l This Is Smart Steelworks

As addressed in the POSCO the Lighthouse Factory #1, a smart steelworks collects and standardizes all factory data via PosFrame; then, the PosFrame self-learns the data to provide optimal process conditions and controls the various operations at steelworks.

The smart process minimizes the occasions for heuristic decisions preventing human errors. Objective and accurate data can lead to a eureka moment where innovative solutions are discovered. Wait, does this mean human labors are now dispensable?

Such hasty assumption is a surefire way to creating unnecessary anxiety. What the smart technology does is to let the workers delegate simple repetitive tasks to the machine, instead channeling their sophisticated skills and mental energy into perfecting the steelworks operation. The workers feed the result back into the PosFrame fostering the ultimate synergy between technology and humans.

How did smart technology transform the steelmaking operations at steelworks? Let’s find out.

[Order process] Small Lot Orders Go Smart

POSCO’s Order Management Group handles the planning for the entire steel production. One of their crucial tasks includes processing small lot orders — the order quantity for these small lot orders is too small that they typically do not meet a steelworks’ minimum production volume requirements.

Before smarticization, workers manually analyzed and processed the small lot orders — from checking the small lot order standards to figuring out whether and how to bulk-process chunks of small lot orders. The workers manually set the standards and modified them every half-year, the worst-case scenario being small orders sometimes ended up buried and left out. Some urgent orders were constantly classified as ‘small lot’ orders never receiving greenlight for production. In such cases, the order setting had to be reset so it can be incorporated with other orders. This used to take almost six hours or more. The average time for processing the entire small lot order is about 12 hours.

With ‘Smart Task,’ AI automatically organizes small lot orders — based on the analysis of all previous data, the system derives 12 factors impacting small lot orders. The AI also self-learns, so it can self-evaluate the orders. The processing accuracy is currently at 97%. To minimize production costs, the system also predicts the planning volumes so that small lot orders can be integrated with other orders — the production accuracy for which is at 99.99%. Compared to the previous 12 hours, the new procedure takes only an hour.

[Ironmaking] POSCO’s Smart Ironmaking Becomes the National Core Technology

The Smart Task comprehensively controls multiple factors such as furnace ventilation, combustibility, molten iron temperature, etc.; whereas, in the past, all of these were left to manual labor.
For the smooth operation of the Smart Task, the ironmaking department at Pohang Steelworks first defined variables that impact the furnace condition and subsequently integrated various information. In technical terms, this process is called ‘standardizing data.’

Under the Smart Task system, various indicators that were previously subject to the workers’ heuristic knowledge have been organized automating the manual processes. Now, the temperature of molten iron becomes data through IoT, and high-definition cameras conduct regular check-ups on raw materials and fuels.

POSCO started accelerating the establishment of an auto-control system in 2017. Through deep learning, AI self-learns, predicts and manages the data. Beyond simple automation of the current tasks, the system predicts and controls the potential variables and derives optimal results.

Even if the current furnace condition is satisfactory, the smart system goes above and beyond to make sure the furnace maintains its optimal condition. The Pohang No.2 blast furnace became a testing ground for the automated smart technology for the first time in Korea upping the daily molten iron production by 240 tons. The smart technology applied to the Pohang No.2 blast furnace received the government designation as the National Core Technology in July this year.

[Steelmaking] POSCO Goes PTX: Posco sTeelmaking eXpress!

The extremely high temperature required for steelmaking operation makes it difficult to measure temperature in real-time, which was why the daily operation was entirely dependent on field workers’ heuristic knowledge. Variations and human errors were inevitable in this system. Abnormalities at stages required not only a full-stop of the procedure directly involved — but also the procedures before and after.

The steelmaking Plant 2 at Pohang Steelworks has developed an integrated control system — it controls the timing, temperature, and the ingredients from the converter to the continuous casting process. There are potentially 125,000 probable procedures involved in the production for any given steel product.

POSCO digitized and standardized these procedures completing the PTX system by July 2018. With PTX, continuous processing became a reality — from converter through continuous casting without temporary halts or delays. Live-assessment of estimated arrival time, temperature, and product components also became available.

