Strategic Integration of Real Options for Enhanced Valuation and Optimization in Mining Project Planning under Uncertainty: A Comprehensive Review
DOI:
https://doi.org/10.47672/ijpm.2004Keywords:
Mining, Project Evaluation, Real Options, Planning Models, Project Analysis, UncertaintyAbstract
Purpose: In the contemporary landscape of mining projects, the imperative to navigate through periods of uncertainty has driven the exploration of alternative strategic tools rooted in project flexibility. Real options have emerged as a pivotal strategic approach, offering the means to adapt and refine mining projects under unpredictable conditions
Materials and Methods: This paper provides a comprehensive review and discussion on the strategic application of real options for optimizing mining project planning in the face of uncertainty. Organized into four sections, the paper begins with a general introduction to real options in Section one, delving into their strategic and technical classifications. Section two critically examines and reviews the indispensable role of real options in the realms of mining investment and project planning. The third section is dedicated to an in-depth discussion of the strategic tools inherent in real options, specifically focusing on their valuation and optimization aspects within mining project planning. The final section provides a discussion and conclusion on the strategic application of real options for optimizing mining project planning under uncertainty.
Findings: The review identifies that real options offer valuable strategic flexibility in addressing uncertainties inherent in mining project planning. Through a detailed examination of their application, it becomes evident that real options can enhance decision-making processes and improve project outcomes by allowing for adaptive responses to changing conditions.
Implications to Theory, Practice, and Policy: The strategic integration of real options into mining project planning presents significant implications for theory, practice, and policy. Theoretical implications include advancing understanding of decision-making under uncertainty and the role of flexibility in strategic planning. In practice, the adoption of real options can lead to improved project outcomes, increased resilience to market fluctuations, and enhanced risk management strategies. From a policy perspective, recognizing the value of real options may inform regulatory frameworks and promote the adoption of flexible planning approaches within the mining industry, ultimately contributing to sustainable development and resource management.
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References
Abdel Sabour, S. A., & Poulin, R. (2006). Valuing real capital investments using the least-squares Monte Carlo method. Engineering Economist. https://doi.org/10.1080/00137910600705210
Adelman, M. A., & Watkins, G. C. (1995). Reserve Asset Values and the Hotelling Valuation Principle: Further Evidence. Southern Economic Journal. https://doi.org/10.2307/1060989
Adner, R., & Levinthal, D. A. (2004). What is not a real option: Considering boundaries for the application of real options to business strategy. In Academy of Management Review. https://doi.org/10.5465/AMR.2004.11851715
Ajak, A. D., & Topal, E. (2015). Real option in action: An example of flexible decision making at a mine operational level. Resources Policy. https://doi.org/10.1016/j.resourpol.2015.04.001
Ajak, A. D., Lilford, E., & Topal, E. (2018). Application of predictive data mining to create mine plan flexibility in the face of geological uncertainty. Resources Policy. https://doi.org/10.1016/j.resourpol.2017.10.016
Ajak, A. D., Lilford, E., & Topal, E. (2019). Real Option Identification Framework for Mine Operational Decision-Making. Natural Resources Research. https://doi.org/10.1007/s11053-018-9393-4
Akbari, A. D., Osanloo, M., & Shirazi, M. A. (2009). Minable reserve estimation while determining ultimate pit limits (UPL) under price uncertainty by real option approach (ROA). Archives of Mining Sciences.
Aminul Haque, M., Topal, E., & Lilford, E. (2016). Estimation of Mining Project Values Through Real Option Valuation Using a Combination of Hedging Strategy and a Mean Reversion Commodity Price. Natural Resources Research. https://doi.org/10.1007/s11053-016-9294-3
Amirshenava, S., & Osanloo, M. (2018). Mine closure risk management: An integration of 3D risk model and MCDM techniques. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2018.01.186
Anonymous. (2018). GDP growth (Annual %) - China. The World Bank.
Ardian, A., & Kumral, M. (2020). Incorporating stochastic correlations into mining project evaluation using the Jacobi process. Resources Policy. https://doi.org/10.1016/j.resourpol.2019.101558
Armstrong, M., Langren, N., Petter, R., Chen, W., & Petter, C. (2019). Accounting for tailings dam failures in the valuation of mining projects. Resources Policy. https://doi.org/10.1016/j.resourpol.2019.101461
Asad, M. W. A., & Dimitrakopoulos, R. (2012). Performance evaluation of a new stochastic network flow approach to optimal open pit mine design-application at a gold mine. Journal of the Southern African Institute of Mining and Metallurgy.
