How to Attract a Younger Workforce in the Manufacturing Industry: The Challenge of Perceptions

How to Attract a Younger Workforce in the Manufacturing Industry: The Challenge of Perceptions

How to Attract a Younger Workforce in the Manufacturing Industry: The Challenge of Perceptions

By Team ProcessMiner

The manufacturing industry is facing a skilled labor shortage. As the industry looks to the future, one of its biggest challenges will be attracting a younger workforce. The problem is that the current perception of manufacturing jobs is inaccurate. Many people – especially millennials and members of Gen Z – still think of manufacturing as dirty, dangerous work with low pay and little room for advancement. However, this couldn’t be further from the truth.

In reality, manufacturing jobs have come a long way from the days of manual assembly lines. Today’s facilities are clean and safe, and workers are paid a good wage for doing skilled work. On top of that,  manufacturing facilities are equipped with high-tech equipment, including computers, robotics, and artificial intelligence.

However, despite these advances, the manufacturing industry still struggles to attract young workers. In order to change outdated perceptions, manufacturers need to do a better job of marketing themselves as modern and dynamic workplaces where employees can have fulfilling careers.

To attract a younger workforce, manufacturers need to focus on five key areas:

  • Artificial intelligence and technology: Emphasize the high-tech aspects of the job. Many people don’t realize how high-tech manufacturing has become. Facilities are now equipped with computers, robotics, and artificial intelligence. This high-tech equipment is operated by skilled workers who need to have training in computer science and engineering. By emphasizing the high-tech aspects of the job, manufacturers can attract workers who are interested in these technologies.
To attract a younger workforce, manufacturers need to focus on four key areas
  • Offer competitive salaries and benefits: Salaries in the manufacturing industry have lagged behind other industries for years. But as the shortage of skilled workers continues to grow, manufacturers are starting to offer competitive salaries and benefits packages. This is especially true for positions that require advanced skills or training. By offering competitive compensation, manufacturers can attract top talent to their facilities.
  • Provide opportunities for career advancement: Many people see manufacturing jobs as dead-end positions with no opportunity for advancement. But this just isn’t true. There are many opportunities for career advancement in the manufacturing industry. The path to career advancement and management may vary depending on the company, but it typically includes several steps. For example, a worker may start out as an operator and then be promoted to a supervisor position. From there, the worker may be promoted to a manager position. director role, vice president, etc. So, by providing these opportunities for career advancement, manufacturing companies can attract and retain workers.
  • Company culture: When it comes to company culture, how can the manufacturing industry attract young people? It starts by understanding what they want. A study compiled data from more than 1,000 respondents between the ages of 18 and 34 to find out what motivates them when searching for a job. The results showed that 80 percent of respondents said “a company’s culture and values” were very important to them when considering a job. Other important factors included “opportunities for career growth” (78 percent), “competitive pay” (74 percent), and “work/life balance” (70 percent). Clearly, culture and values are key when it comes to attracting young talent.
  • The education system and the changing needs of the manufacturing industry: There is a perception that you need a four-year degree to get a good job – when in reality, many good-paying jobs in the manufacturing industry only require a two-year degree or vocational training. Changing this specific perception of manufacturing jobs could motivate more young people to pursue them. One way this can be done is by partnering with schools and community organizations to provide career exploration opportunities for students. We also need to invest in vocational training and apprenticeship programs so students have the opportunity to earn while they learn. By taking these steps, we can ensure the skilled labor shortage does not become a crisis for the manufacturing industry – or for our economy as a whole.

The good news is that the manufacturing industry already has a lot to offer in terms of all of the above. And for the most part, manufacturers have always put an emphasis on company culture, social responsibility, and continuous improvement. 

In terms of continuous improvement, ProcessMiner is the industry-leading AI platform delivering unprecedented autonomous improvements in manufacturing operations. Leveraging the latest state-of-the-art artificial intelligence and machine learning technologies to help manufacturers optimize their production processes, ProcessMiner automatically identifies process improvements and in real-time, implements them autonomously, without the need for human intervention. It’s a manufacturing game changer.

