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Increasing Uptime – Data Science – the way of the future

The maintenance of corrugated machinery involves a lot of nuance – hundreds of thousands of moving parts, each working in tandem and each with its own set of responsibilities and maintenance needs. As parts undergo natural wear and tear from use, they must be carefully monitored to prevent costly downtime caused by failure or related breakdowns. Sometimes, as parts begin to wear out, there are telltale signs, such as decreased output speed, more frequent jams, sounds, and smells, but not all components create such obvious, detectable signs that maintenance is needed. Even with careful adherence to a PM schedule, and even with the most experienced, seasoned operators on the lookout for anomalies, there can still be catastrophic failures or unforeseen malfunctions that take machines offline. As technology advances, there are new ways to leverage early signs of potential issues to detect, and more importantly, predict problems before they happen. That technology has a name: Data Science and it is the key to increasing uptime and profitability of corrugated box plants around the world.

What Is Data Science?

At its most basic level, data science is the intersection of expertise in a specific field, and the gathering, storing, and application of data produced within that field to uncover useful insights that otherwise elude detection or easy perception. Data science is used around the world, spanning every discipline, from detecting and preventing diseases to optimizing baseball rosters; from stopping tax fraud to finding a romantic connection, and everything in between. The field of data science requires a combination of sophisticated data collection and specialized knowledge in the area or field in which the data is to be applied. Depending on the age of the equipment in the facility, corrugated equipment may already be generating and storing volumes of data. However, in most cases, this data is either only looked at retrospectively, or it is not being analyzed at all. Much of this information can be applied insightfully by data scientists to optimize maintenance intervals and equipment performance. But the truly robust ability to predict failures before they begin all comes from another more specialized process called anomaly detection.

What Is Anomaly Detection?

Anomaly detection is a concept used for mining data from machines and other Industrial Internet of Things (IIoT) devices. And while this sounds complicated, at its essence, is the ability to look for any type of activity that breaks with the norm. In corrugated, an anomaly could amount to a strange noise, a jam or lag somewhere on the line, a shudder or vibration that is unexpected, or any number of other occurrences that is unusual.

Even with careful adherence to a PM schedule and the most experienced, seasoned operators, there can still be catastrophic failures or unforeseen malfunctions that take machines offline.

The real utility in applying data science to corrugated lies in predictive maintenance and optimization of maintenance schedules.

While on the surface, each anomaly may be minor if not completely meaningless, detecting and cataloging these anomalies is incredibly insightful to a data scientist, because the anomalies can then be mapped to other problems or breakdowns. When coupled with other operating data and retrospective labeling by machine operators about which anomalies ultimately lead to what specific problems, data scientists and machine learning algorithms can learn to read patterns in machine activity, and they can ultimately be used to alert operators to impending problems while there is still time to fix them.

Increased Profit, Productivity 

The real utility in applying data science to corrugated lies in predictive maintenance and optimization of maintenance schedules. With insight that can detect issues before they start, a proactive approach can be taken to maintenance. Rather than having a machine unpredictably break down during a big project or during peak hours, data science can instead alert the operators and facility managers to a need for maintenance while there is still time to schedule it during non-peak hours. This type of changeover can also happen as routine parts replacement rather than a major repair, which ultimately means lower cost. Think of it as a low engine oil indicator light clicking on in a car, as opposed to a bone-dry engine seizing up and failing completely – the cost savings on the labor alone is enormous, and it is a matter of a quick trip to an auto parts store for an oil top-off, instead of sending the car back to the dealer or even the factory for repair. Another crucial benefit is more accurate maintenance intervals potentially extending the lifespan of parts. Without the use of data science, it is up to a maintenance manual to provide what is essentially an estimate of when a replacement should happen. For another automotive example, this equates to changing a timing belt – there is a recommended mileage interval for changing a timing belt, but nobody really knows when a timing belt is failing until it actually fails. So while changing it out at 100,000 miles preempts major maintenance, you may be spending a day or two without your car and paying for a new timing belt while your existing timing belt is still good for another 30,000 miles. Likewise, data science applied to anomaly detection can lead to a true assessment of when failures are imminent, so costs on suggested maintenance can be deferred until maintenance is actually required.

