In the rapidly evolving landscape of industrial technology, Internet of Things (IoT) predictive maintenance has emerged as a groundbreaking approach to equipment management and operational efficiency. This comprehensive guide explores how IoT is revolutionizing maintenance strategies across various industries.

What Does IoT Stand For?

IoT is the Internet of Things, a technological framework connecting physical devices, sensors, and systems through Internet-enabled communication. In maintenance applications, IoT creates a smart network that:

• Transforms traditional reactive maintenance approaches
• Provides unprecedented visibility into equipment performance
• Enables data-driven maintenance strategies
• Connects previously isolated industrial systems
• Supports real-time monitoring and predictive analytics
  

Read: ‘IoT Can Be Reliable And Easy To Maintain – If We Collaborate’

What Does IoT Mean in Maintenance?

In the context of maintenance, IoT represents a network of interconnected sensors, devices, and software systems that collect, transmit, and analyze real-time data about equipment performance and condition. This technological ecosystem enables:

• Continuous monitoring of machinery and infrastructure
• Real-time data collection and analysis
• Proactive identification of potential equipment failures
• Enhanced decision-making capabilities
• Significant reduction in unplanned downtime

How is IIoT Technology Useful in the Predictive Maintenance of Industrial Processes?

Industrial Internet of Things (IIoT) technology offers transformative benefits for industrial maintenance:

1. Enhanced Monitoring Capabilities

• Continuous real-time equipment tracking
• Comprehensive performance data collection
• Instant alerts for potential issues
• Detailed historical performance analysis

2. Operational Efficiency

• Reduce unexpected equipment failures
• Optimize maintenance scheduling
• Minimize production interruptions
• Improve overall equipment effectiveness

3. Cost Optimization

• Decrease emergency repair expenses
• Extend the equipment life cycle
• Reduce unnecessary maintenance interventions
• Lower overall operational costs

What Technologies Are Used in Predictive Maintenance?

Predictive maintenance leverages multiple advanced technologies:

• Machine Learning Algorithms
• Artificial Intelligence
• Cloud Computing
• Big Data Analytics
• Advanced Sensor Technologies
• Edge Computing

Q/ If a machine dies without a sensor to tell you, how will you find out?
A/ Catastrophe and/or downtime

What Sensors Are Required for Predictive Maintenance?

While increasingly found bundled together in a single unit, critical sensors for effective predictive maintenance include:

1. Vibration Sensors

• Detect mechanical irregularities
• Monitor equipment oscillation patterns
• Identify potential bearing or alignment issues

2. Temperature Sensors

• Track thermal variations
• Detect overheating components
• Prevent potential thermal-related failures

3. Acoustic Emission Sensors

• Capture sound frequency changes
• Identify microscopic structural modifications
• Detect early signs of mechanical wear

4. Electrical Current Sensors

• Monitor power consumption
• Detect motor performance variations
• Identify potential electrical system issues

How Does IoT Help the Process of Automation?

IoT significantly enhances automation by:

• Enabling autonomous system monitoring
• Facilitating self-diagnostic capabilities
• Supporting predictive decision-making
• Reducing human intervention in routine tasks
• Creating intelligent, self-optimizing systems
• Integrating multiple technological platforms
• Providing seamless data communication

Like the Future, IoT Is All About Automation

Conclusion: IoT Is A Major Technology That Will Keep Growing in Importance

IoT predictive maintenance represents a paradigm shift in industrial operations. It can really transform how you and your company approach equipment management. By leveraging advanced sensors, intelligent analytics, and interconnected technologies, businesses can achieve unprecedented operational efficiency, cost optimization, and strategic performance.

As technology evolves, IoT predictive maintenance will become increasingly sophisticated, offering industries worldwide even more refined insights and capabilities.

Read: ‘Finally… The World’s First Edge-AI Native Machine Health Sensing Platform’.
Or reach out and contact us.

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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.

