Last year’s 35-day field test of autonomous operations marked a new phase in the development of industrial AI control, believes Yokogawa Digital CEO, Hiroaki Kanokogi.
“I believe, as human beings, we can solve major problems with new digital technologies like AI. And that’s a great opportunity for everyone on the planet.”
Dr. Hiroaki Kanokogi, President and CEO, Yokogawa Digital Corporation
In March last year, Tokyo-based Yokogawa Digital announced the successful completion of a 35-day landmark trial using a new AI learning system to autonomously control a complex chemical plant process in collaboration with Japanese chemicals and materials company, ENEOS Materials Corporation (formerly JSR Corporation).
In our latest interview with an industry thought-leader, Yokogawa Digital’s President and CEO, Dr. Hiroaki Kanokogi, talks to the MLC’s Executive Editor Paul Tate about what the 35-day autonomy trial achieved, the future opportunities and challenges of autonomous adoption across manufacturing, the impact on human roles in future AI-empowered environments, and how new AI technologies may help to solve some of the world’s greatest problems.
Q: What excites you most about your role at Yokogawa Digital?
A: Digital transformation excites me, and that covers many things. To begin that process requires the collection of data from multiple fields. My biggest interest is how to then utilize that data, not only passively, but also actively. I see passive AI usage to be things like the simple analysis of data. But I would like to do more, to actively use that data to control and manage plants, or supply chains, or anything. That’s the kind of activity that excites me very much.
Q: What was the autonomous factory test project with ENEOS designed to achieve?
A: The ENEOS autonomous factory field test aimed to address two key things. The first was to save waste energy and reduce the total amount of energy used in a complex chemical manufacturing process while ensuring product quality and providing both economic and environmental benefits for the company. The second was to free up human expertise for higher value tasks.
The specific focus of the project was the autonomous control of values and temperature controls in a distillation column in the ENEOS plant. Typically, these are driven by PID (Proportional Integral Derivative) controls to help manage continuous variations. That’s OK, but controlling and reusing the waste heat is the difficult part. It’s a very interesting and good idea to do so, but waste heat can be very unstable and not appropriate for some specific manufacturing processes as it can lead to quality problems, like being out of range of key production specifications. ENEOS had tried various control management solutions but not had good results. So, an operator needed to be assigned to the task of constantly monitoring and manually adjusting the distillation column.
In the ENEOS test, we were able to use our AI reinforcement learning system to quickly learn the optimum conditions in the distillation columns and solve the problem, saving energy using waste heat and producing a good quality product. The test ran continuously for 35 days with the AI system successfully controlling the valves and temperature controls instead of the human expert and without any human intervention. Experts like that are a precious and expensive resource today and being assigned to a manual operation is a waste of their potential skill, especially in times of a labor shortage. But by using the autonomous AI approach, without any human intervention, it freed the expert to focus on higher value tasks, so it helped to solve two of the company’s problems.
“When you apply AI in an oil plant or a chemical plant, safety must be the primary concern. So, in this test we established a specific process to assure safe AI control in a real-world situation where there are some potentially dangerous hazards.”
Q: What key technologies did you use in the test project?
A: One of the key technologies was our AI reinforcement learning system, called FKDPP (Factorial Kernel Dynamic Policy Programming), which was specifically designed for plant control. This was supported by multiple sensors which provided the key process data to drive the AI system.
The second aspect of the trial, which is just as important, was how to ensure the safety of the autonomous system. When you apply AI in an oil plant or a chemical plant, safety must be the primary concern. So in this test, we established a specific process to assure safe AI control in a real-world situation where there are some potentially dangerous hazards. Establishing that safety process, and how to confirm the safe behavior of the AI, was also a very important deliverable from the ENEOS field test.
Q: What makes the FKDPP AI algorithm different from other approaches to autonomous operations?
A: There are many existing AI control technologies, but they are predominantly based on PID theory, traditional control theory. That means the AI’s capabilities are limited within the PID theory. On the other hand, FKDPP was developed to control valves or other equipment directly without PID or traditional control theories. So FKDPP has the potential capability to achieve things that go beyond the traditional limits. Such a dream is very attractive to me.
Q: How would you measure the trial’s success?
A: In the early discussion phase of the experiment, the quality of the final product was the most important metric, plus the energy savings that could be achieved. At first, we expected some human intervention might be necessary, but as it turned out, no direct human action was required at all. The only human involvement was in helping to design the safety layers before the AI implementation, and generally overseeing the progress of the experiment. So, that was an additional successful result. Overall, we achieved the required quality control, as many of the energy savings as possible, and without any human intervention. And the trial ran continually for 35 days, which is a really good duration – not several hours, not three days, but 35 days. It was a great success, and we believe this is a world first for autonomous AI control.
Q: What lessons did you learn about implementing autonomous operations from the trial?
A: It was the first time we had attempted such a large-scale commercial application. We’d previously experimented with AI autonomy on smaller systems, like controlling the air conditioning system in a Yokogawa factory, but we needed to show that it could be used at a larger scale, like in the ENEOS chemical plant. The test proved that AI control could be used successfully for real commercial use at scale. That was a big lesson for us. The other key lesson was how to assure safety with autonomous systems. We worked closely with experts at ENEOS to develop the safety process and that was a great experience and another great lesson for us. It means that our AI control approach has moved to a phase of safe, practical use, and is no longer just in the test phase.
“The skillset required for humans in plants or factories will change after autonomous systems become more widespread, so they can then focus on higher value tasks where the AI helps empower them to be more creative and innovative.”
Q: Where do you go from here? What are your plans for applying this AI control technique elsewhere?
A: The ENEOS test was just the first step. The AI reinforcement learning approach has a very general capability that can be applied to many different kinds of tasks, so we are now discussing further areas where we can apply the same technology. There are many areas where there could be positive trade-offs in real plants, so we are now looking to apply the FKDPP system in a variety of other fields. We want to prioritize the big issues first, and to apply our AI to the most important and most essential parts of plant where some trade off exists and there is room to improve. That could be a in a plant, or in supply chain management, or in energy usage optimization, or many other areas. We’re talking to a number of companies, but these are still confidential conversations right now.
Q: How do you see the adoption of industrial autonomous operations developing over the next few years to 2030?
A: Yokogawa conducted two surveys about autonomous operations over the past couple of years involving more than 500 C-level people. We found that many companies are very interested in the potential of autonomous approaches, and some are already exploring autonomous aspects in their industry sectors. Their motivation is often around traditional drivers like cost reduction or efficiency improvement, but they are also focusing on new issues like meeting sustainability goals, or coping with the labor shortage, or creating more resilient supply chains, and they are looking for new ways to solve these problems.
What may hinder that autonomous development, in my opinion, are perhaps some concerns and fears about adopting such a new and mostly unknown technology. To mitigate those situations, I think we need a strong vision or strategy for the future of industrial AI autonomy, and maybe a trustworthy industrial standard or regulatory environment that will help those companies move forward. And of course, technology to solve cybersecurity safety concerns too.
Q: Where do you expect the adoption of autonomous approaches to be most prevalent as we approach 2030?
A: Our research indicates that all industries and sectors have some degree of interest in autonomous operations. But there are several areas where traditional control approaches have some difficulties. For example, oil refining and other major process industries require high levels safety and that can be a big challenge when adopting advanced technologies. But we have now proved the AI reinforcement learning can be applied safely to such process tasks, so we expect other companies may begin adoption very quickly. Or take the bio industry, for example. The bio industry has some critical challenges in controlling bioreactors and other key processes where AI control, like reinforcement learning, could be very promising. And there are also areas of discrete manufacturing too, where AI control can help support traditional factory automation systems in areas where very precise controls are needed.
Q: Will human roles change over the next few years as a result of increasing autonomous adoption?
A: It’s a very good question. I think initially, people will work closely together with AI and autonomous systems to monitor and supervise their behavior, so if something goes wrong, there is always someone there who can intervene. In that first phase, it will be important that workers have the right skills to be able to work collaboratively with such autonomous systems, and they will need to learn how to do that. But after that, as autonomous adoption becomes more pervasive, I think human workers will be able to focus more on entirely different high value optimization tasks which AI control can’t do, like inventing some new control method or a new chemical formula. So, maybe the skillset required for humans in plants or factories will change after autonomous systems become more widespread, so they can then focus on higher value tasks where the AI helps empower them to be more creative and innovative.
“I think we need a strong vision or strategy for the future of industrial AI autonomy, and maybe a trustworthy industrial standard or regulatory environment that will help those companies move forward.”
Q: Looking ahead, what would you highlight as the greatest challenges and opportunities for manufacturing industry for the rest of the decade?
A: Digitally empowered AI systems will be one of the most promising technologies to move us forward to the next generation of manufacturing. There are lots of opportunities here. It’s almost impossible to imagine how many major opportunities there will be in the future based on these new digital approaches. But there are also challenges. The adoption of new technologies is not always easy, so a strong leadership vision and clear strategy will be important, supported by evolving industry standards. Cybersecurity and safety concerns will also be a challenge in a more autonomous world. We already have a number of problems today, from achieving sustainable development goals to the labor shortage. But I believe, as human beings, we can solve these problems with new digital technologies like AI. And that’s a great opportunity for everyone on the planet.
