The two core concepts of the Forth Industrial Revolution (4IR), a term first introduced at the World Economic Forum 2016, are hyper-connectivity and super-intelligence. The two keywords that best describe hyper-connectivity are the Internet of Things (IoT) and Big Data. For super-intelligence, it would be artificial intelligence – in consideration of which the global companies’ race for AI dominance is understandable, as they brace for the Fourth Industrial Revolution where the concepts of hyper-connectivity and super-intelligence are expected to play pivotal roles.

POSCO is not exempt from this global competition. What they are specifically focusing on is fostering AI experts from within, so they’re organizing related programs to educate prospective AI specialists. This September, they even organized ‘POSCO Industrial-AI Solution Challenge,’ a contest dedicated to various topics on artificial intelligence. The Challenge was open to the general public.

It seems there is no apparent linkage between a steel company like POSCO and AI technologies, but the three topics POSCO assigned for the AI Solution Challenge suggests otherwise. From predicting the cost fluctuations of steel raw materials, figuring out the timing of big swells in the ocean, to calculating how many lunches to prepare for POSCO employees, the scope of AI technology can be far-reaching.

POSCO Newsroom sat with the winners of each challenge category – Team Serio, Zero Coke, and Fast Learner – to hear what they have to say about the contest. All three winners acknowledged the immense potential of AI technology and its applicability to virtually all sectors: AI has no borders.

┃“Braving it out with the AI technology” – Team Serio

Jae-Ho Lee and Dong-Woo Kim, the two members of Team Serio, are from POSTECH (Pohang University of Science and Technology), working in the same research department. The moment when they saw the contest information on the school bulletin board, they knew. That this would be a great opportunity to implement their classroom knowledge to help solve the pressing issues in the field. So they applied with no hesitation.

When asked why Serio had specifically chosen to predict the cost fluctuation of steel raw materials, Lee replied: “This challenge topic has the most economic impact, and we also thought it was the most versatile model as it can apply to other time series forecast of similar nature.”

To come up with a solution, Team Serio utilized the model DA_RNN (Dual-stage Attention based RNN), based on the human cognitive process. When recognizing a particular object, a person first notices an area of interest around her or him. Then, he or she identifies an object within the area of interest, finally recognizing the specific features of the object. That is the typical process of human cognition.

For Lee personally, the most significant achievement from competing in the POSCO AI Challenge was becoming more assured about the prospects of AI research. “Because of the satisfactory result of our model, I think we can brave it out going forward, applying AI technologies more proactively and widely,” he added.

The POSCO AI Challenge has ended, with Team Serio as the final category winner. Even after the contest, however, both Lee and Kim are still testing various approaches that they couldn’t utilize because of the time constraint. For Team Serio, the clock is still ticking.

┃“A Solution As Refreshing As… Coke Zero” – Team Coke Zero

Yong-Woo Cho and Kyu-Soo Han of Team Coke Zero share many interests: they go to the same university. They both major in computer science. They also share the love of… Coke Zero. So it didn’t take long for them to choose their team name. Devising a solution for their challenge topic wasn’t as simple. How did they come up with a solution just as refreshing as their team name, Coke Zero?

For companies like POSCO, ocean cargo shipping is crucial, which is incredibly dependent on wave action. Hence the challenge topic of its AI contest, predicting the big swells. The members of Team Coke Zero who have been studying AI and deep learning since earlier this year found this topic especially intriguing.

To predict the result, they worked with meteorological data from a total of 33 observatories along the coast of the Eastern Sea, from Donghae, Ulsan, Ulleungdo and Dokdo island. The main predictors were water temperature, significant wave height, wave period, wave direction and wind direction. All data were observed at one-hour intervals.

For computer science students like Cho and Han, figuring out the exact causes of big swells was especially challenging. So they built an RSCNN model (Random Shuffle Convolutional Neural Network) to establish the relationships between a total of 248 spatial and temporal predictors. Each model they designed learned a different predictor bundle and the team generated dozens of such models. Through the application of ensemble technique, the team completed a solution predicting either the presence or the absence of big swells in the ocean.

Through the contest, Cho ‘felt more convinced’ about his chosen university major. Coming up with an AI model itself was challenging, but it was also rewarding. He has never had this much fun coming up with solutions for industry challenges. After the contest, Cho is feeling even more dedicated to AI research.

┃“I Taught Myself Artificial Intelligence” – Team Fast Learner

Unlike the other winners, Young-Bin Noh of Team Fast Learner doesn’t come from a science background – he is a Business Management student. He was also a solo contestant with no other teammates. What’s even more surprising was the fact that he studied artificial intelligence on his own. On reasons why he participated in the POSCO AI Challenge, he said that he wanted to receive professional feedback from field experts.

The challenge category Noh had chosen was to calculate how many lunches to prepare for POSCO employees. He chose this topic mainly for two reasons. Firstly, he wanted to practice dealing with text data. Text data mining, aka Natural Language Processing, is widely used and popular, so he always wanted to try his hand at it. Secondly, the challenge category didn’t require much background knowledge. So it was also a practical choice for a business student like Noh.

