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.

Benefits of Sustainable Pulp & Paper Manufacturing

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

Let’s be real: recycling promises do not always work locally. For example, there is a difference between virgin and recycled pad stock on the West Coast vs. the East Coast (B2C). Not to mention that sustainability efforts are extremely important especially in China where they are one of the world’s leading producers of paper. A lack of sustainability could definitely harm sales, production, or both (Harvard Business School).

There are currently no sustainability standards for pulp mills even though sustainability is becoming more important globally due to new technologies being used across industries including manufacturing facilities that have implemented sustainability strategies which have caused them to have higher profits than their peers (MIT Sloan Management Review).

It takes more than 400 years for a tree to remove its carbon footprint. Industry sustainability professionals are working on innovative ways of making paper while maintaining environmental standards and reducing emissions (EPA).

There is also an issue with recycling promises and sustainability in the United States (The UFSJ). 

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.

Manufacturing + ProcessMiner = Sustainability Goals

Sustainable manufacturing minimizes negative environmental impacts, conserves energy and natural resources, and is safe for employees, communities and consumers.

At ProcessMiner, our sustainable manufacturing goal is to optimize the whole product lifecycle of manufacturing systems, products and services, which generates more sustainable products and allows the manufacturing processes to become more sustainable, increasing a company’s social and environmental benefits.

Manufacturing Operational Improvement

  • Address Skilled Labor Shortage
  • Improve Product Quality
  • Increase Yield
  • Increase Throughput

Manufacturing Sustainability Improvement

  • Save Energy
  • Reduce Emissions
  • Reduce Chemicals Usage
  • Save Raw Materials
  • Decrease Water Usage

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.

The Future of Pulp & Paper Manufacturing

The pulp and paper industry is one of the largest industrial sectors in the world. Over the past two decades, it has experienced considerable change, including digital technology advancements, environmental factors, rising energy and labor costs, sustainability objectives, and more.

This post discusses these changes as well as their effects on the sector’s future prospects.

Digital Transformation for Pulp and Paper

The pulp and paper industry has undergone several digital transformations, with mills adopting new technologies to improve efficiency and reduce their environmental impact. For instance, many mills have adopted smart sensors that monitor production processes and help optimize performance. 

Automation has also played a role in the industry’s modernization; some mills have replaced workers with robots to cut costs and improve safety.

 

In addition, many mills have adopted digital technologies to improve their communication with customers and suppliers. This has allowed them to streamline their operations and better meet the needs of their stakeholders.

Ultimately, the goal of these digital transformations is to make the pulp and paper industry more sustainable. By reducing energy consumption and emissions, mills can become more environmentally friendly and protect the natural resources that they rely on. By embracing new technologies, the pulp and paper industry can continue to thrive into the future.

Pulp and Paper Manufacturing: Rising Energy and Labor Costs

The pulp and paper industry has also been affected by rising energy costs. The cost of pulp, one of the largest expenses for mills, is highly sensitive to oil prices; when crude falls (as it did from 2014 through 2016), pulp prices tend to decrease as well.

Many mills have had trouble securing natural gas supplies on favorable terms due to the strong demand for this commodity in North America. As a result, firms’ prospects have diverged across regions and product segments.

Mills in Europe, China and India also face challenges with rising labor costs and increased competition from South Korean conglomerates known as chaebols. While these factors will likely hold Chinese pulp production back somewhat, India is expected to see strong growth in the coming years as it ramps up its own pulp manufacturing.

Sustainability in Pulp and Paper Manufacturing

Environmental factors have been another key driver of change in the pulp and paper sector. Mills are increasingly being forced to comply with stricter regulations governing emissions, effluents, and waste management.

The pulp and paper manufacturing industry is a vital part of the global economy, but it also has a significant environmental impact.

In order to be sustainable, the pulp and paper industry must reduce its environmental footprint while still meeting the needs of consumers and businesses.

In particular, concerns over climate change have led companies to seek ways to reduce their carbon footprint. This has included measures such as installing renewable energy sources at mills and investing in forest conservation initiatives.

There are several ways to make pulp and paper more sustainable. One way is to use recycled materials in the manufacturing process. Another way is to use energy-efficient equipment and processes. And finally, companies can work to reduce their overall environmental impact by implementing responsible management practices.

By making small changes, pulp and paper manufacturers can help ensure that this important industry remains environmentally friendly for years to come.

Smart Manufacturing and Autonomous Control for Pulp and Paper Manufacturing

Critical to optimizing the performance of plant operations and reducing quality variations in pulp and paper manufacturing is proactive and accurate process control. Smart technology is being adopted by the pulp, paper, and packaging industries; this digital transformation in manufacturing reduces the consumption of raw materials, and process variability, drives down costs, increases throughput, and gains a competitive edge with customers.

The ProcessMiner AI-enabled platform delivers process improvement recommendations and optimal control parameters in real-time to your pulp and paper production line. Through our predictive machine learning and artificial intelligence systems, online recommendations are sent for autonomous proactive control. Our platform ensures reductions in cost, scrap, and defects commonly encountered in pulp and paper manufacturing processes.

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.

The Evolution and Required Transformation of Statistical Process Monitoring

In Industry 4.0, there is a need for automated diagnosis of process monitoring alarms along with root cause determination and process adjustment. Computationally intensive and adaptable approaches such as those incorporated into ProcessMiner software are required for these reasons.

Our readers are likely familiar with the industrial practice being broken down into the following four roughly defined eras: Industry 1.0 (use of water and steam power and mechanization starting about 1800), Industry 2.0 (use of electricity, mass production, and assembly lines starting about 1900), Industry 3.0 (use of automation and information technology starting about 1970) and now Industry 4.0 (use of cyber-physical control and big data starting about 2011). In addition to advances in technology, these eras of industrial practice could also be characterized in terms of increases in the availability and use of data.

