Mine planning studies of iron ore deposits are often based on resource models generated by ordinary kriging. Ordinary kriging aims for local accuracy to minimise the variance of expected estimation error but this is at the expense of reproducing variability of the original sampling data. Hence mine planning studies using kriged models are likely to underestimate product variability, and the required blending and stockpiling capacity to reduce variability to an acceptable level. Instead, conditional simulation models of the main chemical variables in iron ore are able to reproduce likely variability to be encountered during mining and processing. This approach is demonstrated in a case study of the Weld Range bedded iron ore deposit in Western Australia, with a comparison of ordinary kriging and conditional simulation models. Conditional simulation models suggest that, compared with the single ordinary kriging estimation, there is likely to be far greater block variability for iron and impurities, a greater requirement for stockpiling and blending capacity, highest variability in initial years of mining and less variability in later years of mining for deeper ore. The conditional simulations provide a more realistic range of mining outcomes for production of high-grade ore, which may be seven per cent less than in the kriged model, as well as for lower expected project net present value (NPV). In year one, the kriged model is conservative for NPV; however, in later years two to five the kriged model has higher NPV than most conditional simulations, suggesting expected NPV based on the kriged model may not be achieved. For improved decision-making in mine planning, a complete range of possible outcomes from conditional simulations of the deposit should be considered rather than relying on a single kriging estimation that may be an end member case in the range of possible mining outcomes.