Noh utilized XGBoost algorithm, which creates hundreds of ‘decision-making trees’ to minimize prediction errors. Decision-making tree is one of the most basic algorithms in machine learning. Like the game of twenty questions, it narrows down the possible answers, ending up with one exact answer at the final stage. To further boost the predictability, Noh employed an ‘ensemble technique’ by creating one more XGBoost.

Noh admits he had to study harder after submitting his proposal, to prepare for the final presentation. The POSCO AI Challenge was an opportunity for Noh to connect with others who were studying data science more creatively. “I enjoyed teaching myself, but I also felt it would be great to learn with others, bouncing ideas off of each other,” he added.

Noh hopes one day the program he developed for the contest can be implemented to help reduce food waste in POSCO cafeteria.

The POSCO Industrial-AI Solution Challenge is now over, but the winners aren’t slowing down. Throughout the contest, they have been running toward the finish line. Now that they have extra time to improve their models, they’re trying to perfect them as best as they can.

The members of Team Serio, Coke Zero, and Fast Learner all said the POSCO AI Challenge was a great learning opportunity, strongly encouraging other AI researchers to participate in similar contests. Simply participating in an actual contest can teach something new, but the contestants can also receive valuable feedback from field experts.

Throughout the interviews, the enthusiastic energy of the young researchers was not only palpable, but also contagious, all of them treating each question with thoughtful consideration. With their dedication, Korea’s AI future seems promising.

POSCO is accepting applicants for the fourth round, offering free education courses on artificial intelligence, big data, and IoT for job seekers as well as the public.

Last year, POSCO along with Pohang University of Science and Technology (POSTEC) created education programs on AI, big data, and IoT in the form of basic online and advanced offline courses. The program comes as part of the company’s social contribution efforts offered free of charge to encourage these skills and broaden job opportunities.

Application for basic online courses are available for everyone until September 2 through POSTEC website. Upon completion of basic online courses, advanced offline courses are open to selected individuals for 10 weeks from September 3 through October 24 at POSTEC. To promote the offline program that provides intensive training in a short period of time, POSCO plans to actively promote these efforts in domestic universities and online communities for job seekers.

The four-month program is designed and carried out by POSTEC professors while equipment expenses are provided by POSCO. Basic online courses started out with six in the first round expanded to 13 this year. The first round of courses have ended on September 2. Over 6,000 students have completed basic knowledge on AI, machine learning, computational intelligence and more. POSTEC plans to take previous feedback into account to further enhance the quality of the courses.

The online programs on AI, big data and IoT were completed by 26,494 students thus far. The advanced offline courses completed by 49 students have successfully entered the domestic workforce  such as interns at POSTEC, regular employees at LG CNS, Kakao, and SK Innovation. POSCO plans to further advance the courses such as blockchain, cryptocurrency, and computer vision for students interested in even more specialized fields.

Meanwhile, Jeong-Woo Choi, who was elected as the 9th CEO of POSCO on July 27, presented ‘With POSCO(Building a better future together as corporate citizens)’ as a new vision at the inauguration ceremony, expressing his aspirations to create a better society through contribution. This program represents POSCO’s major social contribution activity, aimed at increasing job opportunities while enriching the country’s competency in the fourth industrial revolution.

Over the summer holidays, my students and I returned from a field trip to Silicon Valley where we observed its latest technology and trends. Although its main up-and-coming tech such as AI(Artificial intelligence)/Big Data, robot technology, AR/VR, autonomous car, and blockchain was more or less what we expected, we learned a great deal from observing new studies on the front line while witnessing them create opportunities for new enterprises.

What was apparent were the companies investing huge sums into what they consider core technology— AI and big data. Indeed, companies dominating the media have recently adopted AI as their core technology. Amazon had already employed AI technology in their product recommendation engine and dynamic pricing. More recently, Amazon has changed the landscape in retail markets by launching its systems such as Amazon Go and Amazon Drone Delivery.

These new endeavors have indeed adopted what is called deep learning. The photo below was taken at NVIDIA headquarters, the company that had its share price skyrocket higher than any other company over the last two years. The big sign, “i am ai” at the entrance suggests the company’s current business disposition at a glance.

AI Charts a New Course in the Market

AI technology application in the healthcare field is significantly broadening the possibilities of advancement in the medical industry. The screening device for diabetic retinopathy released in April is the first device that does not require the diagnosis of a doctor, unquestionably outmatching previous ones that aided the professionals’ diagnosis. And there are new services combining basic medical information with data generated from wearables predicting the possibility of Parkinson’s disease based on footstep analysis. These innovations are highly anticipated as the future trend that will shape the medical industry.

Retail and healthcare are merely two specific industries among other extensive areas that have adopted artificial intelligence technology. A case in point is POSCO’s accomplishments in process optimization and a decrease in error rates with the application of AI in automatic defect detection and system management automation. POSCO adopted the chatbot based on these frameworks while company-wide efforts are in motion such as conducting training courses for in-house employees in artificial intelligence specialist programs.

