According to a report by market research company Vanson Bourne, unplanned machine downtime costs manufacturers $260,000 for every hour of lost production, and 82% of manufacturers experience machine downtime at least once each year. It’s an extremely costly and common problem, but there is a viable solution that begins with advancing beyond outdated maintenance methods. 

A version of this article was originally published on June 8th, 2021.

Many are embracing Predictive Maintenance for three main reasons:
1) To optimize repair timing
2) To maximize each planned shutdown
3) To be prepared

Predictive Maintenance: Moving Beyond Reactive and Preventative Maintenance

Meanwhile, traditional approaches to machine maintenance don’t do much to limit or remove unplanned downtime. With a reactive approach, for instance, technicians only step in once the minutes of downtime start adding up.

A preventive approach might do a better job of catching issues early, but only if the maintenance happens to be scheduled when a machine begins showing signs of breaking down. Because most factories rely on one or both of these methods for machine maintenance, the vast majority still consistently suffer from unplanned downtime.

The Proactive Power of Predictive Maintenance

Predictive maintenance, on the other hand, offers technicians enough advanced warning to address issues before they cause unplanned downtime. For the first time, factories are able to take a fully proactive approach to machine maintenance thanks to the advent of internet-connected sensors.

These sensors can attach to equipment and monitor key machine health data in real time, then feed that data into a predictive maintenance platform that applies data analytics to identify red flags as soon as they appear. The platform can then send technicians automatic, real-time alerts.

This new generation of technology promises to turn machine health monitoring — historically an overlooked area for maintenance teams — into an asset that manufacturers can leverage to reduce unplanned downtime.

How to Reduce Unplanned Downtime in Manufacturing with Machine Health Monitoring:

1) Optimize Repair Timing

The Vanson Bourne report also shows that the average unplanned downtime event lasts about four hours, and lost productivity during this time can cost manufacturers more than $1 million.

Downtime has huge costs because an unplanned outage brings production to a halt for an unknown reason, which technicians must then scramble to diagnose and fix as quickly as possible. The work is reactive, so there’s no way to know how long diagnostics and repairs will take.

Planned machine downtime is much more palatable. Manufacturers can prepare for these events in advance and schedule exactly what they plan to do. However, production still suffers because equipment might shut down for maintenance it doesn’t need.

Predictive maintenance tools use machine health monitoring to differentiate when a machine does and doesn’t need maintenance. That way, factories can plan for downtime events and incorporate only the equipment that currently needs attention. Technicians can respond early and with keen insight to minimize any negative impacts on production. Once these proactive interventions become the norm, unplanned downtime becomes a rarity. 

2) Maximize Each Planned Shutdown

Say you have 200 machines. Two of them are on the brink of failure, 25 are in very poor condition, 50 show early signs of wear, and the rest are healthy. A crew of five technicians has six planned maintenance windows in the next six months, five of which will be one hour long, and the sixth will be an eight-hour chance to get some serious work done. How does the crew maximize each opportunity? 

Predictive maintenance tools answer that question. Machine health data reveals which equipment requires immediate attention and which can be put off until later. Furthermore, machine health data helps identify where and how a machine requires repairs so that technicians can make the biggest impact in the shortest time window. Each opportunity counts.

With a clear indication of where, when, why, and how technicians need to respond, maintenance teams can use limited resources to make even a large industrial environment (or multiple sites) immune to the issues that cause unplanned downtime. 

3) Be Prepared

When factories can optimize each maintenance opportunity, they can begin to prepare for maintenance in the long term. To put it differently, instead of waiting for the next disaster, maintenance can lay the groundwork for even greater consistency and stability in terms of machine performance. They can prepare individualized plans for each machine, start ordering spare parts, and organize staff based on their skill sets.

With enough fine-tuning, everyone knows exactly what to do so that planned downtime goes systematically. Everyone can use the same preparation and experience to minimize unplanned downtime should it ever occur.

Unplanned downtime used to feel inevitable — a costly disaster waiting to happen. But that was before the era of predictive maintenance driven by machine health monitoring. Downtime in manufacturing will never be the same again.

Read more about eliminating unplanned downtime.

The post Three Steps to Reduce Unplanned Downtime In Manufacturing with Predictive Maintenance appeared first on Augury.

ROI is an important metric. However, according to James Newman, Augury’s Head of Product and Portfolio Marketing, it doesn’t tell the whole story. Once you’ve made all downtime planned, you need to go beyond ROI – and Machine Health – to measure value. 

