Integrating Stochastic Mine Planning Model with ARDL Commodity Price Forecasting

Authors

Lawrence Madziwa, Namibia University of Science and Technology
Mallikarjun Pillalamarry, Namibia University of Science and Technology
Snehamoy Chatterjee, Michigan Technological UniversityFollow

Document Type

Article

Publication Date

8-2023

Department

Department of Geological and Mining Engineering and Sciences

Abstract

Commodity price is of paramount importance to mine planning among other stochastic factors such as mining cost and geological uncertainties. Commodity price uncertainty is critical in the determining block values, and it has been incorporated into mine planning by simulation-based methodologies to deal with the effects of price uncertainty during the mining process. In this article, stochastic optimisation was undertaken using parametric minimum cut network flow algorithm for ultimate pit and pushback design, while stochasticity of commodity prices was incorporated into the planning using Autoregressive Distributed Lag (ARDL) simulation model. The push back design and cashflows in two scenarios, namely when the prices are going up and when the prices are going down, were investigated. Moreover, for each scenario, three types of price inputs were considered: (a) actual prices; (b) ARDL forecasted prices; and (c) stochastic prices generated from ARDL forecasted prices. The results of cashflows indicated that when commodity price moving up scenario, the ARDL forecasted prices underestimate the NPV by about 17% and the stochastic minimum cut underestimates the NPV by 72.27% compared to actual price. Similarly, for the price declining scenario, the ARDL forecasted prices underestimated the NPV by about 3% compared to the actual, while the stochastic minimum cut has an NPV value that is 277% of the actual value. Lastly the value of stochastic programming (VSP) was evaluated. The VSP was about 9.2 million dollars when the prices were going up, while the VSP was about 27.1 million dollars, when price was declining. Moreover, declining commodity price produced negative cashflows which indicate need for risk management with a recourse action to evert losses.

Publisher's Statement

© 2023 Elsevier Ltd. All rights reserved.

Publication Title

Resources Policy

Recommended Citation

Madziwa, L., Pillalamarry, M., & Chatterjee, S. (2023). Integrating Stochastic Mine Planning Model with ARDL Commodity Price Forecasting. Resources Policy, 85(Part B), 104014. http://doi.org/10.1016/j.resourpol.2023.104014
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/809