We may commonly recall AIs as robots in movies such as “HER” and “Ex Machina” that imitate our thoughts and actions. This is, however, not the case. Humans have imitated birds to fly only to no avail, yet later realized the principle behind aviation, and thus invented the airplane. Likewise, there is not much basis to the assumption that artificial intelligence should coincide with our psychology and decision-making process.

On this account, distinguishing strong AI from weak AI is not a relevant matter either. We do not yet comprehend the concept of a strong AI’s “perception”. For now, it is essential to devise a detailed plan to apply its technology by understanding the current AI technology and its future course.

Data-driven AI to Surpass Human Intelligence

How advanced is the current AI technology? Deep learning which was released since 2012 created the age of data-driven artificial intelligence with the evolution of computing and new algorithms. Previous age of AI was based on the logic produced by humans which was fed into computer algorithms. To determine a defect, the computer would logically define it(say a clothing with considerable lint and measurement that exceeds the standard of 0.05 mm) first and confirm whether this fault fits the definition.

A recent data-driven AI is fed a considerable amount of data through the method of induction which the computer then independently recognizes patterns. This is a substantial change from the previous logic-based approach. AI has now advanced to the stage where a human no longer needs to provide the reasoning, but simply providing data enables the computer to reason by itself. The change from human-based logic to a data-driven algorithm that independently recognizes patterns foretells a greater shift than we could image.

The fact that AI started to reason after providing data conveys that we will be experiencing a future with a computer system that reaches new dimensions. As the computer can make assessments based on its own pattern recognition, it will surpass humanity’s accumulated knowledge. Under these circumstances, it may provide results beyond what humanity can comprehend outside the concept of mere “outcome”. This will be an uncharted territory raising questions on how we could accept its new intelligence.

Getting Accustomed to New Ways of Learning

It’s rather engaging to expect how the game of go will shift the strategy board game industry after Lee Sedol’s loss over AlphaGo. To learn the game of go was to acquire its method using the history of human experience. By observation, an increasing number of people are picking up go through artificial intelligence. Our previous experience of learning go was simply through experience and acquired knowledge. AI has now suggested ways that are incomprehensible to the human experience. Professional players are now learning new ways of go through computers. Go players who cannot succeed without handling computers may eventually face defeat.

This illustrates upcoming opportunities and new realities we will soon face. The most crucial matter for companies would be to nimbly search for opportunities that require outcomes exceeding the human logic through the use of AI and data. Not to mention, we should understand the new territories that AI grants and familiarize ourselves with new ways of expanding knowledge.

The purpose of this program was to enhance their understanding of artificial intelligence and new steel products in order to improve the competitiveness of overseas subsidiaries by spreading POSCO Smart Solutions.

At first, in the lecture about ‘Smart Technology and Business Model Innovation,’ they learned about the influence of smart technologies, such as the 4th Industrial Revolution, big data and artificial intelligence, on business models, and in the lecture about ‘Big Data and Cases that Can Be Utilized in Manufacturing,’ they reinforced their understanding of big data modeling.

Also, they learned about Smart POSCO, such as the concept of POSCO Smart Solutions, key activities, CES 2018 and smart factory success stories, creating an opportunity to spread it to overseas subsidiaries.

In addition, they gained knowledge of new steel products such as POSCO Magnesium Alloy Coating Product (PosMAC), thick plate product (high manganese steel), automotive steel (giga steel) and electrical steel (Hyper NO), and shared relevant cases to expand solution marketing in overseas markets.

POSCO ICT Head of the New Business Development Team Mi-hwa Park during POSCO Smart Solution and CES 2018 at Songdo Convention Center.

The participants said, “The brief, but well-organized program was quite useful in that we were able to gain a deep understanding of the trends in information technology, POSCO Smart Solutions and our new products, and learn how to apply them to overseas subsidiaries. We will make efforts to spread POSCO Smart Solutions throughout our overseas subsidiaries.”

Meanwhile, POSCO Group University is planning to provide education on Industrial Revolution 4.0 and POSCO Smart Solutions for new local leaders and general managers to disseminate the necessity of Smartization.

What’s more, manufacturing is no longer a rigid industry that produces uniform, one-sided goods. There is constant communication between customers and businesses for hyper-customization. Not only that, machines and products communicate within a smart factory, and factories exchange data with other factories. Manufacturing is no longer limited to production plants. Moreover, the data generated in the manufacturing process is combined with customer information, and a new service can be created. This combination of manufacturing and services is resulting in creation of added value. In the midst of such drastic change, how can companies stay competitive?

Staying Competitive Through Connection and Convergence

Global manufacturing companies are at the forefront of the 4th Industrial Revolution, as evident in their smart factories. A smart factory collects data generated from the production process using ICT technology, and the system controls all processes from material input to the final product. This has led to flexible production systems with the ability to make various products in one factory or to expand the range of products on the basis of connectivity. Typically, when a company builds a smart factory, it can improve productivity by 20 to 30 percent. A 20 percent improvement in efficiency in the manufacturing sector is a significant, outright increase in global competitiveness.

