The steel market is divided into different segments based on form, production methods, and end-user industries. In terms of form, the market is categorised into liquid steel, crude steel, and finished steel. In terms of production, the market is classified into blast furnace-basic oxygen furnaces (BF-BOF), electric arc furnaces, and other methods. The end-user industry segment includes automotive and transportation, building and construction, tools and machinery, energy, consumer goods, and other industries.
In 2021, the Steel Market Size reached a value of USD 1307.9 billion. The steel industry is expected to experience substantial growth, with projections indicating a rise from USD 1353.28 billion in 2022 to USD 1718.26 billion by 2030. This represents a compound annual growth rate (CAGR) of 3.47% during the forecast period spanning from 2022 to 2030. The market's expansion is primarily attributed to the increased demand for steel resulting from growing infrastructural activities in both commercial and residential construction sectors. Additionally, the availability of domestic raw materials such as iron ore and a cost-effective labour force contribute significantly to driving the market's growth.
According to the India Brand Equity Foundation (IBEF), India ranked as the world's second-largest producer of crude steel in September 2021. The country's crude steel production in the fiscal year 2022 reached 120.0 million tons, showing significant growth compared to the previous fiscal year (FY2021) which recorded 102.5 million tons. India remains globally competitive due to the availability of low-cost labour and abundant iron ore reserves, with the country possessing the fifth-largest iron ore resource in the world.
As per the Joint Plant Committee (JPC), the building and construction sector accounted for approximately 40% of India's steel demand in the financial year 2021. The Indian government's social sector initiatives, such as the Pradhan Mantri Awas Yojna promoting affordable housing for all, the Sardar Patel Urban Housing Project, the 100 Smart Cities Mission, and the development of infrastructure in medium and small towns, are driving the growth of the steel industry in India.
According to the Ministry of Steel, India (MOS), and the Joint Plant Committee, India (JPC), the projected steel consumption volume in India for 2021 was estimated to be around 106 million metric tons, representing a significant increase compared to the previous year.
The steel market in India is characterised by consolidation, meaning that it is dominated by a few key manufacturers. These major players in the market, listed in no particular order, include JSW Steel Ltd, Tata Steel Ltd, Steel Authority of India Limited (SAIL), Hindalco Ltd, and Jindal Steel and Power Ltd (JSPL). Additionally, there are other notable companies operating in the industry.
The Minister of Steel, Shri Beni Prasad Verma has said that Indian Government through its policies has been pursuing adoption of latest technologies for improving efficiency of Indian steel industry. The report of the Working Group on Steel Industry for the 12th Five Year Plan, National Steel Policy and Roadmap for R&D and Technology etc. enlist such measures. The major steel plants in the country have already embarked upon modernization/expansion programme and are phasing out obsolete technologies. Further, new/green field steel plants are being set up with latest world class technologies.
In the fiscal year 2019, India found itself in the position of being a net importer of steel due to two primary factors: limited research and development (R&D) investment and a deficiency in design and engineering capabilities within the steel sector. However, the scenario took a turn in the third quarter of the fiscal year 2020 when India once again became a net exporter of steel. Despite this positive shift, the steel sector continues to face several complex challenges, which have been further amplified by the impact of the pandemic. These challenges encompass various aspects such as global competitiveness in terms of operational efficiency and productivity, the ability to swiftly construct innovative products and other related concerns
One potential avenue to address the challenges faced by the steel sector and enhance its overall performance is through the adoption of digitization technologies. By leveraging advanced technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), machine learning (ML), and cloud computing, the steel industry can unlock a multitude of benefits that can significantly improve productivity, efficiency, adaptability, and sustainability across the production and supply chain.
Through IoT, various components and machines involved in steel production can be interconnected, creating a network of sensors and devices that collect real-time data. This data can provide valuable insights into the performance of equipment, energy usage, and other critical parameters, enabling proactive maintenance, optimising processes, and minimising downtime. Moreover, the integration of big data analytics allows for comprehensive analysis of the collected data, identifying patterns, trends, and areas for improvement, which can lead to informed decision-making and optimised resource allocation.