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In today’s competitive industrial landscape, businesses constantly seek innovative strategies to optimize equipment performance, reduce downtime, and minimize maintenance costs. Predictive maintenance systems have emerged as a game-changing approach that revolutionizes how organizations manage their critical assets and infrastructure.

What is a Predictive Maintenance System?

A predictive maintenance system is an advanced AI-driven approach to equipment management that uses data analysis, sensor monitoring, and sophisticated algorithms to predict potential machinery failures before they occur. Unlike traditional reactive or even preventive maintenance methods, predictive maintenance provides real-time insights into equipment condition, allowing you to:

• Anticipate potential mechanical issues
• Schedule maintenance precisely when needed
• Prevent unexpected equipment breakdowns
• Optimize maintenance resources
• Extend the operational lifespan of critical machinery

This approach transforms maintenance from reactive and panicky “fire-fighting” into a strategy h that directly impacts operational efficiency and bottom-line performance.

Read ‘Why The Confusion? Prescriptive Maintenance Vs Predictive Vs Preventative’.

How Does Predictive Maintenance Work?

Predictive maintenance operates through a sophisticated combination of technologies and analytical processes:

1. Data Collection

• Advanced sensors are installed on critical equipment
• These sensors continuously monitor various parameters such as:
  • Vibration
  • Temperature
  • Acoustic emissions
  • Electrical currents
  • Lubricant condition

2. Real-Time Monitoring

• Collected data is transmitted to centralized monitoring systems
• Machine learning algorithms identify patterns and anomalies

3. Predictive Analysis

• Sophisticated models predict potential failure points
• Maintenance teams receive actionable insights and recommendations
• Required maintenance work is prioritized based on actual equipment condition. Edge AI solutions are now appearing to deal with problems that are time-sensitive – such as safety

Read: ‘The Evolution of Predictive Maintenance Toward Prescriptive Solutions’

Benefits of a Predictive Maintenance Solution

Implementing a predictive maintenance solution can offer you numerous advantages:

Cost Optimization

• Reduce unexpected equipment downtime
• Minimize emergency repair expenses
• Optimize maintenance scheduling
• Extend the equipment life cycle
• Significantly lower overall maintenance costs 
• Improved spare parts strategy

Operational Efficiency

• Maximize equipment performance
• Reduce production interruptions
• Improve overall equipment effectiveness (OEE)
• Enable data-driven decision-making
• Enhance production planning and scheduling

Safety Improvements

• Identify potential equipment failures before they become safety risks
• Reduce workplace accidents related to equipment malfunction – and by minimizing human-machine interaction
• Ensure compliance with safety regulations
• Protect workforce and infrastructure

Strategic Advantages

• Gain a competitive edge
• Improve asset management strategies
• Enable more accurate budgeting and resource allocation

Environmental Benefits

• Reduce waste from premature equipment replacement
• Optimize energy consumption
• Minimize unnecessary resource utilization
• Reduce waste caused by machine malfunction

Conclusion: Predictive Is Just The Beginning

Predictive maintenance represents a transformative approach to equipment management, offering organizations unprecedented insights into their operational infrastructure. By leveraging advanced technologies, manufacturers can unlock new levels of efficiency, reliability, and strategic performance.

As industries evolve, predictive maintenance will become essential for those seeking a competitive edge in an increasingly complex and technology-driven global marketplace.

Predictive Maintenance is a great start. But to go next-level, go prescriptive. Reach out to learn more.

The post Benefits of a Predictive Maintenance Solution 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.

Reactive maintenance remains a surprisingly common approach for many organizations in industrial maintenance. This comprehensive guide explores the intricacies of reactive maintenance, providing insights into its potential benefits and significant limitations.

Reactive maintenance is most often described as “fire-fighting”: when something goes wrong, you try to fix it quickly. While it’s great that it’s being fixed, it’s less great that productivity has ground to a halt in the meantime, and workers often have to perform riskier, dangerous work. But thanks to emerging—and many more established—technologies, reactive maintenance can be thrown in the dustbin of history.