SEE ALSO: How Smart Factories are Changing the Manufacturing Industry

Siemens Electronics Manufacturing Plant incorporates robotics, AI and IoT to its production processes. (Source: Zawya)

If the productivity of a plant can be improved through smartization, it is important to also think about connectivity with the ecosystem that exists outside of the plant. Once a smart factory is built, all the data from customer orders to production and delivery are collected in a system via sensors. The customer, product and production data create meaningful connections with each other and provide extensive insight. Examples of added value creation through meaningful connections include hyper-customized goods, data-driven after-sales services to customers and collaboration among companies connected within the smart factory’s external ecosystem. Such advancements will lead greater product quality, production stability as well as shortened delivery times between value chain suppliers.

The Adidas Speed Factory and GE Brilliant Factory are examples of successful smart factories. Adidas customers choose the materials, colors and design of their sneakers, and have them manufactured and shipped within 24 hours in an automated factory.

GE has built a system that can produce all of their widely-diverse products in one factory. When the factory receives customer orders, it operates in a flexible production system which starts with the necessary raw materials, inputted by the automated scheduling system that controls the entire production process, including the final distribution system.

The Future of Manufacturing

Overall, the adoption of innovative changes in the manufacturing sector is likely to progress from lighter industries to the heavy industries, from B2C to B2B sectors. Small plants, quick manufacturing and B2C companies can more readily adapt to rapid technological and market changes. On the other hand, heavy industries like steel and B2B companies with continuous and heavy manufacturing, large production volume and numerous linked companies are likely to be slower to adapt to changes.

It is much more difficult for traditional, heavy industries to adapt to changes. (Source: Live Mint)

For example, adapting to the changes of the 4th Industrial Revolution in the steel industry may be slow, but it is inevitable. What’s more, the long-term adaptation process is more likely to be systematic and deliberate.

As a leading company in the global steel industry, POSCO is pursuing a long-term, systematic “Grand Design” to reinvent its systems to align with the changes of the 4th Industrial Revolution.

First, POSCO built a pilot smart factory in their steel mill, Gwangyang Steelworks, in 2015 that is currently in operation. The company used IoT to collect big data on site, analyze it in real time and build a smart factory that enables optimal control through AI and self-learning. As a result, the Gwangyang Steelworks is reaping the benefits of a smart factory not only in cost reduction but also in improved steel quality, minimized malfunctions and a safe and stable production environment. This year, POSCO plans to expand and apply smart factories to all of its production processes.

POSCO built a smart factory in Gwangyang Steel Mill.

Smart factory application throughout the entire steel mill will improve overall efficiency through a flexible production system. In addition, the factory will be able to respond directly to various customers in real time based on platform construction with customers within the connected ecosystem. The customized characteristics and design of the steel grade for each customer can be applied to production in real time.

SEE ALSO: How Factories Produce Steel – the Smart Way

Ultimately, a smart ecosystem that links manufacturing, processing and distribution with customer input will lead to a new, innovative ecosystem within the steel industry. In Europe, some companies are experimenting with material libraries and steel distribution platforms. The material library displays a variety of materials for customers to see, touch and test the workability and performance of the materials, and get information about the characteristics, design and delivery times through the order platform. Customers can designate the shipment date on the spot. This will be one of the new promising business models that steel and other material companies will strive towards in the coming future.

POSCO’s Grand Design includes a step-by-step approach to smart factories to expand the use of IoT, AI and Big Data in its production systems. To this end, POSCO ICT has developed PosFrame, a standard software platform that collects basic data of production processes and collectively manages, controls and analyzes the information.

POSCO uses its software platform, PosFrame, for data collection and analysis.

When the software becomes standardized and reliable enough to extend to other sectors, it will be applied to other business areas such as energy and construction, as well as to POSCO’s affiliates.

Manufacturers will have to take into account the heavy production environment, the slow industrial change cycle and the complexity of related industries and affiliates to implement the most effective, long-term, systematic upgrades to its production systems. This will result in a brand-new production and business model for manufacturing companies that will align with the new environment of the 4th Industrial Revolution.

Cover photo courtesy of Sputnik International.

Here’s a look at how steel is produced in a steel mill or factory, and what the “smartization” of steel factories will look like.

So, how is steel made?

Iron Making

To make steel, manufacturers first need molten iron. Molten iron comes from two raw materials; iron ore and coal. Iron ore is converted into sinter ore in a sintering plant and coal is converted into raw coke using a coke oven. The processed materials are poured into a blast furnace where hot air reaching 1200℃ is blown in from the bottom through tuyeres, causing a chemical reaction. This process oxidizes the coke and reduces the sintered ore, creating molten iron.   

Steelmaking

In the steelmaking stage, the molten iron is transported to the steel making plant via a torpedo car, where the liquid is poured into a converter. Then, oxygen is blown into the converter to burn off all the impurities. All that is left is pure molten steel.

