The potential benefits of IoT for mining are obvious. With perilous extractive processes, complex equipment uses, precarious working environments and volatile commodity prices, operational challenges in mining are far-reaching which leave enormous opportunities for improvement and optimization. When implemented correctly, IoT for mining can have a significant impact on worker safety, asset management, resource utilization and operational efficiency with improved visibility and oversight.

While the mining sector has been relatively conservative, the need for digital transformation is understood across-the-board. According to a recent study by Vanson Bourne in collaboration with Inmarsat, 70 percent of surveyed mining companies believe IoT will be a major force to bolster their competitive edge, with almost half reporting that it can unveil cost-saving opportunities. The ability to collect real-time data from the environment, equipment and workers reveals deviations in operational parameters and other bottlenecks, alongside impending workplace hazards. Leveraging this data, automated workflows can be set up to facilitate emergency responses, supply chain planning and equipment maintenance. This helps reduce cycle times while augmenting productivity and equipment effectiveness.

To provide a more concrete picture of IoT for mining, the infographic below encapsulates 13 of the most important statistics about its potential applications, operational values and economic impact.

The digitalization journey inherently starts with reliable data collection at the edge. With the help of new plug-and-play, long-range connectivity, an IoT architecture can overcome the pitfalls of traditional monitoring and control networks to gather data from previously siloed industrial systems and assets. This allows companies to establish an interconnected, unified monitoring network to streamline costs and management activities. At the same time, gathered data can be then integrated into enterprise management systems to empower company-wide decision-making and process automation.