Q: What key leadership skills, attributes, and roles do you feel that senior industry executives now need to lead successfully in an increasingly autonomous world?
A: In a digital world, strong leaders will need to understand not only the business objectives and challenges but the potential of the technology too. Technological advances are happening very fast today, so leaders will need to know what technologies are available and what they can do. More than that. Digital transformation (DX) will connect people in many different divisions of a manufacturing organization. That means leaders also need strong people skills. I believe human empathy will become more and more important because leaders with technology skills will need to convince and engage many people in these different divisions. In other words, I think the leaders of the future will need to become tech savvy humanists to succeed in the digital world.
Q: Finally, if you had to focus on one thing as a watchword or catchphrase for the future of manufacturing, what would that be?
A: It’s about moving from industrial automation to industrial autonomy. We think this vision can take manufacturing much farther in the future. M
Fact File: Yokogawa Electric Corporation
HQ: Musashino, Tokyo, Japan
Industry Sector: Electrical equipment, industrial automation, software
Sales: $2.94 billion (¥389.9 billion – FY 2021)
Net Income: $163 million (¥21.3 billion 2021)
Employees: 17,258 Employees
Presence: 61 Countries
Production Sites: 18 manufacturing sites worldwide
EXECUTIVE PROFILE: Dr. Hiroaki Kanokogi
Title: President and Chief Executive Officer, Yokogawa Digital Corporation
Education: BSc degree in quantum electronics / physics, University of Tokyo; Masters’ degree in quantum electronics, University of Tokyo; PhD in quantum electronics, University of Tokyo
Languages: Japanese, English
Previous Roles Include:
– General Manager, Yokogawa Product Headquarters, Yokogawa Electric Corporation
– Head of Information Technology Center, Yokogawa Product Headquarters, Yokogawa Electric Corporation
– Development Manager, Microsoft Product Development Limited
Other Industry Roles/Awards/Board Memberships:
– Co-inventor, FKDPP Algorithm, Plant Control AI
About the author:
Paul Tate is Co-founding Executive Editor and Senior Content Director of the NAM’s Manufacturing Leadership Council.
AI promises to foster a mutually augmented human-machine relationship, making the workforce more productive and strengthening manufacturing capability.
Artificial intelligence is already becoming a commodity tool that is permeating our lives. If you have used Google maps, Apple maps, or shopped on Amazon, then you have already been touched by AI.
Driven by Data
AI is essentially the simulation of human intelligence using computers. Machine learning (ML) is a subset of AI that is used to automatically learn from data without being explicitly programmed. The two key words here are simulation and data. While we’ve all heard about the great promise of AI, it is still a simulation of human intelligence. Right now, human intelligence is superior to artificial intelligence in many sectors, and certainly in manufacturing. Part of the reasoning here, is that AI has yet to be trained in the vast field of manufacturing. Such training requires data. One thing that we do know from manufacturing is that we have significant levels of automation, and that automation has serious amounts of sensors and sensing capabilities on board. So, while there is still much to be learned about AI in manufacturing, we do have substantial amounts of data for that training.
Understanding the Opportunity
Many initial forays in using AI in manufacturing have been tried. One approach is to take all of the data coming from manufacturing operations and feed them into commercially available AI algorithms to find patterns linking various elements of the manufacturing process to the outcomes (e.g., quality, process health, and the tuning of operations). Many of these attempts are what one would call “black box”, in that they are basically just throwing the data at AI systems to see what happens. This approach has had some limited success. However, more successful approaches have used data with models that are well established but augmented by AI. Augmented is the key word here. An AI augmented model approach uses models based on what we know about our manufacturing processes and supply chains and augments those models to yield more accurate and refined results.
For example, new generation design (CAD) tools employ AI to help design components based on variables such as the load requirements on a part, while minimizing its weight. The approach is generally called generative design, and it creates some very complex structures. One might think that such complex parts can easily be 3D printed – just hit the print button! – as we can now 3D print almost any geometry. However, a person with significant experience in 3D printing will tell you that this kind of print button does not exist and there remain numerous limitations on 3D printed parts. A part generated by an AI design alone may not, in fact, be printable. However, an expert who understands both the goal and the limitations can work with the AI system to generate a lightweight part that can effectively support the required load while still being printable. This is where artificial intelligence works together with real intelligence. The AI is augmenting the human designer, and the human designer is augmenting the AI in this instance. The key is that both are working together.
Improving the Models
Today, we are just starting to take our data and augment our models to help us better understand our processes. Within a few years, we will have improved models that better utilize sensor data to help us predict the future health of our processes. For example, higher end machine tool manufacturers are starting to place more sensors, such as accelerometers, on their spindles. They train their AI algorithms to understand the sensor output when the spindle is healthy, and then have the algorithms use that knowledge to determine if the spindle has developed a problem. Furthermore, the algorithms can often determine a pending problem, and when it is going to happen. So now, a plant might shut a machine down for maintenance in advance of a breakdown. Thus, repair might happen during the weekend, when the machine is scheduled for maintenance, as opposed to waiting for it to break at an inopportune time such as in the middle of a shift.
“AI has yet to be trained in the vast field of manufacturing. Such training requires data. While there is still much to be learned about AI in manufacturing, we do have substantial amounts of data for that training.”
Such applications of AI will not be a silver bullet. The interpolation/extrapolation limitations of AI will still be in place. That is to say, there is a good chance that AI will not do well outside the scenarios that generated its original training data. So, if a completely new problem or situation is presented to AI, its response can be highly unpredictable. Therefore, we will have to understand its limits. For example, there is no algorithm that can accurately predict when a spindle will fail, until the spindle starts to fail. Statistics can be used to estimate failure (for example, based on experience the spindle may be expected to last so many hours of operation), but until the actual failure starts to occur, one cannot use AI, or any other techniques, to precisely predict that failure. For example, statistically speaking, the spindle might be expected to last for 10,000 hours of operation. However, if a severe crash occurs, that life expectancy might be substantially shortened. That being said, if crash data are fed into AI models, we might get a better feel for how much time was taken off the spindle life due to the severity of the crash, so there is another instance where we can augment our spindle life model.
Augmenting the Human Workforce
Just as with automation, people often express concerns about AI replacing humans in manufacturing jobs. Certainly, we will not see the Skynet AI scenario from the Terminator movie series that is in total control. What we will see is that the AI will augment the human workforce making it more valuable and effective. So once again, rather than artificial intelligence, we will see augmented intelligence. Just as with the earlier generative design example, we can use AI to help the human workforce, and the human workforce can be used to train AI.
Mixed Reality AI
One great technology advancement that is presently coming on-line and will be fully integrated into manufacturing operations by 2030, is mixed reality, which is a blending of the digital and physical worlds. For example, in a plant many workers may have to wear safety glasses, so why not make them augmented reality (AR) goggles? Such goggles could ensure that an operator follows the right procedures and adheres to all the required safety protocols. It could also ensure the correct insertion of all of parts into an assembly. Such an approach is the best of both the human and automation worlds.
Automated assembly makes sense in a highly controlled environment when thousands or tens of thousands of assemblies are being made and the components of the assembly are always in the same location and orientation so that automated units such as robots know exactly where and how to put up these parts. But if a part is backwards or falls over for some reason, the robot may have great difficulty picking it up. A human, however, is very adept at determining a part’s location and orientation and picking it up to put into an assembly correctly. Furthermore, AR goggles could ensure that the person picks up the right components and puts them into the right locations. An added benefit to this, is that a digital passport can then be generated for the assembly that records the correct assembly procedure documenting every process step ensuring the assembly’s quality.
“Augmented is the key word here. An AI augmented model approach uses models based on what we know about our manufacturing processes and supply chains and augments those models to yield more accurate and refined results.”
Such a capability can also be used to help to train new workers in a facility and reduce their errors substantially, especially if the AR is used to train the AI by learning directly from the actions and decisions of experienced workers. This does not eliminate the human from the job, as a human is still necessary to physically execute the task. But AI could learn the most effective manner to execute a task and then guide a novice through a similar procedure using AR. All this can be done while building the product’s digital passport, conducting time and motion studies, and ensuring that all safety protocols are followed. It is truly an approach that augments and adds value to the workforce.
If we are recording every move and motion of the workforce, this does raise some privacy concerns which will need to be addressed in the years ahead. We will need to understand what we are learning and recording from the workforce and ensure that their data are protected accordingly. This is not unlike Google maps. We share our location with Google, and in turn, Google provides us with directions to where we want to go. Of course one major concern here is that Google might use our location data to track us and provide that information to third parties. The same is true in manufacturing. How do we ensure privacy among workers and businesses? This does open up a can of worms as, right now, different geographic regions (e.g., EU, U.S., China) have different regulations regarding personal data and its privacy. So, we will certainly have to address these issues which could become increasingly complex for multinational companies over the next few years.