Unfortunately, it’s still a very common maintenance strategy across industries. Why?

What are the Advantages of Reactive Maintenance? 

Despite its limitations, reactive maintenance does offer some short-term benefits:

1. Lower Initial Costs

• Minimal upfront investment in maintenance infrastructure
• No need for advanced monitoring systems
• Reduced initial technology and training expenses

2. Straightforward Implementation

• Easy to understand and execute
• No complex planning or predictive analysis is required
• Minimal training needed for maintenance staff
• An option for non-critical or low-impact equipment

3. Minimal Administrative Overhead

• Reduced paperwork and documentation
• No need for complex maintenance scheduling
• Limited planning resources required

What are the Disadvantages of Reactive Maintenance?

While seemingly convenient, reactive maintenance presents significant challenges:

1. Increased Long-Term Costs

• Higher emergency repair expenses
• Unexpected equipment downtime
• More extensive damage from delayed repairs
• Significantly higher total cost of ownership
• Potential for catastrophic equipment failure

2. Operational Disruptions

• Unpredictable production interruptions
• Extended equipment recovery times
• Reduced overall operational efficiency (OEE)
• Negative impact on production schedules
• Potential loss of customer confidence

3. Safety Risks

• Higher probability of workplace accidents
• Increased risk of equipment-related injuries
• Potential non-compliance with safety regulations
• Compromised worker protection
• Higher insurance and liability risks

4. Equipment Reliability

• Accelerated equipment degradation
• Shortened equipment lifecycle
• Increased wear and tear
• Reduced overall equipment performance
• Higher replacement frequency

Read: ‘Predictive Analytics No Longer Cuts It. Long Live Prescriptive’.

What are Alternatives to Reactive Maintenance?

Organizations have many different, more advanced maintenance strategies, which are also becoming increasingly blended depending on the criticality of a particular machine and the specific use cases the manufacturer is going after.

1. Preventive Maintenance means more regularly scheduled inspections and systematic equipment serving. Benefits include:

• Proactive component replacement
• Reduced unexpected failures
• Lower long-term maintenance costs

2. Predictive Maintenance includes advanced sensor monitor and real-time machine monitoring to generate AI analytics. Benefits include: 

• Precise maintenance scheduling
• Minimal unnecessary interventions

3. Condition-Based Maintenance requires continuous monitoring for data-driven maintenance decisions. Benefits include:

• Immediate issue detection
• Optimized maintenance resources
• Enhanced equipment reliability

4. Reliability-Centered Maintenance includes comprehensive system analysis and strategic maintenance prioritization to manage equipment. Benefits include: 

• Holistic performance optimization
• Advanced risk management

5. Prescriptive Maintenance is the next stage of predictive maintenance, with AI insights that instruct what to fix and when. Benefits include:

• Reduces Downtime
• Optimizes Maintenance Costs
• Improves Equipment Performance 
• Enhances Safety

Conclusion: Reactive Maintenance Should Go The Way Of The Dodo

While reactive maintenance might seem convenient, it represents a short-sighted approach that can significantly impact an organization’s performance, safety, and financial health.

By transitioning to more advanced maintenance strategies, businesses can transform maintenance from a cost center into a strategic value generator, driving innovation, efficiency, and sustainable growth.

Read ‘Why The Confusion? Prescriptive Maintenance Vs Predictive Vs Preventative’.
Or reach out and contact us.

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A leading metals and mining manufacturer, operating dozens of facilities across North America, faced a significant challenge in monitoring one of their most critical pieces of equipment: a rotary kiln with a rotating rate of 23 RPM, far below the lower limit of standard vibration analysis.

Industry – Metals and Mining //
Machine – Rotary kiln //
Fault Type – Gear wear and misalignment //

<25 RPM Kiln Requires Unique Machine Health Approach

The manufacturer’s massive incinerator, essential for melting sand, is the center of the production process. With only one kiln per facility, any unplanned downtime could result in substantial financial losses and production delays. Complicating matters more, the kiln’s internal temperature exceeds 200 degrees Fahrenheit, creating a hazardous environment for maintenance workers. 

