Truck Fill Factor Improvement

Theme: Asset Optimization & Predictive maintenance

Expected Pilot Duration - 12 Months

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Challenge Summary

In SB Mine there are two Orebodies with significantly different Density and Ore Grade. The challenge at SB Mine is the absence of a digital solution to determine which ore a truck is loaded with and accurately measure its volume, hindering load optimization. Developing a solution can enhance efficiency and enable informed decisions.

Challenge Scenario

At SB Mine, the challenge stems from the lack of a digital solution to identify the ore body loaded into trucks and accurately measure their ore volume, leading to suboptimal load conditions. Overloaded trucks result in increased maintenance costs, while underloaded trucks lead to higher cost per tonne of muck.

The absence of a monitoring and feedback system exacerbates these issues, as there is no real-time inventory status available at the SB Mine Ore Dump. Developing a solution that can provide live inventory tracking and feedback would significantly improve operational efficiency and enable informed decision-making for load optimization, ultimately reducing costs and enhancing overall productivity at the mine.

Profile of the end users

Mine Production Team

Get to know live Run of Mine (ROM) and can provide timely feedback to Operators for improvement in performance

Finance Team and MIS Team

Automated Production booking system and removal of Manual Intervention. Real time production booking into ERP system.

Solution Requirements

Fill Factor Improvement from 96% to 100%

Monitoring: The solution should enable daily production monitoring by the Production team for all trips. It should facilitate the identification and approval of any bad scans, ensuring accurate tracking of the ore bodies loaded into trucks.

Report Generation: The solution should generate daily operator-wise Fill Factor Reports, providing insights into the efficiency of each truck's load. These reports should be shared with the ground team to raise awareness and promote improvement in load optimization.

Calibration: The solution should support an annual calibration process to maintain accurate measurements. This calibration ensures that the system remains reliable and provides precise volume measurements for each truck's load throughout its operation.

Installation: The solution should be easily installable and possess a robust design capable of withstanding various weather conditions.

Integration: Seamless integration with the existing ERP ecosystem should be a key feature, allowing for smooth data flow and compatibility with the current operational systems.

Accuracy: The solution should exhibit a high level of precision and accuracy in its measurements, ensuring reliable and trustworthy data for load volume calculations.

Simplicity: The interface of the solution should be user-friendly and straightforward, enabling operators to easily navigate and operate the system. It should also provide feedback and alerts regarding any abnormalities or issues that may arise during the process..

Expected Outcomes

1. Operational KPIs/Metrics:
Baseline KPI
(Unit of Measurement)
Baseline #
(Baseline Period)
Expected Timeline to achieve 100% Target
Fill Factor 95% ~99.5% 12Months

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