The Newsweek Excellence Index spotlights companies that are successful at business while also out to elevate the well-being of customers and their communities. “Like the others on this illustrious list, our values define what we do as a company,” writes Augury’s VP of People, Danelle DiLibero. “And by following these values, we’re here to stand the test of time.”

We’re In The Relationship Business

“We don’t sell sensors and AI. We sell trust,” said Augury Co-Founder and CEO Saar Yoskovitz in a recent interview. “At Augury, we understand that our responsibility doesn’t end with putting sensors on machines. It goes well beyond that to the success and growth of our customer’s users and communities.”

Augury prides itself to be a company that delivers value – in every sense of the word – and that’s why we are proud to be included on the Newsweek Excellence Index 2025, as a company representing the Industrial AI/Advanced Manufacturing industry

Mindful Business Leadership

Newsweek and its partner, Best Practice Institute (BPI), have compiled this index from 1,000 companies in 25 industries and 100 categories.

As they put it: “Corporate success is not defined solely by the bottom line. This year, a group of companies set themselves apart as champions of business excellence by balancing financial success with a dedication to ethical practices, social responsibility and global sustainability. […] These companies prove that thriving requires not just strategic acumen but an unwavering commitment to doing the right thing for their stakeholders.”

We’ll certainly take that.  

Timeless Not Timely

The list features many startups. And we hope they stay on this list for years, along with ourselves now as a midsized company, and that we grow further together. 

Meanwhile, the included legacy companies are notable in that they have withstood the test of time and have been unwavering in their actions. These companies allow the foundation of their values to drive almost everything. They don’t change these values when faced with new situations or trends. These companies are timeless, not timely. They don’t just talk the talk but walk the walk.

Companies like Disney and Hilton consistently fulfill the expectations of those who engage with them. We hope that when people engage with Augury as employees or customers, they have the same feeling.  

People First. Always

The list aligns with one of our core values: People First. This value doesn’t end with our employees, it also includes our customers. We’re people who care and want to innovate while changing the face of manufacturing by helping people do their jobs better. 

 And we’re not here to be a fly-by-night. And we not only want to stay on this list, we’d like to see it grow.


 Read (and watch): “We Don’t Sell Sensors And AI. We Sell Trust”.

The post People First: Augury On Newsweek Excellence Index 2025 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.

Augury’s new R4000-series sensors are already doing what they’re supposed to be doing. However, the real story comes together early next year, according to Augury’s Head of Product and Portfolio Marketing James Newman. That’s when the Cassia X-2000 is deployed as the leading industrial Bluetooth gateway optimized for industrial IoT applications. “With sensor/gateway integration, a whole new IoT chapter can begin: faster, more reliable, more flexible, and able to put AI wherever you need it most. Bring on your edge use cases!”

Life On The Edge

Three years ago, Augury Co-Founder and CPTO Gal Shaul was asked in an interview what emerging technology he believes has the most potential for manufacturing. “I’m really excited about the movement around AI – especially edge AI, which can work at the endpoint and use models as accurately as those using data from a platform. It will enable us to make many new transitions in the market. […] In a few short years, we will be unable to remember how things used to be because it will all be so different.”

For years, edge computing represented the future. Allowing sensor-enabled devices to collect and process data closer to the manufacturing action would enable more AI-led decisions in near real-time, creating a new rainbow of potential use cases to benefit both productivity and those working on the factory floor. But yes, it remained tricky in terms of issues such as security concerns, scalability, and sheer complexity. 

Until now. Now, it’s payback time.

Our new Machine Health sensing platform can now run AI via our new Halo R4000 series sensors, the upcoming Cassia X2000 Enterprise Bluetooth IOT Gateway, and, of course, as we’ve done traditionally, the Cloud. With our wholly new IoT, we are now experimenting with customers to figure out what works best to double down on increasing value.

In other words, welcome to the world of Machine Health 2.0 – where the long-vaunted potential of edge computing can finally reach fruition.   

“AI-powered end-to-end condition monitoring solution designed for cost-effective monitoring at scale with maximum flexibility.” Read the full press release.

Big Story: Rugged, Future-Proof Sensor

The new Halo R4000 series is the world’s first Edge AI-capable Machine Health sensor. As our most miniature industrial-grade sensor yet, the R4000-series features longer battery life, smart and dynamic diagnostics, self-healing connectivity, and wet environment protection – it’s ready to cover any asset that matters, anywhere.

Thanks to the upcoming HexaLock sensor mounting system, designed for greenfield and brownfield environments, the sensors are easier to install and maintain, eliminating some of the challenges of other designs.

One of the main benefits of the R4000 sensor is increased flexibility, in terms of costs, capabilities, and scaling.

Flexibility In Use – And Costs

One of the main benefits of the R4000 sensor is increased flexibility, in terms of costs, capabilities, and scaling.