Initially, the company installed Augury’s Machine Health solution on the kiln, which measures temperature, magnetic flux, and vibration levels. However, the kiln’s slow rotation rate made it difficult to collect reliable data. The kiln is a barrel-shaped steel enclosure, approximately 12 feet in diameter, rotating at a mere 23 RPM, far below the lower limit of standard vibration analysis. This low frequency results in minimal vibration energy, making it challenging to detect subtle changes in the machine’s condition. 

Augury’s Low-RPM Monitoring Solution Uncovers Critical Gear Issue

A lot was riding on one machine–not just continued production, but consumer demand, customer satisfaction, and the company’s brand integrity. Recognizing the limitations of standard monitoring techniques, Augury’s team of experts recommended a novel solution: installing a newly developed high-frequency, ultrasonic sensor. It offered two key advantages that addressed the unique challenges posed by the rotary kiln.

Firstly, it was capable of collecting a longer data samples. By extending the sampling period, it could collect data over multiple rotations of the kiln, providing a more comprehensive picture of the machine’s behavior and enabling the detection of subtle variations that might occur only once per revolution.

Secondly, the ultrasonic component of the sensor enabled the detection of very high, ultrasonic frequencies. This capability was crucial for identifying issues in low-speed components within the slow-rotating kiln, such as the gears in the drive mechanism. It can detect early signs of wear, gear mesh, impacting, inadequate lubrication, or misalignment in these components before they manifest as visible vibrations.

The customer strategically placed sensors at key points including the drive end, non-drive end, and the gearbox. This comprehensive coverage ensured that data was collected from all critical areas of the machine.

Almost immediately after installation, an anomaly was detected. The data showed distinctive peaks indicating potential gear wear or misalignment. Augury’s advanced algorithms analyzed the low-frequency data, and identified these subtle changes. An alert was sent, triggering a collaborative effort between Augury’s analysts and the customer’s maintenance team. 

The maintenance team’s initial inspection of lubrication and other elements appeared normal. However, looking at the persistent abnormal data, Augury’s vibration analysts guided the maintenance team to look for specific signs of mechanical looseness in the internal gear. This data-driven approach led to the discovery and subsequent repair of a problem with the gear mesh. 

After the team made the necessary repairs, Augury’s Ultra Low RPM system continued to detect unusual vibration levels. Further inspection led to the discovery of a loose bolt, which was initially tightened but continued to cause issues. The customer’s team decided to reach out to the kiln manufacturer and they learned that a specialized wrench was required for proper tightening of the problematic bolt. Once the correct tool was acquired and used, Augury confirmed the machine finally returned to normal operation.

Crisis Averted, Downtime Avoided, Savings Realized

Without Augury’s low-RPM Machine Health solution and cross-team collaboration, the customer would have continued operating the kiln with undetected issues. The gear would have eventually failed catastrophically, potentially destroying the gearbox, damaging other kiln components, halting plant operations, and posing a severe safety hazard to workers.

Fortunately, with data-driven detection and early action, the customer avoided an estimated 3,696 hours of unplanned downtime. This translates to approximately 154 days of continuous operation and approximately $108,000 in repair costs.

In the metals and mining industry, where supply chain commitments are critical, the ability to deliver products on time can be the difference between retaining and losing major contracts. By collaborating with Augury and championing the new Ultra Low-RPM solution, this leading mining manufacturer not only avoided a potential disaster but reinforced their position as an industry leader committed to leveraging technology and partnership for continuous improvement.

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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.

The line was running at full capacity, pushing out cases of beer in anticipation of game day. The Superbowl was right around the corner and product demand was rising as quickly as the excitement of the thirsty Rams and Patriots fans.

This article was originally published on June 25, 2019.

If It Works, Why Fix It?