How To Get Started

Data science as applied through machine learning is not new to the world of manufacturing, but it has just recently begun bursting onto the scene in corrugated. Our manufacturer agnostic machine learning platform for corrugated, Helios, has begun rolling out its anomaly detection machine learning dashboard as of Spring 2021. It is important to note that this does not simply replace the need for maintenance personnel, but rather, requires their buy-in and expertise to help the algorithm become “smarter.” When equipping the Helios platform, operators catalog anomalies over a series of weeks and months. Over time, this allows the algorithm’s machine learning to take place, which can lead to predictive maintenance reminders and other insight and analysis for optimizing maintenance intervals and maximizing uptime. Ultimately, Helios serves as a tool that expands and sharpens the insight of the existing maintenance operators, who are now able to couple their own experience with new unprecedented levels of detail detected by the Helios algorithm, increasing uptime and maximizing the profitability of the facility as a whole.

Mark Peyton is the Director of Aftermarket at  SUN Automation Group. He brings decades of industry experience and expertise to SUN where he has held many customer-focused positions. Prior to his 20-year career with SUN, he worked from Langston and United. He can be reached at mpeyton@sunautomation.com or 410-472-2900

Originally published in Corrugated Today, July/August 2021 Issue




Data Science, Machine Learning, and the Future of the Corrugated Industry

Data Science is the ability to take raw data, from a wide array of sources, and make it useful, actionable and valuable. Data science, IIoT and Machine Learning have recently come to the forefront of the technology revolution in corrugated, bringing unprecedented insight into the production floor and beyond. Though IIoT (Industrial Internet of Things) technology has been around for decades, IIoT technology custom to corrugated converting applications is just starting to enter the industry. As we step into this new territory, it is important to understand how these technologies work to fully capture the value it can bring to the future of your operations.

In 2021, Sun Automation unveiled a new corrugated IIoT platform, Helios – expert-driven IIoT and machine learning technology tailored to the needs of the corrugated industry. In developing this platform, we invested our time and resources into data science, IIoT and machine learning and what it could mean for the future of corrugated converting.

Here’s a brief overview of how these technologies work and the relationship they share with the success of your organization.

Live Monitoring

The first step to data science is capturing the data. To capture important data in corrugated, robust sensors and trackers are used to quantify your converting operations into numbers. Components such as the machine’s PLC, heat sensors, speed sensors, tracking software, and other data-capturing hardware help to create a clear picture of your operations in real time. During the live monitoring stage, the IIoT technology will capture and store the machine’s data and present this data to you through visual dashboards.

Since data science is only useful if the data it first captures is clear and accurate, it is important to check the integrity of your live monitoring system regularly to ensure the data it captures is indeed true. Though it is the most basic stage of data science, live monitoring is the foundation for the more complex, smart technologies to come.

Machine Learning

Humans learn to identify and navigate the world around us through experiences and recognizing patterns, often taught to us by others (like parents, teachers, mentors) – machines can now do the same.  As technology advances, IIoT platforms have acquired a higher level of logic and intelligence, through data science, allowing the software to learn how to segment the data into useful information, supervised and unsupervised.

Unsupervised Learning

As your IIoT platform begins monitoring the equipment live, it starts to understand the normal productivity levels, speed, temperature, alignment, and general quirks of the machine, on its own. Once the platform is aware of the normal patterns of your equipment, it becomes skilled to flag data that deviates from the norm. Without user input, the technology can tell which components deviated from their normal speed, temperature, alignment etc. at a specific point in time – hence “unsupervised learning”. Though the software has not yet learned what the anomaly means, it knows the metrics are out of the norm from what it traditionally records on that machine, and it brings the deviation to your staff’s attention.

As anomalies are flagged, production staff become more aware of the factors affecting the productivity, efficiency, and safety of their equipment, helping to prevent and minimize downtime. At this stage, anomaly detection is the first tier of IIoT intelligence – simply recording anomalous data with a question mark. Combined with the knowledge of a veteran operator, that is when supervised learning begins and the power of IIoT comes full circle.

