

Article by Rodrigo Barbosa – Manging Consultant – LATAM
M.Eng, B.Sc (Mining), MAusIMM
Fleet selection and fleet sizing are often treated as technical tasks in mining. A set of calculations, a software run, a spreadsheet output. But the reality is more strategic and far more consequential.
Choosing the right mining fleet affects immediate operational performance, and it also shapes infrastructure planning, capital allocation, maintenance strategies, and even how a site is staffed and organised. Fleet sizing is not an isolated engineering exercise. It is a discipline that connects operational detail to business outcomes.
The most important objective in fleet selection is often the simplest to state and the easiest to lose sight of: getting ore to the plant at the lowest feasible cost, while maintaining safety and reliability.
In other areas of life, we may say the journey matters as much as the destination. In mining, that idea does not hold. Once ore is in the plant, the route it took is irrelevant. What matters is whether it arrived cheaply, safely, and consistently. This is why the best fleet is the one that is cost-effective, not necessarily the one with the most impressive technology or the newest features.
It is easy to fall into the trap of selecting the most advanced equipment or the most sophisticated simulation approach simply because it appears impressive. But mining is not a technology showcase. It is a margin-driven business, and costs determine those margins.
A modern, complex fleet configuration can look like “future-proofing” on paper. However, if that choice increases capital intensity or lifts operating costs without delivering measurable value, it becomes a liability rather than an asset. The right fleet is the one that matches the operational requirement and supports the broader business case.
Fleet sizing in mining is inherently iterative. It is full of nuance and site-specific variables, and those variables rarely behave neatly in a model.
Replanning an operating fleet is particularly challenging. In a greenfield scenario, assumptions can be built into a clean model. In an active operation, the reality is closer to repairing an aeroplane mid-flight. Haul roads already exist, working areas are constrained, and production cannot pause to accommodate a new plan. These constraints demand both precision and pragmatism.
Despite advances in simulation and mine planning tools, modelling accuracy is still limited by data quality and the clarity of assumptions.
Historical performance data can be valuable as a baseline, but it must be validated and calibrated. Seasonal effects and site conditions matter. For example, rainfall can reduce truck utilisation, but it may be poor haul road maintenance that creates the real productivity loss. Small distinctions like this can meaningfully change performance forecasts and fleet requirements.
Dispatch data is rich and structured, but it often contains anomalies. If these issues are not identified and corrected, they can distort analysis and drive poor equipment selection decisions.
In practice, it is common to find inconsistencies between system-reported performance and actual field observations. That is why continuous cross-checking and calibration are essential. Modelling should reflect operational reality, not an idealised version of the operation.
Even subtle design characteristics, such as road curvature or grade profile, can have outsized impacts on mining fleet performance.
Tight turns, steep climbs, and repeated braking events increase fuel consumption, accelerate brake wear, and shorten tyre life. Two haul routes of equal distance may look similar in plan view, but if one includes steeper grades or more elevation change, its long-term impact on maintenance cost and equipment availability will be materially different.
Good fleet sizing and equipment selection is about managing a web of interactions and anticipating downstream effects that may not be obvious at first glance.
Above all, it requires discipline. It means resisting complexity for its own sake, avoiding novelty-driven decisions, and committing to practical solutions that prioritise project feasibility and cost outcomes over theoretical elegance.
In a competitive mining environment, every dollar saved in haulage is a dollar added to margin. This mindset is not optional. It is essential.
The ore does not care how it got to the plant. Neither should we, provided it gets there efficiently, safely, and at the lowest feasible cost.on/, or contact us at training@snowdenoptiro.com for more information.
Rodrigo Barbosa is our Managing Consultant for the LATAM region. He is a mining engineer with an MSc in Mining Engineering. Rodrigo has consolidated and recognised experience in feasibility studies, fleet sizing, mining operations, and related activities in large-scale open-pit operations. He has worked for multinational mining companies such as Votorantim and Gerdau and has served as a consultant for a wide range of commodities within the mining industry. He has been involved in mine planning, mining operations, and technology development for large mining companies.
Rodrigo’s key skills include fleet sizing, feasibility studies, capital projects, key performance indicators, planning, cost management, strategic planning, and operations optimisation.
If you would like to contact Rodrigo: contact@snowdenoptiro.com
Snowden Optiro is a resources consulting and advisory group that provides independent advice, consulting and training to mining and exploration companies, their advisors and investors.
We help mine developers to advance their projects, mining companies to improve their operations and their professionals, and investors to de-risk their investments by the provision of quality advice, training and software in the field of Mineral Resources and Mineral/Ore Reserves.
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