*Review* **The Challenge of Digitalization in the Steel Sector**

#### **Teresa Annunziata Branca <sup>1</sup> , Barbara Fornai <sup>1</sup> , Valentina Colla 1,\*, Maria Maddalena Murri <sup>2</sup> , Eliana Streppa <sup>2</sup> and Antonius Johannes Schröder <sup>3</sup>**


Received: 27 January 2020; Accepted: 19 February 2020; Published: 21 February 2020

**Abstract:** Digitalization represents a paramount process started some decades ago, but which received a strong acceleration by Industry 4.0 and now directly impacts all the process and manufacturing sectors. It is expected to allow the European industry to increase its production efficiency and its sustainability. In particular, in the energy-intensive industries, such as the steel industry, digitalization concerns the application of the related technologies to the production processes, focusing on two main often overlapping directions: Advanced tools for the optimization of the production chain and specific technologies for low-carbon and sustainable production. Furthermore, the rapid evolution of the technologies in the steel sector require the continuous update of the skills of the industrial workforce. The present review paper, resulting from a recent study developed inside a Blueprint European project, introduces the context of digitalization and some important definitions in both the European industry and the European iron and steel sector. The current technological transformation is depicted, and the main developments funded by European Research Programs are analyzed. Moreover, the impact of digitalization on the steel industry workforce are considered together with the foreseen economic developments.

**Keywords:** digitalization; digital technologies; digital transformation; steel industry; digital skills

### **1. Introduction**

Over the last few decades, the European steel sector has undergone relevant transformations. On one hand, this sector has been restructured and consolidated; on the other hand, highly technological production processes and products have been developed. These transformations have considerably impacted the workforce, resulting in numerical reduction and in professional profiles' evolution. In particular, digital transformation and Industry 4.0 contributed to both reducing the need for physical and often cumbersome and repetitive operations and to increasing the demand for highly skilled workforce. In addition, changes in patterns of recruitment as well as in work organization have occurred. In order to be more competitive, the steel sector aims at developing a highly qualified, specialized, and multi-skilled workforce. Nevertheless, due to the skills shortages, recruitment difficulties, and talent management issues, it is important to forecast, identify, and anticipate skill needs.

In order to introduce the current state of this technological transformation, it is important to define the context and to provide some necessary definitions. "Digitization" is defined as "*the action or process of digitizing; the conversion of analogue data (esp. in later use images, video, and text) into digital form*" [1], while the term "Digitalization" refers to the transformation of interactions communications, business functions, and business models into the digital ones. Through the use of "digital technologies" and digitized and natively digital data, digitalization aims at achieving revenue, improving business, replacing/transforming business processes, as well as at creating an environment for digital business. In addition, "digitalization" promotes the integration of digital technologies into areas of a business [2]. On the other hand, "automation" is defined as "*the use or introduction of automatic equipment in a manufacturing or other process or facility*" 1 as well as "*the technology by which a process or procedure is accomplished without human assistance*" [3].

In this context, Industry 4.0 concerns the interoperability, the decentralization of information, the real-time data collection, and the increased flexibility, that represent main aspects of the fourth industrial revolution. This process started with the first industrial revolution, characterized by the mechanization of production performed manually by hand. In this context, steam and water power were used for the mechanization of work [4]. In the second industrial revolution, the introduction of electricity in different processes took place. In particular, in the steel sector, this coincided with the invention of the production area, the improvement of transportation technologies, and the electrification of industrial processes. In addition, the Bessemer process and the open-heart furnace were introduced [5]. The third industrial revolution was characterized by the introduction of Information Technology (IT) and computer technology to automate processes, but still involved human aspects. The use of robots into the processes previously performed by humans and the development of the work based on optimization and the removal of production inefficiencies represent the main aspects [5]. The current industrial revolution, the fourth one, is based on new interconnected technologies in process operations. The term Industry 4.0 was first used in Germany based on initiative of the German Government's High Tech 2020 Strategy [6]. Compared to "Industry 3.0", in Industry 4.0, the machines work autonomously without, or with very limited, human intervention. In particular, the enhancement of automation and connectivity with Cyber Physical Systems (CPS) include smart machines, storage systems, and production facilities capable of autonomously exchanging information, triggering actions, and independently controlling each other. In addition, the Industrial Internet of Things (IIOT) can allow exchange of information provided by sensors that work in real time and transfer data to a local server or a cloud server, where the analysis of the data is performed through the development of predictive models. The final aims can be, for instance, product quality management throughout the entire production chain or early detection and forecasting of anomalies in the processes and prediction of residual lifetime of critical components by means of tools exploiting the data (often Big Data) captured by the sensors (according to the paradigm of Predictive Maintenance).

