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?

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.

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

• 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

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.

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.

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 (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.

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

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.

Interesting fact: there are currently no sustainability standards for pulp and paper mills even though sustainability is becoming more important globally.

Another interesting fact: It takes more than 400 years for a tree to remove its carbon footprint.

Why Sustainability in Pulp and Paper Manufacturing is Critical to Our Future

If a consumer purchases 20 percent recycled content bond paper, but only 2-5 percent is actually used as 20 percent recycled bonds, what happens to the rest? Is it even recyclable? This may lead to contamination of the recycle stream or lower quality fibers being reused.

Also, what is the raw material sustainability of a recycled product? It takes about 1000 lbs. of wood to make 1 ton of pulp so sustainability is now an issue with virgin and/or recycled pulp (USDA).

Paper mills now have sustainability initiatives which can be implemented through various means, including:

• reduce deforestation by promoting sustainable forest management
• improve energy efficiency in production facilities
• treat wastewater prior to release into the environment
• use high-quality diesel fuel produced without heavy metals or sulfur
• invest in research and development for new technologies that promote sustainability within the industry

Meeting sustainability standards requires continuous evaluation of process planning and assessment of technology selection which impacts critical environmental factors such as cost-effectiveness vs. sustainability (Global Paper).

Environmentally preferable purchasing (EPP) is an EPA policy that encourages the selection of products that have less environmental impact throughout their lifecycle. This can encompass sustainable forestry, recycling content, post-consumer material content, design for environment consideration, packaging weight reduction, and renewable energy content (EPA).

As EPP continues to be a priority in sustainability initiatives for pulp and paper mill sustainability planners over the next couple of years it will become more important for mills to understand what EPP consists of before making product selections or planning production schedules with sustainability in mind (IndustryWeek). Using these strategies will help mills reduce their carbon footprint as well as maintain environmentally responsible practices.

Innovating Manufacturing Technology for a Sustainable Future

At ProcessMiner, our sustainability focus is on innovating solutions for the manufacturing industry’s most pressing challenges. 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.