Before PTX, the temperature accuracy was at 80%, but now the system predicts the temperature with 90% accuracy. The use of raw materials also became more efficient.

[Continuous Casting] Prediction Made Simpler and Faster, Saves 600 million

Quality control is absolutely crucial, but it’s impossible to go through each and every material with a fine-tooth comb. So the typical inspection involved a through, 100% surface inspection of sample materials. If and when any abnormalities were detected in the particular sample, all other materials produced in the same batch had to be retrieved, subsequently undergoing thorough inspections.

The Pohang Steelworks No.2 and No.4 continuous casting plants annually inspected 340,000 tons of sample materials — only 30,000 tons found to be defective.

With a relatively small amount of defective materials, POSCO strove to minimize the time wasted, and to maximize the company’s resources. By applying artificial intelligence, POSCO collected a vast amount of data on steelmaking and continuous casting processes thereby establishing a model to predict defects. This was done by creating criteria of what constitutes as abnormalities in quality. Since the adoption of the new system in March 2018, the system now only screens defective materials, eliminating unnecessary testings. Furthermore, it pinpoints the reasons for the defect — the new system is expected to save over 600 million KRW every year.

[Rolling] AI Analyzes and Auto-controls Flatness Factors

Thick plate products first undergo the slab-heating process subsequently going through two rolling procedures. After that, according to the customer’s requirements, it undergoes an accelerated cooling process called Thermo-Mechanical Control Process (TMCP). TMCP involves spraying high-speed water on a steel plate rapidly cooling it to a designated temperature to improve the strength and the welding capacity of steel products. The process requires special equipment — and the water-spraying technology is highly advanced. Without accurate maneuver, the flatness of a steel product can be compromised.

In the past, the flatness of thick plates was manually analyzed, twice — once when the plate was hot (hot flatness) after TMCP treatment; and again, when the plate cooled down (cold flatness) during the shearing process. Measuring flatness in such a manner was extremely time-consuming, and it often caused a functional overload — it was challenging for the factory engineers to control the TMCP by manually inputting the results one at a time.

The plate plant at Gwangyang Steelworks created a learning model by analyzing the influential factors for hot and cold flatness — ultimately the company figured out the conditions that can achieve the most optimal flatness. Now, the engineers can simply monitor the optimal working condition values produced by AI supervising the smooth operations of each process. The correction needs for the products during the post-TMCP process are now reduced to 50% which saves about 1.3 billion KRW annually.

[Surface Treatment] Another National Core Technology, POSCO Surface Treatment Goes AI.

Continuous Galvanizing Line (CGL) involves processing cold-rolled coils with continuous heat treatment, putting them in zinc pot, ultimately producing galvanized iron plates. POSCO’s Giga Steel is also produced through CGL process.

After plates are taken out the zinc pot, an air knife controls the coating amount by cutting out zinc before it solidifies on the surface. The target coating weight varies for each product, and the coating weight could be measured only after the air knife procedure and solidification completed. This is why the live-control of coating weight remained challenging.

To address the challenge, POSCO developed AI-based control model for coating weight — it uses deep learning for precise management of such factors as steel grade, plate thickness, width, operating conditions, and target coating weight. Previously, the accuracy rate for predicting coating weight was projected at 89% — now, with 99% accuracy, the technology has been applied to all surface treatment plants at both Pohang and Gwangyang Steelworks. Just as POSCO’s smart ironmaking technology, POSCO’s smart surface treatment technology also received government designation as National Core Technology.

l Smart Steelworks Enhances Safety

A truly smart steelworks operation prioritizes workers’ safety as well as the environment. POSCO is no exception. What makes POSCO exceptional in this domain, especially when it comes to its application of smart technology, is the way the company utilizes smart CCTVs. POSCO developed a smart CCTV system — unlike general CCTVs which simply record video footage, POSCO’s smart CCTV auto-detects patterns and movements at production facilities. When the system recognizes any abnormalities, it promptly alerts the administrator in charge.