Supervised Learning

Supervised learning takes IIoT platforms to a new level of intelligence, through user input. Just like veteran operators become masters of machines after decades of listening, tinkering, and learning, the more the platform observes and interacts with your machine and staff, the more valuable the data science becomes. As downtime and flagged anomalies occur, your staff can label the recorded data as events such as maintenance intervals, known equipment failures, sheet jams etc. in the platform. Combine this operator-labeled data with the metrics from the machine, and the IIoT platform will learn to identify the patterns that lead to known events and can notify operators of expected downtime. This level of artificial intelligence can provide insight to improve maintenance schedules, part procurement and ultimately equip your staff for maximum productivity and minimum downtime.

As anomalies are identified, solved, and labeled in the IIoT platform, through supervised and unsupervised learning, your current and future operators become more equipped with the knowledge they need to better manage the ups and downs of your unique operations. Machine learning is unique to each asset in your fleet and the data models are trained specifically to your operations, creating an intelligent digital blueprint for the success of your equipment. That is the beauty of data science and machine learning, the more experience the platform has with your equipment and staff, the smarter it becomes.

The Future of the Corrugated Industry

Data science and machine learning technologies could easily be the solution to many common pain points in the corrugated industry. With its ability to report data not readily understandable, identify the source of problems without operator intervention and predict machine failures from legacy data, an IIoT platform provides incredible opportunity for increased uptime, improved performance and a high ROI. Solidifying your future starts now. Since operator interaction with the IIoT platform is so critical to prevent future downtime, the time to invest in an IIoT solution is soon – before your veteran operators retire, so this recordable data is used for machine learning and benchmarking your equipment.

We believe the future of the corrugated industry involves personalized, artificial intelligence that works alongside your production staff to create the most intelligent, efficient and profitable systems. We believe, the future is Helios. Learn more about the Helios IIoT Platform, powered by Sun Automation, and calculate your ROI to see how data science and machine learning could impact your future in the corrugated industry.


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Introducing Helios, The New Corrugated AI/Machine Learning Platform

(Glen Arm, MD February 26, 2021)  — Helios, the new AI and Machine Learning platform tailored specifically to the corrugated converting industry, launched today. The platform is OEM-agnostic and engineered to provide corrugated manufacturers access to robust, actionable insights into the performance of their machines — enabling minimized downtime, optimized maintenance schedules, and maximized profit. Helios is a product of SUN Automation Group.

“IIoT makes every bit of data actionable,” says Helios Director of Technology Matthew C. Miller. “So many corrugated plants rely on human intuition and experience to drive their decisions. With Helios, anomalies that are imperceptible to even the most well-trained operators can be detected in real time and acted upon. And the machine learning capabilities will mean that the platform only gets smarter the more data and user reactions that it is able to process.”

The new platform is designed to minimize downtime, maximize profitability, and decrease the opportunity cost associated with only taking machines offline for preventative maintenance (as opposed to for major malfunctions). These high-level benefits manifest themselves in specific cost and resource-optimizing operational benefits and actionable insights. 

Some of the most beneficial insights are preventative/proactive parts ordering, knowledge about the exact time and cost of parts replacements, the ability for operators to pinpoint the source of slowdowns and other issues, and operator-efficiency training to help machine operators learn and adapt to best practices.

“We understand that data is only as powerful as the actionable insights it can provide,” says Chris Kyger, President, SUN Automation Group.  “That’s why we are so excited to bring Helios to the corrugated industry. This incredible technology will help box plants increase productivity and efficiency while reducing costs and downtime.” 

Helios provides core insights from an accessible, user-friendly dashboard enabling three key benefits: remote monitoring, predictive maintenance, and anomaly detection. 

Remote monitoring provides deep insights into current and historical machine operation and performance that can be seen and accessed in real time from any device. Predictive maintenance optimizes machine maintenance intervals using artificial intelligence that adapts based on the machine operation and usage. Anomaly detection notifies users about abnormal machine states that allow operators to react to a potential issue before the failure occurs. More robust predictive analytics will be phased into the platform over time.