Continuous Casting

This is where steel finally becomes solidified into different shapes such as slab, bloom and billet. Liquid steel is poured into molds and cooled as it passes through a continuous casting machine until it solidifies into the desired shape.

Hot steel passes through rolling machines to be rolled into specific sizes and thicknesses. (Source: Global Sourcing Blog)

Rolling Process

During the rolling process, steel is heated once more to achieve various sizes and thicknesses. Steel slabs are heated to over 1100℃, then pass through rolling machines. This results in hot-rolled coils that can be shaped for different uses such as thick plates. They can also be processed into long, wire-shaped rods for billets. Often, the hot-rolled coils are rolled at room temperature for cold-rolled coils. Cold-rolled coils can be fabricated to produce galvanized and electrical steel products.

“Smartizating” these processes will involve converging IoT, Big Data, and AI to connect the different facilities, IT systems and workers in order to collect and analyze data for optimization.

POSCO is “smartizing” the steel-production process

POSCO is a steel company looking to lead the industry in adopting smart factories. CEO Kwon Ohjoon made “smartization” one of the 4 key priorities for POSCO starting back in 2014, and Kwon will continue to increase those investments.

POSCO smart factory incorporates artificial intelligence to enhance safety and efficiency.

SEE ALSO: How Smart Factories are Changing the Manufacturing Industry

In 2016, POSCO established its Smart Solution Council in order to research AI, big data and IoT applications. In the same year, POSCO ICT’s smart factory platform, PosFrame, was completed and installed in POSCO’s Gwangyang Steel Mill. PosFrame allows engineers to collect and monitor big data. So far, the company has saved over USD 14 million by incorporating the new technology in its production practices.

Here are some other features of POSCO’s Smart Factory.

POSCO’s Smart Blast Furnace

The Pohang Blast Furnace No.3 became a smart furnace in 2017, following a 102-day repair period. The furnace is now equipped with automated sensors that monitor and control its internal conditions using AI technology. Smart sensors monitor the blast furnace for factors like raw material quality and ventilator status, preventing breakdowns and ensuring a much longer lifecycle.

SEE ALSO: Will Artificial Intelligence Lead to Breakthroughs in the Steel Industry?

Worker Safety

Smart sensors can be used for more than just process monitoring. The company is working toward a full implementation of smart sensors for safety purposes, using IoT to create a better working environment.

Steel manufacturing involves high temperatures and high pressure levels, which is dangerous for workers who come in close contact with the equipment. With smart sensors monitoring all of the factory information, workers will instead be monitoring operation from a safe distance.

Workers in POSCO’s smart factory wear smart sensors for safety.

Wearable sensors, in conjunction with factory smart sensors, will be able to detect if and when workers are approaching potentially dangerous areas, and will alert them. These sensors will also detect and alert with regards to any impending accidents, or life-threatening situations like gas leaks, explosions, or fires.

Also, dust, sulfur, and nitrogen compounds will be removed via a high-plasma method, creating an eco-friendly steel plant and a healthier environment for workers.

POSCO will continue to add more smart features to its steel mills to increase efficiency, safety and sustainability. In efforts to learn and implement new technologies, POSCO CEO Kwon Ohjoon visited GE’s smart factories to learn about their technology earlier this year. POSCO also held the 2017 Smart POSCO Forum to share its insights with clients and affiliates, all as part of its expanding smartization efforts.

The fourth industrial revolution is heralded by big data, the internet of things, artificial intelligence, virtual reality and augmented reality, 3D printing and other technological innovations. It’s happening at rapid speed. The steel industry needs to keep up and take advantage of all that this revolution has to offer.

However, the industrial revolution did not happen overnight, and advancements in human development traces back to the Iron Age.  

The Iron Age and How it Impacted Civilization

After the Bronze Age was the Iron Age. People started using iron and steel for tools. This was a huge shift for the world, and helped improve nearly all spheres of life from the spread of written language to more effective agricultural practices. Iron offered more choice. People could forge this material into whatever they needed for tools, weapons, or ornaments and decor.

Blacksmiths would have used tools like this during the Iron Age.

Tools and weapons created during this era often used steel, which was a vast improvement on the bronze items from the previous age. Items made from steel were just as light as bronze, but stronger, so users were able to get more out of each item they created. The strength of these tools made it possible for farmers to plant and harvest more land, faster. It enabled people to sell or trade for livelihood.

The Iron Age is a great example of how the right materials can make life better for everyone.

The New Structure of Steel

As the world moves through the fourth industrial revolution, it is evident the same thing is happening. Instead of improvements in specific materials and tools, however, technology is changing things.

The steel production process can be completed autonomously if the fourth industrial revolution continues the way it is going. This means that manufacturers will be able to have very detailed and thorough control over production, using smart technology that will factor in every element from the surrounding environment to minute details of the material itself. The steel production process will be able to use real-time data to optimize every facet of operations, saving money and time.

Robot arms with artificial intelligence take part in the steel production process. (Source: Telenor Connexion)

The steel value chain is affected by changes in the provision of after-sales service, owing to new technology. Manufacturers can use smart technology to track how users interact with their completed products and offer better customer service as a result.