Atari, S., Bakkar, Y., Olaniyi, E. O., & Prause, G. (2019). Real options analysis of abatement investments for sulphur emission control areas compliance. Entrepreneurship and Sustainability Issues. https://doi.org/10.9770/jesi.2019.6.3(1)
Barbu, A., & Zhu, S. C. (2020). Monte carlo methods. In Monte Carlo Methods. https://doi.org/10.1007/978-981-13-2971-5
Benndorf, J., & Dimitrakopoulos, R. (2013). Stochastic long-term production scheduling of iron ore deposits: Integrating joint multi-element geological uncertainty. Journal of Mining Science. https://doi.org/10.1134/S1062739149010097
Benndorf, Jorg, & Dimitrakopoulos, R. (2018). Stochastic long-term production scheduling of iron ore deposits: Integrating Joint Multi-element Geological Uncertainty and Ore Quality Contro. In Advances in Applied Strategic Mine Planning. https://doi.org/10.1007/978-3-319-69320-0_12
Biswas, P., Sinha, R. K., Sen, P., & Rajpurohit, S. S. (2020). Determination of optimum cut-off grade of an open-pit metalliferous deposit under various limiting conditions using a linearly advancing algorithm derived from dynamic programming. Resources Policy. https://doi.org/10.1016/j.resourpol.2020.101594
Bixby, R. E. (2012). A Brief History of Linear and Mixed-Integer Programming Computation. Documenta Mathematica · Extra.
Black, F., & Scholes, M. (1973). Black, F., & Scholes, M. (1973). Black and Scholes, The pricing of options and corporate libilities.pdf. The Journal of Political Economy.Black and Scholes, The pricing of options and corporate libilities.pdf. The Journal of Political Economy.
Bloomberg. (2020). Bloomberg Commodity Index Report
Botin, J., Del Castillo, M. F., & Guzman, R. (2013). A real options application to manage risk related to intrinsic variables of a mine plan: A case study on Chuquicamata Underground Mine Project. Journal of the Southern African Institute of Mining and Metallurgy.
Cardin, M. A., Deng, Y., & Sun, C. (2017). Real options and flexibility analysis in design and management of one-way mobility on-demand systems using decision rules. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2017.08.006
Chatterjee, S., & Dimitrakopoulos, R. (2020). Production scheduling under uncertainty of an open-pit mine using Lagrangian relaxation and branch-and-cut algorithm. International Journal of Mining, Reclamation and Environment. https://doi.org/10.1080/17480930.2019.1631427
Chatterjee, S., Sethi, M. R., & Asad, M. W. A. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2015.07.012
Chen, W., Tarnopolskaya, T., Langren, N., & Lo, T. (2015). Switching surfaces for optimal natural resource extraction under uncertainty. Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015. https://doi.org/10.36334/modsim.2015.e6.chen
Cortazar, G., & Casassus, J. (1998). Optimal timing of a mine expansion: Implementing a real options model. Quarterly Review of Economics and Finance. https://doi.org/10.1016/s1062-9769(99)80100-8
Cortazar, G., Gravet, M., & Urzua, J. (2008). The valuation of multidimensional American real options using the LSM simulation method. Computers and Operations Research. https://doi.org/10.1016/j.cor.2006.02.016
Cox, J. C., Ross, S. A., & Rubinstein, M. (1979). Option pricing: A simplified approach. Journal of Financial Economics. https://doi.org/10.1016/0304-405X(79)90015-1
Damodaran, A. (2015). Equity Risk Premiums (ERP): Determinants, Estimation and Implications The 2015 Edition. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2581517
Dehghani, H., & Ataee-pour, M. (2012). Determination of the effect of operating cost uncertainty on mining project evaluation. Resources Policy. https://doi.org/10.1016/j.resourpol.2011.11.001
Dehghani, H., Ataee-pour, M., & Esfahanipour, A. (2014). Evaluation of the mining projects under economic uncertainties using multidimensional binomial tree. Resources Policy. https://doi.org/10.1016/j.resourpol.2014.01.003
Del Castillo, F., & Dimitrakopoulos, R. (2014). Joint effect of commodity price and geological uncertainty over the life of mine and ultimate pit limit. Transactions of the Institutions of Mining and Metallurgy, Section A: Mining Technology. https://doi.org/10.1179/1743286314Y.0000000069
Del Castillo, M. F., & Dimitrakopoulos, R. (2019). Dynamically optimizing the strategic plan of mining complexes under supply uncertainty. Resources Policy. https://doi.org/10.1016/j.resourpol.2018.11.019
Dimitrakopoulos, R. (2011). Stochastic optimization for strategic mine planning: A decade of developments. Journal of Mining Science. https://doi.org/10.1134/S1062739147020018
Dimitrakopoulos, R. G., & Abdel Sabour, S. A. (2007). Evaluating mine plans under uncertainty: Can the real options make a difference? Resources Policy. https://doi.org/10.1016/j.resourpol.2007.06.003
Driouchi, T., & Bennett, D. J. (2012). Real Options in Management and Organizational Strategy: A Review of Decision-making and Performance Implications. International Journal of Management Reviews. https://doi.org/10.1111/j.1468-2370.2011.00304.x
Echeverria, A., Juarez, G., Recabarren, M., Ronda, J., & Vila-Echague, E. (2013). Open-pit strategic mine planning under uncertainty - robust and flexible plans. Applications of Computers and Operations Research in the Mineral Industry.
