Mining operations are extremely critical for the global economy, almost every industrial activity depends on minerals and metals extracted from the Earth.
This includes base metals like iron ore, copper; precious metals like Gold, Silver and Platinum and even Rare Earth Metals like lutetium, gadolinium, holmium and Cerium.
The operations themselves are quite technical and complex, requiring large industrial machinery, specialized equipment and spare parts.
Inventory Management in mining generally refers to optimizing the inventory levels of the equipment and spare parts to ensure the perfect balance between overstocking and running out of critical spares.
It can sometimes also refer to the management of the inventories of the raw ore itself, all the way from refining to the final product.
What Does "Inventory" Mean in Mining?
Mining inventory spans two very different domains, the equipment that keeps operations running, and the ore product moving through the value chain. Both are critical; both require entirely different management philosophies.
Maintenance Inventory (MRO)
Technical equipment, spare parts, and consumables needed to keep assets running. Managing these correctly is what separates planned maintenance from costly unplanned downtime.
Run-of-Mine & Bulk Inventory
The ore product itself at every stage — from the raw stockpile just extracted, all the way to the refined copper concentrate or gold bar awaiting shipment.
Maintenance Inventory Management
Maintenance inventories in mining refers to technical equipment, spare parts and consumables.
It is widely considered as one of the most asset intensive industries due to sheer scale of operations that generally require;
Specialized Equipment
Bucket wheel excavators, draglines, jumbo drills, raise borers
General Heavy Equipment
Haul trucks, dozers, drill rigs, excavators
Add to the fact that global mining companies spread out their operations across dozens of plant locations, sometimes even in different continents, and it becomes easy to see that managing maintenance inventories is a critical challenge.
Why Multi-Site Complexity Multiplies the Problem
A single global miner can operate across 30+ sites spanning multiple continents, each with different OEM suppliers, local lead times, and regulatory requirements.
This makes centralised inventory visibility and standardised part numbering across sites two of the most valuable, and least achieved, capabilities in mining MRO management.
Part duplication across sites (the same physical component catalogued under 4 different part numbers) is one of the leading causes of excess stock. Industry estimates suggest 20–35% of mining MRO inventories contain duplicate or near-duplicate items.
Retos comunes
Consumption Planning
Spare Part Materials are managed at scale and procurement + maintenance teams need to plan the requirements in advance to ensure material availability beforehand.
To plan this better, maintenance, asset management and reliability teams undertake an exercise “Demand Forecasting”, this is typically done within a software system like an EAM or a CMMS software.
They mostly rely on rudimentary mathematical models to predict demand in the future;
For Example – A Simplistic Forecast Model
A simplistic model of this forecast looks something like this;
If production volume of 100 Tons requires 10 units of materials, then 200 tons would require 20 materials.
In reality, the math is much more complex but the logic of the forecast is similar.
As one can imagine, this is simply a mathematical forecast and does not account for the complexity in actual day-to-day mining operations.
An incorrect forecast, coupled with a “stockout fear” can easily lead to excess stocking of “Dead Materials”
Similarly, an incorrect forecast can also lead to “Stockouts” of critical mining equipment, that can lead to prolonged extension of downtime. In rare cases, this can also lead to the asset failure itself.
Dead Stock Accumulation
Excess materials sit in warehouse indefinitely, tying up working capital and warehouse space. Some parts reach obsolescence before they are ever used.
Critical Stockout
A required spare is unavailable when an asset fails. Downtime extends until the part can be sourced, expedited, and delivered — often at premium freight cost.
Where EAM and CMMS Systems Fall Short
Most EAM and CMMS platforms (SAP PM, IBM Maximo, Infor EAM) use Min/Max or Reorder Point (ROP) logic as the default replenishment method. These rules are often set once at implementation and rarely revisited, even as failure rates and production volumes change over time.
The result: stocking parameters that were calibrated for 2018 production volumes are still driving 2025 purchasing decisions, with no automated mechanism to flag that the rules have drifted from operational reality.
Criticality Analysis
One of the main challenges that a mining inventory management solution aims to tackle is “Minimizing Downtime”
~30%
McKinsey, 2022
Average share of maintenance budget lost to excess/obsolete inventory
20–35%
Deloitte Mining, 2023
Average unplanned downtime for large mining operations per year
140 hrs
ABB / WEF, 2021
Of that downtime, hours attributable to MRO / parts availability
~42 hrs
ABB
When Production Stops, Costs Don’t
In industrial environments like mining, asset failure is common, and critical asset failure is costly. A critical asset failure can easily result in a total halt in operations of the entire production facility.
This means that all the fixed and operational costs linked to that mining facility continue to stack on – with 0 throughput, directly impacting the bottom line.
