How Data and Advanced Analytics are restructuring the Mining and Metal Industry?

Mining companies create volumes of data from equipment and processes, but only a small portion of this data is used to make decisions. Latest advances in machine learning (ML) and data analytics in the mining industry have leveraged data from sources within and beyond the value chain to deliver real-time decision support and insights about the possibility of future events. Key technologies in this environment include artificial intelligence (AI), ML, data and advanced analytics, simulation modeling, and digital twinning to optimize production and predictive maintenance.

Wherein AI and ML enable miners to get insights into where to explore and what lies under the ground by using satellite imagery and geophysical maps, advanced analytics can look at massive data sets and recognize opportunities for improvement that humans are unable to see. Advanced analytics helps miners to identify operational bottlenecks or waste patterns. It also improves predictive maintenance and increases efficiencies of daily operations.

A report on Digital Transformation in the mining and metals industry by the World Economic Forum discloses that although effective analytics is still in infancy, they have started to add value to mining operations. And the mining and metal industry is estimated to expand between 2016 and 2025 with the potential value of USD 10.6 billion.

Innovations that Use Data Analytics in Mining Organisations

Managing data and using data analytics could be puzzling for mining companies. New technological advancements are offered to the mining industry, providing companies with a chance to analyze equipment and operations. Besides, advanced analytics also offers a vast range of benefits to mining firms.

Opportunities of Data Analytics in the Mining Industry

The mining sector is sitting on a virtual goldmine of complicated and massive data sets. However, investment insufficiency in analytical technologies refrains the industry from unlocking the true value of the system and making full use of big data development tools.

Today, mining companies realize that data analytics can enhance operation efficiencies, real-time planning and boost automation of entire workforce management, ensuring safety. The key is to analyze data, determine its usefulness in a particular field. Following are the benefits of data analytics that the mining industry could exploit:

Speed up Efficiency

With agility and automated processes during operations, data extraction, processing, separating and concentrating the best components is quicker.

Smart Partnership

A single data source across the company accelerates collaboration between multiple departments, ensuring new partnership models with operators, OEMs and service providers to monitor through cloud or networks.

Business Intelligence

Business intelligence (BI) helps to detect areas that are real cost drivers, allowing quicker decision-making and enhancing performance.

Enhance Logistics

Transportation is the core of the mining industry, and automating the process could benefit it widely. Advanced data analytics could help determine areas that need an efficiency boost and improvement.

Safety and Security

By capturing operational, sensor and people data, data and advanced analytics offer actionable insights based on real-time monitoring of employees in mining. These include temperature, location, and heart rate, CO, gas, wind speed, dust etc. Data analytics could also help warn about the risks like tunnel collapse, such as near misses. Thus, it ensures safer operations.