Furthermore, the ability to share production data from one production facility to other facilities, or from one machine tool to another, is going to become important as lessons learned in one location could be equally as valuable to others at different locations. This highlights the value of data, but it begs the question, why would factory owners open their cyber doors to others and share their data? Clearly, new business and privacy models and rules must be developed to make it both worthwhile and safe for companies, and industries, to share some of their data.
So, I believe that by 2030 we are going to see AI capabilities permeate throughout manufacturing. We are also going to see it really take off in mixed reality settings that will increasingly look like something out of a science fiction movie. This will result in a safer, more efficient, and higher value-added manufacturing ecosystem. But it will also result in some very different business and socio-economic models that will change the way manufactures operate, think, and profit in the years ahead. M
About the author:
Professor Thomas R. Kurfess is Executive Director at the Georgia Tech Manufacturing Institute, Professor and HUSCO/Ramirez Distinguished Chair in Fluid Power and Motion Control at the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology, and a member of the MLC’s Board of Governors.
How technology, analytics, and training improved IPG’s business performance.
Company Fact File
Company: Intertape Polymer Group
Sector: Packaging and protective solutions
HQ Location: Sarasota, Fla.
As a packaging and protective solutions company, Intertape Polymer Group (IPG) has a proven track record of ensuring things are secure and wrapped up nicely. But in 2019, the company looked inward at its processes and people and started a journey to ensure that its operations and technologies would be in a successful Manufacturing 4.0 package.
To get there, IPG undertook a digital transformation journey aimed at empowering all employees with technology, analytic insights, and training to make a meaningful impact on business performance. The journey included a multi-faceted approach to implement various M4.0 technologies across IPG’s business operations with a focus on delivering bottom line savings and scaling the technology deployment.
“Lean permeates IPG’s culture. It’s part of who we are and how we do things,” said Jai Sundararaman, IPG’s Vice President of Business Transformation. “It’s now getting more and more integrated with these different technologies.”
IPG started the journey with five key focus areas:
- Digital Analytical Platform. Harnessing IPG’s equipment performance analytics to improve operational efficiency.
- Augmented Reality (AR). Training and developing employees and attracting new talent.
- 3D Printing. Reducing spare parts cost and supply constraints.
- Machine Health. Monitoring key assets 24/7 to reduce downtime and extend asset life.
- Cloud Based Maintenance Management System. Centralizing asset information and reducing operating costs through proactive maintenance and spare parts inventory reduction.
These initiatives were piloted in IPG’s manufacturing environments so the company could understand the real impact, resources needed, and savings realized. As the journey progressed, the company continued to implement these technologies across the organization in subsequent phases.
The digital transformation journey
IPG began its M4.0 and digital transformation journey by creating a roadmap, vetting and piloting technologies, and working with different vendors to identify partnerships that would provide a scalable business solution.
“It takes hard work, discipline, a problem-solving mindset, and resilience for an extended period before you crack the code,” noted Sundararaman.
But as IPG progressed, the pilot projects enabled the company to identify impactful technologies and the capital and resources needed to implement them. Project leads worked with cross functional teams to apply each facet of the deployment strategy, and IPG continues to implement these projects across the organization based on their potential impact on processes and bottom-line savings.
The business impact of contextualized data and M4.0 technology
As IPG forged ahead, it found particular value in understanding data. By analyzing data in real time, the digital analytical platform opened the door to take a deeper look at process parameters to understand its impact on yield and quality. Ongoing yield and quality improvements put IPG in a competitive position to continue to deliver value to customers through lower cost and higher quality. The digital analytics platform was piloted in five plants, and as the project continues to scale, there is potential to save millions in the next five years across 15 plants.
Along the way, the company’s A3 problem solving methodology became twice as productive by increasing the availability of accurate, real-time, contextualized data.
“IPG realized a 75% cost savings while reducing lead time for machine parts from three weeks and three months to less than a few hours in most instances.”
“A3 is a classic example of how we strategically look at the opportunities to go about problem solving,” Sundararaman said. “Now you leverage digital mindsets, toolsets, and skillsets to go make it happen.”
These improvements led to a new process control ideology called centerlining – the process of categorizing each product run by uptime and quality metrics, then using analytical tools and custom applications to determine optimal set points and identify failure correlations. Process hack-a-thons using the centerlining framework became the process engineering focus in order to improve uptime, reduce waste, and improve product quality and consistency. Required process accommodations for uncontrolled process variables, such as outside weather conditions and ambient plant temperature, were analyzed to maintain optimal process conditions.
“A hack-a-thon is a mini-Kaizen,” Sundararaman said. “It’s a different way of going about it. Two to four hours of sitting with all the data and the right people brings up all kinds of insights that never existed before – correlations and possibilities that were not commonly looked at in the past and how we went about solving problems. Now it’s at your fingertips.”
Meanwhile, Machine Health IOT sensors and the AI algorithms have been used to predict machine failures by monitoring the vibration, temperatures, and key variables of the machine, resulting in significant reduction of downtime. In one plant alone, IOT sensors showed promise in the first four months, as reduced downtime translated to real savings.
The company also implemented 3D printing in more than 13 sites, printed more than 1,000 parts. Along the way, IPG realized a 75% cost savings while reducing lead time for machine parts from three weeks and three months to less than a few hours in most instances.
“It takes hard work, discipline, a problem-solving mindset, and resilience for an extended period before you crack the code.”
Tying maintenance together, IPG’s cloud-based maintenance management system was deployed at five sites to track more than 50 thousand SKUs. The goal of this system is to reduce inventories across the plants with consistent and centralized reporting as IPG scales up across all plants.
Lastly, IPG used AR technology to reduce the onboarding time for new employee training. At the two pilot sites, IPG improved labor efficiencies by 12%. As the project is scaled across sites, IPG expects to improve safety process and reduce training costs while improving yield and quality. The AR technology allowed the company to engage employees in new ways. Continuing to scale up will help develop the workforce of future.
Driving operational excellence across the board
One way for IPG to achieve its operational excellence goal is to exceed customers’ expectations by providing superior products at a lower cost and a higher quality. Further, any waste reduction in IPG’s operations directly impact its commitment to reducing its environmental footprint. The M4.0 technologies that IPG is implementing have helped the company gain a deeper understanding of its processes and equipment to solve complex problems and achieve savings beyond its normal continuous improvement capabilities.
As IPG scales technologies across its operations, it will continue to learn and realize additional savings moving from local optimization initiatives to a globally harmonized enterprise solution. Early data from the company’s asset health monitoring and maintenance management system has shown tangible dividends by reducing spare parts cost and improving uptime by proactively predicting equipment failures.
All these M4.0 technologies have provided significant impact on IPG’s operations. As the company continues to scale up across its more than 30 global operations, it presents an enormous opportunity to create additional value to the business and to our customers.
Beyond what’s been accomplished so far, Sundararaman sees a continued focus on digital and IPG will continue to seize the opportunity as it scales.
“This has now taken root and we’re moving into other functional areas,” he said. “In supply chain we’re going to start rolling out digital tools and capabilities. On the commercial side we’re looking at analytics and capabilities. So it’s spreading across the enterprise.”
Stocking the trophy case
As if improved competitiveness, quality, and workforce engagement and reduced costs, environmental impact, and downtime were not enough, IPG’s efforts have earned significant external praise. For its overall effort, IPG was recognized with a 2022 Manufacturing Leadership Award for Enterprise Integration and Technology. Sundararaman was honored in 2021 by MLC with the Digital Transformation Leadership Award. That same year, the company’s Tremonton, Utah operations – where the digital analytical platform was piloted – won the MLC Engineering and Production Technology Leadership Award and was chosen by Industry Week for the Industry Week Best Plant Award for its outstanding achievement for showcasing improvements made with M4.0 technologies.
These accolades add a nice bow to the M4.0 package IPG continues to create. M
About the author:
Jeff Puma is Content Director for the Manufacturing Leadership Council
Manufacturers will be pressing ahead with their M4.0 investments as they expand their digital deployments in operations, a new MLC survey reveals.
Despite economic uncertainty, manufacturers are moving ahead with digitizing plant and factory floor operations and are anticipating significant progress in doing so by 2025.
This is one of the key findings of the Manufacturing Leadership Council’s new survey on Smart Factories and Digital Production (formerly called Factories of the Future) that was conducted in January. The survey was designed to assess how manufacturers are utilizing digital technologies across their production plants and factories, what technologies they expect to invest in to further their digitization plans, what benefits they expect from digital transformation of their operations, the challenges in achieving those benefits, and the potential impact of digital transformation on the industry’s competitiveness.
Here are the key findings. Selected graphs from the survey follow.
Economic Outlook and Impact on M4.0 Investments
- Manufacturers’ outlook for the U.S. economy in 2023 is a mixed bag, with 38% expecting a recession to occur later this year. But 23% expect moderate growth in the economy and no recession, while 16% expect inflation to ease and growth to rebound in the second half (Chart 1).
