HomeHow to better anticipate peaks in demand or maintenance stops in a mining company
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In our previous post about end-to-end integrated planning for a mining company, we showed how machine learning and mathematical modeling can be combined to minimize the sales penalties in iron ore deliveries. However, this is only one benefit of this kind of tool, and here we will address another advantage of using an optimization model: how a company can better anticipate peaks in demand or maintenance stops.
Even though we are all aware that they are one of the main waste of Lean Manufacturing, stocks are sometimes essential to achieve efficiency. It does not mean that a company cannot have stock, but rather that it should be used wisely. In a mining company, it has a primary function of allowing the blend of different ore qualities and dealing with ore quality variability. Besides that, it can be efficiently used in a production process as a buffer: in some moments, intentionally produce more than necessary and store it to meet a future demand or to supply during a production stop resulting from planned maintenance.
However, this buffer has to be built carefully: it should not be a blend of the production excess, but well separated per quality. Another important aspect is that it should be made at each stage of the process, and not only at the end of the production. Both these practices increase the flexibility of the company, which will be better prepared for the cases mentioned above (demand peaks and maintenance stops).
The question that remains is when the company should start storing material and, also, what material. These decisions involve many aspects:
- When will there be scheduled maintenance or a peak in the demand?
- What is the demand for this moment?
- What is my production capacity? How much does it cost to increase productivity at certain moments?
- What is my storage capacity? How much does it cost?
- What are the penalties in case of late delivery?
Considering all these variables it is clear that it is a complex decision, which can be better made by applying an optimization model as an auxiliary tool.
Using our example from the previous post, where we have a generic iron mine, we simulated one planned maintenance of one day in the last week of the third month:
Obviously, this results in lower availability of the machines for production, and the model needs to reoptimize the solution, adjusting ROM consumption and delivery planning. There was a reduction in the delivery of LQ contracts, resulting in a profit reduction of only 0.29%. To achieve such result, optimization identified as the solution to anticipate part of the production to the weeks before:
As we can see, the maintenance affected the profit of the company, which is expected behavior. However, using this kind of model it is possible to simulate and determine when it is the best moment to execute the maintenance, as well as how the production and delivery planning should be modified to better adapt to the reduction of the production capacity. This can be a key decision to improve company results!
Co-authors: Fabio Silva - Senior Manager at Cassotis Consulting
Emmanuel Marchal - Managing Partner at Cassotis Consulting