Abstract:
Cost estimation for surface coal mines is a critical practice that affects both
profitability and competitiveness. New mines require these costs to be
estimated using available information before a project begins. The
competitive advantage of a new mine depends on it being both efficient
and cost-effective. Low-cost producing mines have a higher chance of
survival in a low-price environment than do high-cost producers. The
competitiveness and profitability of a coal mine is based on the costs of
production and the supply position on the cost curve. There is no single
method of cost estimation, and the available methods consider only one or
a few variables, leaving out multiple variables that could significantly
affect the estimation of mine costs. Mining companies are thus searching
extensively for a method that will increase accuracy in the estimation and
evaluation of mining projects
This paper highlights the shortcomings of the available approaches
and proposes a data envelopment analysis method to develop a frontier for
effective surface coal mines, and the use of a parametric method for
modelling the costs and productivity of new mines to ensure effective
competitiveness. The models will extend the capability of estimation and
the accuracy of estimates using the efficient decision-making units, by
considering the optimal mine-specific and external variables affecting
costs.