- The economic context, however, does not appear to be constraining Manufacturing 4.0 investments. Fully one-third of survey respondents said they expect M4.0 investments to increase this year, while another 51% said they expect investments to continue unchanged (Chart 2).
Status of Digital Adoption
- The focus of M4.0 efforts has shifted to broader deployments, with 33% reporting that they are currently implementing M4.0 company-wide, compared with 24% last year. There was also a slight uptick in those implementing single M4.0 projects, to 17% this year from 15% last year (Chart 4).
- The shift to more significant deployments is also reflected in the stage of digital adoption by functional area. For example, the percentage of those reporting an advanced stage of digital adoption in production and assembly operations rose to 16%, from 9% last year. Those reporting they had reached an advanced stage in equipment maintenance operations rose to 13%, from 8% in 2022 (Chart 5).
- End-to-end or extensive digitization of a variety of factory-related operations is still aspirational at most companies, but intentions over the next couple of years are very strong, with some areas anticipated to experience exponential progress.
- By 2025, for example, nearly 10% of respondents expect to have their full factory operations completely digitized end-to-end, compared with none reporting so today. Even more pronounced in terms of intentions is plant floor equipment maintenance and service operations. By 2025, 42% expect to have extensive digitization of this process in place, compared with only about 5% today (Chart 7).
- Similar anticipations of extensive digitization – rising to double digits by 2025 from single digits today – are evidenced in production/assembly (45% in 2025, compared with 9% today; Chart 8), IP-enabled plant floor networking (60%/25%), integration of plant floor equipment data with quality systems (39%/4%), and integration of design and production processes (32%/9%).
- Outside the four walls, integration with suppliers and customers is also slated for significant adoption. Today, only 4% say they have extensively integrated production functions with customers and suppliers, but by 2025, 26% expect to have done so (Chart 9).
Factory Organization and Management
- Very few manufacturers, only 3% according to the survey, expect their factory operations to be run autonomously. The overwhelming sentiment, by 88% of respondents, is that the future state of factory models will be a hybrid of humans and machines, incorporating elements such as robotics, digital production systems, and digital processes (Chart 10).
- Nevertheless, there is considerable agreement that future factories, with the aid of AI and machine learning technologies, will be self-managing and self-learning facilities. Sixty-three percent of respondents partially agree with this characterization, and another 14% fully agree with it (Chart 11).
- In assessing their technical security level against potential cyberattacks, 57% of respondents said they felt partially secure, while 30% said totally secure. Only 9% indicated they felt vulnerable to attack (Chart 12).
M4.0 Technology Usage
- The survey assessed the current and planned usage of 21 technologies, all of which are in use to some degree today by respondents. The five technologies which garnered the highest percentages of those saying they planned to use them by 2025 are smart planning and scheduling tools (54%), digital twins (53%), adaptive process control technologies (50%), digital threads (47%), and machine learning and AR/VR technologies (both at 49%).
- AI also had a strong showing in terms of planned usage by 2025, with nearly 36% of respondents expecting to use the technology within the next two years. By 2025, the most desired applications of AI are in production optimization, equipment maintenance and service, and in distribution, logistics, and inventory management (Chart 13).
M4.0 Opportunities and Challenges
- The chief challenges manufacturers identified in implementing their M4.0 plans remain largely the same as they have been over the past handful of years. Top of the list this year were data and systems integration (49%), the need to upgrade legacy equipment (at 48%), and the lack of skilled employees (38%) (Chart 14).
- The most sought-after benefits from M4.0 are also repeats this year. Better operational efficiency topped the list this year (59%) followed by better decision making (51%) and cost reduction (50%) (Chart 15).
- Just over half of respondents (50.5%, down from 53% last year) )opined that M4.0 would provide their companies with a unique competitive advantage, as opposed to just table stakes (46%), but a notable increase occurred in the number of respondents saying that M4.0 would be a game-changer for the industry (61%, up from 56%) in 2022 (Charts 16,17). M
Part 1: STATUS OF DIGITAL INVESTMENT AND ADOPTION
1. Mixed Bag on Economic Outlook for 2023
Q: What is your company’s outlook for the economy in 2023?
2. Majority Sees M4.0 Investments Continuing Despite Economy
Q: How does your company’s outlook for the economy translate into M4.0 technology investments for 2023?
3. The State of Digital Maturity
Q: How would you assess the Manufacturing 4.0 digital maturity level of your manufacturing enterprise? (Scale of 1-10, with 10 being the highest level of digital maturity)
4. Companywide M4.0 Implementations Increase
Q: Which activity best describes the primary focus of your company’s M4.0 digital efforts today?
5. Production/Assembly Most Advanced with M4.0
Q: At what stage of M4.0 digital adoption are the following functions in your company
6. Level of M4.0 Integration With Business Strategy
Q: How far has your company’s Manufacturing 4.0 strategy been integrated with the overall company business and manufacturing strategy? (Scale of 1-10, where 10 is fully integrated)
Part 2: MEASURING DIGITIZATION
7. Only a Fraction See Full Operational Digitization by 2025
Q: To what extent are your factory operations fully digitized end to end today, and what do you anticipate they will be by 2025?
8. Big Gains Seen in Production/Assembly Digitization by 2025
Q: To what extent are your production/assembly processes digitized today and what do you anticipate they will be by 2025?
9. Much Progress Foreseen in Integrating with Customers by 2025
Q: To what extent are your production functions electronically integrated with customers and suppliers today and what do you anticipate they will be by 2025?
Part 3: FACTORY ORGANIZATION AND MANAGEMENT
10. Hybrid Human/Machine Factory Model Expected
Q: What is the expected future state of your factory model?
11. But Self-Learning/Managing Facilities Also Foreseen
Q: Thinking about the impact of technologies such as AI and machine learning, to what extent would you agree or disagree with the following statement: “Tomorrow’s factory will evolve to be a self-managing and self-learning facility.”
12. Cyber Defenses Seen as Secure by Strong Majority
Q: As factories become increasingly networked and digitized, how would you rank your company’s technical security level against potential cyberattack/disruption to plant floor systems and assets?
Part 4: M4.0 TECHNOLOGY USAGE
13. Smart Tools, Digital Twins Highest on 2025 Plans
Q: Where does your company stand in regard to the following technologies in its production operations?
Part 5: M4.0 OPPORTUNITIES AND CHALLENGES
14. Data Issues, Legacy Equipment Are Top Challenges
Q: What do you feel are your company’s primary roadblocks to implementing your M4.0 strategy in your production operations? (Select top 3)
15. Better Operational Efficiency, Decision Making Are Chief Benefits
Q: What are the most important benefits and opportunities your company hopes to realize from embracing M4.0 in your production operations? (Select top 3)
16. Slight Majority See M4.0 Conferring Unique Advantage
Q: Do you believe that M4.0 digital adoption will create a unique competitive advantage for your company or is it merely table stakes to remain in the game?
17. Strong Majority Sees M4.0 as Game-Changer for Industry
Q: Ultimately, how significant an impact will M4.0 technologies have on the manufacturing industry?
About the author:
David R. Brousell is the Co-Founder, Vice President and Executive Director of the Manufacturing Leadership Council,
Survey development was led by Paul Tate, with input from the MLC editorial team and the MLC’s Board of Governors.
Smart factories looking to combine fast incremental innovation with deployment at scale need to begin with a cloud-based modern ERP.
Although global manufacturing has made tremendous strides in optimizing labor productivity and asset efficiency, the next leap in operational performance can only be unlocked through optimizing operations from manufacturing to supply chain.
This notion of optimizing processes within manufacturing and supply chain functions to achieve operational excellence is broadly defined as smart manufacturing. The term arose in the mid-2000s, prompted by the advent of new technologies such as the Industrial Internet of Things (IIoT) and artificial intelligence (AI). The National Institute of Standards and Technology (NIST) defines smart manufacturing as “Fully-integrated, collaborative manufacturing systems that respond in real-time to meet changing demands and conditions in the factory, in the supply network, and customer needs.”
Similar concepts like digital and cyber manufacturing converge under the smart manufacturing umbrella. Global standards to further define smart manufacturing are still a work in progress. However, a vast majority of large manufacturers are accelerating investment in smart factories. Smart manufacturing has assumed a national strategic importance — it has even been identified as a national priority, as detailed in the White House Critical and Emerging Technologies Report in 2022.
How Does a Smart Factory Work?
Smart factories combine human creativity, digitally connected machines, assets, and AI-powered analytics. This augmentation of human intelligence with machine intelligence helps fuel adaptability and speeds up the capacity to customize outputs based on real-time data and insights. The visibility, agility, and resilience of smart manufacturing make it vital for more efficient supply chain models and overall business operations. Today, dozens of so-called smart factory technologies have given birth to a myriad of smart manufacturing use cases.
Smart factory technologies can be categorized into three buckets:
- Cloud-scale data management and analytics — Implementing predictive intelligence and forecasting capabilities, these digital technologies will also enable IT-OT convergence to support end-to-end digital continuity from design to operations, such as digital twin, closed-loop engineering design.