You’ll naturally still want the full Cloud-backed coverage for your critical assets. This enables deep and descriptive diagnostics and provides the raw data that helps our vibration analysts truly understand what’s going on with a particular machine. However, the sensor chip can now also process this data more efficiently before sending it to the Cloud, thereby saving bandwidth and transmission costs. 

However, in terms of headline news, the chip will also let you run sophisticated AI models on the edge for less critical assets to provide actionable insights, such as basic anomaly detection – helping customers unlock cost-effective total plant coverage. 

Thanks to collaboration with various partners and early adopters, we have solved this IoT connectivity problem.

Bigger Story: New IoT 

All these new innovations – the sensor, the HexaLock, and the Cassia X2000 Enterprise Bluetooth IOT Gateway – means new IoT. 

We all know working with Internet of Things technologies isn’t always easy – too many things can go wrong between reading the data and getting it to the Cloud, where the more in-depth AI magic can happen. Thanks to collaboration with various partners and early adopters, we have solved this IoT connectivity problem.

For instance, the gateway can apply AI for specific value-added applications, such as auto lubrication.

Opening The Innovation Floodgates

Due for general implementation in early 2025, the Cassia gateway is currently being tested in the field with customers. Significantly, and thanks to the increased efficiency of the overall system, we can further increase the number of sensors to 40 per gateway – more than twice what we could do previously.

The gateway’s ability to cover a much wider range will also help many scenarios, not least of which is safety – particularly in harder-to-reach or environmentally restrictive areas where it’s best to minimize human-machine interaction. 

You’ll also be able to run custom firmware from the gateway itself. For instance, the gateway can apply AI for specific value-added applications, such as auto lubrication.

Can you tell we’re excited? The time has truly arrived to make a difference. Are you ready to double-down with us.

Do you have a use case that might benefit from AI on the edge? Reach out! Let’s talk!
Or first, check out the full press release.

The post Finally… The World’s First Edge-AI Native Machine Health Sensing Platform 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.

Augury recently hosted its first L.A. Manufacturing Meetup in a suite in Dodger Stadium. The evening proved to be a real winner as a new way to trigger meaningful conversations with the larger manufacturing community. It also represented Augury’s new Field Marketing & Event Manager Sam Maxwell’s first months on the job. “It was inspiring how people connected – thanks to being in the same manufacturing ballgame.”

It was a big night – and a culmination of my first 90 days at Augury as our Field Marketing & Event Manager. It was a trial by… baseball. On August 24, Augury invited 30 manufacturing professionals to Dodgers Stadium for a learning and networking event capped by an exciting game between the L.A. Dodgers and the Tampa Bay Rays. 

I stepped into the project on my second day and spent the next three months keeping my eye on the ball while working step by step with colleagues to take this one evening from concept to execution. It was a fun way to explore what my role would look like moving forward. 

It was also the first time Augury had hosted an event like this – one where we could give something back to the local manufacturing community and offer those who work the factory floor to connect and share their experiences. 

You could tell it was already a success when everyone walked in. People introduced themselves. They were excited to mingle with peers. In fact, we really had to wrangle everyone together for the fireside chat. 

A Whole New Ballgame

It all began with the knowledge that we had many connections in and around L.A.. And we wanted to say thank you to our base. So, we started brainstorming ideas on what out-of-the-box experience we could offer them. A unique dinner? A factory drive-by with an ice cream truck? It was about breaking the trade show cycle of walking stands for six hours a day and perhaps meeting up for a free drink during a company happy hour. 

Now, here’s my full disclosure: I grew up a baseball fan. After getting a college degree in sports management, my first job was an internship with Major League Baseball. 

I started digging, and indeed, Dodger Stadium proved to be a central location and was perfect for the one-to-one intimacy we wanted to create for our guests. 

My manager said, “Go!” And we went full blast. Yes, there were plenty of road bumps, but a few days before the day, we could step back and go: “Hey, we made it!” I love it when a plan comes together.  

Hitting It Out Of The Park

You could tell it was already a success when everyone walked in. People introduced themselves. They were excited to mingle with peers. In fact, we really had to wrangle everyone together for the fireside chat. 

Our special guest was a food and drink manufacturer customer who talked – directly from his heart – about his company’s predictive maintenance journey. He spoke about how he came to Augury after trying a competitor that did not apply true AI but was a threshold-based system – and hence came with too many false alerts. He also emphasized the importance of real-time support from human reliability experts. 

Attendees nodded knowingly. After all, they were all in the same manufacturing ballgame.  

Now imagine what kind of records Shohei could break if he knew what pitch was coming next. Will it be a fastball? Or will it be a curveball? 

Driving It Home

Meanwhile, an exciting game played out: it was very back-and-forth before the Dodgers lost 9-8 in extra innings. In the process, Shohei Ohtani became the fastest player in major league history to hit 40 home runs and steal 40 bases.

