Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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Artificial Intelligence Machine Learning AI Industry

Shining Up a Rusty Industry with Artificial Intelligence

By Nand Kishor |Email | Feb 9, 2018 | 9174 Views

One of the primary activities companies generally tend to pursue with analytics and data is to plan and optimize operations, and it is a long-term focus of the â??operations research' approach to analytics. It has always been done on a relatively smaller scale, however, using individual models with only limited variables. The cognitive tools, especially machine learning can take this activity to the next level in all parameters.

AI may not be known for its role in manufacturing and operations, but there is an opportunity to use these tools to improve the efficacy and effectiveness of these important industries drastically. 

For instance, the steel manufacturing startup Big River Steel, attempting a major transformation in this most industrial of industries, isn't known for its use of leading-edge machine learning. 

Big River Steel makes extensive use of sensors, control systems, and machine learning-based optimization. Big River has developed a variety of technologies to improve the practice and profit of steelmaking when working with the AI consulting firm Noodle.ai. Indeed, the company's CEO, David Stickler, specifies, â??We are a technology company that happens to make steel.â??

Big River uses machine learning in the following six major areas;
  1. Demand prediction:  Big River succeeds by using capital wisely, so it needs to accurately predict demand for steel. It employs machine learning models using macroeconomic data, historical demand for steel, manufacturing activity, and the activity of large steel consumers.
  2. Sourcing and inventory management: Big River's raw material is scrap, so it needs to predict the availability. Noodle.ai has produced a â??scrap indexâ?? and is working with Big River on a hedging approach for buying scrap steel.
  3. Scheduling optimization: What to produce, when is an important decision for any steel mill is particularly critical when one of its most important inputs is electrical energy. This is exactly the case with Big River. The optimization models help in maximizing energy consumption at off-peak times and thus minimizes energy costs.
  4. Production optimization: All steel mills have unplanned events such as, breakouts and cobbles. These events reduce production and are costly as well as dangerous. Machine learning models can predict when they are most likely to happen and minimize their occurrence.
  5. Predictive maintenance: Big River uses machine learning models to identify the optimal times for maintaining key machines and equipment with an increase in industries.
  6. Outbound Transportation Optimization: Big River works with customers and shippers to minimize the costs of outbound transportation and to optimize delivery windows for customers.

Integration of these applications creates maximum benefits. It is attempting to create end-to-end optimization of the mill's performance and profitability. The company has models for interconnecting different parts of business plans & operations and can optimize across the enterprise. The integrated approach to planning and optimization is at its beginner level and the refining of it requires more data, algorithm tuning, and substantial computing horsepower. 

Source: HOB
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