This review paper derives from an analysis that was performed in the context of a multinational initiative funded by the European Union through the Erasmus Plus framework. This project is devoted to the development of a Blueprint for "New Skills Agenda Steel", is entitled "Industry-driven sustainable European Steel Skills Agenda and Strategy (ESSA)", and has two main aims:


The paper is organized as follows: Section 2 introduces the context of digitalization and provides some important definitions in the European industry and the European iron and steel sector. In Section 3, the main developments achieved within projects that received funding by European Research Programs are analyzed. Section 4 discusses the impact of digitalization on the steel industry workforce and the foreseen future economic developments. Finally, Section 5 provides some concluding remarks.

#### **2. The Framework of the Industrial Digital Transformation**

#### *2.1. Digital Transformation in the European Industry*

The European industry is deeply committed to integrate the digitalization concept into its production and organization in order to be more competitive in the globalization context. The application of digital technologies allows implementing new processes along the entire value chain, through manufacturing and sales to services. On this subject, digitalization represents a holistic approach covering all areas and functions of a company to exploit digital potentials and analyze each stage of its value chain. For this reason, it is important to underline that digitalization is not a simple transfer from "analogic" to digital data and documents. It is rather the networking between the business processes, the creation of efficient interfaces, and the integrated data exchange and management [7].

The digital transformation is the key aspect of the ongoing industrial revolution [8]. Some new Key Enabling Technologies (KETs) are represented by a. new generation of sensors, Big Data, Machine Learning (ML), Artificial Intelligence (AI), Internet-of-Things (IoT), Internet-of-Services, Mechatronics and Advanced Robotics, Cloud Computing, Cybersecurity, Additive Manufacturing, Digital Twin, and Machine to Machine (M2M) communication. The digital technologies could be ex novo applied to a new plant, or can be adapted to existing plants [9]. In order to achieve industrial production optimization, the application of these technologies aims at developing new skills and new competencies as well as new business models. This will result in the improvement of industrial competitiveness and efficiency, due also to a higher inter-connection and cooperation, sharing resources (e.g., plants, people, and information). On this subject, European industries are committed to addressing some important challenges:


These challenges can be reached through the real-time capability, interoperability, and the horizontal and vertical integration of production systems enabled by Information and Communications Technology (ICT) systems, representing the main features of Industry 4.0 [11]. Furthermore, in order to achieve a flexible production, it is important to also have flexible work, through the self-organization and multi-tasking skills, according to education and lifelong learning initiatives.

Industry 4.0 is based on an intelligent networking of machines, electrical equipment, and modern Information Technology (IT) systems allowing processes optimization and increased productivity of value creation chains [12]. Its strategy is based on intelligent factories exploiting the combination of embedded production system technologies with intelligent production and resulting in a new technological age. In this context, in a recent study, three distinct manufacturing systems in Industry 4.0 have been presented [13]. In the first one, an Intelligent Manufacturing System (IMS), also known as smart manufacturing, the employment of advanced manufacturing technologies, and information enable the optimization of the production process of goods and services. The second manufacturing environment is viewed as IoT-enabled manufacturing, relying on the exploitation of smart manufacturing objects (SMOs); the third major manufacturing is a cloud manufacturing. The intelligent systems involve the development of manufacturing systems that can be organized into a functional network.

Although different possible scenarios have been developed, it has recently estimated that the full shift to Industry 4.0 could need 20 years [14]. This long period can be due to the fact that digital technologies strongly vary with company size. In particular, only 36% of the surveyed European companies with 50–249 employees have implemented industrial robots in their production processes, compared to 74% of European companies with over 1000 employees [15]. However, larger companies have more possibilities to employ some IT/ICT specialists, but Small and Medium-sized Enterprises (SMEs) more intensively use mobile internet and social media (44%) [16].

Although the new digital technologies can make companies more efficient and productive, resulting in opening to new markets, some critical aspects on the future have been recently depicted. According to Pfeiffer [17], Industry 4.0 does not present only positive perspectives, but it can be considered as part of a newly emerging global production regime [18], particularly affecting the low-skilled works and repetitive tasks [19], such as manual operation of specialized machine tools, shot-cycle machine feeding, repetitive packaging tasks, monotonous monitoring tasks, and many warehousing and commissioning functions in logistics [20]. However, even if the digitalization may improve processes, the human experience cannot be replaced. In particular, the mining and metal sector in emerging economies needs to pay gap between highly skilled digital workforce and more traditional workforce [21]. On this subject, evolution in workforce skills are needed, in order to be in line with the industrial requirements. This includes not only attracting and recruiting new talents, but also re-skilling current employees with training programs, which can improve skills in different disciplines, such as science, technology, engineering, and mathematics. In this process, a key aspect is represented by continuous learning, also based on an interdisciplinary perspective. This will produce a future adaptable workforce contributing to increase the competitiveness of the companies as well as their own competitiveness and "appeal" in the work market. Furthermore, re-designing work processes aims at reducing the skill mismatch between jobs and employees and securing their jobs in the future [22].

