2
underground mines, even under changing conditions. In
addition to the flexible parameterization of all input vari-
ables, the tool allows for quick and easy comparison of dif-
ferent scenarios to optimize planning.
Literature Review—State of the Art in Underground
Mine Design and Cost Estimation
Mine planning, and consequently cost estimation, follows
the lifecycle of a mine: from exploration through various
production phases to closure and decommissioning. There
is no standardized procedure for the specific approach to
mine planning. Based on common formulas and applica-
ble regulations, each external consultant or internal plan-
ner applies their own system. However, different stages of
mine planning, which correspond to a range of estimation
accuracies, are universally distinguished, as illustrated in
Figure 1.
An extensive literature review and a survey of mining
operators and service providers conducting mine planning
in Germany and globally revealed that no standardized
approach exists. [2–6] Depending on the planning stage,
initial cost estimates are typically based on values from
cost models or benchmarks from other operations (e.g.,
specific costs per meter of drift development). From the
(pre-)feasibility study stage onwards, more precise calcula-
tions are conducted based on the summation of material,
operational, and capital costs. This approach follows the
technological chain of the extraction process (see Figure 2),
along with auxiliary (e.g., mine ventilation) and second-
ary processes (e.g., energy supply). Each process is broken
down into its smallest sub-processes, and costs are incre-
mentally aggregated, a method known as “First Principle
Estimating” [7]. This requires the planner to possess exten-
sive knowledge of mining processes, which is why such
planning is generally carried out by mining engineers. The
accuracy of these estimates depends on the quality and cur-
rency of the individual cost data. For these detailed calcu-
lations, companies, depending on their size, typically use
Microsoft Excel templates tailored to each project. The
research showed that in all cases, planners manually con-
solidate the results of various calculations. Any change in
external conditions generally necessitates a complete recal-
culation of all processes. Scenario comparisons are only
performed if explicitly requested by the client. [8 pp. 8–25]
A literature review and a survey of mining planners
were also conducted regarding the availability of cost data.
It was found that there is only a very limited number of reli-
able, up-to-date costs available for the calculation of under-
ground mines. For the early planning stages, various cost
models can be referenced, as illustrated in Table 1. The data
foundation of these models varies, but generally, only a few
mining operations—often limited to one country—pro-
vide data that can be up to three decades old as the basis for
these models. Furthermore, only a few specific parameters
Description of the planning stages
Accuracy class Accuracy range for
cost calculation
Project Approval and Execution
Class 1 ± 3-10 %
Bankable Study
Class 2 ± 5-10 %
Feasibility Study
Class 3 ± 10-15 %
Pre-Feasibility Study
Class 4 ± 20-25 %
Scoping Study
Class 5 ±30-35 %
Pre-Scoping Study
-±35-100 %
Figure 1. Mine planning stages (adapted from [1 pp. 7–24])
Figure 2. Technological chain of the extraction process with
Drilling and Blasting
underground mines, even under changing conditions. In
addition to the flexible parameterization of all input vari-
ables, the tool allows for quick and easy comparison of dif-
ferent scenarios to optimize planning.
Literature Review—State of the Art in Underground
Mine Design and Cost Estimation
Mine planning, and consequently cost estimation, follows
the lifecycle of a mine: from exploration through various
production phases to closure and decommissioning. There
is no standardized procedure for the specific approach to
mine planning. Based on common formulas and applica-
ble regulations, each external consultant or internal plan-
ner applies their own system. However, different stages of
mine planning, which correspond to a range of estimation
accuracies, are universally distinguished, as illustrated in
Figure 1.
An extensive literature review and a survey of mining
operators and service providers conducting mine planning
in Germany and globally revealed that no standardized
approach exists. [2–6] Depending on the planning stage,
initial cost estimates are typically based on values from
cost models or benchmarks from other operations (e.g.,
specific costs per meter of drift development). From the
(pre-)feasibility study stage onwards, more precise calcula-
tions are conducted based on the summation of material,
operational, and capital costs. This approach follows the
technological chain of the extraction process (see Figure 2),
along with auxiliary (e.g., mine ventilation) and second-
ary processes (e.g., energy supply). Each process is broken
down into its smallest sub-processes, and costs are incre-
mentally aggregated, a method known as “First Principle
Estimating” [7]. This requires the planner to possess exten-
sive knowledge of mining processes, which is why such
planning is generally carried out by mining engineers. The
accuracy of these estimates depends on the quality and cur-
rency of the individual cost data. For these detailed calcu-
lations, companies, depending on their size, typically use
Microsoft Excel templates tailored to each project. The
research showed that in all cases, planners manually con-
solidate the results of various calculations. Any change in
external conditions generally necessitates a complete recal-
culation of all processes. Scenario comparisons are only
performed if explicitly requested by the client. [8 pp. 8–25]
A literature review and a survey of mining planners
were also conducted regarding the availability of cost data.
It was found that there is only a very limited number of reli-
able, up-to-date costs available for the calculation of under-
ground mines. For the early planning stages, various cost
models can be referenced, as illustrated in Table 1. The data
foundation of these models varies, but generally, only a few
mining operations—often limited to one country—pro-
vide data that can be up to three decades old as the basis for
these models. Furthermore, only a few specific parameters
Description of the planning stages
Accuracy class Accuracy range for
cost calculation
Project Approval and Execution
Class 1 ± 3-10 %
Bankable Study
Class 2 ± 5-10 %
Feasibility Study
Class 3 ± 10-15 %
Pre-Feasibility Study
Class 4 ± 20-25 %
Scoping Study
Class 5 ±30-35 %
Pre-Scoping Study
-±35-100 %
Figure 1. Mine planning stages (adapted from [1 pp. 7–24])
Figure 2. Technological chain of the extraction process with
Drilling and Blasting