- Connectivity — Leveraging IIoT to collect data from existing equipment and new sensors
- Intelligent automation — Traditional automation (plant control systems, MES, distributed control), and IIoT-enabled automation (machine vision, drones)
Another way to describe a smart factory is the ability to create a closed-loop, data-driven optimization of end-to-end operations. This closed-loop smart factory undergoes continuous procedural improvement to self-correct and self-optimize — it can self-learn (and even teach humans) to be more productive, adaptive, and safe. Generally, the first milestone of the smart factory is to use advanced analytics to drive decision support. However, the ultimate goal is to reach a stage of operations where the smart factory can self-optimize performance across a broader network, self-adapt to and learn from new conditions in real- or near-real time, and autonomously run entire production processes.
“The modernization of ERP and transition to the cloud is the most vital step to bring the smart factory to fruition.”
The closed-loop optimization paradigm of a smart factory is based on three iterative steps:
- Data acquisition — Leveraging IIoT and modern database technologies to acquire and curate disparate sets of valuable data across manufacturing and supply chains. Through a network of edge devices and connected portals, AI-powered systems can compile data sets related to operations, market trends, logistics, or any other relevant source.
- Data analysis — Leveraging machine learning and AI to analyze the gathered disparate data. This data analysis enables varied used cases, from predictive maintenance of assets, pricing intelligence, and customer experience to supply chain planning and scheduling. The iterative nature of the optimization allows the study of workflow efficiencies over time to find the global optimum for any operational decision.
- Intelligent factory automation — After the data acquisition and subsequent data analysis, optimized workflows are determined, and instructions are sent to the machines and humans within the system. These assets and workers can be anywhere within the smart factory or downstream in logistics or aftermarket services. The data sets that can be compared and analyzed present almost infinite possibilities of combinations to inform digital factory optimization and supply chain forecasting.
Data and Cloud Connectivity
When we talk about smart manufacturing, we are fundamentally talking about data and its analytics. Optimization, resilience, adaptability, agility — these are all outcomes of more innovative manufacturing, but what is the engine driving all those improvements? At its core, it is only data. One can say that any smart factory’s existence is predicated on the ability to harness all of its operational data accurately and economically. The size of this data grows exponentially with the maturity of a smart factory. How does the smart factory remain viable?
“The cloud is the only viable conduit through which all data and information can flow seamlessly across a smart factory.”
Whether public, private, or hybrid, the cloud is the only viable conduit through which all data and information can flow seamlessly across a smart factory. The global connectivity enabled by cloud is also the only way smart factories can ensure each area is working with real-time data and enterprise-wide visibility to every site, asset, and supply chain.
Start with Modernized ERPs
For any organization, Enterprise Resource Planning (ERP) is the central location where all operational and financial data is collected, maintained, and shared—making ERP the de facto single source of truth. When you consider ERP in conjunction with its other components, such as WMS, MES, and PLM, it has a strategic vantage point that provides end-to-end visibility of any organization’s operations. Hence, the modernization of ERP and transition to the cloud is the most vital step to bring the smart factory to fruition. A cloud-based ERP makes applying AI/ML to operational data possible. The sheer span and reach of an ERP system make it an obvious first choice for organizations looking to achieve scale in smart factory initiatives.
Organizations looking for quick wins and rapid scalability of smart factory initiatives can realize the enormous value of cloud-enabled ERP solutions with these vital use cases:
- Kickstarting a smart factory — Integrating systems such as ERP, CAD, PLM, MES, and CRM can enable holistic business decision making.
- Integrated business planning in a smart factory — The ability to conduct scenario-based planning with an overarching objective of maximizing profit, reducing resources, and minimizing risks
- Making ERPs self-learning knowledge system the smart factory backbone — A closed-loop self-learning ERP system can transform business and manufacturing processes and help optimize decision making.
- Improving OEE of capital equipment in a smart factory — Through AI/ML, IoT, and cloud, an ERP serves as an always-learning knowledge system that can monitor equipment data in real-time, recognize failure patterns, and help organizations transition from time-based to a predictive maintenance paradigm of equipment.
- IoT, AI/ML, and ERP integration — When an ERP, with the integration of AI and ML technologies, has access to IoT data, the system is empowered to bridge the intelligence gaps many businesses face in pursuing new business models such as servitization.
- Optimized product quality — With ML, an ERP system gets tracking/tracing capabilities that can help businesses predict which product or process characteristics cause failure as well as enable closed-loop product design based on the product’s entire lifecycle.
Smart factories are a journey, not a destination. The capabilities of a smart factory will expand with the digital maturity of its owner. Manufacturers embarking on smart factory initiatives must consider long-term scalability. Cloud-based modern ERP is the starting point for creating an integrated platform that allows a smart factory to combine fast incremental innovation with deployment at scale. M
About the author:
Chirag Rathi is a strategy lead for Infor’s Industrial Manufacturing business unit. A recognized thought leader in IT/OT convergence and the role of post-modern ERP in the context of Industry 4.0, he has experience in the development of digital transformation strategies for industrial technology companies striving to make the pivot to XaaS.
Setting a clear business case and conditions for success helps companies take incremental steps to deliver transformational value.
Manufacturers are under unprecedented pressure to produce high quality, low-cost products at pace. Compounded by the complexities of resource scarcity, complicated supply chains, and mounting legislation, efficiency is everything. The solution? Manufacturing 4.0 (M4.0), otherwise known as Smart Manufacturing.
M4.0 is regarded as the next industrial revolution and refers to the powerful combination of technological innovation, changing operational strategies, and new ways of working. At the heart of M4.0, and fuelling the transformation of the manufacturing sector, is digitization. Every transformative technology driving M4.0 relies on careful and programmatic digital integration. Bringing these technologies into existing models is proving to be an iterative process with a distant horizon. However, incremental steps, when done carefully, can deliver significant value.
The Devil’s in the Data
Technologies like robotics, 5G connectivity, Machine Learning (ML), and Artificial Intelligence (AI) provide access to valuable data that can streamline and accelerate material-efficient design and enable revolutionary digital production methods. Digital twin technology heralds the arrival of the metaverse in manufacturing, virtually representing existing functions such as production lines to help businesses assess the impact of specific scenarios without pausing processes or compromising safety.
Data is difficult to use in these scenarios without the right tools in place. It is typically fragmented, isolated, and can be ‘dirtied’ by a lack of data governance within organizations. With 73 percent of all data collected floating unused in the digital ether, it is no wonder that businesses struggle to maximize the value of their digital assets.
“To identify the most impactful technology, focus on where you have operational challenges or gaps in accurate and accessible information needed to make better business decisions. ”
Data is often localized in a range of systems along the operations planning, operations management, and operations execution landscape. These systems include ERP (Enterprise Resource Planning), MRP (Material Resource Planning), MES (Manufacturing Execution System), SCADA (supervisory control and data acquisition), for example. Integration of these trapped data sets, combined with basic analytics capability, can deliver important insights to key areas of efficiency leakage like quality; however, companies often struggle to justify the investment in data itself, without clear demonstratable business cases.
Other opportunities like low-capex solutions such as connected sensors – measuring vibration, temperature, vision, and more – may help to diagnose manufacturing operations issues and provide a tangible example of the M4.0 value proposition. For example, a vibration sensor may be calibrated to monitor and record machine cycles, detecting production disruption and correlating to machine failure to provide the basis for predictive maintenance capability. This maintenance event and resulting machine downtime can be integrated into the production schedule, which in turn can be connected to customer facing capabilities like Available to Promise (ATP) and Capable to Promise (CTP) that help drive revenue. These connected capabilities, enabled by M4.0 technologies, help drive quantitative improvements.
Manufacturers can also incorporate third-party data, such as trading party transactional information, to gain greater oversight of the end-to-end supply chain. Better visibility leads to improved risk management while pre-empting disruption and anticipating opportunities. Procter and Gamble, for example, uses digital twin technology to check product quality in real-time directly on the production line, maximizing equipment resiliency while optimizing energy and water use to avoid waste.
How to Get Started
The investment required to drive M4.0 cannot be justified in a simple manner. It is impossible to do it all at once, but is manageable when broken down into smaller pieces. The most effective way to do this is through a defined, carefully timed and executed roadmap.
Start by understanding what information is required to diagnose, anticipate, or report issues, and ultimately what is the desired outcome. This might be addressing the causes of production disruption, such as quality issues, safety, or obstructed views of planning or execution. Then, break down your strategy. What iterative steps need to be taken to solve the situation? What role will technology play in providing data where there are gaps? What is the most effective M4.0 method for your problem? Unilever, for example, is currently implementing a new digital roadmap fueled by automation at one of their largest ice cream factories in Caivano, Italy. The ambition is to free workers from repetitive, routine work, allowing them to upskill while reducing operating costs and improving the safety and efficiency of the factory.
“Avoid moving forward with a specific technology for technology’s sake, only pursuing the opportunities that will deliver real, measurable value.”