In terms of company culture and social responsibility, now, more than ever, workers are looking for opportunities to use their skills to make a difference in the world. Young people want to know their work is purposeful, and that they are contributing to something larger than themselves. Luckily, there are plenty of stories of businesses in the manufacturing industry making positive social and environmental impacts—it’s just a matter of getting those stories out there! 

The Manufacturing Skilled Labor Gap and Social Media

Fortunately, the internet provides plenty of opportunities for manufacturers to share their stories and connect with potential employees. Platforms like Instagram, Twitter, Facebook and LinkedIn offer creative ways to showcase your company culture and values, as well as highlight any community initiatives you may be involved in. Video content is also extremely effective in this regard—after all, millennials are said to be “visual learners.”

By creating engaging content and promoting it across social media channels, you can reach a wider audience of potential employees and get them excited about working in the manufacturing industry! 

Final thoughts… The manufacturing industry is facing a skilled labor shortage, which we all know, will only worsen as baby boomers retire en masse in the coming years. The time is now for manufacturers to start thinking about how they can attract a younger workforce. 

By taking steps now to rebrand manufacturing as an attractive option for young people, you can ensure that your business will have access to the talent it needs to thrive well into the future.

Bridging the Skilled Labor Gap in the Manufacturing Industry

Bridging the Skilled Labor Gap in the Manufacturing Industry

Bridging the Skilled Labor Gap in the Manufacturing Industry

By Jeff Fulgham, ProcessMiner

The skilled labor gap is a real and pressing concern for manufacturers. As baby boomers retire or leave the workplace, there are simply not enough skilled workers to replace them. This is expected to have a major impact on the manufacturing industry as companies struggle to find the talent they need to keep production going.

In fact, according to a study by The Manufacturing Institute, more than two-thirds of manufacturers are concerned about the lack of skilled labor available to replace these retiring workers. 

According to McKinsey, in the next ten to fifteen years, the pulp, paper and packaging industry will be under an immense amount of pressure to keep up with rapidly changing developments. New business models brought about by an increasingly online world, along with a heightened need for innovation and commercialization of products, will mean that the talent pool within these companies will be put under immense strain.

Furthermore, digitalization is set to have a profound effect on manufacturing processes – meaning that even the content of work will be transformed. Consequently, it is clear that forest-products companies must start to invest in their talent pool now, in order to avoid being left behind in the years to come.

The days of workers toiling away in dirty, dangerous factories are coming to an end. With the rise of automation and artificial intelligence, many jobs that have traditionally been performed by human beings are now being done by machines. This shift is already underway in the pulp, paper and packaging industry, where 60 percent of all tasks can now be automated. In the coming years, this trend is only going to accelerate, with more than 30 percent of physical and manual skills becoming obsolete.

This presents a major challenge for companies in the industry, who will need to reskill their workforce to keep up with the times. But it also presents an opportunity. Those companies that are able to embrace the future of work will find themselves with a major competitive advantage.

So the question is: which side are you on?

As baby boomers leave the manufacturing workforce, they take with them years of experience and knowledge.

The labor shortage in the manufacturing industry has been steadily worsening over the years as more experienced workers retire without passing on their skills to the next generation. The result is a widening skills gap that threatens to hamper the industry’s ability to compete in the global marketplace.

The knowledge these baby boomers are taking with them cannot be easily replaced, which means the lack of experienced workers that can operate and maintain sophisticated equipment is a real and present danger to the manufacturing industry.

That being the case, manufacturers are turning to artificial intelligence (AI) to help bridge the gap. By leveraging AI technologies, manufacturers can train machines to perform tasks that previously required human expertise.

Big Data and Industry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry - Paper Factory

AI can help to close the labor gap in three ways: by providing a digital workforce that can operate 24/7, by training the next generation of workers, and by improving productivity, thereby reducing the need to replace workers one-for-one.

AI in manufacturing can be used to help with things like quality control and product inspection. By using AI, manufacturers can reduce their reliance on human labor. This is especially helpful in industries where there is a lot of repetitive work that needs to be done.