Corrugated manufacturers will have access to a free Helios demo, allowing them to experience the platform. The site also provides a Return On Investment calculator that can showcase the benefits that Helios can offer to operations of all sizes and scales. Visit the Helios website to view more: https://gohelios.us/ 

SUN Automation Group is a corrugated converting industry leader in innovative solutions and aftermarket support and services. Since 1986, SUN has led the way for automated production solutions in the corrugated manufacturing space. 


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Why the Corrugated Industry needs Machine Learning Technology

As the capability to make industrial machines more intelligent improves, there are now more reasons than ever to invest in technologies that will future-proof your equipment. The retirement of many veteran machine operators coinciding with the influx of new, undertrained workers is at the root of many headaches for converters today. An increase in unplanned downtime, operator safety risks and machine maintenance costs are just some of the consequences of this staffing predicament.

However, the traditional operator challenges plaguing the industry these last few decades are issues that have been solved in many other manufacturing sectors through technology. So why not in corrugated too?

There are now IIoT (industrial internet of things) solutions designed to increase your bottom line and compensate for the qualified operator shortages in the corrugated sector. With remote monitoring, machine learning and artificial intelligence, IIOT can not only read and report your machine data, but over time can learn and predict maintenance needs to your production staff. This is the kind of visibility that allows staff to make more informed, strategic decisions for the betterment of your organization.

 Mitigate workforce challenges

As more veteran operators are rightfully entering retirement, converters are finding their skills and experience incredibly hard to replace. Their experiential knowledge acquired through many years of operating the same converting equipment may have never been passed down, walking out the door with some of your best people. As younger, less-skilled operators and maintenance technicians move into those positions, you may have noticed that your operations have become a bit more clunky and inefficient, since they simply don’t know the quirks and tendencies of the machines.

Corrugated IIOT technology brings the power of machine learning and artificial intelligence to your production floor, providing actionable insight used to optimize your fleet operations and maintenance activities effectively. The software acquires a wealth of tribal knowledge as your team interacts with it, training the machine to later predict machine failures and suggest submitted resolutions – educating operators for years to come. In the age of endless information at your fingertips, it’s time to use that technology to capture and share key equipment knowledge that keeps your operators safe and your presses running.

Increase your bottom line

As your operators become more skilled and informed by IIoT systems, the results are simple: cost savings and reduced downtime. With the average cost of downtime at $1,000 an hour, according to industry benchmarks, you need to keep your unplanned downtime to a minimum to remain profitable. IIot software is capable of saving time and costs by notifying your team of optimal maintenance intervals, allowing your team to consolidate schedules for maximizing your technicians’ time. Additionally, the visibility provided by IIoT software allows your production staff to quickly identify machine anomalies and, overall, optimize your fleet and staff. Multiply these efficiencies across multiple machines, and your production floor’s ROI increases dramatically.

Connect and inform leadership

Visibility is the key to informed decision making. Being aware of your production numbers, unplanned downtime, maintenance schedules and more, is crucial to optimizing your production floor for efficiency. With corrugated IIoT software, your production staff have the machine-learning tools they need to capture and predict important events driving the productivity and safety of your converting equipment. However, without this technology, equipment knowledge tends to be siloed, maintenance schedules are based on tradition rather than data and your operations slowly fall behind the competition. Effective leadership is informed leadership and with the machine-learning technology of corrugated IIoT software, your staff will have actionable data to structure their fleet and team for maximum productivity and profitability.

The future is here

The cost of a single converter developing an IIoT machine learning software for their plant is simply unreasonable – it would take years to see your ROI. But unless you’ve purchased new capital equipment in the past few years, your existing machines may not be equipped with the technology needed to digitize your operations and data. That is why Sun Automation has created a IIoT, machine-learning solution for converters seeking visibility into their machine and operator performance – Helios. In 2021, Sun Automation launched Helios, a AI/machine learning platform that monitors, learns, and predicts important events driving the productivity, safety and profitability of your corrugated equipment. We are excited and eager to bring the power of IIoT to the corrugated converting industry! Visit the helios website to learn more, calculate your ROI with Helios and schedule a live demo >>> www.gohelios.us


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