Operation type | Cost / hour | Reference | Basis |
Large copper mine | $200K–$300K | Oliver Wyman, 2022 | 100Ktpa+ operation |
Large open-cut coal | $100K–$180K | Accenture, 2021 | Thermal / coking combined |
Gold mine (mid-tier) | $250K–$400K | EY Mining, 2023 | At $1,900/oz gold price |
Iron ore port terminal | $400K–$600K | Rio Tinto / BHP filings | Bulk export terminal |
Lithium / battery mineral | $150K–$250K | WEF, 2023 | Spodumene / brine ops |
This means that all the fixed and operational costs linked to that mining facility continue to stack on – with 0 throughput, directly impacting the bottom line.
This makes it imperative that maintenance teams, asset managers and reliability professionals have a clear, reliable view of which parts and assets are critical.
Criticality assessments are done both at an activo as well as a spare part level, and are typically assigned a score between 1-10, with 10 being highly critical.
This means that all the fixed and operational costs linked to that mining facility continue to stack on – with 0 throughput, directly impacting the bottom line.
This makes it imperative that maintenance teams, asset managers and reliability professionals have a clear, reliable view of which parts and assets are critical.
Criticality assessments are done both at an activo as well as a spare part level, and are typically assigned a score between 1-10, with 10 being highly critical.
They are weighed based on consequences and risks like;
The limitations with this approach;
Static scoring
It doesn't account for dynamic factors like supplier lead time changes or parts interoperability.
Expert Dependency
The process is time-consuming and relies heavily on one team's knowledge, often without cross-checking.
Obsolescence Blind Spots
Parts approaching end-of-life or at procurement risk are rarely flagged in time.
Why Supplier Lead Time Changes Everything
A part scored as “Criticality 6” with a 2-week lead time carries very different risk than the same part with a 26-week lead time from an overseas OEM. Static criticality models rarely capture this distinction. Below are typical lead time ranges that should feed directly into stocking decisions:
| Part Category | Typical Lead Time | Nivel de riesgo | Recommended Stocking Strategy |
|---|---|---|---|
| Consumables (filters, belts) | 1 to 4 weeks | Bajo | Min/Max with local supplier agreement |
| Standard mechanical parts | 4 to 12 weeks | Moderate | Reorder Point (ROP) with safety stock buffer |
| OEM-specific components | 12 to 26 weeks | Alta | Hold on-site; review annually vs. criticality score |
| Long-lead capital spares | 26 to 52+ weeks | Crítica | Insurance spares; mandatory stock regardless of cost |
Mantenimiento predictivo
Advances in predictive maintenance and the ability to interpret Sensor Data has equipped asset management and maintenance teams with insights as to which assets are likely to fail and for what reason.
This is generally done to prioritize maintenance tasks before they happen.
However, this data rarely feeds into the inventory strategy, even though it easily can.
For example: If a machine, during its daily usage, heats beyond a certain threshold temperature, it is likely that the heating is caused due to a specific part failure.
Overheating of this specific machine is caused due to failure of only 2 parts in the asset.
This data, when fed to the Inventory Management solution, will add this new information into the “consumption requirement” data.
How Sensor Data Should Flow Into Inventory Strategy
The Goal
The objective of inventory management in Mining Operations is to simply ensure;
Primary Goal
Minimizing Downtime, especially caused due to "Unavailability of Spares"
Secondary Goal
Killing Excess Dead Stock
Run of Mine & Bulk Inventory
These are inventories pertaining to “product” itself, across various stages of its lifecycle.
Run-of-Mine or the “Raw Ore” stockpile itself that has been extracted from the earth but not processed for the final product
The final product, is the Copper Concentrate or the Gold Bar itself that are waiting to be shipped to final customer or an intermediary for further processing.
Maintaining production and bulk inventory like stockpiles and Run-of-Mine ore is less about logistical and supply chain difficulties and more about Blending and Buffer Management.
The goal is to ensure the processing plant (mills) receives a consistent feed despite the inherent variability of the earth.
The most critical strategy is Blending for Consistency. The mills are calibrated for a specific grade and hardness of ore. If the feed varies too wildly, the chemical recovery process becomes inefficient, leading to millions in lost minerals.
To manage this, miners maintain multiple stockpiles of different grades (High, Medium & Low), creating a “designer feed” that keeps the plant running at peak recovery rates.
Another essential approach is Buffer Management, often referred to as the “Lungs” of the operation. Mining and processing operate at different cadences and it is important to decouple the two to ensure independent continuity in operations.
So a truck might break down in the pit, or a crusher might go offline for maintenance. Strategic stockpiles are positioned between these stages to decouple them.
This ensures that a two-hour stoppage in the mine doesn’t lead to Mill-downtime, and vice versa, a mill stoppage doesn’t force the mine to stop digging.
Grade Segregation and Stockpile Rotation are used to prevent “ore loss” or “dilution.” High-value ore is never mixed with waste or marginal material unless intentionally blended.
Managers also use a “First-In, First-Out” (FIFO) approach for certain ores that might degrade or oxidize when exposed to air, which can interfere with the flotation or leaching process if left sitting too long.