  • A digital workforce is not subject to human limitations, such as breaks or vacations. They can also be easily scaled up or down to meet changing labor needs. Additionally, because they are not human, they do not need to be paid and can work for free.
  • The second way AI can help is by training the next generation of workers. By using historical data and collecting domain expertise from current workers, AI can create models that show the most efficient way to complete a task. These models can then be used to train and support new workers, significantly reducing the time it takes them to ramp up and be productive. 
  • Properly deployed, the combination of predictive analytics and AI can provide next-level insights that can reduce the need for human intervention and allow few employees to deliver the output/impact of many. For example, by predicting a problem before it occurs and taking action to correct it in real-time, manufacturers have been able to reduce staff, improve quality and increase production.

ProcessMiner, an industry-leading AI platform, helps manufacturers autonomously optimize product quality, reduce waste, save raw materials, and improve overall equipment effectiveness, 

ProcessMiner is at the forefront of helping manufacturers combine IT and OT data to unlock hidden insights and deliver returns of more than $500K per year, per manufacturing line. The ProcessMiner platform automatically delivers improvements to overall operational effectiveness in the areas of availability, performance, and quality.

Manufacturing Needs to Stay Competitive in Today’s Ever-Changing Business Landscape

The manufacturing industry is changing rapidly, with artificial intelligence and technology playing an increasingly important role. Manufacturers that keep up with the changing landscape of technology and make it known that these advancements are being used will appeal to young workers. Potential employees want to see that the company is modern and keeping up with current trends, and investing to improve workers’ skills – factors that are important to many younger generations.

AI can provide a number of benefits to the manufacturing industry, including increased efficiency, improved quality control, and reduced costs. In addition, AI can help manufacturers train new employees and keep existing employees up-to-date on new technologies and processes. By investing in AI, manufacturers can stay competitive in today’s ever-changing business landscape.

Jeff Fulgham - ProcessMiner

Jeff Fulgham, ProcessMiner Business Advisor

Jeff Fulgham is a Senior Executive with over 40 years of experience in corporate strategy, strategic marketing, enterprise sales, and M&A. Jeff is passionate about innovation and aligning unique solutions with unmet customer needs to solve big challenges.

Jeff’s experiences include executive-level roles at General Electric, Banyan Water, a venture capital-backed startup, and Solenis, a private equity-owned carve-out. He has successfully led global marketing, services and research teams, acquisitions and divestitures, and is a recognized expert in addressing the global water crisis, helping major global clients optimize water & energy resources and reduce waste.

AI Supporting Effective Wastewater Operations

Big Data and Industry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry

AI Supporting Effective Wastewater Operations

As the skilled labor gap continues to widen in the wastewater industry, treatment facilities are increasingly turning to on-premise SCADA, Edge Computing, and Cloud AI systems to help monitor plant operations.

By Stephen Vasconcellos, Ph.D., ProcessMiner Scientific Advisor

When it comes to wastewater operations, having accurate and timely data is essential for efficient decision-making. While humans are capable of collecting and analyzing this data, artificial intelligence (AI) can help take things to the next level.

In today’s ever-changing landscape, wastewater facilities are being forced to adapt and change the way they operate. The pandemic has created a perfect storm of supply chain issues and manpower shortages that have put a strain on these facilities. In response, many facility operators are re-thinking the way they do business.

Thanks to its ability to quickly process large amounts of data, AI can make sense of patterns that would otherwise be difficult for humans to spot. This can help wastewater operators more effectively manage their systems and improve the quality of service they provide.

The Wastewater Industry is in the Midst of a Skilled Labor Gap

One of the most important changes is in the way that wastewater treatment plants are monitored. Historically, the first line of defense has been the professionals who monitor these systems daily.

However, with the increased demands placed on these workers, it is becoming more and more difficult to maintain this level of monitoring.

Big Data and Industry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry - Paper Factory

Operators at small municipalities are asked to wear multiple hats, and staffing levels can be dangerously lean. Often they are the water, wastewater and street departments.

Mid-sized municipalities operate 24/7 but no one is there during the night in case an issue arises. 

On top of that, the wastewater industry is in the midst of a skilled labor gap with an aging workforce and few young people coming in to replace them. As baby boomers retire, the lack of qualified workers will become even more pronounced.

Wastewater operators play a critical role in protecting public health and the environment – as wastewater is one area where a mistake can have catastrophic consequences.

As the skilled labor gap continues to widen in the wastewater industry, treatment facilities are increasingly turning to on-premise SCADA, Edge Computing, and Cloud AI systems to help monitor plant operations.

