Defining the “Right Problem” Matters
Product Price Function Model Example Using BlendOpt
In a mine planning project, complex pricing models are typically substituted with a product price that tracks an index. This might be justified by a lack of information as to how product blends will deviate from quality specifications or based on an assumption that product quality attributes will have relatively little influence on product price. Production plans created by marketing teams in this way implicitly assume price penalties from product quality will not significantly influence what products should be sold.
Figure 2. Cumulative product tonnes in optimised plans by BlendOpt where product price is
based on an index (bottom panel) or adjusted by a quality-based price model (top panel).
The above bar chart (Figure 2) shows obfuscated results from a project where product planning first took place in BlendOpt using only an index price. This is compared to plans where optimisation involved the modelling of prices with adjustments based on quality attributes including ash, moisture, calorific value, and sulphur. While the profit implications were significant (> 4% increase in revenue, results not shown), more surprising was the extent that the product mix differed between scenarios. In particular, the results in this bar chart and Figure 3 display greater than 50% reductions in the amount of low ash thermal coal sold (see “TH 6%” in Figure 2) and a 9.6% increase in metallurgical coal sold when product price function models are accounted for during optimisation. These non negligible financial and product differences may suggest a potential gap within the market planning exercises undertaken by some coal producers.
Figure 3. Integrated production plan schedule from BlendOpt Analytics with product price based on an
index (bottom panel) or adjusted by a quality-based price model (top panel).
Product Cost Model Example Using BlendOpt
An accurate model of upstream and downstream operating costs can influence the relative financial benefits that are measured between plan alternatives. In one project, BlendOpt was configured using a cost model that accounted only for cost differences between washing and bypassing coal. BlendOpt was configured in this way because it was assumed other costs would not influence the optimality of coal processing and product planning decisions. Later, the same client configured BlendOpt with a more detailed cost model including mining, rail, port, and other costs that resulted in a total modelled cost that was comparable in magnitude to sales prices. Mathematically optimised production plans displayed substantive material differences across the two scenarios (results not shown). Optimisation with the detailed cost model resulted in plans where metallurgical coals represented over half of all coal sold. In contrast, optimisation with only a CHPP cost model indicated optimal plans would create less than 20% metallurgical coal by weight. The difference between these scenarios was surprising to the client and the origins of this unexpected result were ultimately attributed to: 1) historically unprecedented price compression within the market and 2) an unexpected modelling effect discussed below.
Figure 4. Influence of downstream costs on profits of two products.
The chart in Figure 4 attempts to explain how costs that are upstream and downstream of the CHPP can have different consequences to plan optimality. In the illustrative example in Figure 4, two products are created from a single seam with a 20% higher processing yield associated with the thermal product. As more post-CHPP costs are added to the cost model, this has a disproportionate cost impact on products created from coal processed at higher yields. For instance, a high ash thermal coal is likely to have its total cost increase more than a coking coal as costs downstream of the CHPP increase due to the thermal coal tending to be composed of more unwashed or high yield clean coal. The implications of this basic finding are significant: modelling an accurate balance of upstream costs (which are based on ROM tonnes) and downstream costs (which are based on product tonnes) will influence financial comparisons between plan alternatives and thereby affect the composition of a mathematically optimised plan. Plans that do not accurately account for these costs may deviate considerably from the financially optimal plan.
Challenge Pre-Existing Assumptions About Planning