have McKinsey And Its Consulting Rivals Got Too Big?’.

“Not long ago the consulting industry looked indestructible. Fees rocketed during the Covid-19 pandemic as clients sped up efforts to digitize their businesses, diversify their supply chains, and respond to growing calls to bolster their environmental, social and governance (ESG) credentials.” 

But now there’s a slump in demand and widespread layoffs due to a potent mix of shrinking markets brought on by deglobalization, a waning interest in ESG, and seemingly endless technological disruption. 

But the consultants are not standing still, and are actively developing digital tools to improve their workflows. Many are also partnering up with the companies developing the AIs. “Such partnerships look like a welcome source of growth for the consultants. In time, though, they could become a drag – especially if they are successful. The quicker corporate clients become comfortable with chatbots, the faster they may simply go directly to their makers in Silicon Valley.”

However, these short-term gains from AI could lead the consultancy bureaus to irrelevance – “something for all the strategy brains to stew on.”

The Future Is Tasty!

It’s a problem for food developers: the tastings. According to ‘Oreo Owner Mondelez Taps AI to Tweak Its Classic Snacks’, this is especially true if you happen to be a health fanatic developing sugar products.

“I used to work in Sour Patch Kids, and if you did a tasting every day for a week, it was a nightmare,” says one food developer. 

But now AI is transforming the process. “Food scientists there use the AI tool to create optimal recipes by specifying desired characteristics, including flavor (‘buttery,’ ‘in-mouth saltiness,’ or ‘vanilla intensity,’ for instance), aroma (‘oily,’ ‘egg flavor,’ ‘burnt,’ among others) and appearance (‘amount of chips,’ ’roundness,’ ‘chip edges’ are considerations). The tool also considers parameters like the cost of ingredients, their environmental impact and their nutritional profile.” 

“Earlier iterations of the tool that weren’t given as much data made some unhinged suggestions. ‘Because [baking soda is] a very low-cost ingredient, it would try to just make cookies that were very high in baking soda, which doesn’t taste good at all,’ said one manager.”

So yes, you will still need human Subject Matter Experts in the loop to serve as tasters and brand stewards. “The brand steward is key… They’d be telling the tool: This is what the essence of an Oreo is.”

Nice work when you can get it.  

The Future is GenAI. If We Take A Deep Breath…

In other new use case news, “OpenAI and Google have unveiled their next generation of products,” according to ‘What Do The Gods Of Generative AI Have In Store For 2025?’.

In short, playtime is over, and now it’s time “to release clever products that prove there is a market for all this ingenuity.” However, the rush to produce has meant some of these products have been “marred by glitches”.

It seems GenAI  is evolving so quickly that the technology is defining the product. “You are normally taught not to be a hammer looking for a nail,” says Kevin Weil, OpenAI’s chief product officer. But “every two months computers can do something that we have never before been able to do.”

What is the moral of the story? Take a deep breath, people. Take a deep breath and lead with the nail: that problem that really needs solving. 

The New Crystal Ball On The Block

In summary, the future is hazy. No one and nothing – including crystal balls – are sure how it will play out. 

One thing is certain: the new telegraph in town is called AI. And 2025 might be the year it offers yet sunnier forecasts. 

Read last month’s Manufacturing – The News: ‘Will AI Become the Time Person of The Year?’.

The post Manufacturing – The News: The State Of Crystal Balls For 2025 appeared first on Augury.

In a recent edition of the Manufacturing Meet Up podcast, hosts and industry veterans Alvaro Cuba and Ed Ballina look back at 2024 and dust off their crystal balls to see what they see coming up in 2025. Let’s summarize!

 

It was a big year in manufacturing, and Ed and Alvaro discuss the stories and trends that shaped 2024 – and how they see 2025 developing. And here’s a spoiler: challenges remain, but the future looks bright as long as humans and machines work together and keep team-building. Watch the full episode! 

What Did 2024 Mean For Manufacturing?

1) Cost And Productivity Pressures

It’s all getting rather expensive for many reasons. Yes, we’re still dealing with the wake of COVID: incurred costs and wobbly supply chains. And yes, the labor market is still very tight. And for sure, consumers are getting tired of paying the price.

Manufacturers are looking for value and cutting costs, and this won’t be changing anytime soon. In fact, manufacturing has always been about making the best possible products for the least amount of money. So, it’s something we have to live and deal with. That’s where tech comes in…

2) Booming Global Market For AI

You couldn’t miss it: AI hit not only factories but also your family and communities. It’s everywhere and only set to grow. The global AI market for manufacturing is estimated to go from US$4 billion in 2023, to US$156 billion in 2033, growing at a CAGR of 45%.

Most companies believe AI will be a pivotal technology to drive growth and innovation in the sector. People are starting to see the value of the coins coming in. AI adoption in manufacturing is set to go from 26% in 2022, to 86% as of now, and to 93% in two years – which basically means everyone.

In short, it’s a revolution!  

3) Connected People

People still matter – big time. Yes, there’s a “Silver Tsunami” of retirements. But there’s also a “New Collar” class rising. Trades are at an all-time high in demand and get paid attractively well. Not to generalize, but these folk are not only out to punch the clock but want to find meaning in their work – and AI can help with that. Instead of working in breakdown mode, you can use Predictive Maintenance technology to get you to the equipment and address any issues before anything terrible happens. With no greasy messes to clean up, you can focus more on solving root causes. With a connected workforce, it’s no longer just about brawn but also brain. 

With purpose, everyone wins.  

