Cash flow modelling during project evaluation is enhanced when probabilistic methods are applied to the inputs. One of the important inputs is the quarterly production profile. Typically, the steady-state production profile provided to project evaluation practitioners is deterministic, with each quarter the same as every other quarter. This is unrealistic as actual production profiles from existing mines exhibit a range or quarter-on-quarter variance. Monte Carlo stochastic simulation of the production profile can be used to provide a range of possible outcomes, but how are these ranges selected? This paper describes the use of quarterly production variance from a large database of mines worldwide as a benchmark to inform the appropriate selection of ranges. The coefficient of variation is used to rank the production from mines, with quartile analysis applied to indicate industry best, median and worst variation. Due diligence of existing mines and evaluation of future production performance can be quantitatively assessed using this approach. Examples of the effect on value are provided._x000D_
CITATION: Stewart, C A, 2016. Production variance benchmarks and their use in project evaluation and due diligence, in Proceedings Project Evaluation 2016, pp 201-213 (The Australasian Institute of Mining and Metallurgy: Melbourne).