As he logged in to the platform to check a machine health alert he received that morning, the Plant Maintenance Manager was a bit skeptical. He’d initially doubted the investment in the continuous monitoring machine health program that had been paid for by corporate.

Original Equipment Manufacturer (OEM) maintenance recommendations had worked fairly well in keeping their assets more or less functional through preventive maintenance tactics. And most problems were simply worn bearings from lack of lubrication or misalignment here and there.

He didn’t really see the value in dealing with anything more complicated. As long as the assets in his care didn’t critically fail unexpectedly and interrupt production, everything would be fine.

Worst. Timing. Ever.

But today was different. The alert he received pointed to a bigger mechanical issue than normal. This signaled a possible emergency within the plant’s filler and seamer equipment. This malfunction could bring down the whole line for hours or longer.

This couldn’t happen at a worse time. Right before the Superbowl—management would get involved and he didn’t need the scrutiny.

Empowered to Act Fast

Using his remote monitoring platform, the Maintenance Manager confirmed that the sensors were correct. They had to act fast. He alerted the repair team to the problems with the filler/seamer and they quickly diverted production to another functioning line.

By shifting as soon as the problem was detected, catastrophic failure was avoided and the equipment repairs were made without expensive, unplanned downtime. Soon, the production line was back at full capacity, overtime costs were mitigated, and the beer was saved. 

The plant avoided a catastrophe that would have lost 960,000 cans of beer by fixing the filler and seamer malfunction before it halted production. The unscheduled downtime for their 24/7 operation could have lead to reputational risk with long-time customers—in addition to profit and production loss.

“It certainly would have taken the whole machine out, which would have halted the line for eight hours or more,” he later recalled.

Canning Concerns

Beverage manufacturers choose aluminum cans because they’re airtight and light-proof. This ensures product quality while still being lightweight, stackable and recyclable. But packaging success still depends on plant equipment working flawlessly. 

The seamer is one of the most critical parts of a can production line because the double-seaming process ensures the package is sealed completely. And this has to happen as soon as the can is filled. When misaligned rolls or eroded chucks in the seamer equipment don’t fold the can body flange and top curl together accurately, the result is incomplete seals that lead to leaking or spoiled product. 

Variations in temperature, machinery looseness, and wear can all cause problems with seamers on a canning line. Toolings can be loose or the chuck and roll can be set up too close or too far apart. This results in split seams, low or high free space, or high seam gaps, among other problems. Issues may also rest with the aluminum can bodies themselves—because the material is easily bent and microscopic differences can result in inaccurate seaming. 

A Million Beers at Stake

Sometimes, a problem in the seaming is detected at Quality Control. This gives technicians a heads-up that values are out of specification, and that equipment needs to be adjusted. But manual tests take time and expertise. In addition, minute issues that can’t be detected by the human eye can be easily overlooked. 

The margin of error was huge that day. Nearly a million cans of beer were at stake and more if there were issues responding to the repair process after a breakdown. Besides the lost product and repair costs, eight hours of unplanned downtime would lead to unanticipated labor and shipping costs as delivery schedules could be thrown off. And if some of the product made with the malfunctioning seamer was accidentally shipped, it might spoil. And this leads to expensive, and reputation-damaging, product recalls, or even legal action. 

Trusting Technology

For beverage manufacturers working on tight timelines while prioritizing product quality, it’s clear that machine health plays a crucial role in their success. Smart and advanced sensors can monitor and protect the canning process at every step.

Monitoring the main drives on core machines, pumps, packers, fillers, seamers, conveyors, and labelers can ensure that even the earliest developing machine faults are detected the moment they arise—and disasters can be averted. 

Big Savings, Speedy ROI

After this save, the Maintenance Manager was sold on this continuous monitoring machine health program. Not only had he kept the line running, but he’d saved an estimated $300,000 in unplanned downtime expenses. That, plus the saved product, added up to an ROI that was 10 times greater than the initial rollout cost. Corporate was pleased to hear about the great success.

As a result, the sensors were later extended to the plant’s other can and glass lines to provide further machine health insights that had the potential to save even more time, money…and beer.