What Will Matter In 2025 

1) Industry 5.0: Looping In The People

So all this Industry 4.0 tech is great for the shop floor – and job satisfaction. But we must do more to complement it with the front-line people using it and their skills. It’s estimated that around 44% of roles have evolved thanks to human-AI collaboration (and this will only continue). And, the idea behind Industry 5.0 is to empower people with the relevant skills to do their jobs better and to also make them more versatile and resilient.

Yes, you need to connect the workers to the tech. Still, you also have to connect them with each other – down the line, in the different factories in your portfolio, with the customer service people, supply people, quality people, etcetera. 

In other words, Maintenance and Operations will become better friends in 2025.

2) Next-Level Connectivity And Data

Once you connect the people, you need to connect the systems. Yes, we’re talking about bringing Machine Health together with Process Health so everyone can collaborate to achieve full Production Health. We’re talking about next-level connectivity for complete transparency and supply chain optimization. Once all these elements are joined, the results will be incredible.

3) Sustainability. The Real Thing

This is the year that sustainability starts to become less symbolic and more systemic. Before, we had C-Suite sustainability, which made a nice picture in the annual reports. And that’s important: we need goals to work towards in terms of benefiting the planet. But the real sustainability is in the lines. With tech-empowered operators and supervisors, your lines will run better – and that means less waste and energy use. And that brings serious dollars. 

By doing a better job in a more relaxed way, those working the line also have more time to think and introduce even more innovations. In other words, sustainability can start paying for itself. Ka-ching. But again, this is what manufacturing folk have been doing their whole careers. It’s always been a balancing act between productivity, quality, customer service, and all the rest. What Alvaro and Ed are saying is that it will all just get easier in 2025.

Happy New Year! Watch the full Manufacturing Meet-up episode: ‘2024 Trends, 2025 Predictions’.

The post Manufacturing Meet Up: 2024 Trends, 2025 Predictions appeared first on Augury.

At Augury, we use a rainbow of AI techniques: picking the right AI application for each specific purpose. “It’s all about using the right tool for the right job,” says James Newman, Head of Product and Portfolio Marketing at Augury. “Whether it’s Industrial AI, GenAI, or Causal, they all have particular strengths that can work to make your work easier and more impactful.” 

It’s safe to say that Augury has always been in the Reliable AI business. And happily, we continue to create more tools that are reliable when applied correctly and for the proper use cases. We’ll use any technique to improve our models in real-time. It’s what we do. 

Meanwhile, there’s a push to regulate AI, and many governments already have regulations – or will have them soon. In general, it’s about making emerging AI technologies transparent, explainable, safe, and based on FAIR data. These are all reasonable ideas everyone should aspire – in the name of creating  Trustworthy AI.

Reliable AI – AI that works to produce the proper desired outcomes – is very much part of this vision. And as you all know, in an industry like manufacturing, you cannot afford mistakes in terms of both safety and the bottom line.

State Of The Art Industrial AI – And Beyond

Our so-called bread-and-butter AI is industry-renowned for its ability to predict when a machine will break down – in fact, it’s even guaranteed. Designed for purpose-built solutions, this AI will continue growing with more use cases, capabilities, and new ways of leveraging insight. And, we’ll continue to use our neural networks to do the heavy lifting in terms of in-depth modeling. 

We will also keep experimenting with new and emerging forms of AI. For instance, our recent success with our Machine Health algorithms in applying Continuous Learning, which represents one of the more significant milestones in our quest toward overall expert-level AI, was primarily thanks to the rich – and accurate – training data created by Generative AI.  

“Yes, GenAI has a reputation for hallucinating. But it would be best to remember it’s a tool, not an outcome. Gen AI’s accuracy is 100% based on the model it’s being executed against and the parameters around which it is being controlled.”

GenAI Is Your Friend If It’s Used Right

Yes, GenAI has a reputation for hallucinating. But it would be best to remember it’s a tool, not an outcome. GenAI’s accuracy is 100% based on the model it’s being executed against and the parameters around which it is being controlled. GenAI is not evil. It’s down to the people to control it.

The large language models (LLMs) of GenAI work by sucking up lots of data, learning the patterns, and then working to predict the following pattern – in an often-unknowable way. It’s about answering a question that resembles the answers people usually give. Hence, when it doesn’t have the data to fill in the blanks correctly, it starts hallucinating.

In the case of Continuous Learning, we ensured the LLMs we developed only had access to quality data – namely, the over 500 million hours of Machine Health data taken from over 100 types of machines and dozens of industries. In other words, reliable data begot reliable outcomes.  

“By embedding a GenAI agent into our platform, we are now able to let our users engage with our AI very quickly and in the natural language they prefer – bringing our best-of-class AI to the front row, as it were.”

Complicated But Doable: GenAI As Reliable AI Assistant

It will take time to unfold all of GenAI’s potential and value, and it will take even more time for a high-risk industry like manufacturing that cannot afford to base its decisions on a hallucination. GenAI is still coming fast, but it will require a lot of work underneath it. 

However, one other GenAI use case we will see in the short term is related to how the inner workings of all of Augury’s AIs have been largely hidden from our users. Yes, you are accurately told what machine needs fixing and how to do it within a specific time frame before it becomes a problem. However, the users could generally only dig deeper in a rather manual and cumbersome way. 

By embedding a GenAI agent into our platform, we are now able to let our users engage with our AI very quickly and in the natural language they prefer – bringing our best-of-class AI to the front row, as it were.  

An AI To Help Explain AI 

Naturally, we’re not talking about just throwing ChatGPT at it, which would spark hallucinations and insufficient insights. You need to control the data that ChapGPT is dealing with carefully – and the same goes for any custom LLM we develop.