These systems can provide real-time optimization on a variety of critical metrics, including influent wet well levels and flow rates, pump run status and cycle count, blower status, pump runtime hours, flow totalization, pump failures, and power failure and generator status.  

These systems provide simple alarm dialers for notifications, which can be a valuable tool in keeping the plant running smoothly.

By constantly monitoring these key operational indicators, treatment plants can reduce downtime, improve efficiencies, and ultimately provide better service to their communities.

Stephen Vasconcellos, Ph.D., ProcessMiner Scientific Advisor

Dr. Vasconcellos earned a B.S. in chemistry from Rensselaer Polytechnic Institute, and M.S. and Ph.D. degrees in physical chemistry from the University of Massachusetts, Amherst.

He was a member of the American Chemical Society, and a division member of Colloid and Surface Science and Environmental Chemistry. He completed the IRI Technology Manager Course at Harvard University and GE’s TLDC in 2016.

Data Science: How It Can Potentially Transform the Manufacturing Industry

Big Data and Industry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry

Data Science: How It Can Potentially Transform the Manufacturing Industry

The manufacturing industry is ripe with opportunities to utilize data science to bring in operational improvement and sustainable improvement, which will reduce the consumption of raw materials, process variability, drive down costs, increase throughput and support a sustainable environment.

By Mahendra Reddy, Principal Data Scientist, and Dr. Chitta Ranjan, Director of Science, ProcessMiner

Over the past few decades, the manufacturing industry has gone through multiple stages of transformation aided by four industrial revolutions. Now we are on the cusp of another major revolution led by data science and artificial intelligence. Though data science is pervasive in many industries, the manufacturing industry by and large continues to make decisions based on past experiences. 

However, the manufacturing industry is ripe with opportunities to utilize data science to bring operational improvement and sustainable improvement, which will reduce the consumption of raw materials, process variability, drive down costs, increase throughput and support a sustainable environment.

As an example, at ProcessMiner our research and development team has developed a data science solution that has helped an F500 and one of the largest plastic injection molding manufacturers to reduce scrap rates by more than 50 percent.

The Power of Data Science to Solve Real-World Manufacturing Problems

There is a vast amount of data available in the manufacturing industry. But many manufacturers still struggle to collect and use data due to various reasons. The major one is the presence of legacy systems. 

As the first step, manufacturers must start by laying the groundwork for a safe and secure environment before they start extracting valuable data from machines as the manufacturing industry is highly prone to cyber-attacks.

Creating internal systems of data collection such as plant historians and the use of IoT to extract data from machines is perhaps the largest source of data in the manufacturing industry.

Once safe and secure systems are established to collect data, manufacturers can use data science for various applications:

Big Data and Industry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry - Paper Factory
  • Predictive Analytics: Predictive analytics in the manufacturing industry can be used in multiple ways – real-time monitoring of machine performance, prediction of quality of the products, and scrap reduction, to name a few.
  • Preventive Maintenance: Machine breakdown and unplanned downtime are the major contributors to increasing overhead costs in manufacturing. According to some estimates, almost every factory loses at least five percent of productivity, with experiences as much as a 20% loss, due to downtime. Data science can be used to predict machine breaks in advance so that manufacturers can plan the downtime better.
  • Demand Forecasting and Inventory Management: One of the basic goals of manufacturing is Just-In-Time (JIT) – Making the right products in the right quantities at the right time. Demand forecasting is an important strategy to achieve this goal. Historical data can be leveraged to forecast the demand for a product which will enable the manufacturers to manage inventory effectively.
  • Supply Chain Management: Supply chain management is a complex problem to solve due to its unpredictable nature. Data science can be used to analyze vast sets of data from multiple sources such as inventory, work-in-processes, inbound shipments, present market trends, consumer sentiments, along with weather forecasts, and make optimal decisions.

The Journey Toward AI-Enabled Autonomous Optimization for Continuous Manufacturing

When ProcessMiner began the journey towards AI-enabled autonomous optimization for continuous manufacturing, we knew it was going to be a game-changer in the manufacturing industry. Fast forward to a few years later, we made a significant impact in the pulp and paper and plastics industries. 

