In the realm of mining and metals operations, a diverse spectrum of decision-making strategies exists, ranging from the reactive to the predictive. These strategies are applied across various facets of the industry, from risk management to maintenance to process monitoring. While the industry has embraced reactive, proactive and predictive strategies, the full extent of their applicability in the context of process monitoring remains less understood.
Reactive decision-making is known for its simplicity, flexibility and cost efficiency. However, operators find themselves in a perpetual firefighting mode, responding to events as they occur. Unfortunately, this approach often leads to suboptimal operational performance and inefficiency.
The primary issue with reactive decision-making for process monitoring lies in its timing. When an unforeseen event unfolds, it takes a significant amount of time before an operator is even alerted to the problem, which subsequently puts the operation in abnormal conditions during that time or even causes downtime. Once alerted, they must then rapidly analyze multiple parameters and select the best course of action in real time. This heavy reliance on expert personnel is not only resource-intensive but also prone to human error. Even with experienced operators, there is no guarantee that they will always make the optimal decision, particularly in complex and dynamic situations.
This reality underscores the challenge of retaining experienced operators over the long term. High turnover rates and the need for continual exposure to various scenarios make it difficult to maintain a pool of experts capable of consistently making the right decisions. Thus, the need for a paradigm shift becomes evident, leading us towards the second type of decision-making: proactive decision-making.
Proactive decision-making represents a fundamental shift in strategy, offering several advantages, including risk reduction, higher operator satisfaction and long-term planning. Instead of reacting to unfolding events, this approach emphasizes the identification of potential sources of disruption and the prevention of conditions that could lead to these events. Operators actively strive to minimize the likelihood of high-risk situations arising. This shift is highly desirable as it significantly reduces overall risks associated with operational practices.
Nevertheless, it is essential to acknowledge that proactive decision-making is not without its costs. Prioritizing risk mitigation and conservative strategies may sometimes limit operational efficiency. This shift recognizes that, on occasion, the pursuit of operational efficiency may need to yield to risk avoidance.
However, predictive decision-making stands out as a more efficient approach. This method builds upon the proactive framework by leveraging the power of data analytics and advanced technologies. Instead of merely identifying and mitigating known risks, predictive decision-making leverages historical data, real-time monitoring and machine learning algorithms to foresee potential issues before they materialize.
By harnessing the predictive power of data, operators gain a distinct advantage. They can proactively address issues with a higher degree of certainty, effectively balancing operational efficiency with risk management. Predictive decision-making allows organizations to optimize their processes while maintaining a robust safety net against unforeseen events.
Nevertheless, several challenges are associated with predictive decision-making. Firstly, it heavily relies on the quality and availability of data; inaccurate or incomplete data can result in unreliable predictions. Secondly, the implementation of predictive monitoring systems can be complex, often requiring expertise in data science and machine learning. Additionally, there is a substantial upfront cost involved in establishing predictive monitoring systems, including investments in sensors, data infrastructure and analytics tools. Furthermore, the risk of false positives with overly sensitive predictive models may lead to unnecessary activities and costs. Moreover, predictive models are not infallible, and unexpected failures or issues may still occur if the models do not accurately predict them. Lastly, resistance to change from operators and stakeholders can also pose a challenge, particularly when predictive measures disrupt established routines or necessitate significant process changes.
Nonetheless, transitioning from reactive to predictive decision-making is a strategic move that holds significant value due to its potential to maximize efficiency and yield numerous benefits for operations. However, in reality, a complete migration to predictive decision-making for process monitoring may not be feasible. Therefore, optimizing the decision-making spectrum emerges as a more attainable and pragmatic goal.
In embarking on this journey, it is essential to establish a “Predictive Migration Readiness Index” (PMRI) to comprehensively evaluate the readiness of various facets of process monitoring for the transition from reactive to predictive decision-making. The readiness assessment, vital for the shift from reactive to predictive decision-making, encompasses several pivotal factors. These include data availability, the digital maturity of the operation, the potential for data-driven models (assessing feasibility and complexity), operator readiness, technology infrastructure, process complexity, the value proposition of migration and change management preparedness. A higher score within this index means a heightened level of preparedness for the migration towards predictive decision-making.
Ali Vazirizadeh, a metallurgist, is co-founder and director of Aisimpro, a company that specializes in AI implementation for the mining industry.
