Loading and Moving Machine Operators, Underground Mining

Operate underground loading or moving machine to load or move coal, ore, or rock using shuttle or mine car or conveyors. Equipment may include power shovels, hoisting engines equipped with cable-drawn scraper or scoop, or machines equipped with gathering arms and conveyor.

The occupation "Loading and Moving Machine Operators, Underground Mining" has an automation risk of 39.7%, which is very close to its base risk of 40.0%. This relatively moderate risk indicates that while automation technologies are capable of performing a significant portion of the tasks within this role, there remain substantial barriers preventing total automation. The typical tasks involve operating large, complex machinery in unpredictable environments, requiring a combination of technical skill and situational awareness. Mining environments are inherently variable, and factors such as geological differences and safety hazards introduce complexity that automated systems may struggle to handle with full reliability. Thus, current automation development is able to address repetitive and controlled tasks, but falls short when it comes to nuanced human judgment and adaptability required underground. Looking at the most automatable tasks, those that involve direct physical manipulation and routine machine operation are most at risk. For example, "Handle high voltage sources and hang electrical cables," is a task that can often be performed by automated systems with articulated robotic arms or specialized automated machinery. Likewise, "Drive loaded shuttle cars to ramps and move controls to discharge loads into mine cars or onto conveyors" falls squarely within the domain of automated guided vehicles (AGVs), now increasingly used in modern mining operations. Additionally, "Pry off loose material from roofs and move it into the paths of machines, using crowbars" represents a repetitive, hazardous activity that automation could improve upon, both in efficiency and in worker safety. Broadly, these tasks are characterized by their repeatability and their relative independence from highly variable, real-time judgment. However, several tasks remain highly resistant to automation due to their need for real-time problem-solving, communication, and adaptability. Examples include "Push or ride cars down slopes, or hook cars to cables and control cable drum brakes, to ease cars down inclines," a task reliant on continual assessment of real-world variables and nuanced control. Directing other workers—such as "Direct other workers to move stakes, place blocks, position anchors or cables, or move materials"—involves both leadership and dynamic response to immediate physical environments. Likewise, "Maintain records of materials moved" requires a level of oversight and integrates information from multiple sources in ways that are still difficult for fully autonomous systems. The bottleneck skills for this occupation, with Originality marked at levels of 2.0% and 1.4%, further illustrate that creative problem-solving is not a principal requirement; however, the moderate automation risk originates from the need for real-time situational judgment. Overall, while substantial portions of the work can be automated, critical tasks still depend on human behaviors that are complex to replicate with current automation technologies.