Click here to learn more about continuous machine health monitoring.

The post Save the Beer, Save the Day: Utilizing Machine Health in the Beverage Industry appeared first on Augury.

Thanks to its PepsiCo Labs division, PepsiCo has successfully avoided the much-maligned “pilot purgatory” in digital transformation projects. A recent article, ‘How PepsiCo Avoids Pilot Purgatory with Innovation Partnerships,’ outlines five key lessons from PepsiCo Labs’ approach, focusing on its partnership with Augury to reduce unplanned machine downtime in Frito-Lay manufacturing plants.

Escape Velocity: Avoiding Pilot Purgatory

Launched in 2018, PepsiCo Labs has completed over 100 pilots, fostering strategic partnerships with startups to solve core business challenges. By following the lessons PepsiCo learned along the way, companies can improve their success rate in bringing pilot projects from idea to implementation efficiently, avoiding the common pitfall of pilot purgatory, which affects 75-85% of digital projects, according to McKinsey & Company.

PepsiCo’s Lessons Learned:

1. Be Focused: PepsiCo Labs defines the problem and success criteria before starting a pilot. In Augury’s case, the goal was to reduce costly unplanned machine downtime in manufacturing plants.
   
2. Pick the Right Partner: PepsiCo Labs sources potential partners globally, screening hundreds of companies before selecting the best fit. Augury was chosen for its AI-driven Machine Health Solution, which predicts and prevents machine failures.
   
3. Clearly Define Stakeholders: PepsiCo Labs provides crucial support and mentoring to pilot partners by connecting them to expertise within PepsiCo. Augury CEO/Co-Founder Saar Yoskovitz notes in the article: “PepsiCo Labs supported Augury by giving us access to manufacturing and technology leaders that helped us navigate how to work best with PepsiCo. It was a perfect match of complementary expertise to help us be successful.” 
   
4. Start Smart, But Have Scalability in Mind: The pilot with Augury began in four Frito-Lay factories, allowing for a manageable scope while considering future scalability. Augury’s solution was designed to be easily expandable without significant additional staffing.
   
5. Create a Review Period and Be Ready to Pivot: PepsiCo Labs conducts formal reviews of pilot projects, comparing results from different partners. After one year, Augury’s solution significantly reduced unexpected breakdowns and increased productivity. The article also emphasizes the importance of being willing to end unsuccessful pilots and learn from failures. 

Onward and Upward

“Today, Augury’s Machine Health has been rolled out to 36 Frito-Lay sites in the U.S. and Canada and two more in the U.K. To date, Augury has helped mitigate a whopping 900 unplanned downtime events, effectively dodging an estimated 4,500 hours of unplanned downtime.”

Read the full article ‘How PepsiCo Avoids Pilot Purgatory with Innovation Partnerships’.

The post Beyond the Pilot: Five Lessons From PepsiCo On How To Effectively Scale Innovation Partnerships appeared first on Augury.

A sustainability report using CRANE’s model, which estimates future emissions reductions, shows how companies such as Augury allow manufacturers to immediately tackle environmental issues while also streamlining production. “The report is saying nothing exotic: by running your assets more efficiently, you emit less. It’s a double win,” says Augury’s James Newman.

This article was originally published on September 19, 2022.

Better Production For Plant and Planet

“It’s always good to plan ahead – but not if it paralyzes you in taking action now,” says James Newman, Augury’s Head of Product and Portfolio Marketing. He’s feeling bullish in response to a recent report that highlights how manufacturers can start today – not one day – in cutting their emissions. 

It’s as simple as making your machines run more efficiently. “It’s really not rocket science. Augury’s AI-driven insights into Process Health and Machine Health work to improve production for both your plant and the planet.” 

“It’s really not rocket science. Augury’s AI-driven insights into Process Health and Machine Health work to improve production for both your plant and the planet.”

