Flatten the curve: How to use AI in mining and metals


Mining and Metals companies face an unprecedented market and volatile commodities pricing.

Tasked with making important decisions even in the midst of uncertainty, companies must increase tonnage while reducing costs.

The answer lies in deploying artificial intelligence (AI)-driven technologies. However, companies must first lay the proper data foundation to succeed.

The price of gold has been trending upwards for years. In 2016, prices started just below $US1100 ($1440) per ounce.

In 2019 those prices skyrocketed by 18.8 per cent before reaching peak pricing of just under $US2100 an ounce in 2020.

While increased pricing for certain commodities is certainly beneficial to mining and metals companies, those increases are not without volatility.

That volatility makes it difficult to plan, particularly as the need for new technologies and assets continues to grow.

In the commodities market pricing has traditionally been in cycles of 10, 15 or even 20 years. Shortening pricing cycles make it challenging for companies to cope with price uncertainty.

This makes it extremely difficult to know whether to make strategic capital investments now or wait for a downward cycle to rebound.

In addition, volatility torpedoes the accuracy of long-term strategic plans.

In this market, companies must be nimbler than ever, which means finding new ways to increase tonnage while reducing costs.

Increasing output while reducing production costs is every company’s dream. However,  achieving that dream requires optimiSed assets, processes, and maintenance strategies without compromising team member safety.

Now, a number of companies are looking for new ways to improving efficiency and overall production by embarking on a digital transformation journey.

Digital transformation enables key stakeholders to turn operational data into quantifiable business results.

Unfortunately, many companies are moving too quickly on new deployments, focusing on implementing new AI technologies without first having the right operational data foundation in place.

The lack of a solid data foundation means data quality is poor and users are mired in data prep tasks.

In fact, 53 per cent of data science time is spent performing data prep, and the result is a mere 1 per cent of all collected data is used for decision making.

It’s impossible to extract value out of predictive analytics and AI technologies with such limited insight into operational data.

While many companies are allocating budgets towards these advanced technologies to improve maintenance strategies and asset health, funding to improve data quality is often overlooked.

Companies that lack the right data quality are missing out on critical insights and capturing lost revenue.

A solid operational data strategy will not only enable organisations to leverage more data to gain better insights, it’s imperative to capitalising on AI technology.

Companies can use operational data to provide insights to move from a reactive to a condition-based approach, but it requires the right foundations.

With the help of advanced analytics and artificial intelligence, companies can then move to predictive—and even prescriptive—strategy to optimise production processes and flatten the curve of unpredictable pricing cycles.

Join the webinar “Essential rules of adopting AI in mining and metals” on April 15 at 12 PM AEST to learn:

  • A simple definition of AI and the benefits of adopting it into your operations
  • AI maturity model
  • The anatomy of a successful AI project
  • Real-world examples and success stories

Registration here.

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