Either way, this AI agent will help users better understand what’s happening with the AI in the background. Those working on the factory floor can start querying the platform directly for supporting evidence: Why must I look at this machine? What is the metadata? Can I compare the metadata with the metadata of another machine? Has this happened before? Who fixed it and how?

This is where GenAI can shine: engaging with a trustworthy model to give you additional insight. 

“It’s not just about considering how one thing impacts another but also how it affects many different factors and what changes you need to make to get your desired result.”

What Will Causal AI Mean For The Future Of The Factory Floor?

In many ways, Causal AI offers the perfect fit for fully transparent and safe AI. Because it’s all about learning cause-and-effect relationships between different data sets, these Causal AI models are very explainable thanks to their very construction. 

As we aspire towards full Production Health, we can loop in more knowledge models based on domain expertise – combining data sets from maintenance, production, operations, quality, etc. As we bring in these other data sets, we’ll use Causal AI to examine all the cause-and-effect relationships between what we see on the machines and what the system produces. 

In short, it’s about finding true causality rather than just the correlations offered by GenAI. It’s not just about considering how one thing impacts another but also how it affects many different factors and what changes you need to make to get your desired result. 

“In short, Reliable AI is not about a single methodology.”

The Dance Of The AIs – As Choreographed By Humans

Causal AI will be huge for manufacturing. By allowing manufacturers to find relationships they may not have seen before, Causal AI will spark whole new ways of doing things to optimize processes. 

And to help explain these intricacies, a GenAI agent may be looped in to help – but without losing the power of the Industrial AI that forms the backbone of highly accurate, and dare we say it, reliable AI insights. It’s this dance between different technologies that will define the future of manufacturing. 

In short, Reliable AI is not about a single methodology. It’s about using the right combination of methods to provide reliable answers to people in reliability. 


Stay tuned for more exciting Augury AI info! Reach out!

The post Reliable AI: Providing Reliable Insights For Reliability Professionals appeared first on Augury.

Augury’s Saar Yoskovitz returned to the New York Stock Exchange’s Floor Talk to update the audience on the current state of Industrial AI, the need to shift the focus from Generative AI to Reliable AI, and how implementing digital tools needs to be backed by impact and cultural change.

Time To Rethink The Manufacturing Toolbox

“The manufacturing leaders we work with understand that the tools they used to have in their toolbox are no longer available,” says Saar Yoskovitz, Augury’s Co-Founder and CEO, during an interview on NYSE Floor Talk.

“Manufacturers can no longer just offshore to China because of geopolitical issues. They can’t just hire more people because skilled talent is very hard to find. They can’t raise prices because of the economy and consumer pushback. And at the same time, there is increased scrutiny around sustainability, from both the consumer and regulations.”

In other words, more and more manufacturers are investing in AI and other digital tools to attain their productivity and business goals. In this process, the question becomes: “How do we leverage new technologies to create real business impact and fundamentally change how our teams are structured and organized?”

Talking Innovation

Judy Khan Shaw hosts Floor Talk, a taped interview segment from the New York Stock Exchange. The show is renowned for allowing innovative business leaders to discuss timely topics, initiatives, and milestones.

After his first appearance in March 2023, Saar returned in November 2024 to discuss how manufacturing and industrial AI have evolved over the last 18 months since his previous visit. 

Three Insights Into Why Industrial AI Is On The Rise

According to Saar, Generative AI is coming out of its hype cycle, and expectations are coming back down to earth along with it. And there’s three reasons for this:

1) The Understanding That AI Is A Tool, Not An Outcome

“You must begin with the business outcome or impact that you want to solve for, and then walk it back to find the right tool or technology they need to use to get there,” says Saar.

2) There Is No Room For Mistakes – Especially In Manufacturing

The large language models behind GenAI are known for their hallucinations – which is not ideal for making life-and-death decisions that involve safety. “The algorithms and models must be trained with the relevant domain expertise. This is what we call Reliable AI, not Generative AI. For example, our models and algorithms have been trained on over half a billion hours of machines we’ve monitored. So, real-life machine behavior is fed into the model to create the best outcome for the user.” 

3) Digital Transformation Is Also About Cultural Transformation

“How is this technology being used at the end of the day? We talk about copilots and AI agents, but do they actually impact the day-to-day life of a maintenance technician or an operator?” In other words, tech is useless if it isn’t used by the users to its true potential. Hence, a change in tech involves a corresponding change in culture. “And I think that’s crucial as we implement more and more technologies,” says Saar. 

Looking Ahead To 2025

“From a macro perspective, the industrial market’s headwinds will remain,” according to Saar. “So, how do we leverage technology to overcome them? I really believe the opportunity is greater than the challenges.”

Watch the full interview.

The post Augury CEO On NYSE Floor Talk: “The Opportunity Is Greater Than The Challenges” appeared first on Augury.

In a recent edition of the Manufacturing Meet Up podcast, hosts and industry veterans Alvaro Cuba and Ed Ballina discussed digital transformation, its impact on maintenance and reliability, and how its transforming manufacturing. They even offer a snappy history lesson. Let’s recap!

Coming To A Shop Floor Near You (If It Hasn’t Arrived Already)

The numbers don’t lie. Industry 4.0 is coming, and it’s coming fast, according to a number of recent surveys. For instance, in 2022, only 26% of manufacturers had started doing something with AI; two years later, that number is 86%. Today, the global market for artificial intelligence is around $4 billion; in 10 years, it’s predicted to be $156 billion. Meanwhile, in the last couple of years, 44%, almost half of the roles and responsibilities of the positions in the manufacturing arena have been changed and upgraded thanks to artificial intelligence. 