Mea Culpa

It’s our fault, really, that some clients come to question our value. 

We deliver on our promise to make unplanned downtime obsolete – thanks to the alignment between our tech and those working on the plant floor, taking our advice and doing the actual maintenance. 

We prove ourselves with a fast and vast return on investment. In fact, we just upgraded our Value Calculator, which shows potential customers how much time and money they can save with Augury’s Machine Health solution. Indeed, ROI remains a bottom line that cannot lie. After all, every procurement and financial department love these happily crunchable numbers across time and space. 

A Proven Worth

And, of course, we’ll be sticking to our promise of eliminating unplanned downtime. It remains the Achilles’ heel of manufacturing in terms of both costs and safety.  

It’s only become more relevant with continued supply chain challenges – waiting months for a single essential spare part can devastate your business. And then there’s the workforce shortages: with not enough people to hire, manufacturers are forced to do more with less. As a result, Machine Health isn’t going anywhere. It’s proven its worth.

But it doesn’t mean we can’t expand on our ambitions – and how we measure them. 

Awkward Conversations

It’s legitimate for those customers who have been with us for three or four years and are now enjoying almost 100% uptime thanks to our system, to question what value we are bringing them now that downtime is no longer the price of doing business. 

They are looking at the numbers and seeing we are not saving them that five or six million dollars as we did three years ago. Our case was clear.

Dream Team

Indeed, their maintenance teams on the floor are not questioning our worth. We’ve become a fundamental part of what they do, and they still have wins almost every day. And to their credit, they are the core of a successful Machine Health program. 

Teams are responding precisely as they are supposed to – fixing things before they become a real problem. They’ve learned to run the assets better. For instance, they know if a bearing reading changes, it’s time for lubrication. They know what will happen and don’t have to wait for the danger alarm.

This is something to celebrate. But with no full danger alarm, we can no longer count it as preventing downtime…

Building A Value Narrative

Our champions working at our client manufacturers – usually those working on the floor – understand this paradox. They’ve reached out to our customer success managers to help them better describe the worth of our program. And for them, we were inspired to write a FAQ: ‘Do you have any tools for selling the results of our program to our leadership?’. 

There is indeed a much larger story to tell regarding what value Augury brings to the table. A larger value narrative can work to loop in the bigger picture that goes beyond simple ROI. And such a narrative can grow from answering new questions. 

Digging Deeper: Telling The Story In New Ways

Meanwhile, we continue to travel the road of continuous improvement – refining and expanding our Machine Health offering. There are still so many directions to take from here. While often more challenging to quantify, it’s not impossible. We already know from customer conversations they are finding value in all sorts of places.

Look at the number of resolved root causes as the workforce goes from firefighting to planned maintenance based on real-time insights. Has the morale changed? How about your safety numbers?

Look at the retention numbers. Are fewer people retiring early? Are you hiring more younger workers who are attracted to cutting-edge AI tech? Is there increased job satisfaction?

In fact, once you start digging, countless questions may have value in their answers:

·      What business problems did Augury solve?

·      Are there the stories that bring the program’s success to life?

·      What’s changed? In terms of how you do maintenance? In when you do maintenance? Do your maintenance and operations teams interact differently? Did you stop doing things you no longer had to do?

·      How has Augury helped you go after root causes and improve Process Health?

·      What do the users of Augury say about how their day-to-day life has been transformed? In terms of productivity? Job Satisfaction? Safety?

·      Are there other metrics you can measure besides avoided downtime hours?

Yes, our original pitch was and continues to be helping you eliminate unnecessary downtime. But our goal has always been more than that: it’s been to be your partner and co-pilot as you continually up your game and elevate the total value you create for your organization.

The post How Do You Measure Success Once You Beat Unplanned Downtime? appeared first on Augury.

Industry: Building materials
Machine: Comerio calendar drive
Fault type: Bearing wear
Outcome: Saved more than $6M in downtime/maintenance and achieved 94x ROI

Setting the stage for success

A prominent building materials manufacturer adopted Augury’s Machine Health in February of 2023 with the goal of reducing downtime on key assets at their roofing facility. The company’s previous tool–a hand-held device – had proven ineffective at detecting failures, resulting in both high hopes for a more advanced continuous monitoring solution, as well as some skepticism among the maintenance team.  

Following installment of Machine Health at the plant, Augury representatives kept in frequent contact with the company’s resource owners, maintenance managers, and schedulers, informing them of monitoring updates and checking in on the status of assets. 