Industry 2.0 and Statistical Process Monitoring

Statistical process monitoring (SPM) originated with Walter Shewhart’s work at Western Electric during the Industry 2.0 era. Shewhart was the first person to recognize that the time order of industrial data could be very informative. Data collection was slow, manual, and expensive in his day. Sampling frequencies were typically low. The first Shewhart chart in 1924, for example, involved a plot of proportions of non-conforming items with the data aggregated over months. Any calculations had to be simplified in order to be done by hand. The Shewhart control limits were set at plus and minus three standard deviations of the control chart statistic from the centerline. The Western Electric Handbook, which operationalized Shewhart’s ideas, was first published in 1956.

There were some major advances in SPM methods during Industry 2.0. Harold Hotelling introduced the first chart for monitoring multivariate data in 1947. S. W. Roberts introduced the exponentially weighted moving average (EWMA) chart in 1959 and E. S. Page introduced the cumulative sum (CUSUM) chart in 1961. These methods made better use of information in recent data. It does not seem, however, that the EWMA and CUSUM methods had much of an influence on industrial practice in this era.

Richard Freund of Kodak introduced the acceptance control chart in 1957. The purpose of his chart was to widen the Shewhart chart control limits to avoid the detection of process shifts that were considered to be too small to be of practical importance.

As another notable contribution, in 1959 J. E. Jackson proposed a multivariate monitoring method based on the use of principal components. It was much later, however, before the principal component methods could be used to their full potential.

Industry 3.0 and Statistical Process Monitoring

The quality revolution in the U.S. started around 1980, well into the Industry 3.0 era. The primary driving force behind the quality revolution was quality and price competition from Japanese companies. Statistical monitoring methods became widely used and in some cases oversold. W. E. Deming was the de facto spiritual leader of the quality revolution because much of the Japanese quality success was attributed to his teaching in Japan in 1950 and later. Deming was a strong proponent of process improvement by continual variation reduction. He also advocated the use of the Shewhart control chart, writing that no method of monitoring was better. In particular, he was opposed to the widening of the Shewhart three-sigma control limits.

As the amount of data increased during Industry 3.0, more and more SPM methods were developed. Computation became much less of an issue with software, such as Minitab® and JMP®, becoming available. There were additional multivariate methods introduced such as the multivariate EWMA chart and multivariate CUSUM charts. As sampling frequencies increased, it became important to account for autocorrelation in the data. Manufacturing processes are almost invariably multi-stage, so methods were developed to account for incoming quality, not just outgoing process quality, at each stage.

Methods were developed in Industry 3.0 for monitoring processes where there was more than just the one variance component assumed with Shewhart control charts. For example, with batch data, there could be variation between batches as well as variation within batches. There were extensions required for the monitoring of functional data (called profile monitoring), where quality is best characterized by a relationship between a response variable and one or more explanatory variables. One of the first applications of profile monitoring was developed at the U.S. National Bureau of Standards for monitoring calibration curves. Some methods were also developed for monitoring image and video data.

Industry 4.0 and Statistical Process Monitoring

Although standard Industry 2.0 and 3.0 statistical process monitoring methods can remain useful, they fail to work well, if at all, in many Industry 4.0 applications. Industry 4.0 data are often high-dimensional with high sampling frequencies. Control charts have been developed to signal for data that indicated a statistically significant process change. With massive amounts of data, any change, however small, becomes statistically significant. In an increasing number of cases, the focus needs to be on detecting, to the extent possible, only process changes of practical importance. In effect, control charts and warning systems, in general, need to be tuned to detect shifts of practical interest, which will vary by application.

Having a smoke alarm that is activated by the striking of a match would be very undesirable in a kitchen, for example, but such sensitivity might be desirable in an airplane lavatory. Thus, there needs to be a return to some of the basic ideas of Richard Freund and the acceptance control chart. The focus needs to be on the practical importance, not just the statistical significance, of process changes.

There are other complicating issues that can arise with Industry 4.0 data. The use of sensor technology leads to high dimensional data, so dimension reduction methods can be helpful to take advantage of the resulting duplication of information. The high sampling frequencies require decisions about the appropriate level of data aggregation. Sampling frequencies will rarely be synchronized for the measured variables, as assumed in Industry 3.0 multivariate methods. In particular, quality variables are often measured at much lower frequencies than process variables and are often characterized by delays such as those due to lab work.

The types of data in Industry 4.0 can vary as well with attribute data, variables data, profiles and image data all required for decision making. In addition, there can be complicated relationships between variables involving correlation and causation.

The data involved in Industry 4.0 applications can be far too complicated to be handled by traditional statistical process monitoring approaches. Modeling and using the process data effectively, even by process experts, is often impossible. In Industry 4.0 there is a need for automated diagnosis of process monitoring alarms along with root cause determination and process adjustment. Computationally intensive and adaptable approaches such as those incorporated into ProcessMiner software are required for these reasons.

Tom Tulloch ProcessMiner

Bill Woodall

Scientific Advisor, ProcessMiner, Inc.

Bill is Professor Emeritus in the Department of Statistics at Virginia Tech. He has been active in the quality monitoring and improvement area for nearly forty years. He has published many papers on Industry 3.0 monitoring methods and some on Industry 4.0 applications. A full list of his papers can be found at www.stat.vt.edu/people/stat-faculty/woodall-bill.html. Papers are available upon request.

Email: bwoodall@vt.edu.