Blending for Consistency
Multiple stockpiles of different grades are maintained and blended to create a consistent "designer feed" that keeps the plant running at peak recovery rates.
Buffer Management
Strategic stockpiles positioned between stages decouple mining and processing operations, protecting each from stoppages in the other.
Grade Segregation & Rotation
High-value ore is never mixed with waste or marginal material unless intentionally blended, preventing ore loss and dilution.
FIFO Approach
Certain ores that degrade or oxidize when exposed to air are moved on a First-In, First-Out basis to prevent interference with flotation or leaching.
Approach to Inventory Management
Managing inventories for the Stockpile is more of a “Science” based problem, from a value perspective, the approach shifts towards inventory reconciliation.
Because bulk piles are massive and irregular, traditional counting is impossible. Modern operations use drones equipped with LiDAR or photogrammetry to create 3D models of stockpiles.
This provides a highly accurate volume calculation which, when cross-referenced with lab assays of the ore grade, allows the company to report the true “stored value” of the inventory to stakeholders and tax authorities.
| Method | Volume Accuracy | Survey Time | Cost per Survey |
|---|---|---|---|
| Manual / Visual Estimate | ±15 to 25% | Days | Bajo |
| Traditional Ground Survey | ±5 to 10% | Hours to Days | Moderate |
| Drone with Photogrammetry | ±1 to 3% | 30 to 90 minutes | Low to Moderate |
| Drone with LiDAR | ±0.5 to 1% | 30 to 60 minutes | Moderate to High |
Mine-to-Mill Optimization is the overarching framework that ties this together. It treats the entire flow – from the blast in the pit to the finished product – as a single integrated system.
By adjusting the size of the rock during blasting (fragmentation), the mine can reduce the energy needed for crushing later, essentially using the inventory stages to optimize total energy costs and throughput.
Mine-to- Mill Systems
The Three Core M2M Technologies
Blast Design & Execution Systems
These tools manage the “first stage of crushing.” In M2M, blasting isn’t just about moving rock; it’s about pre-conditioning it for the mill.
Operational Logic: Software calculates the optimal explosive energy distribution based on rock hardness. It uses high-precision GPS on drill rigs to ensure every hole is placed exactly where the model dictates.
Fragmentation Analysis Systems
These are the “sensors” of the M2M chain. They measure the success of the blast.
Operational Logic: Using high-speed cameras or LiDAR installed on shovels or conveyor belts, the software uses edge-detection algorithms to measure the dimensions of every rock in real-time.
Geometallurgical Modeling (The "Block Model")
This is the foundational database. It doesn’t just record where the ore is, but how it will behave in the plant.
Operational Logic: Software layers metallurgical data (bond work index, hardness, mineralogy) onto the 3D mine map.
Key Players and Their Platforms
| Empresa | Key Platform / Software | Primary Focus |
|---|---|---|
| Orica | BlastIQ | The global leader in blasting. Their software focuses on digital blast optimization to ensure consistent fragmentation that reduces mill “choke” events. |
| Hexagon Mining | Mine-to-Process (M2P) | They own Split Engineering, the industry standard for fragmentation analysis. Their suite links mine planning directly to processing sensors. |
| Metso Outotec | Geminex | Focuses on the “Mill” end. They provide Advanced Process Control (APC) that adjusts grinding circuits based on the incoming ore data received from the mine. |
| Dassault Systèmes | GEOVIA (Surpac/MineSched) | Specialized in the 3D modeling and short-term scheduling that ensures the right “blend” of ore is delivered to the plant at the right time. |
| RPMGlobal | XECUTE | An ultra-short-term planning tool. It manages the “live” schedule of trucks and shovels to ensure the plant never runs out of feed (Buffer Management). |
| Maptek | BlastLogic | A high-performance blast accuracy system that reconciles “as-designed” blasts vs. “as-built” blasts to identify why fragmentation might be failing. |
How They Operate: The "Feedback Loop"
These systems typically operate in a continuous cycle across three departments:
The Pit (Drill & Blast)
Using Orica or Maptek, engineers design a blast. Sensors on the drill rigs (Measure While Drilling - MWD) detect that the rock is harder than the original geological model suggested. The software adjusts the explosive load in real-time.
The Conveyor (Fragmentation)
As the ore moves toward the plant, Split-Engineering cameras scan the rocks. It detects that the "fines" (small particles) are lower than expected. This data is instantly pushed to the Plant Manager.
The Mill (Process Control)
The system receives the "low fines" alert. Because the mill will have to work harder to grind the larger rocks, the software automatically slows the feed rate and adjusts the ball mill's power consumption to prevent a grind-out or circuit overload.
The Bottom Line
Large companies like Rio Tinto or BHP don’t use these as standalone tools. They integrate them into an Integrated Operations Center (IOC).
The software serves as a single source of truth, ensuring that the Mine Manager isn’t being rewarded for “cheap blasting” if that cheap blasting is causing the Mill Manager to spend double on electricity and liner wear.
