To reduce operational costs and comply with shifting environmental regulations, diesel and tire usage can be optimized for greater savings and reduced emissions. By analyzing run scenarios to improve the efficiency of load and haul routes or calculating truck haul and return cycle times, companies can benefit from reduced unit costs.
However, vast amounts of data generated by today’s mining equipment through sensors and AI mean that leveraging it effectively can be an initial challenge. Beyond collecting data, the ability to analyze it to guide strategic decision-making will ultimately be what sets mining companies apart from competitors.
Challenges with Leveraging Data Analytics for Load and Haul Cost-Effectiveness
Deploying more sensor-equipped vehicles and machinery and leveraging AI may initially pose these challenges in effectively utilizing data.
1. Data Aggregation and Reliability for a Bird’s Eye View
Mining machinery by various brands may have inherent biases in the data recorded or shared. For example, while it is still possible for various tire monitoring systems to exchange data with other brands’ systems, each is designed to prioritize its own equipment. This hinders mining operators from getting a full, accurate overview of the performance of machinery across various brands used by a mine.
By consolidating diesel and tire usage data into a single dashboard, mining teams can better understand and interpret data, facilitating management’s decision-making. A bird’s eye view will enable better understanding of data collected by the variety of fleet machines and tires used by different brands by a mine, allowing improved energy and cost saving efforts.
2. Data Security, Infrastructure and Management
The security of a mine’s data infrastructure is paramount to smooth mining operations. Security threats such as ransomware and targeted hacking attacks by organized criminals can be extremely costly when they lead to false tire pressure readings, suspended operations and indefinite mine downtime. As such, the focus of mining companies cannot be placed purely on how data is collected, but how it is stored, organized and shared for efficient retrieval and analysis.
3. Data Interpretation, Analysis and Strategic Decision-Making
Despite large amounts of data available by collection through vehicle and tire sensors, making connections between information can still be a challenge. For instance, by running scenario analysis, operators may discover that one load and haul route consumes more diesel or leads to more tire wear over time.
However, it may take many hours of machinery use before patterns emerge, such as the duration between a tire cut and the failure of a tire or vehicle. Finally, once patterns such as return cycle times, bucket fill factor and diesel usage are detected, thorough analysis still needs to be conducted by mining teams to effectively leverage this information for greater savings.
Leverage Data More Effectively by Asking the Right Questions
With evolving mining regulations in Indonesia, optimizing diesel and tire usage through data analytics is an opportunity companies can begin to leverage. By asking critical questions on how to enhance a mine’s operational excellence, significant cost savings can be unlocked and new policies affecting your mine’s operating expenditure can be better managed.
- Are your data analytics efforts for your mine aligned with your budget?
- Are your employees onboard with new technology and effectively utilizing data?
- Is your data infrastructure secured against cybersecurity threats?
Unison Mining’s team can work with you to ensure your data analytics and cost savings initiatives are aligned with your budget and achieve full employee buy-in for sustainable change.