In other words, we are very much talking about accelerated change. And if it hasn’t already arrived on your shop floor, it will very soon. 

According to industry legends and early adopters Alvaro Cuba and Ed Ballina, during a recent episode of the Manufacturing Meet Up podcast, it’s best to prepare yourself.  

Another Industry History Lesson à la Alvaro and Ed

What are we talking about when we talk about Industry 4.0? Ed and Alvaro gave an edifying summary of how we got here. And they give credit where credit is due: our furry and feathery friends in the animal world.

It all began many eons ago when monkeys started using sticks to scoop up delicious mouthfuls of ants, and birds began dropping clams from high heights to crack them open for a nice little chowder fest. Indeed: animals were the original tool inventors and jumpstarted what we’ve come to call manufacturing. 

Regarding human progress, 4.0 began with 1.0 in the 18^th century using mechanization and steam. Then in the first part of the 20th century, we embraced electricity and mass production. And then, at the beginning of the 21^st century, with the Third Industrial Revolution, it became all about the internet, automation, and electronics. 

4.0 Is Happening Fast But Not Fast Enough

So, we’ve finally arrived at the fourth big transformation – Industry 4.0 – which includes smart manufacturing, digitalization of the supply chain, synchronization, the full ecosystem, and everything related to AI and the Internet of Things, etc.

At its core, it’s about the connections between people, processes, and technology and making these connections as efficient as possible – not only in specific parts but across the entire end-to-end supply chain.

So yes, it’s an ambitious revolution. However, despite the surging statistics mentioned above, the implementers are still struggling to find savings. But with an onslaught as big as this one, the slow start can be considered natural growing pains while organizations try to figure out what works and what doesn’t. 

Top Advice For Embracing AI In Manufacturing

Happily, Alvaro and Ed have already walked this road many times and offer quality advice: 

1) Start Small: With Detecting Equipment Failures

Most manufacturers start with a particular use case: predicting asset failure. When assets don’t blow up, there is no collateral damage. Plus, there’s no greasy mess to clean up. And as we all know: a safe and clean workplace makes for a happier workplace. Hence, you’ve already won most of the battle.

From here, you can continue a process of continual improvement and expand to quality, process, yet more safety, etcetera.

2) Make Sure You Use Quality Data

You know the old saying: crap in, crap out. So maybe you should snap on some snazzy sensors to collect lots of quality and immediately interoperable data?

3) Think Security

You have to consider government regulations and hackers who may be after your luscious IP. So perhaps that’s another reason to have your own internal system to collect data. 

4) Keep Your Eye On The Three Balls: People, Process, And Technology. And In That Order!

Start with your people: What training do they need to get prepared, open, and excited about what is to come? They shouldn’t be worried about losing their jobs – they are just getting tools to do their jobs better. As for processes, AI can do a lot: analytics, data, data mining, and the ability to help make fast and accurate decisions. Say goodbye to trial and error! Only then can you start thinking about increasing automation, etc. 

5) Think Big: The True Value Comes When You Start Putting Different Systems Together

Machine Health is great, Process Health is great, and safety is great. But the true value arrives when you start combining these and other aspects for even deeper insights. As you strive for full Production Health with maintenance and operations joining forces, the benefits don’t just add up; they multiply because of their synergy with each other. 

6) Benchmark, Benchmark, Benchmark

How do you know you’re winning if you don’t keep score? It can start simple: tracking your mechanical uptime and/or your energy usage. But as your various solutions start to interact and influence each other, you will also need more complex KPIs that may go beyond your regular P&L. Overall carbon footprint? Labor turnover? Supply chain partner satisfaction? Etcetera.

But yes, for now, the low-hanging fruit remains elaborating on your line uptime. How much are you saving in terms of operating costs, waste, and labor? 

7) Don’t Be Afraid. Trailblazers Came Before You

This stuff has been around for a while. There are now many paths that have been carved into the woods for you to follow. Take advice! Learn from others! We’re already basically drowning in too much data. Let’s use it. This is the promise of AI: it can streamline all this data so we don’t have to spend all this time gathering and analyzing it ourselves. Thanks to AI, the data is now given to us in bite-sized actionable chunks. 

Listen, like, share, and subscribe to the full podcast ‘AI and the New Era of Manufacturing’. 

The post You Know You Want To: Embracing Digital Transformation In Manufacturing appeared first on Augury.

With uncertainties around climate and geopolitics, one thing is clear: AI is coming of age as it improves outcomes across industries and even wins two Nobel Prizes. What’s next? Will AI become Time magazine’s Person of the Year? … Read all about it in our regular round-up of manufacturing-related news.

It’s been a big year for AI. Last month, AI was validated by winning the Nobel Prizes for both Physics and Chemistry. Not so shabby for a bunch of zeros and ones.  

A Certain Vigilance Might Be Called For

But yes, as vast as the promise, the rise of AI also comes with its own array of warning labels, as the bestselling historian Yuval Noah Harari ably communicates in his latest book, Nexus: A Brief History of Information Networks from the Stone Age to AI.

“At the heart of Nexus is the idea that networks – whether social, political, economic or technological – are the bedrock of human cooperation and power. Harari argues that the strength of human societies has always come from their ability to create and sustain networks of information. These, in turn, allow large-scale collaboration and the distribution of resources, knowledge and authority,” according to ‘Has AI Hacked The Operating System Of Human Civilisation? Yuval Noah Harari Sounds A Warning’.