The implementation of new digital technologies could produce significant improvement in workforce health and safety as well as energy efficiency and CO<sup>2</sup> utilization increase. In addition, innovative and more customized goods and services could be achieved [23]. In the global market and, consequently, in the global competition, the European manufacturing industry will have some market opportunities related to digital technologies. For instance, the 10-year China strategy plan, the *Made in China 2025* [24] strategy, aims at upgrading Chinese industrial base by focusing on 10 key industries. This will offer attractive opportunities for some European businesses in the short and medium term, about critical components, technology, and management skills.

In this context, the European manufacturing industry is leaning towards a greater new production system, increasingly flexible and tailored to the needs of customer. This evolution will preserve its global competitiveness thanks to high-value goods and high-quality products, as well as integration, digitalization, speed, flexibility, quality, efficiency, and security.

#### *2.2. Digitalization in the European Steel Industry*

The steel industry is a highly energy intensive industry. However, it is a modern, energy and CO<sup>2</sup> efficient sector, with high value-added production and niche products for the world market, based on an outstanding R&D network [25]. The European steel industry has an annual turnover of EUR 166 billion and it provides the 1.3% of EU GDP. Concerning the workforce, in 2015, it provided 328,000 direct jobs with an even greater number of dependent jobs [26].

The digitalization process in the European steel industry represents a pre-condition for Industry 4.0, as Industry 4.0 is much more than digitalization, but rather a paradigm/philosophy than a technology [27]. The current situation of digitalization in the steel sector is that its workforce is aging. An experienced workforce knows industrial processes very well, but it is less comfortable with digital tools and collaborative work and, sometimes, it is resistant to training and learning activities. For instance, in order to improve the culture of multigenerational digital innovation, the attempt of connection between the workforce under 30 with the older leadership team has been carried out within a Tata Steel site [21]. All over the world, including Europe, innovation, technology, quality, and highly skilled people are the basis for competitiveness [25]. In addition, preserving industrial knowledge and

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a skilled workforce is an important asset for the iron and steel sector [26]. Nevertheless, also in Europe, the digitalization process lacks uniformity. For instance, in the Czech Republic, the steel industry is still not in line with the other European countries in adopting new technologies and, in particular, in digital transformation, although recent investments have been done for modernizing production and reducing environmental impacts [28].

Due to the complexity of the production processes, the application of new technologies can result in the optimization of the entire steel production. By combining process automation, information technology, and connectivity, the digitalization of the steel production can be beyond a conventional automation of the industrial production. In the future, the intelligent combination of different tools, such as plant and laboratory experiments, physical modelling, and computational modelling will play a significant role in the digitalization process in the steel sector. For instance, recent advances in modeling steel continuous casting have been achieved. As this process involves many interacting phenomena, model verification and validation of model predictions represent the key factors. On this subject, each aspect of models needs to be verified with known solutions and validated with measurements to trust the predictions and improvements arising from modeling studies [29]. The digitalization in the steel production can not only produce quality, flexibility, and productivity, but also ensure the visibility of the real-time operational data and provide insight for a better and faster decision-making along the value chain [30]. Over the last few years, clear implementation of digital technologies has been made in the steel sector. However, the application of a decentralized, unmanned autonomous system, for assembling components, to the continuous process of steel, is difficult and expensive [31]. In addition, the application of digital systems in some production processes, such as Energy management and Water and Wastewater management, aims at achieving continuous improvements in the steel sector regarding quality, costs, energy consumption, and environmental performance. In this context, digital technologies help to adapt and to integrate innovative and emerging techniques to the steel production processes. For instance, novel pollution prevention and control techniques under development and new techniques addressing environmental issues can provide future economic or environmental benefits to this sector [32,33]. In addition, an example on modelling and prediction of the gas consumption to minimize ordered and supplied gas quantity error can be provided. On this subject, a linear regression and the genetic programming approach have been used. The achieved good results can contribute not only to steel production optimization, but they represent a methodological approach, which can be applied to other energy consumption optimizations [34]. Furthermore, as the steel production based on the EAF consumes a large amount of energy, better knowledge of the consumed energy within the EAF operations is fundamental. In a recent study, in order to predict the electric energy consumption during the EAF operation, two models have been considered, one based on linear regression and the second one on genetic programming [35].

The main challenges of the European steel industry on Industry 4.0 are related to legacy equipment, uncertainty about the impact on jobs, and issues of data protection/safety. In addition, results from a recent study [36] showed that the technical barriers are considered less important than the organizational ones. On one hand, internal management represents the driving force for implementing the Industry 4.0 projects, but on the other hand, technology and production are less crucial. Furthermore, the main possible explanations about lack of qualified personnel are the increased use of digital technologies, the lack of suitable educational programs, and the delays in training provision after the introduction of a technological innovation. Nevertheless, one of the main challenges of the European steel industry consists of attracting and retaining qualified personnel [5]. Nevertheless, it is difficult to integrate new technologies and processes among site workers, especially when it comes to older employees. In addition, there is a strong age gap between the workers currently employed and prospective employees creating knowledge transfer issues. Finally, there is a lack of investment in training and education from steelmaking companies and an insufficient amount of in-house training provided by companies.