The whole event got me thinking… Augury’s Machine Health Solution tells you what to expect regarding your machines – so you can happily avoid unplanned downtime while maximizing production. Now imagine what kind of records Shohei could break if he knew what pitch was coming next. Will it be a fastball? Or will it be a curveball? 

However, that wouldn’t be very sporting, would it? 

Luckily, business is a whole other game. No one wants to lose if they don’t have to.

If you are interested in attending such an event, let us know where you want us to go next Reach out!

The post A Great Night For Discussing Manufacturing (And Baseball) appeared first on Augury.

It’s official – there’s a teenager in the house. To celebrate, we’ve compiled 13 of the nicest gifts we have ever received: impact statements and compliments from customers, partners, analysts, and the press.

1.

“It is by far the best thing that has ever come into this mill in the 28 years I have worked here in maintenance. Work smarter not harder.” — Anonymous, Pulp & Paper Industry

Read the Article

2.

“We scouted the world for different solutions, big companies and small companies, and we objectively tested them side by side. Augury was the solution that came on top.” — Anna Farberov, General Manager, PepsiCo Labs

Read the Case Study

3.

“Since implementing Augury and 24/7 machine monitoring, we have drastically reduced unplanned downtime and also found some gaps in our maintenance strategy.” — Michael Hill, Predictive Maintenance Lead, Purina

See Video

4.

“Augury’s made a difference in our maintenance program because it’s really the first and most critical step in our transformation of getting away from that calendar-based maintenance.” — Travis Schell, Divisional FP&R and Automation Manager, Hill’s Pet Nutrition

Read the Case Study

5.

“We had a problem getting the mechanics to trust what our technology was telling us. We have several tenured mechanics, and they had been taking care of the equipment for a long time. The failures that we find with Augury are very high in the P-F curve so when we asked them to change the component, they could not understand why we were changing good components out. We then started doing autopsies on the problems that we found. You would be surprised how many mechanics will come around to see what you are doing if you start tearing down a motor on the shop table. Once they see the problems that you found with Augury, they started to understand what this technology can do and how it can help identify the problem.

We are lucky that we have our upper management support with our predictive maintenance program. They realize the savings and help us to make the program a success.” — Roy Smithson, Maintenance

Read on our knowledge base, The Endpoint

6.

“‘The value of startup vendors such as Augury has initially been the combination of hardware and software predictive-maintenance solutions, especially machine learning-driven.’ said Emil Berthelsen, vice president and analyst at IT research and consulting firm Gartner Inc.

By using multiple data sources, such as historical and operational data, acoustic sensors and images, Mr. Berthelsen said, ‘the quality and levels of predictive-maintenance insights continues to improve.’”

Read The Wall Street Journal

7.

“We saw ROI of nearly four times the investment within six months, just in repair savings. If we include savings from downtime avoidance, it would be much, much higher.” — Adi Segal, Head of Maintenance & Service Department, BAZAN Group Oil Refineries Ltd.

Read the Article

8.

“My mission was to make the world’s best pet food. My mission wasn’t to be the world’s best at machine learning and end-to-end systems and predictive maintenance. So I wanted to find that strategic partner that was one of the best in that space and they could enable me to go fast and wide. Because there’s a time-to-value here from a money perspective, the longer it takes me to get this technology in, the more downtime I’m incurring.” — Terry LeDoux, Former VP Digital Manufacturing, Retired, Nestle Purina

Read Terry’s insights

9.

“The beauty of the solution that we have together with Machine Health, partnering with Augury, is…state of the art hardware and software delivering value immediately and maximizing on an ability to deliver something to the customer that they see now…We’re creating value together. That’s a very different dynamic, right? That’s a true SaaS model of technology and digital to basically embrace your customer and say, not only am I delivering you something that’s incredible, it’s going to get you the value you are looking for and the ROI you wanted to exceed your expectations.” — Carlos Gomez, VP, Global Partnerships & Alliances, Baker Hughes

See the Interview on Beyond The Line

10.

“Getting these wins up front and early is very important. The news spreads like wildfire around the company. People start saying, ‘Hey that’s neat, we need that at our facility!’ Of course, we were also collecting KPIs from the beginning, and not only on whether we prevented equipment failure. We also covered time to reaction, time to getting a notification, time to taking action… We’re tracking all of those. And once we saw they were all tracking the right direction, it gave us the confidence to move forward.” — Gary Binstock, Director of Technology for Strategic Innovation and Alliances, Colgate-Palmolive

See all of Colgate-Palmolive’s wins

11.

“But here, it’s not just human workers trying to hear signs of machine failure above the factory fray. Sensors attached to equipment are also listening out for indications of hardware faults, having been trained to recognise sounds of weary machines that risk bringing production lines to a grinding halt.

PepsiCo is deploying these sensors, created by tech firm Augury and powered by artificial intelligence (AI), across its factories following a successful US trial.”

Read the BBC Story

12.

