A Fresh Take on All-in Sustaining Costs (AISC)

Tarrant Elkington, General Manager and Executive Consultant at Snowden Optiro explores the evolving role of All-in Sustaining Costs (AISC) in mining and proposes a refined approach to improve cost transparency and benchmarking.

In the dynamic world of mining, understanding cost structures is paramount. The All-in Sustaining Cost (AISC) metric has long been the industry standard, offering insights into the expenses associated with mineral production. However, as the industry evolves, so must our tools for evaluation. It’s time to rethink AISC to ensure it reflects the true financial health of mining operations.

Overview of All-in Sustaining Costs (AISC)

Introduced by the World Gold Council in 2013, AISC was designed to provide a more comprehensive and standardised measure of production costs in the gold industry and then more broadly adopted within the mining industry. This metric goes beyond the basic “cash costs” of production to include expenses that sustain a mining operation over the long term.

What does AISC include?

AISC covers a wide range of costs:

• Exploration and study costs (sustaining): Expenditures necessary to sustain current operations, not for expansion.
• Direct production costs: Labour, energy, consumables, and royalties.
• Sustaining capital expenditures: Investments required to maintain production levels, such as equipment replacements and mine development
• Administrative costs: Corporate overheads related to running the business.
• Environmental and closure costs: Reclamation and mine closure provisions.

Greater detail is shown in Table 1.

Source: World Gold Council (2018)

By providing a more holistic view of operating costs, AISC helps investors and stakeholders assess the long-term profitability and sustainability of mining operations. However, as with any metric, it is not without its challenges.

The traditional AISC curve: A quick overview

Traditionally, AISC curves plot individual mines or companies along the x-axis, ranked from the lowest to highest cost, with the cost per ounce of production on the y-axis. An example, for Australia/New Zealand gold project is shown in Figure 1.

Source: Aurum Analytics (2024)

This arrangement provides a snapshot of the industry’s cost distribution, highlighting which operations are more cost-effective and which are less so. The curve is often divided into quartiles:

• Lowest quartile: Mines here are the most cost-efficient.
• Top quartile: These operations have higher production costs and may be more vulnerable if metal prices decline.

While this model has been instrumental in benchmarking and strategic decision-making, it has its limitations—most notably in its treatment of by-product credits.

The by-product credit conundrum

As often observed on the Money of Mine podcast, a significant challenge with the current AISC calculation is its treatment of by-product credits. According to the World Gold Council, by-product revenues (e.g., copper or silver generated from a gold mine) are deducted from the overall production costs of the primary commodity. While this can accurately reflect the cash costs for some operations, it often leads to distorted results.

In some cases, negative AISC values are reported, where by-product revenues exceed the total costs associated with the primary metal. While this may sound appealing, it can obscure the real cost structure and operational efficiency of a mining operation.

A real-world illustration

Consider a mining operation with the following annual production and financials. The inputs are shown in Table 2.

Using the traditional AISC formula, we subtract the by-product revenue (copper) from the total sustaining costs:

• AISC (including by-products): $250 million (gold costs) – $300 million (copper revenue) = -$50 million
• AISC per ounce of gold: -$50 million / 200,000 ounces = -$250 per ounce

In this scenario, substantial by-product revenues not only cover all gold production costs but also create a “negative” cost per ounce of gold. Mines with similar characteristics include Cadia (Australia), Ernest Henry (Australia), and Norilsk Nickel (Russia). While these results are technically accurate, they fail to provide a clear understanding of a mine’s operational efficiency.

A new perspective: AISC as a percentage of total revenue

To better understand a mine’s financial health, I propose a revised metric:

• AISC (excluding by-product credits) divided by total revenue (including all by-product credits).

This approach provides a more transparent view of how much of a mine’s revenue is consumed by sustaining costs, offering a percentage that reflects operational efficiency.

Applying the new metric to our example:

• Total revenue: $400 million (gold) + $300 million (copper) = $700 million
• AISC percentage: $250 million / $700 million ≈ 35.7%

This indicates that approximately 35.7% of the total revenue is allocated to sustaining costs. To express this as a per-ounce cost for gold:

• AISC per ounce of gold: 35.7% * $2,000/ounce = $714 per ounce

Or alternatively if reporting against copper peers:

• AISC per tonne of copper: 35.7% * $10,000/ounce = $3,570 per tonne

This method avoids the pitfalls of negative AISC values and gives a clearer picture of the operation’s cost structure.

How to represent production on AISC curves?

Implementing this revised metric raises a question about how to measure production on the x-axis of AISC curves. Two potential options are:

1. Equivalent production basis: Convert all production to an equivalent basis, such as gold equivalent ounces (GEOs). For example: GEO= Gold revenue / total revenue x gold ounces produced. This method accounts for multi-commodity operations in a single unit of production.
2. Revenue as the measure of production: Use total revenue directly as the production metric for the x-axis. This simplifies the comparison by avoiding conversion factors and is commodity-agnostic.

My preferred option: I favour using revenue as the measure of production. It simplifies the comparison process, allows for cross-commodity benchmarking, and provides a transparent representation of an operation’s scale and financial performance.

Figure 2 shows an example of the proposed approach.

Advantages of the proposed approach:

• Enhanced transparency: By excluding by-product credits, this metric provides a clearer view of actual costs, eliminating distortions caused by dominant by-products.
• Improved comparability: Standardising the metric across different commodities facilitates meaningful benchmarking. Additionally, projects within the same commodity with different end products, like lithium, can be compared in a fair way.
• Reflective of operational efficiency: Linking sustaining costs directly to total revenue highlights how efficiently a mine operates.
• Resilience assessment: This metric better indicates a mine’s ability to withstand price downturns.
• Commodity-agnostic benchmarking: The approach allows for cross-commodity comparisons, enabling diversified companies to present all assets within a single framework. Or all projects in a certain region could be compared, across commodities. There are a lot of applications for this.

Your thoughts?

What do you think of this proposed approach? Could this revised metric provide a clearer picture of mine efficiency and resilience? How do you feel about using revenue instead of production volume for AISC curves? I’d love to hear your thoughts. Please share your ideas in the comments or send me a message.

References

Aurum Analytics (2024). Australian & New Zealand Gold Operations: September Quarter 2024. Via LinkedIn.

World Gold Council (2018). Guidance Note on Non-GAAP metrics: All-In Sustaining Costs and All-In Costs

If you want to know more about this topic, feel free to reach out to Tarrant Elkington. You can contact him here.