In March 2023, following two months of baseline monitoring and data collection, Augury detected two faults, one of which was for a comerio calendar drive–a critical piece of plant machinery that ensures high – quality output, allowing for a higher degree of accuracy in rubberizing. Augury’s Reliability Success Manager (RSM) knew that preventing a breakdown was paramount for the company, and followed up with the maintenance team on the alerts. Based on previous experience with similar machines, he recommended they investigate the drive, and warned that a failure could cost them three weeks of production across two shifts, on top of overtime maintenance costs.

A second alert and machine repair establishes trust

Believing they could continue running the machine safely, on-site teams decided to delay repairs while they continued watching the machine for deterioration. At the same time, another machine went into alert – a cooling fan also vital to the entire production line. Drawing on past experience, the RSM recommended the maintenance team investigate the machine for possible bearing wear. The inspection confirmed the theory and the bearings were replaced on an outage day, saving over $200k in downtime and repair costs.

Following two more months of monitoring of the comerio drive, the RSM grew increasingly concerned about a catastrophic failure. Showing them data that suggested a further spike in vibration levels, he urged the maintenance team to act immediately. The maintenance manager agreed and instructed his team to perform an investigation, which confirmed  Augury’s finding that the machine was close to failure. 

AI data, human insight, and collaboration pays off

Despite having delayed the repair, the maintenance team still had time to acquire the spare part and locate a specialist in time to perform the work – a scenario made possible by the early Machine Health monitoring detection and continual RSM guidance. 

The bottom-line savings for the business equaled nearly $6.5 million from avoided downtime, maintenance costs, and lost production – a figure representing a colossal 94x return on investment.

The post Early Alert Helps Manufacturer Save $6.2M in Downtime appeared first on Augury.

Forget house flipping–that’s for amateurs. You gotta go bigger than that. Flip your old factory into a modern, high-tech workplace. It all starts with designing your downtime, on your time.

Unplanned downtime. It dogs pretty much everyone involved in maintenance and reliability. But what’s worse is unnecessary downtime. Downtime that could have been scheduled, avoided, or even prevented if machine failures were predictable.

That’s exactly what our two experts tackled in the first webisode of Flip This Factory: Design Your Own Downtime. 

Meet the Experts

Dave Penrith, recently retired, spent his 36-year career with Unilever in the UK, ultimately serving as global Chief Engineer. 

On the other side of the Atlantic, Terry LeDoux was VP of Nestlé Purina North America before retiring after a 33-year career in information systems.

Both Dave and Terry are champions of digital transformation, with deep experience in the successful rollout of Industry 4.0 initiatives. Here are their key takeaways on how to start fixing downtime for good, without using temporary hacks.

#1: Turn Knowledge and Insights Into Wisdom 

According to Dave, you’ve got to start by thinking about it as a process. “There’s a lot of solutions out there that will give you data. But you’ve got to move that data into knowledge and insight and then into wisdom. Machine health is the first step in that journey. It’s a foundation you can build everything else on top of.”

Terry agreed. “Most of us have some form of CMMS or maintenance solution that we work with in our factories. But over time that data becomes less and less accurate as players change. So sometimes you just gotta take a step back and do something very simple…actually walk your product flow. Anything that your product flow touches that could stop that product flow is probably critical.” 

#2: Measure What You Treasure

It’s easy to get lost in a sea of acronyms, metrics, and complex measures. (OEE anyone?) But our experts are firm on this point: simple is better. 

“Strip it back to the basics,” Dave said. “Production is about boxes on trucks. How many boxes could you have put on the truck and how many did you actually put on the truck? Start from that level first because otherwise, there’s a risk that we get comfortable hiding behind some of the complex definitions.”

Once you know how big or small your problem is, you’re on solid ground to find a solution. At that point, “…it’s about trying to find the tools and right vendors that will help get you there faster,” Terry said.

#3: Shift the Culture 

People can be resistant to change. So listening to the shop floor users, making it simple, and communicating the benefit is key.

“Nobody ever solved the shop floor problem in a boardroom,” Dave said. “We’re trying to shift culture and behavior from problem-solving to problem-avoidance. We’re trying to give the shop floor folks the tools to fix their own problems.”

Moving from problem-solving to problem avoidance is a paradigm shift that needs reinforcement. After all, maintenance techs are trained to go fix whatever is in the red, melting down, and setting off alarms.

