Strategic Iron Ore Storage Using a Fuzzy Mean-Reverting Real Options Framework: A Case Study of Gol Gohar

Abstract

Mining projects operate under significant uncertainty, driven by volatile commodity prices, fluctuating costs, and long investment horizons. Traditional valuation approaches, such as discounted cash flow (DCF), often fail to capture the value of managerial flexibility under these uncertain conditions. To address this limitation, this study develops a real-options-based framework to evaluate the strategic decision of storing iron ore rather than selling it immediately. The storage decision is modeled as an American call option, where the underlying asset is the spot price of iron ore and the exercise price represents storage cost. To better reflect actual market behavior, iron ore prices are modeled using a mean-reverting process instead of the commonly used geometric Brownian motion. In addition, key parameters; including volatility, mean reversion rate, and storage cost, is treated as triangular fuzzy numbers to account for epistemic uncertainty and limited data availability. A modified binomial lattice is then constructed to integrate both mean reversion and fuzzy parameter propagation. The proposed framework is applied to the Gol Gohar iron ore mine as a case study. Results show that storage becomes economically attractive when the spot price falls below 92 USD per ton. The maximum option value, estimated at 11.6 USD per ton, occurs when prices are significantly below the long-run mean. Sensitivity analysis indicates that volatility and storage cost have the strongest influence on the option value, while mean reversion plays a moderate but meaningful role. Overall, the study provides a practical and robust decision-support tool for mining managers. By incorporating both stochastic dynamics and fuzzy uncertainty, the model offers a more realistic basis for strategic decision-making in volatile commodity markets.