To identify the most impactful technology, focus on where you have operational challenges or gaps in accurate and accessible information needed to make better business decisions. This includes internal observation, but also looking outside of your organization at how competitors and partners navigate digital opportunities. Next, define your key focus areas and identify the gnarliest questions. Answering these critical questions will bring the most value. Then, examine your existing processes and infrastructure. Can they integrate with more advanced systems? Can you run systems in parallel? What are the initial costs of integration and migration? Build a transformation framework into your roadmap, with key milestones. Consider key dependencies and potential impacts. Determine if external partners can play a role to advance the realization of benefits.
Build a Case for Change
You will then have defined your specific operations opportunity and created a roadmap to apply M4.0 technologies to these opportunities. Success now relies on two things: communication and unification. The entire organization needs to understand the processes you intend to embed, how, and most importantly, why. Clear, cross-department communication will help teams understand the opportunities that M4.0 will bring and recognize the rationale for digitization. Inspire your teams by creating a compelling case for change. Communication should include partners and suppliers, as they are likely to be impacted positively. Business alignment is vital. The process or technology you want to introduce should pursue a shared, overarching purpose.
“Manufacturing companies that understand the central role of data and its criticality to enabling value integration across people, process, and technology are leading the way in enterprise digitization.”
With full business and supply chain buy-in, and a clear rationale that reflects organizational objectives, you can move to the next stage. Test your chosen change, setting up pilots to assess the impact and finetune your approach. If you do not see the expected outcome, take a step back. Avoid moving forward with a specific technology for technology’s sake, only pursuing the opportunities that will deliver real, measurable value. At the height of the COVID-19 pandemic, for example, PA developed an AI enabled forecasting capability to predict 30-day forecasts of COVID-19 trends with 75 percent accuracy, supporting global manufacturing companies to manufacture and distribute their products in line amid demand fluctuations.
Nail Then Scale the Opportunity
The questions that often accompany significant developments, especially those requiring investment and a measured level of risk, are how quickly adoption will happen, and how easily will it scale? Our research into breakthrough innovation found that once a clear business case is made and the conditions for success are set, scalability will take care of itself.
Investment in M4.0 technologies provides greater visibility, aiding strategic and operational planning, and transforming reactive behavior into a predictive, proactive approach. Data-driven disruption in manufacturing improves order management, production monitoring, predictive maintenance, resource management, and inventory optimization, all while supporting cohesive feedback loops between sales, marketing, and manufacturing teams. The benefits are clear, bringing significant improvements that increase quality, efficiency, profitability, and customer satisfaction.
Manufacturing companies that understand the central role of data and its criticality to enabling value integration across people, process, and technology are leading the way in enterprise digitization. These outlier organizations are utilizing end-to-end digital integration to develop ‘shop floor to top floor’ capabilities, optimizing operations through better processes and people management. M
About the authors:
Michael Platz is a Supply Chain and Manufacturing Operations Expert at PA Consulting.
Shanton Wilcox is a Partner and America’s Leader in Manufacturing at PA Consulting.
The NAM recently released its Top 8 Manufacturing Trends for 2023—a guide to the opportunities ahead and the resources that the NAM can offer. Here is what to look out for this year and beyond.
Advanced and emerging technology: Manufacturers are investing in a multitude of new technologies, including artificial intelligence, virtual reality, machine learning and more. Automation and robotics are enhancing workers’ abilities but will also require many more high-skilled employees. Though the workforce shortage is a challenge, digital technologies will help manufacturers become more resilient, efficient and profitable.
- NAM resources: How do you maximize these opportunities? The NAM has resources for you, including the Manufacturing Leadership Council (the NAM’s digital transformation arm), the Innovation Research Interchange (the NAM’s innovation division) and the MLC’s Manufacturing in 2030 Project.
Supply chain resilience: As manufacturers face long lead times, increased costs and a scarcity of raw materials, they are taking steps to boost supply chain resilience through reshoring, cybersecurity, increased supplier pools and more.
- NAM resources: Manufacturers can benefit from resources like CONNEX Marketplace, which helps connect nearby manufacturers and suppliers; the NAM’s Supply Chain Hub—a continually updated collection of webinars and policy documents focusing on supply chain issues; and useful case studies highlighting best practices.
Talent disruptions and opportunities: Manufacturers are confronting a range of challenges around the workforce, including labor shortages and skills gaps, while also figuring out how to take advantage of previously untapped talent pools.
- NAM resources: If you are searching for ways to enhance your employee benefits, The NAM Manufacturers Retirement 401(k) and Savings Plan offers manufacturing employees secure benefits for the future, while Innovation Management Workshops give manufacturing leaders the skills to build innovative and creative teams. Meanwhile, the Manufacturing Institute—the 501(c)3 nonprofit workforce development and education partner of the NAM—promotes a range of events and initiatives designed to expand and diversify the manufacturing workforce as a whole.
Cybersecurity: The threat from bad actors is real, and strong cybersecurity has become critical to manufacturing operations up and down the supply chain. At the same time, manufacturers will have to be on the lookout for new cybersecurity reporting requirements.
- NAM resources: The NAM can help, with support like the NAM’s complimentary Cyber Risk Assessment. NAM Cyber Cover offers cyber insurance and risk mitigation, and you can check out these videos from manufacturing executives laying out best practices for cybersecurity defenses.
Post-pandemic growth and expansion: Long-term goals shouldn’t be downgraded, despite an uncertain economy. Manufacturers should keep pursuing technological advances, navigate government incentives and stay open to mergers, acquisitions and other investments.
- NAM resources: The NAM Incentives Locator helps manufacturers find funds and tax credits to help their business, while the MLC offers networking opportunities for manufacturing leaders.
Tough economic outlook: There’s no doubt that manufacturers face economic headwinds. That means manufacturers need to look for ways to be nimble and responsive to changing realities and able to work more efficiently than ever.
- NAM resources: Tools like NAM Shipping & Logistics give manufacturers discounts on shipping and freight, while NAM Energy offers conversations with energy advisers who can help adjust energy use strategies. IRI Coffee Houses promote virtual conversations with innovation leaders to discuss new developments and opportunities.
Sustainability: Manufacturers are committed to strengthening operations and maintaining a healthy planet at the same time. More than ever, manufacturing companies are looking for ways to reduce carbon emissions.
- NAM resources: The NAM provides resources that can help manufacturers with innovating for sustainability, as well as rethinking end-of-life technology value. Manufacturers can also learn about how digital solutions drive sustainability in manufacturing.
Looking ahead to 2030: Changes in the manufacturing industry and in the world around us—from population growth to the rise of a new middle class to increased interconnectivity—have manufacturers planning for big changes in the next decade.
- NAM resources: The IRI offers a forum for manufacturers to connect with R&D leaders, while the MLC’s Next Phase of Digital Evolution report shows how manufacturing leaders can plan their long-term futures.
Learn more: Take a look at the full guide for more details and to find out more about the NAM resources that will help manufacturers deal with these key trends.
Asset visibility is essential to maintaining a secure operational environment while also providing real-time insights.
Asset visibility plays a vital role in operational technology cybersecurity. When organizations continuously inventory and classify assets in their facilities, it becomes much easier to protect the entire OT environment.
“Asset management is really the foundational layer of cybersecurity, and it’s a critical component for effective communication between executives and operations staff,” said Ben Miller, Dragos Vice President of Services. “When executives hear about new OT threats in the wild that could impact anything from gas crackers to safety instrumented systems, how can they even get their arms around the relevance of these emerging OT risks to their business if they don’t have an inventory of assets identified and classified?”
Effective asset management enables teams to discover latent vulnerabilities, insecure configurations, and rogue assets. With a comprehensive asset inventory, security teams can determine if new threats apply to their environments more quickly, and they can respond faster to security incidents as they unfold. A clear OT asset portfolio gives decision-makers better information for planning their cybersecurity roadmaps and complying with security and safety regulations. Plus, once assets are properly managed, performance and efficiency can be analyzed and improved, maximizing uptime and profitability.
Unfortunately, many organizations struggle to get a clear view of the OT assets running in their industrial facilities. According to the Dragos 2021 Year in Review report, 86% of services customers have extremely limited or no visibility into the assets in their OT.
“With a comprehensive asset inventory, security teams can determine if new threats apply to their environments more quickly, and they can respond faster to security incidents.”
“Some of the visibility challenges are probably technology-related, but there’s also a need to broaden asset owners’ definitions of what should be considered within an asset inventory,” said Miller. “Comprehensive asset inventories need to include all the components and devices that support your operational process, whether those components are physical or virtual, software or hardware; and it’s not just about noting the existence of the asset.
“To make the data actionable, you need to capture and regularly update its version, firmware status, and configuration state,” Miller said. “Beyond each individual asset, it’s also critical to understand the relationships they have with one another and the communication pathways they establish inside and outside of the organization.”