Our industry-leading autonomous control solution delivers process improvement recommendations and parameter control changes in real-time to the production line. Our platform ensures high-quality output while driving reductions in scrap, defects and waste commonly encountered in complex manufacturing processes.

Below are a couple of successful examples:

  • ProcessMiner team developed a data science solution that helped an F500 plastic injection molding manufacturer to reduce scrap rates by more than 50%.
  • Our artificial intelligence platform achieved unprecedented autonomous chemistry control for the tissue mill. Using a closed-loop controller in conjunction with quality parameter predictions, the mill was able to control its strength chemistry autonomously to ensure optimal chemical feed and adhere to target parameters. As a result, there was a 25% reduction in wet strength chemistry, a 33% reduction in wet tensile variation, and a 98% increase in target adherence.

There are various challenges to applying data science in manufacturing. A few of them are:

  • Lack of subject matter expertise: SME knowledge is of paramount importance in business understanding, knowing the manufacturing process, and identifying the problem to be solved.
  • Lack of readily usable data: The data in the manufacturing industry in most cases is not readily available to be used. It needs to be sourced from various data sources and be made available to extract insights.
  • Complex nature of the manufacturing process: The data science community has achieved state-of-the-art solutions in areas such as computer vision and natural language processing. But the manufacturing process is highly complex and has temporal and spatial relationships between multiple process variables. There is a lot of scope for pushing the boundary further to achieve better results.

The manufacturing industry is undergoing a significant transformation in recent years. In this article, we have outlined how data science can potentially transform the manufacturing industry if we set up the right tools for data access and get the required subject matter expertise. 

As we have demonstrated successfully, it is critical that the manufacturing industry takes full advantage of one of its most important assets, DATA. Coordinating the implementation of manufacturing process data with applied data science and domain expertise is a major lever in modernizing the production process.

Authors: Mahendra Reddy, Principal Data Scientist, and Dr. Chitta Ranjan, Director of Science, ProcessMiner

Mahendra Reddy

Mahendra Reddy

Principal Data Scientist, ProcessMiner

Mahendranath Reddy is the Principal Data Scientist at ProcessMiner Inc. He is an accomplished data scientist with 6+ years of experience in applied machine learning. Previously, he worked at Tiger Analytics LLP and Ericsson as a data scientist.

He graduated from the Indian Institute of Technology Madras in 2013 with a bachelor’s degree in Mechanical Engineering. While in college, he is a high achiever and he published a paper in the International Journal of Heat and Mass Transfer in March 2014 under his Professor’s guidance. While working at Ericsson, he won the ACE Award which is given to individuals for their focused and value-added contributions.

Dr. Chitta Ranjan

Dr. Chitta Ranjan

Director of Science, ProcessMiner

Dr. Chitta Ranjan is the Director of Science at ProcessMiner, Inc. He is author of the textbook, “Understanding Deep Learning: Application in Rare Event Prediction.” He earned his Ph.D. in Statistics from the School of Industrial and Systems Engineering at Georgia Tech. Prior to this, he completed his bachelor’s in IE from IIT Kharagpur, India.

He specializes in developing real-time AI systems. He is a recipient of best paper awards for his research. He also serves on multiple boards of directors in the analytics and engineering space. He has also worked at Pandora Media, and eBay.
Dr. Ranjan is an active contributor to the AI community and an influencer. His publicly available content on YouTube, TowardsDataScience, GitHub, Python Library (PyPi), journal publications, and other open-source contributions have over half a million readers. Globally, he has over 10k followers.

Big Data and Industry 4.0: Significant Cost Savings in the Pulp and Paper Industry

Big Data and Industry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry

Big Data and Industry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry

Perceptions in the pulp and paper industry are that we are behind other industries, and this is not necessarily correct; the reality is enough thought leaders and entrepreneurial activities are approaching our industry, and, in fact, we are right on schedule.

By Dr. Chris Luettgen

Professor of the Practice, Chemical and Biomolecular Engineering College of Engineering; Associate Director, Pulp and Paper Renewable Bioproducts Institute; Director, Georgia Tech Pulp and Paper Engineering, Undergraduate Certificate Program and Foundation

In the pulp and paper industry, there is a lot of excitement about the potential of big data analytics and Industry 4.0 to optimize plant operations further. Driven by global demand, sustainability efforts, an aging workforce and margin pressures, the time has come for the pulp and paper industry to reap the full benefits of AI.

