## 5 Conclusions  
Although various solutions exist in the current market to monitor parameters related to the physical and chemical stability of TSFs, some mining operations remain characterized by manual, low-level monitoring techniques, limited technological innovation, insufficient data, and inadequate data processing and integration, as well as lacking early warning and communications systems for surrounding communities. Mining operations generate large volumes of data to monitor operational processes and the physical and chemical performance of equipment and processes, but only a fraction of these data are used to improve decision-making regarding the management of TSFs. However, with the adoption of new sensor types and IoT approaches, the volume of data handled is expected to significantly exceed that generated today. Recent advances in AI, ML, CC, and data analytics will allow mining operations to take advantage of data from different sources within and outside the value chain to provide real-time decision support and insights into the probability of future events that cannot be predicted with traditional analysis methods. All of these technologies will allow sensor data to be converted into practical metrics and then automate complex calculations to deliver a uniform interpretation of the data. In addition, data can be monitored in real time, allowing management to escalate when necessary and receive alerts to prevent problems early. An online, real-time monitoring system for mine TSFs would allow the visualization of physical and chemical stability indices, which can be constructed by processing the information derived from monitoring the relevant parameters and variables related to these structures' stability and security. This monitoring, depending on each parameter and current and future technological developments affecting monitoring techniques, may include direct monitoring information of parameters that can be measured online and in real-time, while those that require further analysis are achieved either by processing the data through specific models or software or via laboratory analysis. The stability indices should include an analysis based on the risk associated with the variables' behavior. Without digitalization in the sense of changing the data format from analog to digital, data exchange would be cumbersome, and changing the current operating model to one based on the use of digital technologies would also be impossible to achieve. Similarly, without interoperability, mining equipment and computer systems would remain bound to current data structures, which have hindered initiatives to improve mining processes for years. To achieve a smart monitoring model for mine TSFs, from an Industry 4.0 paradigm, a permanent technological surveillance program must be established to identify emerging technologies and channel relevant information to key parties. The acquisition of these technologies must be supported according to their potential effects on mining processes. Technological surveillance can reduce the time that elapses between the occurrence of technological advances and their detection by industry by analyzing technological suppliers, patents, and publications. The identification and use of external sources of knowledge is especially relevant today, given the growing complexity that globalization entails. The mining industry's potential to evolve toward becoming a benchmark industry for the adoption of Industry 4.0 technologies requires support from all stakeholders and interest groups in the mining ecosystem. The fourth industrial revolution, like its predecessors, has brought a fierce debate regarding its benefits and potential threats, particularly the fear of job loss and replacement with technology. Therefore, these impacts must first be understood and measured, and then susceptible interest groups must be properly informed and educated.