“Whenever we prevent a failure, of course we prevent everything that comes from that. We reduce the amount of labor time, machine time, we reduce all the inputs that are used in our operating machines—including energy, water, and gas emissions. Any improvement we have in operation and the reduction of the inputs is helping us meet our sustainability goals. We really feel that we have a partner in Augury and this has made it very, very successful.” — Gofna Liss-Rubin, Open Innovation Manager, Osem-Nestlé

Watch the Video

13.

“Augury isn’t a program. Programs end. Augury is a culture. And this culture is saving downtime, money, and negative environmental impact at scale.” — Bill Hollman, Corporate Operations Manager, Nefco Biosolids

The post Happy 13th Birthday Augury! appeared first on Augury.

Augury’s recent ‘Beyond the Line’ event showcased how the company’s tagline ‘Predicting A Better Future’ is now a self-fulfilling prophecy, writes James Newman, Augury’s Head of Product and Portfolio Marketing. “We are building what manufacturers need to create a better future. And with four core elements coming together, manufacturers can enter that future today.” 

Self-Fulfilling Prophecy

We unveiled our new Production Health blueprint during our recent ‘Beyond the Line’ event – showing how manufacturers are at a watershed moment in optimizing their production goals. In short, Augury, buoyed by its tagline “Predicting A Better Future,” is already building what manufacturers need to make this better future happen. 

Yes, it remains a work in progress – as any journey of continual improvement should be – but it’s already one offering value to our customers. Four core elements are now coming together that will transform our industry as manufacturers gain more control over achieving their goals.

1)    Machine Health 2.0
2)   Process Navigator
3)   Fusion Diagnostics
4)   Ecosystem

1) Machine Health 2.0 (The Ever-Evolving Foundation)

The blueprint builds on our long-established foundation of Machine Health. Augury has built its business and reputation on making unplanned downtime a thing of the past for our clients. While Machine Health is the foundation for everything that follows, it continues to become broader and deeper. For instance, we can now speak confidently about Machine Health 2.0 as we continue to make advances in infrastructure, improved IoT, and next-generation AI.  

2) Process Navigator (Looping In Clear Prescriptive Insights For Operations)

With precise and real-time actionable insights into the state of your machines, the next logical step is to have the same clarity and control over your Process Health. Our Process Navigator offers dynamic real-time simulation you can use to optimize your operations – and then change when needed. 

During the ‘Beyond the Line’ event, a comparison was made to the history of navigation. Too many manufacturers still rely on “paper maps” to negotiate their processes. But now we’ve gone beyond paper maps and even static GPS routes. We have achieved a Google Maps level of navigation for both machines and the processes: the ability to provide turn-by-turn instructions that adapt to “traffic conditions” and can be calibrated for one’s particular needs – be it speed, energy efficiency, or both. Indeed, you are now able to adjust your production process for multiple objectives.

The next step is supervised self-driving processes. (Tune in next year.) 

It allows us to know how processes are affecting machines and how machines are affecting processes – and not in retrospect, but in real-time.

3) Fusion Diagnostics (Breaking Down The Wall Between Maintenance & Operations)

It may sound fancy, but Fusion Diagnostics does what it says. It’s the connective tissue that allows the tearing down of the wall between maintenance and operations – call it a sledgehammer or a wrecking ball. Call it whatever you want. But it’s the unification we always needed. It allows us to know how processes are affecting machines and how machines are affecting processes – and not in retrospect, but in real-time.

With Machine Health, we already had turn-by-turn instructions on what to do about your machines. With the launch of Process Navigator for Process Health, you now have turn-by-turn instructions on what to do about your processes. And now, Fusion Diagnostics brings machines and processes together – which is entirely different from the two silos they live in today. We are bringing in two vast and complex data sets that AI can use to turn new and previously unseen connections into actionable insights with turn-by-turn instructions that impact the entire organization collectively and not in isolation.

Fusion Diagnostics reconnects all the teams that should have been connected from the beginning – giving them the insights that matter to both: optimizing production.

Do you want to manage your energy use? Go ahead. Do you want to manage waste to meet your sustainability goals? Go for it. 

Thanks to real-time operator-driven actions that respond to changing needs, every shift can deliver its best work. After all, your best route today might not be your best route tomorrow.   

“Unlike in Middle Earth, there isn’t, and there will never be, a single app or platform to rule them all. It’s only by the combined efforts that we will complete this vision of creating a better future.”

4) Ecosystem (No Solution Is An Island)

To attain that better future, you need to realize you can’t do it alone. No one can afford to be an island anymore. And now, with large partners in the ecosystem, we can offer customers more expanded capabilities much sooner than we dreamed of – whether that’s better synching capabilities with Baker Hughes, simplified technology stacks with Schneider Electric, or changing how you manage your assets with IFS Ultimo. 

