6 ways metals companies can create value with AI and ML

Thomas Callaghan, Alison Bauter Engel, Jackson Hurst, Bob Lavoy

Metals 4.0 technology has seen artificial intelligence (AI) lead metals companies to experience digital transformation, but not everyone is quick to invest. Why is that?

While many metals leaders are investing in cloud-based technologies like enterprise resource planning (ERP) systems, some struggle with understanding AI and machine learning (ML) and how that technology can help. Instead, they find themselves facing a lack of confidence to invest, thanks to many unanswered questions:

Can our metals company use AI and ML?
How can we avoid adoption failure?
How can we maximize the return on investment (ROI)?
How complex are AI and ML to use?

Leaders in the metals industry need to move beyond the proof of concept for AI and ML – they need to see proof of value.

What are the benefits of AI and ML for metals companies?

With access to metals-specific ERP solutions that include AI and ML, metals companies can lead their market by decreasing costs, increasing efficiency, and capitalizing on new opportunities. But knowing where to start is often the most challenging issue facing metals companies.

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Where can metals companies start with AI and ML?

Smart technology can offer so much, but it sometimes can be overwhelming to know where to start. While a brilliant IT leader might be ready to take a metals company into the future with smart technology, it still takes understanding and buy-in from executive leaders and employees.

Without a good understanding, investing in AI and ML can feel wildly complex and can lead to the frustration of wasted investment if it’s not done well. So here are some effective steps that don’t require a Ph.D. in mathematics:

1. Start with the problems that need to be solved

Investing in AI and ML technology shouldn't be motivated by the fact that it’s the newest technology that other companies use. It has to work for a particular metals company. Rather than starting with technology, companies should focus on the problems they’re trying to solve. Key stakeholders should work together to determine barriers to growth and identify opportunities where technology can help. Then they can ask how AI and ML could provide an effective solution. Failure to start with the problems that need to be solved could lead to investment in unneeded or useless technology.

2. Focus on accurate data

Maximizing AI and ML technology requires accurate data from companies and their outside sources. Unfortunately, companies that invest in smart technology often don’t see the ROI because their data is inaccurate. For example, in forecasting demand, technology likely will not be helpful if historical sales data is not accurate, complete, and reliable.

When considering AI and ML investments, metals businesses should determine the availability of accurate data:

How many sources are collecting the data needed to solve the identified problem?
How readily available is the data, and what steps are needed to access it?
Who in the business is responsible for the data and has access to the data needed?

Once a company has gathered the correct data, it can begin to use the data with AI and ML technology to improve the business.

3. Increase efficiency with automation

Gathering data from documents and processing it manually can be time-consuming, tedious, and harmful to the bottom line. Human error also can lead to data being mismanaged, altered, or even deleted. However, technology can help. Optical character recognition (OCR) is an example of an automation metals companies can use to save time and money and to help eliminate the headaches of poor data.

OCR is cost-effective software that can convert scanned images into digitized data using ML models. Given that data accuracy is essential to maximize the ROI for AI and ML, OCR presents an intelligent way to deliver data quickly, efficiently, and with greater accuracy than manual entry.

4. Avoid machine downtime with predictive maintenance

The introduction of networked technology such as the internet of things (IoT) has made it possible to monitor machines and provide real-time data to production teams. While connected devices can provide instant information, it still takes an additional step to determine if an issue exists with a machine or production process. Many metals companies face the problem of depending on a technician's experience or gut feeling to determine if maintenance is needed.

The good news for metals companies is that sufficient information already is available to show proof of value for predictive or preventive maintenance. With machines connected, the collection and use of data can train AI and ML models to predict maintenance needs. In worst-case scenarios, the models can catch anomalies that point to a larger production issue. The bottom line is that AI and ML can be highly effective in predicting maintenance needs.

5. Improve inventory management with demand forecasting

In the past two years, many metals companies have been negatively affected by supply chain uncertainty and volatility in the metals industry. At times, some have struggled with running low on inventory or having too much on hand. The implications of both can affect customer delivery dates and increase overheads.

With demand forecasting, ML models can use historical data and external market reports to build a more accurate picture of product supply and demand. Some AI services can be tailored for metals companies to deliver more accurate demand forecasts.

6. Decrease costs with optimization

Metals companies can gain a competitive advantage by using AI and ML to optimize in areas like planning, production, inventory, pricing, and supply chain. The technology can help identify the areas of greatest need by finding inefficiencies and bottlenecks in processes. AI models can then pinpoint opportunities for improvements, cost savings, and increased profits.

Step into the future with AI and ML

Some metals companies struggle to see the ROI in smart technologies because they focus too heavily on what the technology can do rather than asking what needs the technology can meet.

At Crowe, our metals team combines decades of experience in the metals industry, ERP implementation, and specialized data science teams to maximize the potential of AI and ML. Let us help you replace the uncertainty of AI and ML with the confidence to lead your metals company into the future.

Thomas Callaghan

Managing Director, Data Science

+1 615 360 5589

mail

Alison Bauter Engel

AI Studio Operations Leader

+1 312 880 4853

mail

Jackson Hurst

+1 312 759 1022

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