Eivazy, H., & Askari-Nasab, H. (2012). A mixed-integer linear programming model for short-term open pit mine production scheduling. Transactions of the Institutions of Mining and Metallurgy, Section A: Mining Technology. https://doi.org/10.1179/1743286312Y.0000000006
EY's Global Mining & Metals Center. (2015). Business risks facing mining and metals 2015-2016. Business.
Frimpong, S., & Whiting, J. M. (1997). Derivative mine valuation: Strategic investment decisions in competitive markets. Resources Policy. https://doi.org/10.1016/s0301-4207(97)00029-9
Gottesman, A. (2016). The Binomial Option Pricing Model. In Derivatives Essentials. https://doi.org/10.1002/9781119280941.ch7
Groeneveld, B., & Topal, E. (2011). Flexible open-pit mine design under uncertainty. Journal of Mining Science. https://doi.org/10.1134/S1062739147020080
Guj, P., & Garzon, R. (2007). Modern asset pricing - A valuable real option complement to discounted cash flow modelling of mining projects. Australasian Institute of Mining and Metallurgy Publication Series.
Guj, Pietro, & Chandra, A. (2019). Comparing different real option valuation approaches as applied to a copper mine. Resources Policy. https://doi.org/10.1016/j.resourpol.2019.01.020
Gupta, P., Li, G. Bin, & Yu, J. (2015). From Natural Resource Boom to Sustainable Economic Growth: Lessons for Mongolia. IMF Working Papers. https://doi.org/10.5089/9781475570632.001
Han, J., Jentzen, A., & Weinan, E. (2018). Solving high-dimensional partial differential equations using deep learning. Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.1718942115
Hasan, M., Zhang, M., Wu, W., & Langrish, T. A. G. (2016). Discounted cash flow analysis of greenhouse-type solar kilns. Renewable Energy. https://doi.org/10.1016/j.renene.2016.04.050
Hazra, T., Samanta, B., & Dey, K. (2019). Real option valuation of an Indian iron ore deposit through system dynamics model. Resources Policy. https://doi.org/10.1016/j.resourpol.2019.01.002
International Council on Mining and Metals (ICMM). (2020). Impact of COVID-19 on the mining sector: A year in review.
Inthavongsa, I., Drebenstedt, C., Bongaerts, J., & Sontamino, P. (2016). Real options decision framework: Strategic operating policies for open pit mine planning. Resources Policy, 47(July), 142-153. https://doi.org/10.1016/j.resourpol.2016.01.009
Kazakidis, V. N., & Scoble, M. (2003). Planning for flexibility in underground mine production systems. Mining Engineering.
Kazakidis, Vassilios N. (2001). Operating risk: Planning for flexible mining systems. In ProQuest Dissertations and Theses.
Khan, A., & Asad, M. W. A. (2020). A mathematical programming model for optimal cut-off grade policy in open pit mining operations with multiple processing streams. International Journal of Mining, Reclamation, and Environment. https://doi.org/10.1080/17480930.2018.1532865
King, B. (2011). Optimal mining practice in strategic planning. Journal of Mining Science. https://doi.org/10.1134/S1062739147020110
Koushavand, B., Askari-Nasab, H., & Deutsch, C. V. (2014). A linear programming model for long-term mine planning in the presence of grade uncertainty and a stockpile. International Journal of Mining Science and Technology. https://doi.org/10.1016/j.ijmst.2014.05.006
Laing, T. (2020). The economic impact of the Coronavirus 2019 (COVID-2019): Implications for the mining industry. In Extractive Industries and Society. https://doi.org/10.1016/j.exis.2020.04.003
Lambrecht, B. M. (2017). Real options in finance. Journal of Banking and Finance. https://doi.org/10.1016/j.jbankfin.2017.03.006
Le Bellac, M., & Viricel, A. (2017). The Black-Scholes Model. In Deep Dive into Financial Models. https://doi.org/10.1142/9789813142114_0005
Lemelin, B., Abdel Sabour, S. A., & Poulin, R. (2006). Valuing Mine 2 at Raglan using real options. International Journal of Mining, Reclamation, and Environment. https://doi.org/10.1080/13895260500430294
LI, S. xing, & KNIGHTS, P. (2009). Integration of real options into short-term mine planning and production scheduling. Mining Science and Technology. https://doi.org/10.1016/S1674-5264(09)60125-3
Li, S., Dimitrakopoulos, R., Scott, J., & Dunn, D. (2007). Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry. Australasian Institute of Mining and Metallurgy Publication Series.