POSCO developed an optimized smart CCTV system that meets various demands of steelworks operations by integrating previously accumulated footages. From last year, POSCO began installing the smart CCTVs at Gwangyang No. 3 surface treatment plant, No.2 steelmaking plant, No.1 coke plant, and Pohang No. 2 plate plant.

Smart CCTVs automatically recognizes and analyzes various information such as the product quality and safety information, reducing the amount of time workers merely ‘stand by’ just to check all the information manually. At a steelmaking plant, for example, smart cameras can measure the temperature of molten iron. At a surface treatment plant, the smart camera detects the subtle color difference of coil-ends allowing prompt response for potential quality defects.

Furthermore, multiple imaging devices, such as thermal imaging cameras, can proactively detect and prevent fire hazards. At all corners of POSCO steelworks, smart CCTVs gather information and identify potential hazards before anybody does.

POSCO’s smart steelworks is an ongoing project. With the establishment of smart technology, POSCO will continue to address the challenges remain unsolved — POSCO aspires to complete the smartization process to ensure production efficiency, top quality products and improved safety.

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.

CEO Kwon explores CES 2018

As such, CEO Kwon made his first official visit to the 2018 Consumer Electronics Show (CES) in Las Vegas earlier this month to stay on top of the latest trends in connectivity and smart solutions that can be applied to the manufacturing process in smart factories.

SEE ALSO: POSCO CEO Ohjoon Kwon Visits CES 2018

Ultimately, a smart factory for POSCO would entail using smart sensors to pick up data, smart analysis through AI and automated production processes. Moreover, the smart factory would store and analyze its big data and apply it to make future improvements and prevent malfunctions. Aligned with many of POSCO’s business growth engines, major topics and themes at CES 2018 included smart homes, smart cities, autonomous cars and AI, and more than 3,900 companies put forth their best innovations.

Smart Connectivity

As more and more cities undergo smart transformations, the next big trend will be data collection and analysis through 5G connectivity. It was no surprise to see numerous innovations in smart transportation, smart grid, public safety, healthcare, data analytics and more at CES 2018. Moreover, city officials from early-adapting cities such as Kansas City, Miami and Las Vegas took to the stage to discuss their cities’ smartization plans. Some of the topics discussed included using GIS mapping software, smart construction, inclusive technology and autonomous transportation to enhance citizens’ productivity, efficiency, safety and well-being.

CES 2018 was a platform for smart city discussion. (Source: Bosch)

CEO Kwon visited related booths to learn about new technologies POSCO could apply to its businesses. Currently, POSCO’s smart factories are run by POSCO ICT’s smart platform called PosFrame, which allows the factory to apply a digital genome map to operations via data collection, instantly convert smart factory initiatives in domains, act as a common platform for new technologies and apply the same, standardize smart factory model to other facilities through reusable software.

Also at this year’s CES, POSCO ICT signed an MOU with DPR Construction, a leading smart construction company to apply PosFrame to construction sites for more efficient, cost-effective and smarter construction processes.

Smart Transportation

Another major theme at this year’s CES was mobility. Namely, autonomous and electric mobility. Companies from all across the board from traditional car manufacturers and software companies to gaming companies lined up to showcase their new innovations.

Even as the market for sustainable EVs and autonomous EVs look to expand in 2018 and beyond, EVs only make up about 1 percent of the global fleet of cars and there’s still a ways to go before EVs become the norm. Drivers need to feel safe in their EVs or autonomous EVs, want greater range on a single charge and need to see more charging infrastructure in their communities before making the switch.

SEE ALSO: Ask an Expert: Electric Vehicles and the Future of the Automotive Market

What’s more is that just because a vehicle runs on electric batteries, it doesn’t automatically mean the vehicle is eco-friendly or transmits less harmful emissions. Manufacturers and consumers alike have to take a lifecycle approach to assess the car’s impact on the environment from production to end of life recycling.

POSCO works with numerous partners to develop leading solutions for electric vehicles.