Industry 4.0 involves using digital technologies to create smart factories that are more efficient and responsive to customers’ needs.  While manufacturers have long been using data analytics for process monitoring, fault detection and equipment health prognostics, these new tools represent the latest trends in manufacturing.

With the end goal of increasing levels of productivity, improving product quality, reducing scrap and lowering costs, simply put, big data delivers the insights and outcomes necessary for success. Digital technologies, like machine connectivity and intelligent automation, leverage big data and analytics to deliver these outcomes.

Big Data and  aIndustry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry - Robot

Many manufacturers are exploring these new technologies to gain a competitive edge in the market. However, digital transformation is still uncharted territory for most pulp & paper companies. Only 15 percent of endeavors are eventually deployed successfully in production, often due to the lack of a clear priority and resources, both the people and the investment levels to succeed. While technology lays the groundwork for managing and analyzing this data, success is dependent on people using the resulting insight, and that is where the challenge lies and state-of-the-art AI platforms have helped.

The pulp and paper industry is striving to become more digital but lags behind other sectors in its application of these technologies. TAPPI’s recent survey on pulp and paper manufacturing practices found that 36 percent of pulp and paper manufacturers were already using some type of Industry 4.0 technology (automation, connected devices, robotics), an increase from 25 percent in 2016. However, except for automation systems (21 percent), few pulp and paper producers reported significant impacts from their investments in smart factory technologies: 51 percent said their investment had a minimal impact on productivity and only four percent said they experienced substantial gains in productivity (defined as anything more than 10 percent). In addition, only 16 percent reported gains in quality with Industry 4.0 applications.

Big Data and Industry 4.0: Exciting Opportunities Offer Significant Cost Savings in the Pulp and Paper Industry - Paper Factory

The pulp and paper industry is lagging behind other sectors in applying Industry 4.0 technologies like big data analytics, robotics, and connected devices. As a result, manufacturers are struggling to find ways to harness the power of these new technologies without sacrificing the principles that have made pulp and paper companies successful for more than a century. Nevertheless, pulp and paper producers need to recognize the potential impact of these new tools. If a company does not adopt these new technologies quickly enough, competitors will gain a significant advantage in the market, with respect to raw material costs, quality to specification, and consistent downstream customer satisfaction.

There is a perception that the pulp and paper industry is behind other sectors somehow. Still, I believe the reality is that we are not behind, as there are significant activities of thought leaders, and entrepreneurs entering our industry. There are demonstrations occurring throughout our industry that are proving the real financial benefits of predictive and constantly updated machine learning. 

True AI is only a breath away.  I also do not think that perceptions are important. What is essential, at least to me, is that pulp and paper producers invest in whatever they believe provides them with a competitive advantage. And if that is Industry 4.0 technologies, so be it!

The savings and cost justifications for investment in AI and smart manufacturing are multi-fold: chemical savings, energy savings, reduction in waste production, all of which will translate to higher yield, and overall reduced raw material costs. In addition, the pulp and paper industry is fascinated by big data analytics and Industry 4.0 – researchers expect AI to be an important driver of technological change in pulp and paper production within the next three years. However, pulp and paper producers are struggling to find ways to harness the power of these new technologies without sacrificing the principles that have made pulp and paper companies successful for more than 100 years – improved labor density and material efficiency.

Ultimately, it gives engineers and operators the tools to solve problems proactively rather than constantly fighting fires. We see many mills that are so lean, particularly with respect to real domain knowledge. Those who are present are continually fighting the fires of keeping the sheet on the reel, resulting in little time for continuous improvement, let alone step-change improvement. The promise here is that these industry 4.0 tools will free up the personnel and take them away from the fires that will douse themselves. The result is that talented staff members can work on the next thing.

Today, there many examples where AI and ML platforms have demonstrated a tremendous ability to deliver highly predictive outcomes before they happen, resulting in optimizing operations and delivering material cost savings. It is satisfying to see the growing application of advanced technologies across the pulp and paper industry. In addition, we are now seeing compelling use case studies which should convince industry executives of the benefits of jumping into the Industry 4.0 world.  Good luck with your own operational issues, stay safe, and keep the sheet on the reel.