“But what really needs to happen is to focus on the green, and publicly recognize people who are having great days,” said Terry. “You need to understand what they are doing to have those great days. And very often it’s that they’re using the tools that you put out there in the right way. Or they put forth ideas on better ways to use those tools. So recognition and reward is a small investment that really drives a lot of improvement.”

For folks on Dave’s team, that might mean winning the “Scotty Award”, named after Star Trek’s chief engineer, Montgomery Scott. “Scotty seems to save the Enterprise every week or every episode. So we had a Scotty award for someone who’s gone out of the way and done something different and saved the day.”

#4: Data, Served Fresh and Hot

Most reports are a look back at what happened–what Dave likes to call “cold” data. So any good machine health report is ideally forward-looking, predictive, and prescriptive. 

That data helps teams plan forward and across the organization. It’s where the idea of problem avoidance comes back in.

“You need fresh, hot data that helps you see what’s coming down the pipe,” Dave said. “Imagine a scenario when you’re running production and you’ve got a forward-looking system and it tells you that an asset is going to fail within seven days. You know that you’re going to have to take some time out to address that potential failure. But let’s say you also know that there’s going to be a material outage within three days because the suppliers are falling short. Just imagine having the ability to make those two interventions coincide. What you’re doing is combining what could have been two sources of loss into a single point of planned loss. Taking advantage of that is planning and designing your downtime.”

#5: Find a Strategic Partner

When it’s time to move forward, it is critical to find a vendor who doesn’t just throw data over the transom. 

“My mission was to make the world’s best pet food,” said Terry. “It wasn’t to be the world’s best at machine learning, end-to-end systems, and predictive maintenance. I wanted to find that strategic partner that could enable me to go fast and wide because, from a money perspective, you’ve got to consider time-to-value. The longer it takes me to get this technology in, the more downtime I’m incurring.”

Dave noted that while you might be able to DIY sensors and data, what you won’t have is the “hundreds and thousands of running hours of benchmark performance for a range of different assets and cycles. We couldn’t get to knowledge or insights and we definitely could not get to wisdom.”

Simply put: your strategic partner needs to be predictive, forward-looking, and always on.

Bonus Tip: Think Big, Start Small, Scale Quickly

When it comes to new tech solutions, lots of manufacturers find themselves stuck in pilot purgatory. But it shouldn’t, and doesn’t, have to be that way. 

Terry shared that his teams set a good, baseline measurement so they knew what success would look like. Then, at the end of a 90-day pilot, if they had 70% success, it was time to go big with speed.

For Dave, scoping the right size pilot is critically important too. “Manufacturing systems are complicated, and complicated things will fail in complicated ways. When a pilot is so narrow, there’s a risk that nothing fails. And the learnings are in the failures more than they’re in the successes. If you can get the correct size of pilot, to prove that it works, it will go big.”

In Terry’s opinion, “If you don’t jump on this next Industrial revolution and move your workforce to a mobile environment, you are going to fall behind. Yes, this is all new stuff but fail fast and pick yourself up. Learn from it and go try something different.”

Dave agreed, adding, “Don’t overcomplicate the metrics, and remember, it’s a people journey first.”

The post Design Your Downtime: 5 Tips from Industry Experts appeared first on Augury.

Vibration is everywhere. Whether it’s related to bearings, banjos or bacteria, vibration research continues to inspire a rainbow of use cases… Read all about it in our monthly round-up of manufacturing-related news.

Living In Dizzying Times

‘Future Computers Could Run on Lab-Grown “Brains”’
‘Can AI Generate a Way to Pay for Itself? The AI Hype Is Marketing, Baby’
‘I Saw the Face of God in a Semiconductor Factory’

It’s all rather dizzying. So, for this edition of ‘Manufacturing – The News’, we thought we’d try to bring things back down to Earth.

So, let’s talk vibes!

And indeed, buried behind all the dramatic stories, the gentle art and science of measuring vibrations seems to be enjoying a media moment.

An Avoidable Train Wreck

Apparently, the inability to recognize a worn-out bearing led to the recent toxic train disaster in Ohio, USA.

“If you put an accelerometer on a bearing and you’re monitoring the vibration levels, the minute a defect happens in the bearing, the accelerometer will sense an increase in vibration, and that could be, in many cases, up to 100,000 miles before the bearing actually fails,” according to ‘Hot Box Detectors Didn’t Stop The East Palestine Derailment. Research Shows Another Technology Might Have’. “It would have detected the problem months before this happened. There wouldn’t have been a derailment.”