AI and ML technologies further enhance the capabilities of the steel sector by enabling intelligent automation and predictive modelling. AI-powered algorithms can autonomously optimise production schedules, monitor quality control, and streamline operations. ML algorithms can analyse historical and real-time data to detect anomalies, predict equipment failures, and optimise energy consumption. These technologies empower the industry to make data-driven decisions, improve overall efficiency, and reduce costs.
The utilisation of cloud computing in the steel industry offers numerous advantages. Cloud platforms provide scalable and flexible computing resources, allowing companies to easily handle large volumes of data and perform complex calculations without significant upfront infrastructure investments. Cloud-based collaboration tools enable seamless communication and information sharing among stakeholders, fostering better coordination and agility in the supply chain. Additionally, cloud-based storage and data backup solutions enhance data security and disaster recovery capabilities.
By embracing digitization, the steel industry can achieve greater visibility, traceability, and transparency throughout the production and supply chain. This enhanced visibility enables efficient inventory management, optimised logistics, and improved customer service. Furthermore, digitization supports sustainability efforts by enabling the monitoring and reduction of energy consumption, waste generation, and environmental impact.
Leveraging IoT, big data analytics, AI, ML, and cloud computing, the steel industry can revolutionise its operations and address the challenges it faces. Digitization offers the potential for increased productivity, enhanced efficiency, improved adaptability, and sustainable practices, ultimately paving the way for a more competitive and resilient steel sector in the digital era.
Now, let us delve into the discussion of specific use cases where AI and ML can contribute to achieving the digitization of the steel industry. The application of these technologies brings a range of possibilities and benefits to various aspects of steel production and supply chain management. From predictive maintenance and quality control to demand forecasting and supply chain optimization, AI and ML offer valuable tools that can revolutionise the industry. By harnessing the power of data analytics and intelligent algorithms, the steel sector can unlock new levels of efficiency, productivity, and sustainability, driving it towards a digitally transformed future.
Artificial Intelligence (AI) and Machine Learning (ML) play a crucial role in achieving digitization within the steel industry. The integration of AI and ML technologies offers numerous benefits and opportunities for transforming various aspects of the industry. Firstly, AI and ML can enhance the automation of processes, enabling the steel sector to streamline operations, reduce manual intervention, and improve overall efficiency. By leveraging AI algorithms, the industry can optimise production schedules, predict maintenance requirements, and minimise downtime by identifying potential issues before they escalate. ML algorithms can analyse vast amounts of historical and real-time data to identify patterns, trends, and anomalies, facilitating data-driven decision-making and enabling continuous process improvement.
In India, steel manufacturing companies are at varying stages of their digital transformation journeys. While some firms have successfully implemented AI technologies to optimise production processes and reduce losses, others are actively exploring ways to enhance business intelligence through the utilisation of AI and analytics.
1. Predictive Maintenance: The steel industry can use AI and ML for predictive maintenance by analysing real-time data from equipment sensors. This helps detect potential failures, allowing proactive maintenance scheduling and optimising production efficiency. For example, Blast Furnaces can be monitored using thermal imaging to identify hot spots and prevent damage to the equipment. This technology predicts the health of various equipment components without disrupting production.
2. Quality Control: AI and ML algorithms analyse production and quality data to improve product quality. Steel manufacturers can implement data-driven quality control measures to ensure consistent and high-quality output. For instance, in the case of SBM (Special Bar Mill), a rolling mill for producing rounds, maintaining quality is crucial. By using industry-grade cameras and a deep learning model, defects like scratch marks and dents can be identified in real-time. This allows for timely corrective actions, preventing a large number of quality issues.
3. Inventory Management: AI algorithms can analyse historical demand patterns, market trends, and other relevant factors to provide accurate demand forecasting. This enables steel companies to optimise inventory levels, reduce carrying costs, and avoid stockouts or excess inventory, improving overall supply chain efficiency. AI algorithms are used for inventory management. By analysing historical demand patterns, market trends, and other relevant factors, these algorithms provide accurate demand forecasting. This helps optimise inventory levels, reduce carrying costs, and prevent stock outs or excess inventory. As a result, overall supply chain efficiency improves, benefiting steel companies by reducing costs and ensuring timely delivery of products.
