Maximizing Efficiency in Mining Operations: Deep Dive into Time Usage Models for Underground Equipment (Part 2)

Maximizing Efficiency in Mining Operations: Deep Dive into Time Usage Models for Underground Equipment (Part 2)

By Danie Bezuidenhout

In Part 1 of this series, we introduced the Time Usage Model (TUM) as a crucial framework for categorizing and understanding how time is spent in mining operations. This structured approach, particularly beneficial in high-capital, complex industries like mining, allows companies to maximize asset utilization, identify bottlenecks, and optimize decision-making. In Part 2, we delve into advanced aspects of the Time Usage Model as applied to underground mobile mining equipment, drawing on insights from Ray Ballantyne's "Challenging the Norms" and exploring how detailed classifications within the TUM can address unique operational challenges underground.

Advanced Time Classification for Mobile Underground Mining Equipment

In underground mining, mobile equipment is subjected to a dynamic environment that requires a nuanced breakdown of operational time. As Ballantyne explains, a TUM for underground operations must capture not only the high-level categories like Utilized, Standby, and Maintenance hours but also secondary and tertiary breakdowns that reveal specific operational details. By implementing this level of classification, mining operations can accurately track critical operational states and ensure that productivity indicators such as availability, utilization, and operational efficiency are calculated consistently.

1. Key Classifications and Their Role in Underground Operations:

• Direct Operating Hours (DOH): Refers to the time equipment is performing its primary task with the power source running, such as drilling, hauling, or loading. This category also allows tracking of multiple primary tasks specific to underground operations, enhancing performance insights at the task level.
• Indirect Operating Hours (IDOH): Represents essential but non-primary tasks like moving equipment, cleanup, or setup activities. Despite being necessary for overall productivity, IDOH is differentiated from DOH to avoid conflating core production tasks with supporting activities.
• Standby and Maintenance Hours: These hours include various states where equipment is either waiting for operational instructions or undergoing maintenance. Ballantyne advises against categorizing delays as negative when essential tasks (such as safety meetings or equipment setup) are involved, advocating instead for clear distinctions that foster accuracy.

2. Improving Data Integrity and Reducing Ambiguities:

For any TUM, data integrity is paramount. Ballantyne emphasizes that classifications like "Damage" or "Opportunity Maintenance" should not dilute the model, as they introduce subjective assessments that can skew reporting. Instead, clear rules should guide the model to ensure that data capture remains objective and that secondary classifications like Failure vs. Damage or Scheduled vs. Unscheduled maintenance are handled within the broader context of equipment condition and usage, not subjective judgement calls.

3. Leveraging TUM Insights for Operational Excellence:

Through consistent use of TUM across operations, companies can benefit from improved asset availability, optimized maintenance scheduling, and data-driven decisions that drive efficiency. With a robust TUM, mining companies are better positioned to:

• Benchmark Against Standards: By applying standardized classifications, companies can compare performance internally and externally, driving continuous improvement across sites.
• Refine Maintenance Strategies: Differentiating maintenance types within TUM enables proactive maintenance, reducing unscheduled downtimes and extending equipment life.
• Address Operational Bottlenecks: Detailed tracking of Indirect Operating Hours reveals inefficiencies, informing targeted operational adjustments that boost productivity.

Concluding Part 2

With a well-defined Time Usage Model tailored to underground equipment, mining operations can unlock insights that drive efficiency, safety, and productivity. By adopting Ballantyne's recommended classifications, companies can ensure that their TUM aligns with both operational realities and strategic objectives.

Stay tuned for further insights as we continue this series, discussing how data from TUM can inform broader asset management strategies in Part 3.

Reference: Ballantyne, R. (2019). Challenging the Norms: Time Usage Model for Mobile Underground Mining Equipment. RAK Developments.

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