“Manufacturing represents around 24% of global carbon emissions,” according to the ‘2022 Eclipse Carbon Optimization (ECO) Report – Quantifying Sustainability for Technologies Enabling the Industrial Evolution’. “The overwhelming majority of the carbon intensity comes from energy and waste. Eclipse Ventures’ portfolio companies focus on enabling increased efficiency through higher yield, more productive assets, and ultimately, far lower energy intensity per unit of production. Some of the companies addressing the carbon intensity of the physical world include Bright Machines, Augury,  and Arc.”

Industrial Evolution

The Eclipse Carbon Optimization (ECO) Framework uses the open-source CRANE’s model, which estimates future emissions reduction “much like an investor would evaluate an investment’s future revenue and profit potential.”

Developed by climate nonprofit Prime Coalition and in partnerships with ESG advisory firm Rho Impact, Eclipse Ventures applied this methodology to 11 companies in its portfolio to better understand their carbon reduction potential.

“With our ECO Framework, we are able to dynamically assess a technology’s economic value potential and its environmental value potential, both now and 10 to 20 years into the future,” the report states. “Our goal is to reveal the true promise and impact of that technology in order to build and increase investor and public confidence in companies transforming physical industries.”

“It’s great they are now able to make these kinds of assessments,” says James. “It clearly measures the before and after states of Augury’s solutions so you can see the tangible impact it has on your energy consumption, which by extension is directly tied to your energy emissions. It’s an amazing tool – and will only become more accurate moving forward.”

“We can double a manufacturer’s impact by doing what they should be doing anyway: making their assets run better than they were running before.”

Cutting Emissions. Now. And Without Fuss.

The report estimates what Augury brings to the table in terms of cutting emissions: “Due to higher uptime, more efficient energy use, and higher yield, Augury cuts emissions by ~12%, resulting in 3 MMtCO2 reduced annually by 2040.”

To put it into more concrete terms: saving 3 million metric tons of CO2 annually is the equivalent of taking around 750,000 gas-powered cars off the road every year.

“But what I think will get people most excited is how this doesn’t involve anything exotic,” notes James. “Most sustainability programs involve buying new assets, switching over to alternative fuels or some sort of offsetting – and there’s nothing wrong with any of these efforts. But we can double a manufacturer’s impact by doing what they should be doing anyway: making their assets run better than they were running before.”

Impact: Not Additive But Multiplicative

“And this is really just the tip of the iceberg,” says James. “I see a lot more good news in what the report doesn’t cover. Not only are they using the lowest possible numbers to manage expectations, but the report is also very USA-centric. I’d like to know the numbers on a world level – especially when it comes to the global manufacturers who are our current target verticals.”

As the report makes clear, the CRANE analysis also did not factor in the energy and utilities sector, where Augury is now also active, but only included “traditional” manufacturing rotating equipment based on the company’s existing verticals. “Further, we do not assume any emissions reductions from the elimination of product waste during manufacturing downtime,” the report states. “We will make adjustments based on Augury’s progress in the next report.” 

“Indeed, you’ll see a huge jump in our performance in the next report,” says James. “Especially since it will also include the impact of our acquisition of Process Health leader Seebo. As we leverage our ability to combine what we do on a machine level and on a process level, the impact will not be additive but multiplicative.”

 “It has become even more evident that our physical industries bear the greatest responsibility for fulfilling the needs of people worldwide.”

Time is Ticking

Eclipse obviously takes seriously its mandate to invest in technologies that can drive sustainability today, and not just in some unknown future.

“In recent years, it has become even more evident that our physical industries bear the greatest responsibility for fulfilling the needs of people worldwide,” the report states. “Yet, the aging systems and processes upon which physical industries were built have brought forth dire consequences – our environment is at a critical juncture, and if we do not act now, the well-being of our society and planet will be jeopardized.”

To learn more about how Augury is meeting the challenge in our race against the clock, reach out! Meanwhile, you can also read more about sustainability and ESG here.

The post How Manufacturers Can Go After Sustainability Goals appeared first on Augury.