CMA

60 The implementation of digital transformation systems has brought in series of benefits, such as productivity increase by the automation of production and decision-making processes, reduction of waste, improvement of equipment utilization, and maintenance costs reduction. This new mining methodology is not only about the adoption of new technologies, but it will also demand organizational changes, specialized knowledge, and expertise. ‘Digital Transformation’ has been extensively used in recent years, mainly to describe the adaptation process of mining organizations to new digital technologies. Digital Technology involves 1. specific information – data collection 2. computation – analysis of data 3. communication- Reports generation for users, 4. connectivity technologies which vary from one industry to another. In the case of mining, it is possible to identify a set of tools that will and are already affecting the processes at the operational mining sites and corporate units of a mining company. It involves a transversal process of change across the complete value chain of the mining industry, from the exploration to the production of final products, their commercialization, and even the closure of operation sites. Following are the key elements of the digital transformation process • Remote Operation including automation & robotics in Mining operation This is major element of digital transformation. The benefit of the automation of processes, use of robots in critical activities, and remote operation centres (ROC) for the improving of safety, by reducing the number of operators required in hazardous sites. This significantly reduce OPEX and CAPEX of mining operations. Reduced manpower is required at the mine site, fewer or none supporting infrastructure is required, including housing installations, hospitals, or schools. Also, other expenses are reduced, such as transportation of operators. The impact on costs is larger as the location of the mine is more remote, distant, and isolated. The use of autonomous equipment, such as Dumpers, LHDs, and drilling, is expanding rapidly. For example, global equipment manufacturers Caterpillar and Komatsu have already started manufacturing autonomous trucks for large-scale mining operations in Australia, Brazil, Canada, and the USA. Already till February 2020, a total of 459 autonomous haul trucks were operating in mining operations around the world (still only 1% of total population) and continuously increasing in numbers. Advantages of automation of equipment are not only help in promoting green mining but also provide safety, enhanced productivity, and reduced operational costs, increasing equipment’s utilization (due to the continuous operation), reducing variability in the production outcome, and improving tires, consumables, and components performances. • Sensors for mining equipments for real time data capturing This is a network of physical objects, such as sensors, equipment, machinery, and other sources of data. The elements connected to this network can then interact, exchange information, and act in a coordinated way. With the advances in internet technology, it is possible to establish low-cost networks. Additionally, the development of smart sensors allows real-time capture of data from machines and equipment across the operation. This generation of data is the base to conduct an integrated planning and control, considering the different units within the operation, and support the decision making process. • Analysis/Interpretation of Data (Artificial Intelligence) Artificial intelligence methods are also being applied for mineral exploration and optimize the prospection and exploration activities, reducing costs and improving their accuracy in mining planning. With the application of Information Technology, and real-time data capture, mining operations have enormous amounts of data available related to production, processes, and performance of machines, among others. Through advanced analytics methods, it is possible to transform this information allowing its use for a better planning of activities and to support fast and effective decision-making processes for the operation, thereby reducing wastage of resources. Predictive models can also be developed to enhance maintenance of equipment, therefore improving productivity and effectively reducing the stores & spares etc.