And there wouldn’t be a headline like this: ‘Health and Environmental Fears Remain After Ohio Derailment and Inferno’.

Driving It Home

Meanwhile, people are using the precision measurement of vibration for everything from catching car problems, to figuring out how toothed whales make their sonar sounds, to listening to plants express stress, to manufacturing the perfect pre-war banjo.

Researchers are now even measuring the vibration of living bacteria to test for drug-resistance. “The measured vibrations are extremely small, at least ten thousand times smaller than the thickness of a hair,” according to ‘SoundCell, Spin-Off Of Graphene Flagship Partner TU Delft, Receives €350,000 From UNIIQ’.

“To accurately measure the vibrations of a single bacterium, SoundCell uses membranes made of graphene – a nanomaterial unique for being the world’s thinnest known material as well as a material that is highly flexible, conductive, and strong. Due to the extremely sensitive properties of graphene and the biological processes in bacteria, SoundCell provides insight into whether a bacterium is still alive (vibrating) after administering an antibiotic.”

Currently, antibiotic resistance is measured by cell division time – which could take days or weeks. Now, this new technology could bring it down to mere hours so doctors would still have time to try other options.

Let It All Synch In…

Vibration may indeed be more than just a passing fad. In a now semi-classic column in Scientific American, it was hypothesized that vibration may actually account for sentience – and more.

“Over the past decade, we have developed a ‘resonance theory of consciousness’ that suggests that resonance – another word for synchronized vibrations – is at the heart of not only human consciousness but of physical reality more generally,” according to ‘The Hippies Were Right: It’s All about Vibrations, Man!’.

But yes, more research is required in this area. So meanwhile, let’s really stick to more Earthly matters…

Learn more how Augury’s AI-driven vibration analysis boosts efficiency and eliminates downtime for manufacturers.
Or read: ‘Manufacturing – The News: Why Can’t People And Robots Just Get Along?’.

The post Manufacturing – The News: Positive Vibrations appeared first on Augury.

Until recently, supply chain reliability was the worry of supply chain people. But in a post-COVID-19 world, they’re no longer alone. Everyone worries about their favorite products making it to the shelf.

In this post, VP of Global Engineering Services at Colgate-Palmolive Warren Pruitt shares how Augury’s Machine Health Solutions has saved his company time, money and energy.

This article was originally published in 2020 on LinkedIn.

At Colgate-Palmolive, we’ve been bolstering our supply chain reliability by moving toward predictive maintenance of our machinery – a model that preserves “machine health” through 24/7 monitoring via wireless sensors combined with analytics powered by artificial intelligence (AI).

Unlike preventative maintenance (scheduled routine check-ups for equipment), this digitally enabled method diagnoses problems early, allowing for quick remediation and minimized downtime. It’s analogous to always wearing a glucose monitor or EKG rather than waiting until your annual doctor’s visit to discover something’s amiss.

Even better, the data fed from our machines to our partner Augury’s software platform gets compared to their learnings from over 80,000 other machines operating around the world. That massive analytical scale brings us insights on how to optimize the performance of equipment and make ever-smarter choices on how and where we deploy it. What’s possible only gets more compelling as this AI solution harnesses more data to create better “health outcomes” for our machines and our business.

Finding the Right Solution

This conversion process started a few years back as we searched for a robust way to do predictive maintenance. Like Goldilocks, it took some trial and error to find the “just right” solution.

Bluetooth sensors needed a person with a smart phone to collect the data. Wired sensors came with a high price tag. But the wireless-sensor solution we’re now using automates that data collection and analysis – monitoring vibration, magnetic flux (energy use) and surface temperature. When issues arise, reliability professionals remotely alert and collaborate with our plant teams as needed.

Although early diagnosis of problems is a key advantage here, there are additional savings from extending use of equipment past what would be typical preventative maintenance schedules. Instead of stopping production as a matter of course, say, every six months, we monitor the near real-time health and performance of our machines and if all is well, we can safely keep them running nine months or longer. This has increased our available manufacturing capacity, allowing higher production volumes, which has been invaluable during the COVID-19 crisis.

Evident Savings

Since implementing this technology at several of our manufacturing facilities, Colgate has already seen significant savings of time, money and energy.

