In our blog, we have already brought up some insights on how a mathematical model can assist many decisions related to the planning of a mining company. Among the many benefits, such as minimizing penalties and anticipating demand peaks or maintenance, today we want to highlight the opportunity of using this kind of tool to better select selling contracts and increase profit.
It is common in the mining industry to operate at a lower risk (process-wise and delivery-wise), selling a single product of average quality. However, there is variability in the ROM quality, which can generate iron ores with different chemical and physical properties. With an appropriate planning tool, and the possibility to separate the products by different qualities, a door is opened to an actual optimization opportunity in the selection of contracts.
Salesforce teams usually have limited information on the operational challenges faced during production while the industrial engineering team has low access to contractually defined product prices. That incoordination regarding what type of product is more suitable can be eliminated by applying the model as a mediator.
Considering mid-term planning, the company might face moments where a contract is about to end, and the decision on whether to renew it or to sign a new one comes up. In this kind of situation, with mid to long planning horizons, operational decisions are not considered. Therefore, it is reasonable to assume the consumption of ROM types predicted by the mine planning during that period.
To demonstrate this decision, we use our established example with a generic iron mine that has three different contracts: LQ (lower quality), MQ (medium quality), and HQ (high quality), as shown in table 1. Considering this scenario, we will define that the LQ contract finishes at the end of month 6, and the MQ contract finishes at the end of month 9. The expiring contracts can be renewed (LQ-B and MQ-B) or two new contracts can be signed, one with higher quality (HQ-B) and one with lower quality (LQ-C), presented in table 2. The new contracts have slightly different specifications, as well as different pricing profiles. All contracts up to debate have no minimum demand.
As a result, the model suggests the renewal of the LQ contract (LQ-B), as well as the execution of the alternative HQ contract (HQ-B) and LQ contract (LQ-C), and not to renew the MQ contract. In other words, the outcome of separating the products between lower and higher quality is higher profit when compared to the sale of a medium-quality product. In our simulation, the annual profit increased by 2.12% compared to the case with automatic renewal and no alternative contracts, showing that the blend of the products aiming to achieve average quality was destroying value.
This kind of decision, also known as client portfolio selection, is crucial to keep the competitiveness and profitability of a mining company. Using such mathematical model as a decision-support tool will result in more accurate decisions and yield higher profits!
Author: Cassiano Lima - Senior Consultant at Cassotis Consulting
Co-authors: Fabio Silva - Senior Manager at Cassotis Consulting
Emmanuel Marchal - Managing Partner at Cassotis Consulting
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