The primary objective of Data analytics is planning and optimizing operations. Companies have been using this technique, albeit on a smaller scale. There is ample scope for C, especially machine learning (ML) to take Data Analytics to a different level.
Artificial Intelligence (AI) has a great role to play in this aspect. AI does not have a reputation of playing any role in the manufacturing industry, especially in the operations side. However, you can use AI to improve the effectiveness and efficiency of important industries.
Steel manufacturing companies can make extensive use of AI tools like control systems, sensors and ML-based optimization. AI has the capability to introduce different technologies for improving the practice of making steel in a profitable manner.
Let us see the six major areas where ML can be of help in the manufacture of steel.
Prediction of Demand: Prominent companies invest their capital wisely. Therefore, there is a need for accurate prediction for the demand of steel. This is where ML comes in. It makes use of tools like macroeconomic data in conjunction with the historical demand for steel.
Sourcing / Inventory management: The normal raw material in the manufacture of steel is scrap. Therefore, there is a need for predicting its availability. AI can produce a ‘scrap index’ whereby companies can explore possibilities of using a hedging approach to buy steel.
Scheduling Optimization: The primary concern for any steel manufacturing company is its products and timing of production. This aspect assumes greater importance when electrical energy is one of the most important inputs. The optimization models minimize energy costs by maximizing energy consumption, especially at non-peak times.
Optimization of Production: There can be unplanned events in steel manufacturing industries. The molten steel can break out of the mold during the casting process. It can also happen that rolled steel can escape from the rollers on to the mill floor. This can cause the production to stop. The situation can be dangerous and expensive. ML can predict such occurrences and thus help in minimizing it. This can optimize the production.
Predictive Maintenance: Normally, steel manufacturing companies have regular maintenance schedules, usually on a weekly basis. AI can help in this process by predicting when the particular machine requires maintenance. Hence you can do need-based maintenance instead of the fixed weekly maintenance schedules. This is very important in case of manufacturing companies that have a large number of industrial machines.
Reduce cost of production: AI tools will help to identify the mathematical correlations that will eliminate the costs of production. At the same time, it will enhance the quality of the production of steel.
Early warning systems: AI allows the industry to take rectification steps on time and prevent untoward accidents. This need-based repairing of machinery plays a great role in enhancing the lifespan of the industrial machines.
Optimization of outbound transportation: Steel manufacturing companies have not yet realized the full potential of outsourcing their outbound supplies. AI can help by optimizing delivery windows for their customers.
Various top manufacturing companies have used ML and AI for improving their operations. But, integrating these two mechanisms can bring in a lot of benefits for such companies. This can increase the profitability of these companies. Certain companies do have such models that interconnect different aspects of their business operations. This helps in optimizing the performance of these companies. Statistics show that ML can increase production capacity by a minimum of 20% while reducing the rates of material consumption by 4%.