For example, the AI detected rising temperatures in the drive motor of one of our tube makers and alerted the plant team. Upon inspection, they discovered a problem with the motor’s water cooling system. By getting it quickly resolved, we prevented the drive from failing due to overheating, which would’ve stopped the tube production line and incurred replacement costs. We figure the savings at 192 hours of downtime and an output of 2.8M tubes of toothpaste, plus $12,000 for a new motor and $27,000 in variable conversion costs.

In another case, the AI provided an early warning that a gearbox in a liquids machine was experiencing structural and operational issues, putting it at high risk of failure that could shut down the line. The team was able to order a replacement gearbox and plan the maintenance to swap it in, bringing the machine to acceptable condition with minimal interruption.

Looking Ahead

The benefits of having real-time access to machine health analytics have been so powerful, we’re going to roll out this technology across our global supply chain. Augury recently helped guide our installation of their technology remotely using augmented reality.

We’re excited to be early adopters and enthusiasts and hope our manufacturing peers join this movement toward digitization and predictive maintenance. The more data we have to learn from, the better results we will enjoy. Which can only mean more reliable availability of products. And that’s a future we can all smile about.

Read more about Colgate-Palmolive as Machine Health innovators.

The post Getting Smarter about Machine Health to Improve Supply Chain Reliability appeared first on Augury.

Augury has recently released a flurry of reports that will resonate with manufacturing professionals – from tips for avoiding downtime, to deep dives into the current market realities for Pharma, Chemicals and Food & Beverage. Read all about it.

This post was originally published on April 22, 2022.

Download Guide To Decreasing Downtime

Whether it’s planned or unplanned, downtime can drag down the bottom line and company morale. In fact, almost half of factory operators find their biggest operational challenge comes from unplanned downtime.

Experts agree the right predictive maintenance strategy can make all the difference. In ‘Your Guide to Decreasing Downtime’, seven manufacturing experts share their tips on:

• Doing a critically assessment
• Selecting the right condition monitoring technology
• Combing condition monitoring methods
• Implementing redundancy
• Getting everyone on board

Ready to transform your operations, gain insights into your machines and decrease downtime? Download your guide now.

New Market Insight Reports for Pharma, Chemicals and Food

While downtime is a universal challenge for manufacturers, each industry has its own pain points to face. That’s why we created brand new market insight reports for Pharma, Chemicals and Food & Beverage.

For instance, a recent industry survey conducted by Pharma Manufacturing and Augury confirmed that supply chain disruptions, the workforce skills gap and capacity constraints are still top industry pain points for pharmaceutical manufacturers. And one area that touches all of these challenges is plant machinery – making it even more essential for machines to run at maximum efficiency.

Meanwhile, the Chemicals industry is also naturally keen to maximize production and sustainability. But the sector also has its own particular concern that can also be addressed by digitalization efforts: safety, for both employees and the environment.

As for Food & Beverage manufacturers, they’ve been hit harder than most as a result of the pandemic – and permanent changes are afoot in the way the industry does business. And while ravaged by issues such as supply chain disruptions, labor issues and unplanned downtime, most surveyed plant floor respondents said unexpected equipment failure remained their biggest threat.

The Big Picture: Make It Real-Time And Condition-Based

These three reports are all deeper dives that build on the general market insight report we recently created with Plant Services – which got a nice write-up in VentureBeat.

“Despite unplanned downtime being the biggest threat to meeting production targets, two-thirds of corporate respondents report an inability to visualize the real-time condition of critical assets across all sites at their company, which can have a devastating financial ripple effect throughout their businesses,” according to VentureBeat.

“Only 26% of manufacturers are employing real-time condition-based predictive maintenance. This technology most commonly uses a combination of AI and IoT to predict machine failures before they occur, minimizing unexpected downtime and keeping production lines running.”

Read all about it: Download Your Guide to Decreasing Downtime and new market insight reports for Pharma, Chemicals and Food & Beverage.

The post Your Manufacturing Pain Points And How To Fix Them. Read Our Latest Reports appeared first on Augury.

A recent report found manufacturers listed unplanned downtime as one of their top concerns for the industry. By using Machine Health data insights, manufacturers can prevent unplanned downtime and optimize planned downtime. Learn how Machine Health data is the key to predicting repairs and providing predictive insights that tell technicians exactly what — and when — to fix.

Unplanned downtime caused by machine failures hits manufacturers hard: Both corporate and frontline workers listed it as one of the top challenges facing the industry. But with today’s technological capabilities, it doesn’t have to be.