However: “The networks that have served humanity so well in the past, enabling unprecedented levels of cooperation and progress, are now at risk of becoming too complex and opaque for humans to fully manage. AI, Harari warns, is not simply a tool that we use; it can make decisions and generate new knowledge independently. Its rise could fundamentally alter the structure of human society. This is why Harari argues for its regulation and control.” Fair enough. 

“Nexus is ambitious, bold and at times, unsettling. It does not offer solutions that are easily within our grasp. But it challenges readers to think critically about what governs our lives and the ways AI could transform them.” Again, fair enough.

To be clear: while Harari recommends vigilance, he also believes: “Obviously, AI also has enormous positive potential.”

“Business technology leaders are winding down two years of fast-paced artificial intelligence experiments inside their companies, and putting their AI dollars toward proven projects focused on return on investment.”

The Holiday is Over: ROI Over Experimentation

Indeed, in the real world, more established forms of AI are ably dealing with very specific problems – whether it’s CuspAI creating materials-on-demand that can be deployed for cheap carbon capture or Every Cure repurposing existing drugs to treat currently untreatable diseases.

In terms of manufacturing, even GenAI has found some use cases that seem viable for the short term, whether using the preferred language of frontline workers or collating documentation and training in a single interface, according to ‘Five GenAI-Enabled Low-Code Industrial Analytics Use Cases Firms Can Get Started With Today’.

And it’s about time: the AI holiday is over. It’s time to get down to business… 

“Business technology leaders are winding down two years of fast-paced artificial intelligence experiments inside their companies, and putting their AI dollars toward proven projects focused on return on investment,” according to ‘Companies Had Fun Experimenting With AI. Now They Have to Show the Returns’.

“When generative AI came along, there was a certain amount of discretionary funding that we could look at to go experiment and test out some of the technology,” says one CTO.

“This is a year where you have to be expecting business results,” says another.

“The problem is, roughly 70% of business customers’ generative AI projects are still stuck in pilot or testing phase.”

“Accuracy and reliability is a big problem.”

“To get over the experimentation hump, businesses need to ensure there is widespread access to corporate data so that technology builders can use it to implement AI […] That’s how you get to the workable prototype that you know is fit for purpose, that you know will move the needle on the business.”

“One way to tell if the needle has moved: AI-based tools must be able to prove their worth in less than 12 months.”

“Another option: breaking down AI initiatives into smaller chunks that are more easily proven out.”

“I’m quite happy that it’s not a magic wand. We understand that it really is a useful, powerful tool, but it fits into the broader ecosystem.” 

“… has helped shrink the process of taking a new model of a regular vehicle from conception to mass production from around five years to about two.”

Stimulating Simulations

In manufacturing and AI, we hear a lot about “digital twins,” which is a fancy way of saying “running simulations.”

“When a factory has secrets to protect it is not unusual for security staff to ask that no photos be taken. This industrial campus in Milton Keynes, northwest of London, however, is particularly cautious. It is the home of Oracle Red Bull Racing, a Formula 1 team involved in a competitive contest that relies on levels of engineering so advanced they would leave most manufacturers in the dust,” according to ‘Digital Twins Are Speeding Up Manufacturing’.

“The digitization of car design and the virtual testing of prototype vehicles in a simulator has helped shrink the process of taking a new model of a regular vehicle from conception to mass production from around five years to about two. […] Carmakers are now trying to create digital twins of their factories and supply chains to plan production more efficiently. As the volume of data grows, AI will help analyze the twins and suggest improvements.”

“Spotting problems before they occur has both safety and financial benefits. It also makes routine servicing more effective. Aircraft used to require their engines be serviced at set intervals, even though some journeys cause more wear and tear than others. Planes flying out of an airport in a desert region, like the Middle East, can ingest gritty dust particles, which abrade components faster. Certain flights are more heavily laden, which adds stress. And some pilots push the throttles harder than others.”

“As the digital twin takes such things into account, maintenance schedules can be tailored to how each engine is actually wearing. This means some engines can stay on the wing as much as 30% longer.” 

“He said the 15 years of experience traditionally required for a human to handle a complex role can be accomplished at the same level by someone with five years of experience working with two AI co-pilots.”

But It’s Not Just About Productivity, It’s About People

“Honeywell CEO Vimal Kapur said at the recent CNBC Evolve: AI Opportunity Summit that he expects AI to be a much bigger growth driver than a productivity fix for the industrial company. That’s because of a generational shortage in skilled labor and the ability of AI copilots to reduce the time it takes for newer employees to become experts at their jobs,” according to ‘Why Honeywell CEO Vimal Kapur Doesn’t Think The AI Payoff Will Come From Productivity’.

“The AI opportunity for Honeywell is creating a new labor pool that can learn and work alongside AI and accumulate and deploy institutional knowledge much faster. He said the 15 years of experience traditionally required for a human to handle a complex role can be accomplished at the same level by someone with five years of experience working with two AI co-pilots.”

“For all the debate about how quickly AI opportunities will materialize, Honeywell’s Kapur is bullish on the adoption curve steepening quickly. ‘Awareness is high, adoption is low, but there will be an inflection point,’ he said. ‘I do believe 2025-2026 will be a big year for adoption of AI in the context of industrials.’”

So, here’s a thought: perhaps Time should name early AI adopters as their Persons of the Year. 


Read last month’s Manufacturing – The News: ‘What Happens After The November Ballot?’.

The post Manufacturing – The News: Will AI Become Time’s ‘Person of The Year’? appeared first on Augury.

In a remarkably cut-to-the-chase interview at the New York Stock Exchange, Augury CEO Saar Yoskovitz slices through the hype and the noise to explain how AI is successfully reshaping manufacturing. In short, it’s about ignoring the shiny bits and focusing on the desired business outcomes. 