Chris Luettgen - ProcessMiner

Dr. Chris Luettgen
Georgia Tech

Professor of the Practice, Chemical and Biomolecular Engineering College of Engineering; Associate Director, Pulp and Paper Renewable Bioproducts Institute; Director, Georgia Tech Pulp and Paper Engineering, Undergraduate Certificate Program and Foundation

Dr. Luettgen’s research focus is on relevant areas of need for the pulp and paper industry – both in continuously improving existing technologies and in transformational research.  Specific areas of research include improved operational excellence of pulp/paper mill technologies; recycled fiber and global wastepaper sourcing; renewable cellulosic feedstock such as bio-based replacements of petro-based products and alternatively sourced bio materials; nanocellulose fibril and crystal applications for enhanced product performance; tissue manufacturing; and big data analytics and predictive process control.

Using Artificial Intelligence to Speed Up Paper Production and Lower Your Pulp & Paper Manufacturing Costs

Using artificial intelligence (AI) in pulp mills, which have traditionally been paper factories that employ human decision-making almost completely, is one of the ways to lower costs. This article explains why AI is crucial for a successful pulp and paper production.

New Levels of Productivity in Pulp and Paper Operations

Global production of paper and cardboard totals more than 400 million metric tons each year.

Digital technologies including machine connectivity, intelligent automation, and advanced analytics, enable new levels of productivity in pulp and paper operations by leveraging large quantities of production data to deliver better insights and outcomes.

Successful digital innovators are seeing throughput gains of 5 to 10 percent, yield gains of up to five percentage points, and significant savings on materials, chemicals, and energy.

New levels of productivity in pulp and paper operations

For the industry, this represents a $4 billion to $6 billion opportunity, and the value is achievable here and now. [McKinsey, Aug 2021] That said, most manufacturers are just beginning to see its full potential on their operations.

Pulp & Paper Manufacturers Should Embrace Digital Technologies to Keep Up with the Competition

Thanks to digital technology, pulp & paper plant managers will know exactly when equipment needs servicing instead of having to wait until it breaks down. AI is being used to identify pulp quality problems up to eight hours ahead of time. This gives pulp producers enough time to make adjustments before they lose yield or even cause a pulp spill.

In addition, artificial intelligence can be used in pulp and paper plants for automated processes requiring little human assistance. This allows pulp & paper plants to cut down on their labor costs as well as improve safety by eliminating tasks that employees perform in hazardous environments.

Pulp Manufacturers Face Serious Challenges in Adopting Digital Technologies

Cut down on pulp and paper labor costs

Three-dimensional imaging provides information about pulp qualities by measuring fiber length, freeness (lining) and specific gravity instantly at the press. The pulp testing equipment sends this information wirelessly to an outside computer where it can be viewed in real-time at mills or companies around the world using web terminals with 3D pulp data.

Virtual reality allows pulp producers to see virtual pulp testing results before they set up lab equipment or make changes at pulp machines. This means they can adjust pulp production settings to avoid pulp quality deviations without the need for actual testing and adjustments, which saves both time and money.

Despite the potential, pulp manufacturers face serious challenges in adopting digital technologies and systems. A typical pulp mill’s production of pulp is around 300,000 tons per year and they typically possess hundreds of pulp machines that produce pulp and paper products on a daily basis.

The need to maintain compliance with environmental standards also affects their ability to incorporate new digital technologies into their existing infrastructure. Pulp producers must focus more on how they can improve their pulp quality before thinking about integrating robotics or AI into their pulp & paper process as these are costly investments.

Impediments aside, pulp manufacturers should embrace digital technologies and find ways to integrate them into their pulp & paper production processes in order to improve product quality and lower costs while enhancing pulp recovery rates.

The pulp and paper industry is one of the largest industrial sectors in the world, worth more than $1.5 trillion. Pulp mills must remember that if they want to increase their market share in a highly competitive pulp & paper industry, embracing digital technologies is the only way forward.

Learn how the ProcessMiner platform uses real-time artificial intelligence to help reduce energy consumption, raw materials, chemical usage and cost. See our quick “How it Works” video.