Along with procedural advancements, technology advancements have allowed best-in-class manufacturing facilities to move away from reactive and preventive strategies and more toward predictive maintenance that uses machine learning. The goal is to make maintenance so data-driven that the notion of a machine failing unexpectedly becomes a thing of the past.

Learn more about how AI and predictive maintenance is driving digital transformation in manufacturing

Preventive maintenance has a number of inefficiencies. Because it operates on a routine schedule, workers may be spending time preemptively upkeeping assets that don’t yet need a tune-up, rather than focusing on higher-value work. Meanwhile, machines close to failure may be overlooked because they’re not scheduled to be serviced yet, which can lead to unplanned downtime when the failure finally occurs. By using technology and continuously monitoring critical machines with AI-driven diagnostics, we can anticipate failures before they occur. This allows you to strategically plan more prescriptive maintenance, reduce the amount of maintenance needed, and eliminate unexpected downtime events.

How Machine Health Leads to Reduced Machine Downtime

Understanding Machine Health provides manufacturers with a multitude of benefits. Using technology, manufacturers can collect data insights on what to fix before machines fail, allowing for more optimized maintenance work. It does this by providing:

1) Improved Machine Visibility

Adding sensors to equipment helps companies continuously monitor Machine Health. These sensors collect real-time data, and AI analyzes the data to help workers understand a machine’s performance status. Enabling predictive maintenance means technicians can see and repair machine issues before they fail.

Improving machine visibility drastically accelerates the reduction of unplanned downtime. How can manufacturers be aware of potential problems if they can’t see them? Rather than time-consuming preventive maintenance, AI insights allow workers to apply predictive maintenance based on where failures are most likely to occur.

2) Save Costs Caused by Downtime

For every hour a system is down, whether it is planned or unplanned downtime, a Fortune 1000 company could lose up to $1 million. And with production targets increasing, that cost may be growing.

While preventive maintenance can let machines slip through the cracks, Machine Health provides real-time insights, alerting manufacturers to which machines that need maintenance. This lets production move away from a calendar system that increases the chances of machine failure. Predictive maintenance alerts teams to potential failures before they occur, providing both a safer work environment for maintenance and less downtime for production lines.

3) Optimize Operations

Machine Health data can be used to optimize operational methodologies. For instance, if the Machine Health status drops off when the production team at a manufacturing facility operates a line at a certain speed, a high-level decision might be made to halt operating the line at that speed. Insights can be continuously harvested and utilized to inform and optimize operational decisions.

Machine Health can play a factor in operational decisions in all sorts of industries. It doesn’t matter what your specific operations are — from machines pulling rollercoaster carts to oil harvesting to vaccine manufacturing — everyone benefits from optimizing operations. From avoiding unplanned downtime to upping safety concerns, machines can help you do all of it — if you listen to them carefully.

Making predictive decisions to perform condition-based maintenance can reduce downtime in production. To learn more about reducing machine downtime in your own business, get in touch today.

The post How to Reduce Machine Downtime by Listening to Machine Health appeared first on Augury.

Manufacturers know critical assets are vital for continuing operations, but what exactly is asset criticality, how do you differentiate critical from non-critical assets, and how can you use asset criticality scores to drive value?

This article was edited on October 10th, 2023.

How To Identify A Critical Asset

Whenever you put a budget together, there are always those “fixed” expenses that will never go away. For a household budget, mortgage payments, real estate taxes, insurance premiums, and some utilities — though largely variable — would fall within that category. A daily latte from your favorite coffee shop, on the other hand, is more of a discretionary expense that you could do without. Tomorrow, you may decide that it’s more prudent to earmark that money for an actual necessity.

That’s pretty much how to identify critical assets in manufacturing.

Asset criticality refers to how important any asset is to keep business up and running. Whether that means that an asset affects the production line, determines inventory, or represents a large-scale safety hazard should machine health decline, critical equipment analysis provides insights into what’s fixed, variable, and discretionary within operations. You can recognize if a certain piece of equipment is worth the expense — or nothing more than a “nice-to-have.”

“Asset criticality refers to how important any asset is to keep business up and running.“

Critical equipment analysis would, for example, keep you from sinking additional money into machine health sensors on something that won’t give you value long-term. Perhaps after conducting a criticality assessment, you find a redundancy, and something once seen as a critical asset would fall into that discretionary category. The first step in uncovering those redundancies is to analyze each asset and assign it an asset criticality score.