Again, real-time information comes into play, allowing steel companies to see exactly how orders are being created, fulfilled and used, as it is happening. Customer needs are met with ease and speed, all tracked and analyzed digitally.

Competition in the steel industry will be affected by this revolution, too. Competitors can see how other companies are using big data and automation to save money and offer a better customer experience, and use those details to revolutionize their offerings. Online steel transaction platforms make this transparency even more accessible, for companies and clients alike.

Implications on the Steel Industry

To keep up with this revolution, and make the most of it, steel companies need to understand the implications on the industry and plan for the future. New technology is only going to become more widespread, more common, and more advanced. Companies need to be ready to react.

Integrating technology and smart software is the ideal way for steel companies, and the industry at large, to take advantage of this new revolution and use it to create better products and better customer relationships.

To get an idea of how to best integrate new tech into existing steel practices, POSCO’s smart steel factory is a prime example, currently being tested at the Gwangyang Steel Mill. POSCO’s smart factories will collect and analyze all of the microdata generated in the steel production process, to determine the cause of every event that impacts quality or production in general.

An image of how POSCO’s Smart Factory will be run.

From facilities to energy and environment, to safety and conditions, the smart factory will monitor everything. And it will respond accordingly to make the best quality steel possible.

Self-learning artificial intelligence ensures that the process of steelmaking is carefully controlled, with adjustments made in real-time to directly and positively impact the result. The smart factories will use technology to fill the gaps where human capabilities are simply no match for software.

The world has come a long way since the Iron Age and through the various industrial revolutions. In the current fourth industrial revolution, steel companies have to take advantage of new technology and innovations by figuring out ways to apply them to steelmaking processes. POSCO has made this a top priority as it continues to research, educate and implement smart technologies to its production processes and materials and will lead the way for steel companies to follow through the fourth industrial revolution.   

Cover photo courtesy of Manufacturing Global.

POSCO’s smart safety features

As a steelmaking company, POSCO has been working towards becoming a “Smart POSCO”, or incorporating the implementation of AI into their production practices. In 2016, POSCO established a Smart Solution Council to research AI, big data and IoT applications. To date, the company has achieved a cost reduction of USD 14 million with the new technology. Even as one of the first steel producers in the world to implement a smart factory for production, the road ahead is a long one. Members of the discussion panel at the 2017 Smart POSCO Forum offered their expert opinions on how companies should move forward with AI research and applications.

Problem Mining

“One of the biggest difficulties in applying AI to manufacturing companies is problem mining. The only way to solve a problem is to find it and define it.” Kim Byung-In, a professor at the POSTECH Department of Industrial and Management Engineering, started off discussions and added, “Along with problem mining, people can learn from the past with reinforcement learning. Reinforcement learning means learning through similar tasks from the past to apply to new assignments. Defining problems and solutions more precisely and systematically managing related keywords can be useful for future tasks.”

Professor Zhang Byoung Tak, an AI professor at Seoul National University and a distinguished professor for POSCO added, “Traditionally, people solve problems, but in the generation of AI, AI will be able to solve problems for us… People now have to focus on defining problems, implementing systems and getting the right kind of data.”

Cooperative Research and Investment

Along with defining the exact problems that AI can provide solutions for, the implementation of AI will also call for a new working culture for companies and industries.

Tech company VUNO CTO Jung Gyuhwan stated, “Each company has its own production or operation method, and different data formats and protocols, but product inspection and screen UI (User Interface) are common areas. If we can find such common areas that can be commercialized and continue to research ways to expand AI applications to different sectors, we can achieve meaningful results together.”

Researchers developing programming and coding technologies

Jung went on to highlight that most AI personnel are unfamiliar with manufacturing. In particular, steel production terms are so difficult that it takes a considerable amount of time to share and understand information. There are also numerous obstacles and obligations for startups entering an unfamiliar development environment full of regulations and restrictions on things like the use of codes.

“In other words, task efficiency is a trade off with information security. I believe that if there is such a process in POSCO where the risk of technology leakage is not high, it will speed up results through collaboration with start-ups. POSCO ICT in Pangyo is a good place for startup employees to work. If we can take advantage of the POSCO ICT databases, collaboration with start-up companies will be easier. As of now, there are not many AI companies in Korea that specialize in manufacturing. Most people don’t even know that there is a lot of data available in the manufacturing industry. That’s why fostering start-up companies specializing in manufacturing now, is a good idea.”

Google’s Fei-Fei Li announces Google’s acquisition of Kaggle. (Source: Venture Beat)

According to CB Insights, M&A (mergers and acquisitions) of AI startups have increased 700 percent since 2011, because they are more efficient and often more insightful when it comes to AI research. Besides investing in existing startups, members of the panel agreed that collaborative competitions such as Kaggle, a contest platform where data scientists from around the world participate in solving problems in industry and society, are a great place to find talent and foster them as start-up companies.

Tackling New Challenges

The flurry of activity around AI research shows just how new the sector really is. People are still finding ways to apply AI across different sectors and industries. In such a volatile environment, how do companies and researchers navigate their way forward?

