Better sampling, preparation, and analysis (SPA) can improve the precision of results for resource grades and commodity trading. What we are often asked by customers is how they might quantify these improvements in terms of economic benefits.

One way to do this is by applying the SPA precision of your results to the selling price of the resource. In this paper, a deeper look into resource pricing methodology for iron ore will be undertaken and how improved precision, on the quantification of the critical elements in this product, can affect the selling/purchase price of this commodity.

Using a real-world example, this paper will show the results of a basic business case study, investigating the return on investment (ROI) for a Sampling Improvement Project (SIP) including a well-designed sampling, sample transport, sampler preparation and analysis facility. The investigation will include the estimated total cost of the SIP, from the problem statement to implementation, together with an estimate on operational costs. This is then compared to the potential profit gains that the SIP could provide.

Primary focus will be on structures/methods used to determine commodity prices, how the measurement of the concentration of the critical elements link to pricing, and how variations in measured vs actual concentrations effect the final price. Also considered will be the economic benefits of faster more reliable data collection as well as improved quality moisture measurement.