An effective asset management program enables asset owners and cybersecurity teams to efficiently:
- Discover, identify, and classify OT assets correctly
- Create and continuously update an asset inventory
- Operationalize asset visibility by leveraging its benefits to increase uptime and profitability
From a cybersecurity perspective, continuous OT asset visibility capabilities make it possible to discover connectivity and communications channels operators didn’t even know existed; pinpoint active threats operating quietly in the environment; and identify insecure configurations, latent vulnerabilities, and rogue assets.
In a recent whitepaper, Dragos identified 10 ways that asset visibility builds the foundation for effective OT cybersecurity:
- Asset visibility and management facilitates an understanding of what “normal” means in your environment.
- A well-structured program verifies all OT assets, including those belonging to the Industrial Internet of Things.
- You’ll be able to identify and visualize asset relationships and communication pathways.
- Security teams can detect threats with high signal and low noise ratios.
- You’ll spot rogue assets that you didn’t realize were on your networks.
- An asset inventory provides critical information for incident response.
- Managing assets properly enables efficient mitigation of vulnerabilities and threats.
- Configuration detection can help to supplement change management.
- Compliance reporting will be easier and more clear-cut.
- A well-executed program will help you justify security investments and plan cyber roadmaps.
A Successful Path to Asset Management
An OT-specific, methodical approach toward data collection and asset inventory creation is critical for a successful asset management program. Recording important information, such as software version, physical location, asset owner, and priority, enables many cybersecurity and performance optimization activities.
“Operational technology environments are increasingly targeted by adversaries who try to weaponize organizations’ own hardware and software against them to disrupt industrial process controls. Implementing forward-thinking cyber strategies can help deter, detect and mitigate such threats,” said Ramsey Hajj, Global Cyber OT Leader with Deloitte & Touche LLP. “Cyber threat identification, detection, and prevention controls can help address OT security risks with steps to increase device visibility, segment OT networks, monitor security for the OT environment, correlate security information from OT and IT networks, and establish security operations centers to support ongoing, proactive efforts.”
“Organizations that want to develop an OT-specific, methodical approach to asset management will need a structured plan to determine and execute data collection requirements.”
IT has a long history of asset management and asset inventorying, so the tools, frameworks, and practices around gaining asset visibility are very well tuned to IT use cases. However, OT has unique environmental challenges that need to be managed across industrial assets — and IT tools, integrations, and processes are not designed to meet these requirements. A few simple examples of IT asset visibility tools and tactics that don’t translate well to the OT environment include:
- IT might utilize forced reboots of desktop computers for patch installations, but in an industrial environment, rebooting a workstation could result in weeks of unplanned downtime and introduce significant safety risks.
- You cannot put an agent on a PLC, because they often run firmware or operating systems that are not compatible with agents.
- An IT administrator who performs a network scan using NMAP in an industrial environment runs the risk of knocking sensitive devices like older controllers offline, disrupting production.
- In traditional IT environments, it would be perfectly normal to use active scanning tools for asset discovery and monitoring, but in industrial scenarios, passive techniques are often preferred – if not required – because they’re much safer.
Organizations that want to develop an OT-specific, methodical approach to asset management will need a structured plan to determine and execute data collection requirements. One resource that many OT asset owners use to guide their development of an asset management program is the Collection Management Framework for ICS Security Operations and Incident Response.[PB1] It provides a prescriptive, impact-driven reference based on years of customer experience that’s uniquely suited for the realities of the OT environment.
Whether organizations leverage the Collection Management Framework or some other method, having a plan that’s uniquely suited to OT environments is key.
It’s Not Just About Security
Asset management provides the foundation for a more secure facility, and it’s also the first step toward real-time insights, end-to-end visibility, and scalable solutions to manufacturing challenges. Smart manufacturing solutions can create insights and augment human intelligence with artificial intelligence to help overcome complex challenges, address key business objectives, and boost visibility and performance across the digital supply network. Predicting machine downtime by analyzing performance trends and actively managing the workforce to track worker safety and performance data are two proven benefits.
According to a 2019 Deloitte and MAPI study, 86% of manufacturers believe that smart manufacturing solutions will be the main driver of competitiveness in five years, but the transformation of legacy operations can be daunting. Transforming the facility requires collaboration between manufacturing, supply chain, and IT. New technologies should be adopted and the organization should focus on becoming more insight-driven.
“Transforming the facility requires collaboration between manufacturing, supply chain, and IT. New technologies should be adopted and the organization should focus on becoming more insight driven.”
Deloitte, Dragos and an ecosystem of solution providers, technology innovators, and academic researchers are working together to demonstrate how smart manufacturing solutions can transform enterprises. One such endeavor is The Smart Factory @ Wichita, an experiential center with a fully functioning manufacturing production line where asset owners can be immersed in custom simulations to understand business challenges and see first-hand how cybersecurity is integrated.
“The benefits of effective asset management are significant – this isn’t just a conversation about cybersecurity, although that’s certainly one of the most important benefits,” Miller said. “Asset management historically was manual and tedious work, but continuous and automated monitoring enables higher accuracy, increased productivity, and more agility in your operations. That’s a big upside, especially given its critical role in managing risk across cyber and safety domains.” M
About the author:
Jennifer Halsey is Senior Industry Marketing Manager at Dragos, Inc.
To ensure adaptability, a smart factory should be part of a broader digital transformation strategy for a manufacturer.
The smart factory, with its digital production model, is gaining momentum, driven by a combination of forces that includes global disruptions and instabilities, supply chain disruptions, and heightened customer demands for digital-first experiences. Usually, when organizations think of their objectives for digital transformation, they tend to lean toward production optimization or cost reductions. However, the success of real factory transformation comes from transforming the way companies capture and provide value to their customers.
Organizations embarked on a smart factory journey that has scaled beyond the pilot phase have experienced unprecedented increases in operational efficiency as a result of greater agility in their operations. However, most companies appear to be stuck in “pilot purgatory.” There may be several reasons for this, ranging from lack of leadership, cultural aspects, workforce readiness, and legacy and disparate systems, to a lack of process standardization as well as cybersecurity risks.
Many manufacturers are already leveraging components of a smart factory in areas such as gaining visibility in production operations using Manufacturing Execution Systems (MES), planning and scheduling using real-time production and inventory data, and leveraging augmented reality for maintenance. But a true smart factory is a more holistic endeavor, moving beyond the shop floor toward influencing the enterprise and the broader ecosystem. The smart factory should be an integral part of a broader digital transformation strategy for an organization. That strategy should have multiple elements that manufacturers can leverage to adapt to the changing marketplace more effectively.
The strategic importance of the smart factory is undeniable as early adopters have reported greater agility in their operations, serving their customers better, and driving more to the bottom line. NTT DATA and Oxford Economics surveyed 528 business and IT executives recently. We found that manufacturing organizations of all kinds are prioritizing revenue growth, cost reduction, increased resiliency, and innovation for the three years ahead. In this article, we will discuss the following aspects of the smart factory:
- Key considerations for smart factories and enablers
- Maturity models, assessments, and framework; how to start, scale, and transform
- Use cases
Key Considerations for Smart Factories and Enablers
1. Data Mastery and Analytics – A foundational pivot for smart factories is data mastery. Data mastery involves the use of data and analytics to find insights that help organizations become more efficient and pursue new business opportunities effectively. Data mastery is about more than building data lakes or empowering senior leaders to make better decisions. It is also about structured and unstructured data flowing through organizational processes to enable decisions at the edges of an enterprise.
In today’s manufacturing environment, accessing, aggregating, and analyzing data remain primary challenges. Manufacturing data come in many different formats (both structured and unstructured) and from many different sources: smart sensors, PLCs, DCSs, HMIs, MESs, motion control systems, vision systems, historians, completed work records, operator and maintenance logs, quality records, automation protocols, batch reports, energy meters, spreadsheets, databases, and a host of other sources. To meet these challenges, a well-orchestrated common data framework that is designed to ingest, aggregate, and condition the entire variety of data from the factory floor and then channel it to the company’s AI Data Pipeline for analysis is critical.
What’s essential to meet this critical need is a mechanism to manage data coming from the manufacturing shop floor. First, the data must be contextualized automatically using artificial intelligence, including an expert system, machine learning (ML) classifiers, and highly sophisticated ML algorithms. Next, a factory/plant digital twin must be generated and configured to mirror the actual manufacturing production lines. The digital twin, a dynamic, empirical mirror of the factory production systems, machines, equipment, and processes, produces a real-time semantic context for all product, production, and process data. Analytics can be performed on real-time data streaming from sensors, machines, and production systems to determine state condition and support best practices based on quality reporting, engineering diagnostics, and KPIs for asset performance Using analytics, users can design new reports and dashboards that address predictive maintenance, process optimization, root cause analysis, statistical process control, KPIs, and process deviation, among other data points. The analyzed data can be visualized as real-time, actionable information for every machine, line, and plant across an enterprise in contextualized dashboards that are designed to present the right information to the right people.