In late October 2024, Augury’s CEO and Co-Founder Saar Yoskovitz sat down with host John Furrier during the East Coast AI Leaders’ Executive Series presented by theCUBE and NYSE Wired. The resulting interview – fueled by the obvious expertise of both guest and host – offers a clear vision of AI’s impact on the manufacturing industry. Watch it: You can learn a lot in just 21 minutes. 

Here are some quotable quotes from the broadcast: 

The Importance of Trust in Manufacturing

“AI is a tool, it’s not an outcome. Let’s focus on what is the business outcome that you want to drive, and only then on what’s the right toolset to achieve it. What we’re seeing specifically in the industrial market is that there is no room for mistakes. I tell the team, ‘At the end of the day, we don’t sell sensors, and we don’t sell AI; we sell trust.’ And if our customers want to see the behavioral change, the cultural change and the impact, and if the maintenance technician or the operator doesn’t trust the system, they go back to their old habits.”

Infusing Reliable AI Into Every Layer of The Stack 

“We’re infusing AI into every layer of the stack, and we mean it. We have a new sensor we just launched a couple of months ago: it’s the industry’s first sensor that is capable of running Edge AI, running neural networks on the Edge. Then, the whole network architecture is also infused by AI and self-healing networks because the reliability of the network and the safety and security of the network are as important as a result.”

Why Manufacturers Are Rushing to Embrace AI

“In our conversations with executives and senior executives in manufacturing, what they’ve noticed is that the tools that they used to have in the toolbox are no longer available. This means they can’t just offshore to China anymore because of geopolitical issues and supply chain risks. They can’t hire more people because they can’t find more skilled talent. They can’t raise prices because of the economy. And now we have sustainability pressure from regulators or the consumers. So, they fundamentally need to think differently about how they run a production line or a factory.”

How To Bypass the Data Tarpit

“One of the biggest challenges in the industry today is we talk about data lakes, but in reality, we have data swamps or data tarpits depending on how grim you want to be. […] That is a huge challenge because every factory, even if they use the same machines by the same OEMs, has a different system integrator that customizes it, et cetera. Our first approach for what we call Machine Health has been to create our own data set. We don’t need to integrate into anything. We come in, we superglue a few sensors on your machine, connect it to the Cloud, and we basically bypass all of the legacy systems while working with IT and working with security, but bypassing all of the legacy systems and creating that direct connection. The time to value could be as quick as one day.”

The Future

“We started as a predictive maintenance company, and then we understood that the problem that we solve is not really a maintenance problem, it’s a sustainability problem, and a supply chain resiliency problem, et cetera. We went broader into what we call Machine Health. And over time, a couple of years ago, we said, ‘Okay, even this is not ambitious enough.’ Now, our customers are asking us to also go into the process and the operation side. We understand that there’s a bigger picture called Production Health. We want to build the operating system of the AI-driven factory.”


Watch the full interview with host John Furrier during the East Coast AI Leaders’ Executive Series presented by theCUBE and NYSE Wired.

The post “We Don’t Sell Sensors And AI. We Sell Trust”  appeared first on Augury.

Both US presidential candidates want to support manufacturing – but in very different ways. In the confusion, many manufacturers are holding back on long-term planning and investment. Too much risk. But at least there wasn’t a shipping strike. And what’s this CapEx vs OpEx thing all about? … Read all about it in our regular round-up of manufacturing-related news.

This week’s cover story for The Economist should be good news to many: ‘The Envy Of The World: America’s Economy Is Bigger And Better Than Ever’.

However, the subtitle asks a painfully relevant question: ‘Will Politics Bring It Back To Earth?’ 

Difference Strokes for Different Folks

“Manufacturing accounts for about 10% of U.S. gross domestic product and an even smaller share of the nation’s jobs. But the sector bears outsized importance since the production of essential goods holds national security implications and many manufacturing workers live in key swing states,” according to ‘Trump And Harris Both Want A Manufacturing Boom. They Have Very Different Plans For Doing it’.

“Harris aims to close corporate tax loopholes and throw government support behind the production of critical goods. By contrast, Trump wants to protect domestic manufacturers with tariffs on foreign products while cutting corporate taxes and easing regulations.”

While imposing tariffs hasn’t had the most outstanding track record, it is also too soon to tell “whether the support for manufacturing provided by the Biden administration has yielded significant gains in output or jobs.”

Time will tell…

How Big A Difference Does A President Make?

“In truth, no president can single-handedly control the growth of specific industries. Larger economic forces like recessions and exchange rates tend to play a much more powerful role. But some policies can help or hinder their progress,” according to ‘To Revive Manufacturing, How Much Can a President Do?’

In short: “local factors are more important.”

For instance, where are potential employees moving? “As much as politicians might promote the number of jobs a manufacturing project creates, it has become more difficult for companies to fill positions. Sun Belt states have attracted more people in recent years with their lower cost of living, and manufacturers have taken notice.”

“Nevada’s manufacturing job base grew more than 13 percent from the beginning of 2020 to March 2023. Some of that had to do with federal policy: For example, the state received a Commerce Department grant to develop its lithium extraction and refining sector as well as battery production and recycling, which has seeded a new industry cluster.”

“But the expansion has been in the works since the early 2000s, when the state began an effort to diversify its economy beyond hospitality and entertainment.”

Besides being actively open-armed when it comes to welcoming manufacturers, Nevada has

“also benefited from its proximity to California, which has lost about 60,000 manufacturing jobs since the pandemic began.”

