Mining resource optimisation : the effect of the cost application methodology on the value of a project
De Klerk, Henri
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South Africa has vast mineral resources and the mining sector has a great impact on the gross domestic product (GDP), gross fixed capital formation (GFCF) and exports. As a result the mining sector employed 2.6% of all the workers in the non-agricultural formal sectors of the economy in 2012. Because of South Africa’s dependence on mining as a contributor to the economy, there are many studies conducted annually to determine the economic viability of proposed mines. During these studies the resource has to be optimised. Optimisation means that portions of the ore that are deemed economic for exploitation should be identified – this portion of the ore is known as the economic footprint. During the optimisation phase costs and prices are applied to the resource. The dilemma in the optimisation phase is how these costs are being applied. Research done during this study has shown that the tendency in practice is to apply benchmarked unit costs for both capital and operating expenditure. This study focusses on the application method of the variable costs during the resource optimisation phase. Time-driven activity-based costing (TDABC) was identified as an alternative to the traditional costing methodology. In context of the aforementioned the primary research objective of this study was to determine the effect of applying TDABC for the variable costs during the resource optimisation phase instead of the conventional benchmarked unit costs. During the research done for this study it has become apparent that activity-based costing (ABC) is a managerial costing tool that is more expensive than traditional costing techniques and that it is not required for external financial reporting. ABC purely is a management decision tool! It enables the manager to manage costs by modifying the activities that are used to produce a product or a service. Because of the costliness of an ABC system TDABC was introduced as an alternative to the traditional ABC system. TDABC addresses the limitations posed by ABC – it is simpler, less costly, faster to implement and applies the practical capacity of resources to calculate the costs. To satisfy the primary objective of the study, a hypothetical coal deposit was constructed in a block model. The model contains 101 million gross tonnes in situ (GTIS) that is reduced to 91 million mining tonnes in situ (MTIS) and 90 million run of mine (ROM) tonnes when the modifying factors are applied. The 90 million ROM tonnes are made up of 35 million tonnes export product, 10 million tonnes domestic product and 45 million tonnes discards. Value distribution models (VDMs) were constructed to determine the economic footprints of the resource. In total six VDMs were constructed; the variable costs that were applied to each are: VDM 01 uses TDABC principles to calculate the variable costs; VDM 02 recalculates the total costs obtained from VDM 01 to unit costs; VDM 03 recalculates the grand total cost obtained in VDM 01 to a single unit cost; VDM 04 applies Wood MacKenzie data, based on export and domestic product tonnes, for a similar mine to calculate the variable costs; VDM 05 applies Wood MacKenzie data, based on total product tonnes, for a similar mine to calculate the variable costs; VDM 06 applies benchmark data for a similar mine to calculate the variable costs. The cut-off value that was applied is zero; i.e. blocks with a value of zero and less were excluded from the economic footprint of each VDM. A production schedule was constructed for each of the six footprints that were obtained. A production schedule enables the calculation of the free cash flow which can be recalculated to a net present value (NPV) that provides a common platform to compare the different footprints. Two scenarios were tested in the financial model. The first scenario’s variable costs were based on the variable costs that were used to determine each of the VDMs and the second scenario’s costs were based, entirely, on TDABC. Therefore, twelve NPVs were obtained. In all twelve NPVs that were calculated the order of the NPVs were the reverse of the order of the discounted variable costs (DVCs); in other words a high DVC yielded a low NPV and vice versa. The results showed no correlation between the NPVs of Scenario 01 and Scenario 02. It is recommended that TDABC be applied to determine the variable costs during the resource optimisation phase. Together with this it is also recommended that various cut-off values are applied during the optimisation phase so that multiple footprints’ NPVs can be obtained so that the most valuable footprint will come to the fore.