And there’s so much more potential and partners we are – and will be – tapping into. Whether it’s automating parts-as-a-service or keeping your assets adequately lubricated, the sky is the limit. And it’s impossible to build all these possibilities ourselves. And certainly, it does not best serve our customers. Yes, we will stay focused on building the central capability set that drives value – see above – but meanwhile, we can also leverage entirely new ways of working – thanks to being part of a larger ecosystem.

To put it in another way (and with apologies to JRR Tolkien fans): Unlike in Middle Earth, there isn’t, and there will never be, a single app or platform to rule them all. It’s only by the combined efforts that we will complete this vision of creating a better future. 

A diverse and robust ecosystem will also allow your technologies to be as connected as your teams need to be. Looping in additional data sources will inspire yet more innovation through new techniques and AI capabilities. 

This is why the blueprint for full Production Health will never be finished. This is a house that will only keep growing.   

Conclusion: Upward and Onward

There will always be people who say, “Wouldn’t that be nice?” We can now say, “It’s already nice.” We are making that prophecy of a better future come true: not in three or ten years or Sunday, but in real-time. Now. The building blocks are now in place.

This doesn’t mean we’re done. There are lots of things to come. But for the first time, the vision is not just a wish but a reality.

Learn how to break efficiency barriers with an ever-evolving Production Health blueprint.
Or cut to the chase by reading the ‘Your Production Health Blueprint‘ one-pager.

Read Part 1 of Beyond The Line: ‘The Manufacturer’s Nightmare’
Read Part 2 of Beyond The Line: ‘A New Bag Of Tricks’
Read Part 3 of Beyond The Line: ‘Bringing The Manufacturer’s Dream To Life‘.

The post The Better Future We Predicted Starts Right Now appeared first on Augury.

There’s always buzz around Augury’s AI. Now, this reputation will only ramp as its Machine Health algorithms apply Continuous Learning thanks to rich – and accurate – training data created by Generative AI. This is big news for manufacturing in terms of increased accuracy, scalability, and customization. In other words: “This is a huge milestone in our journey towards expert-level AI.”

You didn’t need a special sensor to pick up the vibe: something was afoot at Augury. There was a buzz in the air – and on Slack. What was happening? Was there a partnership announcement? Was there an IoT breakthrough? Had the wall between Maintenance & Operations finally been smashed once and for all? Did Augury figure out how to measure success now that unplanned downtime is obsolete? 

A Powerful New Capability: AI That Continuously Learns

As it turned out for this time, Augury’s Machine Learning Operations (ML Ops) and AI teams had created a Continuous Learning system for its Machine Health algorithms, using a large language model (LLM) to process highly valuable insights on the Machine Health platform. This highly-rich training data includes all the customer-generated content like repairs, feedback, fault diagnoses and related comments, as well as fault misses. 

“This is a huge milestone in our journey towards expert-level AI,” summarized Augury’s VP of AI and Physics Assaf Barak. “Having a continuously learning and improving AI system that gives accurate insights in real-time and impacts the user has been the holy grail, and we are now starting to see it happening in Machine Health for the first time. This is amazing.”

“This is like the next evolution,” adds James Newman, Augury’s Director of Product and Portfolio Marketing. “The whole goal of Augury has always been to drive scale: not just for customers, but for us. And the only way you can do that is by continually improving how the AI can replicate what humans do.” 

“This process has allowed us humans to become progressively more specialized. For instance, in the beginning, we only saw birds and bees. We could only later separate the sparrows from the chickadees and the bumble bees from the killers.”

Live. Learn. Specialize. Develop Expertise

Continuous Learning is a data science method in which a machine-learning model is continually exposed to new data, allowing it to develop and improve over time. 

Sound familiar? It should. Humans do something similar by collecting and discarding knowledge based on real-world experience. Though, admittedly, some humans are better at improving over time than others. 

Regardless, this process has allowed us humans to become progressively more specialized. For instance, in the beginning, we only saw birds and bees. We could only later separate the sparrows from the chickadees and the bumble bees from the killers.

Indeed, while Continuous Learning has already been applied in healthcare to predict patient outcomes and disease outbreaks and in finance for fraud detection and risk analysis, it can now be used to provide more accurate and consistent predictions on the health of manufacturing machines and to respond more quickly to specific customer needs.  

“This approach to scanning for signatures differs from other Machine Health Solutions, which are purely threshold-based systems – setting off an alert when a particular variable, such as a higher temperature, is breached.”

A Grand Library Of Machine Music

Augury has built an unparalleled repository of machine-related data. Its algorithms can now be trained on over 450 million machine hours across over 100 types of machines and dozens of industries. This sheer mass of data has given Augury the ability to provide Guaranteed Diagnostics that are over 99.9%+ accurate.