4. Energy Management: ML algorithms analyse energy consumption patterns to optimise energy usage in steel manufacturing. This reduces waste, lowers costs, and promotes sustainability. For instance, a steel plant can deploy sensors throughout the facility to monitor energy consumption, temperature levels, and sound patterns. ML algorithms trained on historical data learn to recognize patterns and correlations between sensor data and maintenance needs. Real-time analysis helps identify impending maintenance states, such as unusual energy consumption, abnormal temperature rises, or irregular sounds indicating machinery issues. The algorithms generate maintenance alerts, enabling proactive measures to be taken by plant operators.
5. Supply Chain Optimization: AI and ML optimise the steel industry's supply chain, improving production planning, logistics, and procurement. By analysing historical data and market trends, these technologies enhance efficiency, reduce lead times, and improve coordination. For instance, demand forecasting models optimise production and procurement based on accurate predictions. Intelligent inventory management systems reduce carrying costs and ensure timely order fulfilment. Collaborative relationships with suppliers enhance visibility and responsiveness. Transportation management systems and route optimization algorithms minimise costs and delivery times. By analysing data and implementing continuous improvement initiatives, companies identify bottlenecks, enhance customer service, increase agility, and adopt sustainable practices.
6. Safety and Risk Management: AI and ML improve safety and risk management in the steel industry by analysing sensor data and historical records. These technologies identify hazards, predict incidents, and enable proactive measures for a safer working environment. For example, computer vision techniques can be used with a small camera to detect objects and promptly alert operators to potential collisions with workers. Another application is monitoring compliance with safety regulations using CCTV feeds and deep learning models. Real-time analysis generates alerts for non-compliance, enabling immediate corrective actions. These examples demonstrate how intelligent cameras and continuous monitoring enhance safety measures by leveraging AI and ML technologies.
Overall, the adoption of AI and ML technologies in the steel industry holds immense potential for enhancing productivity, efficiency, quality control, supply chain management, and sustainability efforts. By leveraging these technologies, steel companies can stay competitive in the market and drive continuous improvement in their operations.
The steel industry has been slow to adopt AI, primarily due to the long-standing nature of some mills, leading to entrenched cultures and processes. The industry predominantly relies on experienced equipment operators and teams of metallurgists who formulate recipes based on trial and error rather than data-driven insights.
Furthermore, data poses a significant challenge. Older plant equipment may lack the capability to capture relevant data, while newer machinery may not be effectively connected to track data consistently across the entire process. While front-end processes can be analysed and improved without connected data, back-end finishing operations prove more difficult. Many capital investments have not considered data collection, and even new facilities often lack comprehensive data capture and the necessary governance for effective management and deployment.
The complexity of steel manufacturing, with its inconsistent steps and the splitting of batches, further complicates the tracking of individual parts' lineage. Despite these challenges, they are not insurmountable. The steel industry's slower progress in AI presents an opportunity to generate cost and yield benefits. It requires embracing an experimental mindset, starting with small-scale initiatives, and being open to change in order to unlock the potential of AI in the industry.
Partnering for Digital Transformation: Nwarch.AI's Role in Driving Innovation Across Industries
We, at Nwarch.AI (a subsidiary of Daten and Wissen), collaborate with numerous industries to support their digitization journeys and achieve remarkable outcomes. Through our expertise in computer vision, deep learning, and data-driven solutions, we empower organisations to enhance productivity, promote sustainability, and ensure a safer working environment. By leveraging advanced technologies, we enable businesses to tackle challenges, optimise operations, and drive positive transformations in their respective domains.
Daten & Wissen is the team of expert AI engineers to help your business to embark on a transformational journey with the adoption of this futuristic technology.
Video surveillance systems hold a hidden treasure trove of valuable data that remains l…Read more