The manufacturing industry has always been a pioneer when it comes to developing and implementing new technologies. Their goal is to adopt technologies that help increase productivity while reducing operational costs. Technologies such as the Internet of Things, Artificial intelligence (AI), and other advances have already found their way into the heavy steel industry.
Steel is an essential metal used for everything from construction to the automobile industry and almost every other industry. The latest technology allows manufacturers to make real-time adjustments based on accurate data. Let's see how AI-powered tools help reduce costs in steel manufacturing.
The Complex Process of AI Training
Introducing an AI solution designed to help improve steel production is not an easy process. In fact, it isn't very easy and can take months or even years to complete. The primary issue is to secure the correct data needed to train the machine learning (ML) model.
Steel manufacturers have to employ experts capable of measuring the right things and understanding how the measures help improve processes. In other words, a data scientist is needed to filter through various types of data and choose the ones that make the most difference.
The data includes all chemical and physical properties of materials used during production. When everything is set up correctly, AI-powered tools can measure the thickness of the steel, what temperatures are needed to meld various parts, and overall energy consumption.
The System Needs Accurate and Relevant Data
We can't emphasize enough how important data is for AI. If you want your steel production to improve, you first must ensure that the ML model is fed with the correct data. If your factory floor uses older machinery during production, you have to install IoT sensors on every unit to generate massive amounts of operational data. Most newer machinery has built-in sensors, making it easier to implement.
However, since most machinery is older, you can connect all machines into a centralized system once installing the IoT sensors. Installing IoT sensors on outdated machinery doesn't come without challenges. You have to ensure that the sensors gather the right data to get insights and improve the operation. The AI solution needs time to learn how the machinery works to set up a baseline for the data it generates. Whenever a machine reaches higher temperatures or vibrations, it will trigger alarms to inform your engineers that there's a problem.
With that said, the system needs to be optimized, so it doesn't trigger the alarm too often. That's why the data it works with must be high-quality and accurate. Otherwise, the system might generate more problems than it fixes.
Steel Supply Chain Management Challenges
Apart from operational challenges on the factory floor, the steel industry has a few unique elements that make implementing new technologies tricky. For example, organizing the supply chain in heavy industries such as this one is much harder than in most other industries. Here are some of the unique challenges you'll face:
1. Inbound Supply From Multiple Sources
Steel is made from processing iron ore. However, it needs other materials during production, which is why steel manufacturers usually require additional processing before the end product reaches the market. Working with different iron ores and materials can lead to various steel grades that might be too low quality for what customers expect.
2. Fault-Sensitive Production
Steel production requires a constant input of various materials in all production stages. The flow must be consistent to increase efficiency and reduce cost. If one element shuts down during an operation, everything has to stop until engineers find and repair the issue. Every production halt leads to massive costs and potential product quality issues. That is why the production needs a steady flow of materials to avoid machinery restarting costs, minimize downtimes and productivity drops.
3. Managing Multiple Sales Channels
One of the biggest challenges steel companies face is sales channel management. Manufacturers have to stay in touch with dealers, agencies, and other organizations that target the same market. Unfortunately, manufacturers don't have much control over the process, and they have limited visibility.
Internet selling and direct sales channels such as e-auctions and marketplaces quickly replace traditional sales channels because they are far more transparent and have shorter sales cycles. Moreover, these marketplaces and platforms allow customers to compare products directly, pushing manufacturers to improve their products. The practice is responsible for 3/4 steel grades developed in the past 20 years.
Using Digital Twin Technology To Improve Production
Steel production lines using IoT sensors generate a lot of data that can be used to improve the operation even further. Leading steel manufacturers use the Digital Twin technology to enhance their operations in a virtual setting before making changes in real life. Digital Twin technology uses the data collected by machines and IoT sensors to recreate the entire product, production line, or operation in a virtual setting, allowing you to test ideas and designs without spending money on prototypes.
However, for the Digital Twin to work, the company has to create an ecosystem that promotes information exchange within the company, as well as business partners. Of course, all equipment must be connected via IoT sensors to ensure that the digital twin has access to accurate real-time data. DT can help optimize supply chains, storage facilities, production, sales channels, and everything in between when set up correctly.
Role of Enterprise AI in Steel Production
Alright, let's say that you've managed to overcome all challenges in implementing IoT and AI into your steel production operation and that everything is working as it should. You should be able to see advances in management and production in a few weeks, but here are all of the benefits you'll get from introducing AI-powered solutions into steel production.
1. Predictive Approach to Supply Chain Management
Steel products have a long manufacturing life cycle, so production and supply chain management have to work non-stop to ensure that products reach the consumers in time. Therefore, supply chain management is one of the most important factors when running a steel manufacturing plant. IoT sensors, digital twins, and other technologies can help you recognize potential issues upfront, allowing you to prepare a better plan for the future.
2. Event Analytics and Their Effects on KPIs
AI solutions collect event data and analyze it to identify influences on planned activities. DT helps create a multitude of what-if scenarios allowing you to compare outcomes after introducing changes in your supply chain. The process will help you find the best solution that impacts essential KPIs.
3. Continuous Learning That Helps Optimize Outcomes
Constant access to big data and analytics will keep optimizing existing processes in the future. AI-powered solutions can analyze vast amounts of data that would take humans decades to process. As a result, data science is becoming more and more important for steel manufacturers, and it's responsible for increasing efficiency and reducing costs.
Conclusion
AI solutions are the future of manufacturing, and it's been adopted by most industries in the past few years. Steel manufacturers are among the first to recognize the power of AI. Most successful enterprises already use it to improve operations, streamline supply chains, reduce waste, and minimize production costs.
The technology is still evolving, but it's already providing businesses with numerous benefits that help save millions of dollars every year. There is no doubt that AI is changing steel manufacturing for the better as the entire sector undergoes the 4th industrial revolution.