“Think of that data as a repository of every piece of music ever created”,” says Hari Viswanathan, Augury’s Director of Product Marketing. “Every machine has music coming out of it in the form of vibration, temperature, and magnetic flux data. And while most failures in machines are unique, there are similarities. Over the last dozen years, our VAs, vibration analysts, have been tirelessly labeling spots with a familiar pattern – for instance, bearing wear. We call these patterns ‘features’, but you can also regard them as musical chords of sorts. And we are always scanning – or listening, if you will – for specific chords that mean specific things. Yes, there might be variations between machines and in different situations – like the variations of those four same chords we hear in pop music – but it will remain that basic chord.”

This approach to scanning for signatures differs from other Machine Health Solutions, which are purely threshold-based systems – setting off an alert when a particular variable, such as a higher temperature, is breached. 

And now, Augury’s patented “listening” approach has been turned up to 11.  

“The LLM doesn’t just collect this information, it cleans it up, sorts it, creates new connections, and makes new labels to tag the data on which the algorithm can be trained. It’s like going from knowing a few basic chords to understanding all music.”

A Whole New Level Of Wow

In the past, these new VA-sourced labels were packaged together regularly, along with customer response feedback as they repaired the problem, to retrain the model. It was all very “manual”.

“Now incredibly,” enthuses Hari, “we’ve used generative AI in the form of an LLM, a large language model, to automatically take that corpus of information that’s in the platform – basically all the communication related to a particular repair – and relabel everything in terms of specific repairs done on specific machines. In other words, the LLM doesn’t just collect this information, it cleans it up, sorts it, creates new connections, and makes new labels to tag the data on which the algorithm can be trained. It’s like going from knowing a few basic chords to understanding all music.”

“And let’s be clear,” adds James. “Until now, GenAI has been noisy. You could not trust it – because its knowledge source was usually the hysterically unreliable internet. No wonder it hallucinated. But we are basing our LLM on our own already trustable AI and database. Quality in, quality out.”

“What is already true for Structural Mechanical Looseness, the same will soon be true in finding the different core causes for Bearing Wear, Anomaly Detection, Rotational Mechanical Looseness, etcetera.”

Endless Encores: Recognizing More And More Core Causes

So what does Continuous Learning mean in the real world of the factory floor? Hari explains: “Here’s an example. Historically, we had one fault type covering Structural Mechanical Looseness – that’s basically when a machine is shaking too much,” says Hari. “Now, because we went through this relabeling process, the LLM could pull out two distinct faults: either it’s not bolted to the ground properly, or the bearing inside is somehow broken. These are two very different types of failures. So now we can drastically increase the specificity around our fault diagnosis.” 

“In other words, what is already true for Structural Mechanical Looseness, the same will soon be true in finding the different core causes for Bearing Wear, Anomaly Detection, Rotational Mechanical Looseness, etcetera.”

How To Monitor Infinite Machines Without Losing Accuracy

“Augury originally started with the idea that we wanted to make our AI the basic equivalent of a Category 1 vibration analyst,” says James. “We wanted the AI to be smart enough to eliminate the grunt work of vibration analysis. Namely, you don’t have to go through endless data looking for problems since we’ll tell you where these problems are. You also don’t have to figure out which spectrums matter. We’ll show them to you. But now, with Continuous Learning, we can go beyond that and be even more specific.”

This does mean that the role of the VA is shifting. “As the algorithms become more accurate and specific, rather than VAs spending time reviewing the algorithms outputs, they can spend more time ensuring the best inputs,” says Christian Smith, Augury’s Manager of Reliability Operations. “They can ensure the digital twin is truly identical and the algorithms are set up for success. It also means they can spend more time with our customers, providing consultation on subjects from asset selection to maintenance practices.”

“And with this capability of Continuous Learning, we will not only know what’s important for all machines, but what’s important for your machines.”

“Our VAs will also remain our greatest source of learning,” adds James. “However, with the increased expertise of the AI, all VAs can now be expert VAs all the time. They no longer have to do the middle-ground work of deciding if something is important; the algorithm will cover that.”

“And with this capability of Continuous Learning, we will not only know what’s important for all machines, but what’s important for your machines.”

“So, now you have the benefits of both the generic model as crowdsourced from all of Augury’s customers, and we can now also build unique and customized models.”

What We Mean When We Talk About Scale

“We were already awesome at diagnosing generically,” says James. “But now we can be awesome at winnowing down to the specific needs of individual customers because now the AI is learning from the customer’s in-house experts – those doing the actual repairs. And with our VAs having to do less verification and validation, they can cover more machines while diving deeper into specific problems. Their work will only become more specialized,” says James.

“So, what does this mean in terms of scale? On one level, nothing changes: you know our AI can help you no matter where you are in the world,” says James. “But on another level, if something about you is out of the norm, our AI already knows it. Because your expert logs have taught it what to do. So, now you have the benefits of both the generic model as crowdsourced from all of Augury’s customers, and we can now also build unique and customized models. We’ve now broken both these barriers. Nobody else can do both. In fact, I’d be interested in being shown a company that has done either at any measurable scale.”

Learn more about how AI and other advancements are putting manufacturers back into the driving seat.