Cybersecurity – As more IoT devices are connected to networks, potential attack surfaces can increase, along with risk from compromised devices. These attack surfaces may not be exploitable or vulnerable today but may be easily exploited in months or years to come. Thus, leaving devices unpatched and connected to the network is not likely feasible. The responsibility of securing these devices should not lie solely with those who deploy the connected device; instead, the responsibility should be shared with the device manufacturers, who may be best positioned to implement the most effective security. Historically, OT networks have typically been implemented quickly without proper precautions being taken to electronically safeguard the industrial control assets they contain. Consequently, they are inherently full of risks, which are often not assessed, categorized, or even known and understood. Gartner reported that typical cybersecurity incident losses range from under $50K to over $100M with an average cost of roughly $3M and reported payments for ransomware incidents ranged from under $100K to more than $2M, with an average cost of roughly $520K. Additionally, organizations took an average of 170 days to detect an incident and an average of 66 days to investigate an incident. The time to remediate was on average about 80 days.
To mitigate the risk of cybersecurity, organizations must adopt a holistic approach across lifecycle assessment, implementation, and management.
Figure 1. Cybersecurity methodology
2 Digital Technologies – Smart factories typically integrate functions beyond the factory floor. While the Smart Factory focus mostly spans across operations, quality, sustainability, safety compliance and technology risk mitigation, it also integrates with the ERP, CRM, PLM, and other value chain applications. Ecosystem and sustainability are beginning to play a very prominent role in the evolving smart manufacturing priorities.
Figure 2. A variety of digital technologies are being leveraged to enable transformation in these functions:
Some of the technology considerations and capabilities to address smart factories from an IT and digital perspective are listed below.
Figure 3. Typical stack and technology considerations to keep in mind.
3 Leadership, Organization, Culture and People – Factory Initiatives need a higher level of engagement to manage change. When a factory becomes smart, and front line and management decisions are more data driven, product lines are getting smarter, the traditional corporate silos are being broken down and replaced with flatter, more collaborative organizational structures and monolithic entities are being replaced by more federalized, distributed, and agile value-chain ecosystems, it requires both a top down and bottom-up approach to manage change, culture, training, and communication across the enterprise.
Figure 4. Gartner found that the top three challenges for Chief Data Officers all had to do with concerns about the required culture and skills to achieve their goals.
Prioritize support at a leadership level as well as on the ground to gain organizational buy-in and ensure adoption. It also requires diverse teams with a broad variety of skill sets. The leadership or the chief digital officer must identify and deploy skill sets required to support the initiative such as engineering, master data management, analytics, edge, and data science to ensure the value extends beyond the four walls of an enterprise. Another key point to consider is upskilling resources, reskilling of employees, and ongoing support and learning. It requires development and assessment of skills required for the long term to ensure ongoing success as the smart factory evolves.
Building skills can pay major dividends such as driving acceptance and adoption of solutions and providing support for employees as they adapt and creating a culture of continuous learning. As smart factories leverage advanced digital technologies, roles within the factory will call for new and different skills than had been needed previously, making it challenging to upskill and train. Organizations should also look at some of the non-conventional models to deploy alternate talent models, collaborate with universities to build a pipeline of talent, and leverage the ecosystem of partners. This will also help attract and motivate young people starting their career in manufacturing.
Figure 5. A six-step approach to managing change.
Maturity Models, Assessments and Frameworks:
A Think Big, Start Small, and Scale Fast Approach
Smart factory investments often start with a focus on specific opportunities. It will be effective to start small, test out concepts in a manageable environment, and then scale once lessons have been learned. Once a “win” is achieved, the solution can scale to additional assets, production lines, and factories, thus creating a potentially exponential value creation opportunity. Moreover, achieving the ultimate true “Lights Out” operation requires a crawl, walk, and run approach as illustrated below. A company’s manufacturing strategy and environment will determine which specific issues to address and the way to unlock value through smart factory solutions. Customizing the approach to each scenario and situation can help ensure the resulting smart factory meets the needs of the organization.
Figure 6. A three-horizon approach to achieving smart factories.
Execution Approach – Pilot Site
Plant Maturity Assessment and Transformation Framework
Before an organization embarks on a transformation, it is important to assess and understand the maturity levels of people, process, and technology. While there is no globally accepted maturity assessment framework for smart factories, the World Economic Forum has taken a leadership role in shaping the future of global manufacturing. A vital component of this initiative is the Smart Industry Readiness Index (SIRI). SIRI is a suite of frameworks and tools that help manufacturers start, scale, and sustain their manufacturing transformation journeys. The assessment findings depend heavily on sharing relevant organizational practices and self-reflection on areas of improvement.
Figure 7. Smart Factory Maturity Assessment
The report should have a comprehensive roadmap that depicts current maturity across process groups and people; a readiness index and levers to improve; capabilities required to implement; the next logical step; and recommendations to start the transformation.
Smart factories encompass operations, quality, sustainability, safety compliance and technology risk mitigation. Use cases and process groups vary depending on specific customer and factory needs. Typically, smart factory use case benefits are focused on productivity increases, cost reductions, improved energy efficiency, inventory reduction, lead time reduction, and improved throughput.
The World Economic Forum has reported some of the top use cases implemented by the Global Lighthouse network companies.
Figure 8. Typical process groups and representative use cases in smart factories.
Key Value Realized by Organizations Implementing Smart Factories
Lighthouses are demonstrating how digitally infused operations go beyond productivity improvements to create sustainable, profitable growth. To be sure, the productivity gains are there, resulting from digital machines and management applications driving output increases at the factory level. Looking past productivity, two ways to drive growth stand out: by adopting new business models and unlocking capacity in the people and production processes. The results are encouraging. These measures optimize resources and infrastructure while enabling workers to realize their potential equipped with powerful digital applications – all without massive capital investment or negative environmental impact.
Figure. 9 Value realized
Though there are many different approaches to smart factory deployment, lessons can be learned from each approach that can lead to significant value creation. These range from people-centric experiences such as change management, putting humans at the center of capabilities, and managing skill diversity to broader operational and technological considerations. If leaders take just one lesson away, however, it is the importance of moving forward. All our recent surveys and research show that organizations broadly agree that the future of manufacturing is smart. The empirical results in global lighthouse factories reported by WEF demonstrate the value of the smart model. While it seems daunting, by starting small with specific program objectives that can produce tangible value, companies can get themselves started on driving sustainable value. For those who are in the journey, it is about accelerating and scaling the benefits. For those just contemplating how to start, now is the moment to begin — or risk being left behind. M
About the author:
Baskar Radhakrishnan is Strategic Advisor, Manufacturing Industry Solutions at NTT DATA
AI is changing the way manufacturers do business—from the production line to the back office and across the supply chain. At the Manufacturing Leadership Council’s Manufacturing in 2030 Project: Let’s Talk about AI event last month in Nashville, Tennessee, panelists discussed how those sweeping changes would alter, and enhance, the manufacturing workforce.
A collaboration between the MLC (the NAM’s digital transformation arm) and the MI (the NAM’s 501(c)3 workforce development and education partner), the event provided key insights for manufacturers into how technology and workforce trends interact with each other. Here are a few key takeaways.
Net positive: “The history of technology adoption is about improving the job quality of individuals on the shop floor. AI helps them to do the job better, provide them with better tools, gives them greater authority and ultimately increases the value-add of their jobs. All of that is a net positive for those individuals,” said MI Vice President of Workforce Solutions Gardner Carrick.
- By leveraging data and enabling greater efficiency, AI will improve communication, increase collaboration across disciplines and stimulate innovation, according to the panel.
- In addition, “AI can even inform the workforce’s creativity by working with it to design a new product or system,” said Jacey Heuer, lead, data science and advanced analytics, Pella Corporation.
Skills needed: While you might expect that implementing AI requires workers skilled in programming, data science and machine learning, manufacturers will also need to expand their bench of critical thinkers and problem-solvers. The panelists had a few tips to help companies along.
- Invest in upskilling programs to make the AI integration process at your company smoother and develop the talent you already have.
- Update job descriptions to reflect the skill sets the company will need in the next five to seven years.
- Consider recruiting for and teaching adaptive skills—skills that enable individuals to adapt easily to changing demands and environments—which can increase the flexibility of your workforce.
- Build partnerships with local schools, community colleges and technical and vocational schools to develop talent pipelines that will meet your needs.
The human-AI collaboration: While AI will take over monotonous, repetitive tasks, the panelists predicted that the industry will continue to center around human labor.
- “You can teach AI to do X. You can teach AI to do Y. [However,] combining the two may be really difficult for AI, while a human can do it better. You’re going to continue to see humans in roles that center on making decisions and telling stories,” said Asi Klein, managing director, industrial products and organization transformation, Deloitte Consulting.
- Meanwhile, AI adoption will likely lead to an increase in available jobs, as more skilled workers will be needed to guide and inform these new processes.
The last word: “Over the last 12 years, we’ve seen a lot of technology adoption, but we have not seen a lot of job loss. In fact, we’ve seen job gains,” said Carrick. “There is a lot of opportunity to reimagine jobs to add value that AI will help to illuminate.”