And so, California wants to follow Nevada’s lead. But again, there’s a familiar refrain at play: “One factor chilling investment is the election itself. Companies know that the outcome will affect taxes, trade policy, subsidies and regulations, so they are waiting for more clarity before carrying out new plans.”

As one industry expert says, “I think we’re kind of stuck here until the end of the year.” 

Going From CapEx To OpEx

Another sign of uncertain times: “The growing demand for subscription models that move tech acquisition from CapEx to OpEx is driving the growth of the device-as-a-service market,” according to ‘Device as a Service Market Expected to Generate $1.8 Trillion by 2031’.

“Increasing demand for subscription-based models that help customers convert the high cost of acquiring new technology from a capital expenditure (CapEx) to an operating expense (OpEx) drives the global device-as-a-service market. Also, rising adoption of DaaS due to its adaptability, cost savings, and data security has supplemented the growth even more.”

Something solid to think about?

At Least There Wasn’t A Strike

One thing is sure: it’s good news that a large and long-term dockworker strike was averted on the US East and Gulf coasts – especially after ocean supply chains were already hit hard by conflict in the Red Sea, drought around the Panama Canal, and the Baltimore bridge collapse. 

“In a statement, the union said that it had reached ‘a tentative agreement on wages’ and that its 45,000 members would go back to work, with the current contract extended until January 15. The union said it was returning to the bargaining table ‘to negotiate all other outstanding issues.’” 

According to the insightful ‘Beneath the Potential Strike at U.S. Ports: Tensions Over Innovation’, one of the greatest of these issues is automation.

“Confronted by the militancy of longshore unions, port operators have deployed automation, in part to limit their vulnerability to labor troubles. Not coincidentally, dockworkers tend to look suspiciously at robots and other forms of innovation, divining threats to their livelihoods.”

“History validates their assumption that their bosses are embracing automation in part as a way to reduce costs. The most obvious example is the advent of container shipping in the 1950s.”

“Most industry experts view automation as both inevitable and positive. The questions are: Who controls the technology, and will workers be cushioned against changes with training programs that prepare them for new opportunities?” 

Post-Election Boost

As perhaps a reflection of better post-election news to come: “British factories recorded their best month for two years during July, with output and hiring rising and optimism building after Prime Minister Keir Starmer’s landslide election victory,” according to ‘UK Manufacturers Show Fresh Signs Of Life After Election, PMI Shows’.

“Output and new orders increased at the fastest rate since February 2022, while manufacturers added staff for the first time in 22 months.”

So perhaps there’s hope in the unknown.

Tune in next year.

Read ‘Manufacturing – The News: Getting Constructive With Creative Destruction’.

The post Manufacturing – The News: What Happens After The November Ballot? appeared first on Augury.

At TechEx 2024 in Amsterdam, industry leaders from LEGO and Whirlpool shared insights on how they are leading the charge regarding Industry 4.0 – while already enjoying significant payback. Meanwhile, on the exhibition floor, exhibiting companies are increasingly offering the “picks and shovels” to smaller operations that are unsure how to tap into the digital transformation gold rush.

One question was often asked during the European 2024 edition of TechEx 2024 in Amsterdam: “If you follow the saying ‘When you’re in a gold rush, sell picks and shovels’, who is selling those picks and shovels for Industry 4.0.?”

Who, indeed? The short answer is that fully nurturing ecosystems that get everyone digging into and applying their data to boost production and efficiency remain largely works-in-progress. Tech innovation in enterprise largely remains a Wild West, with no single player dominating the market and many parties offering many solutions.

Meanwhile, in the last two years, events like TechEx have been largely hijacked by speculative solutions that sought to leverage the potential (and hype) around generative AI. There’s now a definite feeling that this era is over, as people want to see more short-term returns. In other words, if you spend all that money on picks and shovels, you want to see some ROI gold.

Welcome to the new frontier.

Industry 4.0 Needs To Stake Its Claim

The adoption of Industry 4.0 technologies could be a lot faster, especially for SMEs. While most leaders recognize AI as a critical tech for growth and innovation, they generally lack confidence in the readiness of their data and workforce.

In other words, Industry 4.0 remains a tricky terrain for the smaller players to exploit fully. But at least they can learn from – and hopefully, be inspired by – the more prominent companies leading the charge. 

Let’s Play! Lego Leads The Way!

Look at Lego. It’s a company with many luxuries. It’s family-owned and, as such, built on organic growth. Their product and their company are truly global and truly standardized. It also happens to be regarded by many as the most reputable company in the world. Plus, they make fantastic stuff – they’ve even just released a musical biopic about Pharrell Williams. What’s not to get excited about?

And if you just consider the fact that “it only takes 6 Lego bricks to create 915 million combinations,” the possibilities are indeed endless.

For his presentation ‘Revolutionize Your Operations: Unleash the Power of Digitalization and Innovation!’, Lego’s VP of Operations, Jesper Touboel, enthused how a sense of play infused the company’s efforts in applying data to foster fast and informed decision-making. At the core of these efforts is embracing condition-based maintenance – following an established TPM methodology so the wins can be carefully measured along the way. 

“This may not be a maintenance conference,” notes Jesper. “But a lot of new knowledge is coming out of maintenance. Many of us, as engineers, were taught that you need to maintain everything in a certain way. But that’s complete rubbish because we over-maintain a lot.”

After some successful pilots, Lego is scaling its Global Maintenance Efficiency Program to fully and consistently embrace condition-based maintenance across all its facilities. “It really changes your philosophy around maintenance,” says Jesper. “We’ve already been able to extend the lifetime of our equipment significantly.”

But yes, “It’s always difficult to drive change. You need lots of training to make people part of the journey.”