The post Big News At Scale: Augury Achieves Continuous Learning For Its Machine Health AI appeared first on Augury.

As a lead-up to Augury’s Beyond The Line event on June 18th 2024, Augury Co-Founder and CEO Saar Yoskovitz presents a three-part series on how new technologies are aligned to help manufacturing meet its many different, and often conflicting, goals. In parts 1 and 2, Saar summarized the ongoing challenges and emerging solutions. Now, he outlines the required capabilities manufacturers need to flourish in this new era of data-centric manufacturing.

Let’s do a quick recap! We started with ‘The Manufacturer’s Nightmare’, in which we presented a dreaming golfer who realizes his old bag of golf clubs can no longer make him a winner in today’s modern world. And yes, that golfer represents today’s manufacturers who wonder how they can deliver the massive leaps in efficiency, sustainability, quality, and flexibility needed to meet the demands of their customers, investors, and employees. 

In part two, we showcased ‘A New Bag Of Tricks’ – those macro trends in technology and the workforce that are now creating tremendous opportunities for manufacturers. Indeed, a new set of clubs exists that will help turn many of the rosiest promises of the past years into practical solutions – and do things manufacturers previously only dreamed of.

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But how do manufacturers apply those trends to bring this dream to life? How do they create a blueprint for themselves and their strategic vendors to ensure they have the digital infrastructures, new work processes, and highly skilled teams they need to win?

We will now address these questions. 

Capturing The Magic: Think Capabilities, Not Products

Step one: you need to realize that no one can predict all the answers. A strategy for manufacturing crafted even a few years ago, for example, would not have embraced the emerging power of generative AI platforms such as ChatGPT – much less, Chat GPT 4.0. It would have underestimated the advances being made by smaller startups and emerging leaders in applying Machine Learning, AI, and data engineering to basic but crucial challenges such as energy management, asset reliability, and process optimization. 

“Step one: you need to realize that no one can predict all the answers.”

The key is to think about general capabilities, not specific products. And, here are the five capabilities that must be built into the emerging digital ‘tech stacks’ of successful manufacturers:

Five Required Capabilities for Digital Success 

1)    Infrastructure: Reliable Sensing And Computing – From Cloud To Edge

The modern foundation for AI-driven manufacturing will need to be cloud-based. Only by aggregating massive volumes of data can AI models evolve quickly enough and become trustworthy enough to be used confidently in manufacturing. The Cloud is ideal for this task. 

But while models might be built and trained in the Cloud, real-time insights needed for autonomous production optimization will also require work to be done much closer to the manufacturing process. In other words, computing power needs to extend from the Cloud all the way to the Edge – namely, the assets themselves and even the sensors that are collecting and analyzing the vast amounts of data already generated by modern manufacturing systems. 

AI processing at the Edge not only reduces cost but also increases speed. This way, changes to processes or spotting risks to assets or people can be done in minutes and executed in real-time on the plant floor – and not discovered days later through some centralized data science exercise. 

“But while models might be built and trained in the Cloud, real-time insights needed for autonomous production optimization will also require work to be done much closer to the manufacturing process.”

Integrations must be ubiquitous. This is not just about moving data from one place to another, but also integrating disparate tools to take advantage of raw data and insights generated by other systems in the manufacturer’s overall technology stack. 

2)   Insights: Agility in Data Collection and Sharing

Manufacturing systems will throw off ever more data as sensing becomes more ubiquitous and affordable. This will feed more powerful and accurate AI systems and give the people who use them deeper, more effective insights into the tactics needed to optimize manufacturing. 

But as data volumes grow and analysis becomes more real-time, data collection itself will need to become more responsive. Rather than fixed sensing at regular time intervals, sensing regimes must be more dynamic. For example, you might want to increase or vary your data collection when indications suggest deeper analysis is required or when you want to respond to certain behaviors of machines or processes. To do that, AI engines have to create models at the speed of production – which means in a matter of hours and not months.

Like the ‘gain’ function on a radar, which can be turned up or down to achieve greater accuracy or reduce unnecessary signals, sensing depth and power should be variable to match the depth of insight to the task at hand. This will help control costs, reduce bandwidth requirements, and create new opportunities for analysis.  

“The added context provided by a more diverse array of data types will mean faster and more confident decision-making by people and the digital systems that support them.”

3)  Coverage: Everywhere And Beyond

A) Multidimensionality Of Insights And Data Sources

Just as with a self-driving car finding its way through a combination of data sources and sensors (GPS inputs, on-board cameras, LiDAR systems, etc), the AI-driven processes that will help manufacturers navigate complex process changes and asset management strategies will benefit from more and more sources and types of data. The added context provided by a more diverse array of data types will mean faster and more confident decision-making by people and the digital systems that support them. 

This scenario is already emerging in areas such as Machine Health, where classic data inputs such as vibration or temperature are now being augmented with external processes and factors, such as input pressure for water or materials, environmental conditions like humidity, and even the specific SKU being run at any given time.  

