Accurate mobile equipment data for mining professionals.
With HME Benchmark choose from over 1,000 machines and define its operating circumstances, to generate a contextual estimate that details periodic maintenance costs, manpower requirements and machine availability for the term of the estimate. Annual and monthly estimates can be created, and they are based on underlying data collected from over 25 million operating hours logged in large, open pit mines on four continents.
With HME Benchmark you can:
The database includes all common classes of Haul Trucks, Shovels, Dozers, Graders and Wheel Loaders, and is updated every 90 days with inflation adjusted data and new machine models.
The simple user interface applies parameter-driven algorithms to a sophisticated quantitative model that contains generic maintenance plans for common mining equipment. Variables include material, location, workforce experience, climate and road conditions, and estimates can be created for any age machine.
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Michael Currie originally developed the HME Benchmark tool for his own consulting practice but soon realized that it would be perfect for fleet and equipment managers to use in their operations. He is a recognized leader in applying life cycle cost (LCC) analysis to mobile equipment, and provides products and services based on LCC principles to clients operating around the world. With over 25 years of experience that includes senior roles with PricewaterhouseCoopers’ global mining group and Finning, a major Caterpillar dealer, he brings credible industry knowledge to engagements that focus on optimizing operations and solving problems.
Michael specializes in:
Clients include leading mines, mining equipment manufacturers and suppliers, as well as fleet operators in other industries that require robust equipment budgets, resource estimates and maintenance strategy evaluations.
Do you have the information you need to understand mobile mining equipment maintenance?
This eBook explains the fundamentals of mobile mining equipment maintenance.