Digital Twin!

There was standing room only at ‘The Digital Twin: An Essential Step In Industry 4.0,’ presented by Julien Bertolini, an IoT expert for Volvo. Perhaps the audience was lured by its description as a “real-life case study” about establishing a digital twin that could measure the performance of 15 Volvo Group factories around the world.

And indeed, large-scale scaling remains an eternal challenge for many. And Julien’s advice followed closely with those who have come before him: 

–       strong foundational strategy
–       get your data house in order: quality is critical 
–       IT and OT have to bury the hatchet and collaborate
–       clear use definitions 
–       find those scarce skilled professionals 

A Smart Factory In Argentina

If scaling tech is not challenging enough, try doing it in Argentina’s famously struggling economy. Whirlpool, the kitchen and laundry company, did just that. And won. 

In the presentation ‘Whirlpool’s South American Smart Factory,’ the senior manager behind the project, Alessandro Malucelli, described the efforts involved in building a state-of-the-art factory in this remarkably challenging context.

At the time, it was a time of high inflation and import restrictions. “If the country goes to the right, we’re going to have more market – to have an opportunity,” says Alessandro. “Left politicians intended to evaluate South American business, which means there could be an opportunity either way. So, we decided to face a storm to capture benefits later on.”

According to Alessandro, it took 22 months to build a highly agile and automated facility, and the resulting factory has already shown a 20% improvement. This was all thanks to closely following a World Class Manufacturing (WCM) program. Basically, another version of TPM, WCM was originally developed by the Japanese professor Hajime Yamashina as a management and continual improvement approach that aspires to build the best and most cost-effective products or services by involving those working at the facility.  

Onward And Upward

Many larger companies are obviously having success with Industry 4.0 technologies. And the fact is they all started somewhere. So who will help supply and apply the required picks and shovels to manufacturers everywhere?

We’re ready. Let’s get to work.

Time for our pitch: Reach out to learn how Augury can set you on the road to Industry 4.0 payback.

The post TechEx 2024: Searching for the Gold in the IIoT Rush appeared first on Augury.

A survey of 500 APM practitioners reflects how more manufacturers recognize the business benefits of technologies like predictive maintenance (PdM) and the Industrial Internet of Things (IIoT) – and how these benefits will only amplify with AI enhancements. However, the report also comes with a warning: there needs to be more focus on increasing employee satisfaction.

The leading technology research and advisory firm ARC Advisory Group published the report ‘Advanced Asset Performance Management and Its Impact on The Workforce’. Based on an in-depth survey of over 500 end users from various industries in North America and Europe who follow an advanced Asset Performance Management (APM) program, the report contains many relevant recommendations for tracking key metrics, managing expectations around ROI, and scaling. 

However, the report’s key insights relate more to employee satisfaction and how embracing AI seems like the next logical step in continual improvement.

“About 90 percent of the respondents indicated that since the adoption of advanced PdM programs, they were able to add more assets to their asset management programs.” 

A Solid Business Investment – One That Keeps Giving

According to the report, the three top reasons for investing in an advanced APM solution are: 

1.  Improve maintenance practices
2.  Reduce safety incidents
3.  Improve employee job satisfaction

And happily, “the majority of survey respondents indicated that they are able to bring in continuous improvements to their asset management processes and practices as they have adopted advanced PdM practices. About 90 percent of the respondents indicated that since the adoption of advanced PdM programs, they were able to add more assets to their asset management programs.” 

“The one area that clearly needs attention is around employee satisfaction, where a greater number of participants indicated that the benefits were not realized. The same is true for employee retention.”

There Are Only Losers With Employee Dissatisfaction

“While business benefits are very well recognized, what remains to be understood is the impact of advanced APM technology adoption on the industrial workforce,” according to the report. 

“The one area that clearly needs attention is around employee satisfaction, where a greater number of participants indicated that the benefits were not realized. The same is true for employee retention.”

Hence, manufacturers need to examine APM programs more closely from an employee perspective and ensure these align more with employee needs. After all, in an industry already finding it hard to attract and retain talent, there are only losers when it comes to dissatisfied employees. Turnover can, in turn, lead to not only increased recruiting and training costs but also increased downtime and decreased production.  

“Many manufacturers recognized how AI-driven tools can be deployed to increase employee satisfaction through training and retention and skillset expansion. The use of AI can even work to attract talent.”

Can AI Provide More Than Purely Business Benefits?

Now, back to the good news. “AI is another key technology that will have a significant impact on how we manage our assets. Among a list of key areas that AI can impact, survey respondents felt process optimization and PdM benefit significantly with AI enhancements,” notes the report.

Many manufacturers recognized how AI-driven tools can be deployed to increase employee satisfaction through training and retention and skillset expansion. The use of AI can even work to attract talent.  

However, almost half of the respondents saw that the most significant potential for AI was process optimization. “With this asset management focused survey, it was interesting to see process optimization at the top of the list. This highlights the ongoing trend of Process Health and Machine Health coming together. As APM becomes closely tied with business goals, asset management professionals are also thinking about process optimization opportunities.”

Meanwhile: “As PdM becomes more effective, affordable, and easier to implement, end users are able to expand their PdM programs, this not only ensures better and comprehensive asset care but also simplifies asset maintenance and offers a helping hand for employees.”

In short: all this investment is great and will benefit the organization, but let’s not forget the human part. We must make sure that we aren’t just optimizing processes but also optimizing how we help our people become the best they can be. That’s when we will create a true win-win.

Read the full report: ‘Advanced Asset Performance Management and Its Impact on The Workforce’.

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