All of this can help get better insights into machine performance and reliability. Hence, manufacturers should ensure they’re architecting strategies to embrace and demand these integrated data collection and analysis approaches. Here, it will be particularly important to break down insights and contexts into four functional levels:

Forensic: Data and context that help determine after an event what caused it to occur
Predictive: Data and insight that can identify with high accuracy when an event will occur
Prescriptive: Data and insight that can predict an event and provide the best means of intervention to prevent negative outcomes and maximize positive ones
Proactive: Data and insight that’s trusted enough to also autonomously give commands or adjust systems or processes to prevent negative outcomes and maximize positive ones. 

B) Broad Instrumentation And Coverage

The manufacturing process is an incredibly complex and integrated effort involving tens or hundreds of mechanical components, raw materials with constantly varying characteristics, and changing goals (cost optimization, throughput, quality, etc). 

Data will also need to come from everywhere to make insight-driven manufacturing a full success. Every machine type and all key subprocesses will need to be instrumented and understood, and those data sources will need to be brought together into a holistic view of the end-to-end process.  

“Leading manufacturers will harness that power to be able to adjust processes in real-time, within individual shifts, and continually tune the mix and behaviors of machines, materials, and even work processes to get the desired outcome every time.”

C) Holistic Approaches To Assets, Processes, And People

Manufacturers will need to be able to see all the elements of a process through a digital lens. Basically, this involves taking the ‘digital twin’ concept to a whole new level. 

Capturing and understanding the interrelationships between the hundreds or thousands of variables that exist in any manufacturing operation will be essential for optimizing overall production. Long the ‘holy grail’ of manufacturing, that end-to-end visibility can become a reality today due to the intersection of technology trends outlined above. Leading manufacturers will harness that power to be able to adjust processes in real-time, within individual shifts, and continually tune the mix and behaviors of machines, materials, and even work processes to get the desired outcome every time. 

Domains of work that are isolated today (such as asset management not being connected to process optimization, or operating parameters not being able to incorporate changes in weather or raw materials compositions, etc) will come together to enable higher-order decision-making by truly cross-functional teams. 

“Manufacturers must have a culture and rewards system that attracts and retains a new generation of digitally native workers while providing the training and opportunities that keep their current expertise in-house and motivated.”

4)   Engagement: Digital-Ready Workers and Culture

While all these technological advances create the opportunity for a new era in manufacturing effectiveness, in the end, it will, as always, come down to the people who use them. Manufacturers must have a culture and rewards system that attracts and retains a new generation of digitally native workers while providing the training and opportunities that keep their current expertise in-house and motivated. 

While worker, product, and environmental safety will remain paramount, the speed of innovation will become more critical. Manufacturers will need to understand where they can take greater risks and where they can even afford to ‘fail fast’ to try out new approaches and tools. 

The decentralized decision-making prevalent at many manufacturers will need to evolve. The benefits of data-driven manufacturing will be amplified by scale. With more data flowing in and insights flowing out, learning will be accelerated – along with the flow of benefits for all the processes and plants across a portfolio.

And as the individual manufacturer tweaks its in-house capabilities, the external world must also lend a hand…  

“The winning manufacturers in this new era will not be the ones who try to work in islands, but those who see the benefits of connectivity and data sharing.”

5) Ecosystem: Long Live Not Living On An Island

Data sharing across supply chains, with OEMs and other partners (and even aggregated data sharing, in anonymous ways, between competitors) will deliver even greater benefits due to the rapid improvement it can drive in AI models and algorithms.

The winning manufacturers in this new era will not be the ones who try to work in islands, but those who see the benefits of connectivity and data sharing. Speed of innovation and the courage to move quickly will outweigh the traditional benefits of secrecy and proprietary thinking.

The Lean Dream Coming True

And of course, all these capabilities interact and build on each other. With data finally breaking down silos between work functions, the full power of concepts in lean manufacturing, TPM, or similar models will finally be able to be realized. 

Just as IT operations and application security eventually fused with software development to shorten cycle times, reliability, process engineering, and other functions can come together in one integrated approach to optimizing manufacturing. 

So, let’s revisit that golfer’s nightmare in the first post… He’s standing at the first tee and realizing all their once trusty clubs were either gone or useless. However, with this new set of capabilities in place, his nightmare can turn into the dream scenario where the bag is full of a new set of clubs – designed with AI precision, crafted of the latest materials, and proven in the hands of the world’s best experts. Now, that’s the manufacturing world we can build for the future. 

In other words, as with golf, we can now enter the zone.

Read Part 1 of Beyond The Line: ‘The Manufacturer’s Nightmare’
Read Part 2 of Beyond The Line: ‘A New Bag Of Tricks’

Ready for the next era of efficiency?

• Register for our event

The post Beyond The Line (3): Bringing The Manufacturer’s Dream To Life appeared first on Augury.