Calculating Asset Criticality Scores

Asset criticality scores ensure that each asset you use is critical to business, and Augury can help with the process. We start with the big picture. Our team looks at each piece of equipment on the production floor in need of monitoring and then applies our equipment criticality assessment criteria to determine its overall value.

For mission-critical assets that directly support safety, compliance, cost control, or operational throughput, Augury installs our full machine health suite of monitoring and analytics tools to drive the most value.

Learn More About Augury’s Machine Health Monitoring Tools.

For medium-critical assets, we can also provide lower-cost support with Machine Health for Supporting Equipment. This autonomous and predictive monitoring enables full coverage — though there can be some overlap with this functionality in monitoring high-criticality assets. Machine health for supportive equipment monitoring is more probability-based. What you use it for will largely depend on in-house tech and knowledge base to monitor that data.

Learn More About Machine Health For Supporting Equipment.

Options For Non-Critical Assets

And when it comes to non-critical assets that are only tangentially related to value-driving objectives (a fire pump, for instance), we can determine whether monitoring such equipment still has the potential to drive some value for the overall production line. If so, we offer lower-level monitoring tools like our handheld vibration-monitoring device.

Unplanned or extended downtime can cost manufacturers millions, if not billions, of dollars. Conducting critical equipment analysis and implementing machine health monitoring tools accordingly can help prevent it.

If you’d like to learn more about our services, watch Augury’s Solutions Architect Brian Richmond discuss the business case for performing a criticality assessment and gain valuable information on how to create your own matrix in our on-demand webinar.

The post How to Identify Critical Assets and Drive Value appeared first on Augury.

When it comes to servicing niche machinery, it can be especially important to address issues at the first signs of a fault in order to avoid unnecessary downtime that can impact operations and lead to revenue loss.

Industry – Pet Food //
Machine – Hammer Mill //
Fault Type – Motor Fault (Rotor Bar) //

This Win of the Week shows how Augury’s AI monitoring provided early detection of a motor fault on a leading pet food manufacturer’s critical piece of machinery. This insight enabled the customer to order needed replacement parts — which required several weeks lead time — and repair their machine before a costly catastrophic failure occurred.

Image 1:

Machine Health status and alerts are tracked, logged, and visible to the Augury vibration analyst and on-site teams in the Augury platform as issues are detected and addressed.

A hammer mill is one of a pet food manufacturer’s most fundamental and specialized pieces of equipment. When Augury’s AI detected a motor fault indicating possible rotor bar damage on this customer’s hammer mill, the Augury vibration analyst downgraded the machine’s health status to Alarm and advised the onsite team to replace the motor as soon as possible to prevent damage to the machine. It’s extremely difficult to assess the fault severity and remaining machine life when this type of fault appears and is trending upwards, but according to vibration standards, a maintenance team should immediately prepare to replace the motor.

Image 2:

Augury’s algorithms analyze over 840 unique feature sets. Shown below are skirts of sidebands that developed around shaft turning speed harmonics, a textbook indication of possible rotor bar damage.

The customer faced multiple challenges to obtain the needed part, including restrictions on motor efficiency and supply chain concerns due to the pandemic. Because it would require a lead time of several weeks to manage through, the customer took the extra step of verifying Augury’s findings before ordering the part, which an independent testing team confirmed. While waiting for the new motor to arrive, the vibration analyst continued monitoring the machine’s condition and collaborating with the onsite team via the Augury platform, working within their limitations as best they could to avoid total failure and a halt in production.

The onsite team was very responsive, providing feedback to the vibration analyst throughout the process and confirming the success of the repair. After installing the new motor, the onsite team pulled out the rotor of the faulty motor, discovering severe damage, including large burn spots where the connection had failed and gaps where conductive filler had started to deteriorate. As pictured below, it is rare that this type of damage manages to escalate to the point of being so physically evident without the machine failing.

Images 3a and 3b:

Post-repair images showing the damage to the rotor bar. While the image on the right displays typical evidence of moderate damage, the image on the left shows such severe damage that it is surprising the machine was operational.

Without this early detection, the motor would have likely failed catastrophically, potentially halting production for months. Instead, with insights from condition monitoring, the onsite team had adequate time to order the part and prepare for a planned repair before severe secondary damage could occur. Augury helped the customer take the right action at the right time, resulting in zero unplanned downtime.

The post Machine Health Insights Give Pet Food Maker Lead Time to Order Parts appeared first on Augury.