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Subtask Time Estimation

Estimate sub-tasks more accurately through data-driven maintenance task analysis

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Overview

Accurate maintenance sub-task time estimates often rely on geological and geophysical data, which may not be available in sufficient detail or comprehensive enough. This incomplete or uncertain data can lead to inaccurate time estimates, having a trickle effect on other maintenance tasks scheduled.

When estimating sub-task times, a host of factors need to be accounted for that could influence or delay tasks. Delays in the delivery of necessary supplies and materials, logistical challenges such as transportation and supply chain disruptions, weather conditions, particularly in outdoor mining operations and mining equipment availability all contribute to deviations in sub-task times.

Key Client Challenges

How can we incorporate advanced technologies like AI and machine learning into our estimation process to improve accuracy of maintenance planning and scheduling?

What data sources and sensors should we prioritize for collecting real-time information that can enhance our time estimates for mining sub-tasks?

How can we effectively factor in equipment maintenance and performance data to minimize downtime and unexpected delays in sub-tasks?

How YCP Unison Can Help

With emerging technologies, mining companies are now better positioned than ever before to improve these estimates. For instance, accurate surveying and mapping techniques, such as 3D laser scanning and ground-penetrating radar can provide a more detailed understanding of the mine site’s topography, which is key to better sub-task time estimation.

Additionally, big data integration allows for the combining of data from various sources, from geology and equipment performance to environmental conditions, all providing a more complete picture of the mining operation and enhancing sub-task time predictions.