According to Choi Heeyoul, a professor of computer science at Handong Global University, companies need to “start where the people, problem definition and data are ready. Whether it is in production, sales, distribution, etc., if there is the will to solve problems, then results will follow. Especially when collecting data, you need the help of data analysts. For example, classification data for different types of steel can only be analyzed if the history of the classification criteria is recorded. Speech recognition is not any better for AI application than other fields, yet the most active and productive research has been completed in this sector. This is attributable to the will to apply AI, the abundance of accurate data and close cooperation with data analysts.”

Obtaining accurate data is vital for AI research

Professor Zhang Byung-Tak had another approach to offer. “I hope people will boldly take on challenges. The dramatic increase in computing power can now enable attempts that were previously considered reckless…No matter how much data we gather, it will never be enough. We need to approach problems as experiments and just start. One way to go about it is to work in reverse instead of waiting for data to be accumulated. You can start the task to define the data that is needed and determine the coherence of the data to the task.”

Lee Jong-Seok, a Professor at Sungkyunkwan University added, “When we are defining problems, we need to separate them into ones that can be solved in the short term and ones that require long term solutions. Some problems can be solved with minor improvements, while other problems will persist. For the latter, it is important to have patience and invest in a solution over a long term.”

That’s exactly what manufacturing companies are doing. They are collecting data every stage of production all the way to sales and administration. Big data is now available to study and apply to boost companies’ efficiency, sustainability and performance. Those who come up with the optimal software and modeling systems for data analytics will win the competitive edge early on.

UNIST professor Choi Jaesik echoed the panel’s sentiment, in closing, saying, “It’s very uncommon for a manufacturing company to hold AI courses for all their employees. I think POSCO can take pride that…POSCO is a great place to apply AI and achieve results. There is already plenty of data on site and employees are continuously working to obtain more accurate data. I look forward to seeing what POSCO will bring to the advancement of AI.”

POSCO CEO Kwon Ohjoon plans on doing exactly that, by increasing investment in research and implementing AI technology where applicable. Read more on POSCO’s smartization efforts here.

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The 2008 Financial Crisis was a blow to the global automotive industry and its suppliers. It took years to recover, but the economy did start showing signs of growth. McKinsey&Company’s 2013 report, The road to 2020 and beyond: What’s driving the global automotive industry, painted a positive picture of the global automotive market with profits projected to reach EUR 79 billion by 2020, up from EUR 54 billion in 2012, with China responsible for 60 percent of profits.

Their 2016 report, Automotive revolution – perspective towards 2030, offered a similar outlook. Overall global car sales will continue to grow, but at about a 2 percent growth rate, down 1.6 percent from the last five years. New market trends such as electric/autonomous vehicles, shared mobility and stricter environmental regulations will disrupt the market with new challenges and opportunities, but what remains constant is that the majority of growth in global automotive sales will be lead by the rising middle class of emerging markets, namely in India and China.

Thousands of vehicles are scrapped in the Chinese government’s efforts to reduce emissions. (Source: The Daily Mail)

Subsequently, China reached record vehicle sales in 2016. However, in April 2017, it recorded the lowest number of sales in the past two years for passenger cars, down 3.7 percent from 2015. This follows a national sales tax rise early in 2017 to 7.5 percent, coupled with a general decrease in demand for cars as consumption is reaching a plateau. Moreover, China’s sales tax will increase again to 10 percent in 2018.

India’s Booming Automotive Industry

Unlike China, India’s automotive market shows little signs of slowing down. India is now the world’s fastest growing economy as their GDP is projected to increase by 7.2 percent from 2017 to 2018. Their middle class is estimated to triple by 2025, reaching 89 million households.

The government of India is looking to the automotive industry to lead India’s economic growth, as outlined in its Automotive Mission Plan for 2016-2026 and “Make in India” initiative. Under such plans, the government will work towards creating an additional 65 million jobs in the automotive market and a 500 percent increase in vehicle exports by 2026.

A full parking lot of Renault cars reflects the massive automotive market in India. (Source: The New York Times)

It’s no surprise that Foreign Direct Investment (FDI) is pouring in. From 2000 to 2016, India’s automotive industry alone attracted USD 15.79 billion in FDI. For investors, India’s growing middle class of consumers is as attractive as their vast and low-cost labor.

POSCO Maharashtra in India

POSCO Maharashtra is one automotive supplier that is echoing the growth of the Indian automotive market. Their 2Q17 unaudited earnings report showed significant growth from just a year before in 2016. The reported revenue was USD 331.7 million, compared to USD 174.7 million in 2016. The company’s operating profit was USD 32.6 million, up from USD 3.5 million in 2016. Finally, their net profit was USD 22.9 million, another significant increase from USD 15.9 million in 2016.

*Earnings figures were converted from Korean won to U.S. dollars using August 22, 2017, exchange rates.