That’s why POSCO is working with automakers to come up with vital solutions in the auto industry for a greener future. It supplies partners with an advanced high-strength steel (AHSS) called POSCO GIGA STEEL that is both lightweight and super strong, allowing cars to travel with less energy without compromising passenger safety.

POSCO is also a lithium provider and has developed an innovative technology to speed up the extraction process, saving time, costs and damage to the environment. It also works with car makers to apply premium electric steel to partners’ electric motors to increase efficiency and cut costs.

With so many exciting new innovations and technologies surrounding EVs, POSCO will undoubtedly play a significant role in ensuring automakers have the tools they need to realize a green future.  POSCO will work to stay ahead of newly emerging technologies to enhance the efficiency, sustainability and performance of all of its solutions.

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.

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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As many as 700 people attended the forum including POSCO Group affiliate employees, as well as those of POSCO’s major suppliers, outsourcing partners and AI-related professors. Notably, employees from Nastech and Korinox, where POSCO is carrying out smart factory support projects, participated as well.

POSCO CEO Ohjoon Kwon watching a presentation at the forum

“POSCO must continue to take up the challenge of implementing a smart POSCO in preparation for the dawning of the 4th Industrial Revolution. We will apply smart technologies to all processes of the steel mills, as well as to management areas such as finance, HR and purchasing to realize the concept of smart management,” stated POSCO CEO Ohjoon Kwon. “We will actively share the results of POSCO’s smartization process with our group affiliates and customers.”

Mi-hwa Park, head of the POSCO Information Planning Department added, “To realize a smart POSCO, we started the Smart Solution Council headed by our CEO last year, and identified 141 smart projects which applied Big Data and AI to solve practical problems. Approximately 600 employees participated in the projects and reduced costs by KRW 15.7 billion.” She added, “We will develop a POSCO-style SME smart factory model to support innovations for the manufacturing sites of our SME customers and suppliers. Furthermore, we will contribute to fostering SMEs and creating jobs by providing a lighter version of the PosFrame, developed by POSCO, as a customized smart platform for SMEs.”

POSCO CEO Ohjoon Kwon with the excellence award recipients

POSCO also recognized projects that applied Big Data and AI, for excellence in the steel production category. Out of the four, one improved the flatness of steel sheets and another provided improved technology for slab production. The “3D-based design/construction quality improvement” project that uses POSCO E&C’s Virtual Construction to predict risks, and the “power generation output through condenser performance diagnostics” project that can accurately detect errors in POSCO Energy’s power plants were also among those selected.

Forum participants were given an opportunity to experience POSCO’s smart solution platform PosFrame firsthand. PosFrame combines operational insights and IT technology to collect Big Data as well as process and monitor data in a way that allows participants to experience the reality of “Smart POSCO.” It was one of the most popular features of the forum among participants.

POSCO CEO Ohjoon Kwon looking at a PosFrame platform

POSCO also announced its plan to standardize the technologies and processes developed through smart projects, turn them into a technical package and actively incorporate them into its “Smart Solution” business to create added value.

Ki-won Na, CEO of Shinheung Automotive Co., Ltd., an automotive parts maker, said, “We received a lot of ideas applicable to our operations from the case studies presented today, and if we learn about POSCO’s Big Data analysis and utilization techniques in more detail and apply them, I expect it will greatly contribute to improving our productivity.”

POSCO joined hands with POSTECH to launch an advanced AI course earlier this year and is currently training 25 internal AI experts so that they can apply their AI knowledge in their field to solve problems and thus lead the way for a smart POSCO.

Also, POSCO has begun providing free AI education to young, underprivileged jobseekers in July and is now training 100 youths per year on both theoretical and practical knowledge. POSCO is also planning to support the customized SME smart talent development project, including practical education on AI and Big Data skills, for its customers and suppliers.

POSCO is actively planning to complete its smartization, meaning it will apply its “Smart Factory” to all processes of the steel mills, and carry out its “Smart Solution” business, which combines POSCO’s product technologies and services with smart technologies.

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