The POSCO Maharashtra Plant in India

In May of this year, POSCO Maharashtra signed a MoU with Essar Steel to supply 1.1 million tons of flat steel products during the 2017 fiscal year. This is the second year of partnership between the two companies, but the volume of steel POSCO Maharashtra will supply is 30 percent higher than in 2016.

The growing business reflects the work philosophy of POSCO Maharashtra employees: “If [we] supply quality material, they’ll come back again and again. So that’s what we’re doing right now. Although our products might be priced a bit higher in the Indian market, we are supplying quality material and our customers continue to be loyal because they understand that value.”- Chetan Waghchoure, sales representative for POSCO Maharashtra. Read the full interview here.

POSCO Maharashtra’s Chetan Waghchoure during an interview with The Steel Wire

POSCO Maharashtra is taking full advantage of the thriving automotive industry in India and continues to expand its business by reducing production and inventory costs, increasing performance and efficiency and planning to incorporate AI technology into their future production processes.

Market indicators point to India to drive the profitability of the automotive industry. India’s growing consumption of vehicles, open business environment and vast labor force will continue to attract foreign investment and new businesses. The best part is that India is not even close to being fully developed, meaning brighter prospects for future growth and good news for manufacturers and suppliers.

With the emergence of the 4th industrial revolution as a hot topic of the world economy, AI is expected to drive manufacturing innovation by providing breakthroughs in smart factories.

POSCO Smart factory in Gwangyang

In his recent POSCO Issue Report, Kim Ho-In, a senior researcher at POSRI, examined what innovations AI can bring to the upgrowth of smart factories, and identified key issues related to such technological introductions.

Smart Factory Takes Wing with AI

Kim looked at how AI will provide breakthroughs in areas of manufacturing, such as preventive system maintenance, process control, expert process control and robot automation, and how AI  will make smart factories more efficient, less wasteful and more productive.

Traditional stock, inventory and statistical analysis provided insufficient information for preventing factory defaults, and made certain failures inevitable. With AI, however, factories can predict quality deficiency, gather and analyze complex data and improve equipment reliability. Also, by integrating such intelligence with robots, there are now self-learning, versatile beings that can automatically overhaul and optimize system defaults.

Thus the ability to self-teach, analyze and create will lead to a more comprehensive statistical analysis and achieve positive results in preventive maintenance.

The Right Kind of Smart

The different innovative tools in smart factories, however, may have distinct values depending on its application for the industry in question, so while such technological advances may be a breakthrough for manufacturing, companies must apply the tools that are most effective for their field and industry.

AI that can self-teach, analyze and create in a smart factory

For instance, the technology required for “smartization” is entirely different for assembly process industries such as parts production, automotive and electronics, than from mechanisms industries such as metals, chemicals and energy.

In the assembly process industries, customizing production, cutting labor costs and responding to the fast life cycle of products are the major areas for innovation. AI applications like robot automation can guarantee positive results because many production processes can be replaced with robots, resulting in minimized labor costs.

In the process equipment industries, however, improving productivity through optimal control of core processes, controlling the linkage between processes and maintaining the facility are the major issues. The loss of data can be extremely critical in the case of a core facility failure, so preventive equipment maintenance would have a greater impact than robot automation.

The investment size and outcome value for implementation and innovation of smart factories will differ by industry. Thus it is necessary to build up strategies on technologies that are most suitable for each required field.

Smart Reconstruction

Recently, the world’s fifth largest blast furnace located in Pohang Works, went through major renovations to “go smart”. The Pohang Blast Furnace No.3 was first fired up in 1978 with an internal volume of 3795㎥, and was repaired in 1989, 2006 and finally in 2017, when it leaped toward POSCO’s vision of “smartization”.

After a 102-day repair period, the furnace volume expanded to 5600㎥, and the site is now fully equipped with AI to precisely monitor the condition of the blast furnace.

The Blast Furnace No.3 dismantled at the start of the repairing process

Engineers, including Gi Wan Son, Blast Furnace No.3 Repair Quality PJT team leader, worked on all three renovations and provided exceptional knowledge based on more than 40 years of experience with the Blast Furnace No.3. According to Son, the main focus was to build a smart technology infrastructure that utilizes AI and collects big data.

POSCO added smart sensors that automatically monitor the internal state of the blast furnace such as raw material quality and ventilator status. Both measures are expected to help forecast and extend the lifespan of the blast furnace to longer than the average period of 15 years.

[clickToTweet tweet=”POSCO Blast Furnace No.3 will lead the future of  blast furnaces for another 50 years if we incorporate POSCO’s advanced technologies” – Gi Wan Son, Blast Furnace No.3 Repair Quality PJT team leader” quote=”POSCO Blast Furnace No.3 will lead the future of  blast furnaces for another 50 years if we incorporate POSCO’s advanced technologies” – Gi Wan Son, Blast Furnace No.3 Repair Quality PJT team leader” theme=”style6″]

Though in its early stages, AI has already had a significant impact on smart factories and further innovations are expected. The manufacturing industry needs to pay close attention to the future scope of AI development and how it will allow them to overcome existing challenges and open doors to a limitless future.

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