Li, Y., Topal, E., & Ramazan, S. (2018). Optimizing the long term mine landform progression and truck hour schedule in a large scale open pit mine using mixed-integer programming. In Advances in Applied Strategic Mine Planning. https://doi.org/10.1007/978-3-319-69320-0_39
Lilford, E. V., & Minnitt, R. C. A. (2005). A comparative study of valuation methodologies for mineral developments. Journal of The South African Institute of Mining and Metallurgy.
Lipsett, M. G., & Baiden, G. R. (2001). Mining information systems development. CIM Bulletin.
Liu, D., Li, G., Hu, N., & Ma, Z. (2019). Application of Real Options on the Decision-Making of Mining Investment Projects Using the System Dynamics Method. IEEE Access, 7, 46785-46795. https://doi.org/10.1109/ACCESS.2019.2909128
Longstaff, F. A., & Schwartz, E. S. (2001). Valuing American options by simulation: A simple least-squares approach. Review of Financial Studies. https://doi.org/10.1093/rfs/14.1.113
Marcotte, D., & Caron, J. (2013). Ultimate open-pit stochastic optimization. Computers and Geosciences. https://doi.org/10.1016/j.cageo.2012.08.008
Martinez, L. a, & McKibben, J. (2010). Understanding Real Options in Perspective Mine Project Valuation : A Simple abstract discounted cash flow analysis for mine project valuation. Minin 2010.
Matts, C., & Maassen, O. (2007). "Real Options" Underlie Agile Practices. InfoQ.
Meagher, C., Sabour, S. a A., & Dimitrakopoulos, R. (2009). Pushback Design of Open Pit Mines Under Geological and Market Uncertainties. Orebody Modelling and Strategic Mine Planning.
Moel, A., & Tufano, P. (2005). When Are Real Options Exercised? An Empirical Study of Mine Closings. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.233800
Mohammadi, S., Kakaie, R., Ataei, M., & Pourzamani, E. (2017). Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm (ICA). Resources Policy. https://doi.org/10.1016/j.resourpol.2016.11.005
Montiel, L., & Dimitrakopoulos, R. (2015). Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2015.05.002
Moreno, E., Ferreira, F., Goycoolea, M., Espinoza, D., Newman, A., & Rezakhah, M. (2015). Linear programming approximations for modeling instant-mixing stockpiles. Application of Computers and Operations Research in the Mineral Industry - Proceedings of the 37th International Symposium, APCOM 2015.
Moreno, E., Rezakhah, M., Newman, A., & Ferreira, F. (2017). Linear models for stockpiling in open-pit mine production scheduling problems. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2016.12.014
Musingwini, C., Minnitt, R. C. A., & Woodhall, M. (2007). Technical operating flexibility in the analysis of mine layouts and schedules. Journal of the Southern African Institute of Mining and Metallurgy.
Nadarajah, S., Margot, F., & Secomandi, N. (2017). Comparison of least squares Monte Carlo methods with applications to energy real options. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2016.06.020
Nembhard, H. B., & Aktan, M. (2009). Real options in engineering design, operations, and management. In Real Options in Engineering Design, Operations, and Management. https://doi.org/10.1201/9781420071702
Osterholt, V., & Dimitrakopoulos, R. (2018). Simulation of orebody geology with multiple-point geostatistics-application at Yandi channel Iron ore deposit, WA, and implications for resource uncertainty. In Advances in Applied Strategic Mine Planning. https://doi.org/10.1007/978-3-319-69320-0_22
Ramazan, S., & Dimitrakopoulos, R. (2013). Production scheduling with uncertain supply: A new solution to the open pit mining problem. Optimization and Engineering. https://doi.org/10.1007/s11081-012-9186-2
Ramos, A. L., Mazzinghy, D. B., Barbosa, V. da S. B., Oliveira, M. M., & da Silva, G. R. (2019). Evaluation of an iron ore price forecast using a geometric brownian motion model. Revista Escola de Minas. https://doi.org/10.1590/0370-44672018720140
Ratick, S., & Schwarz, G. (2009). Monte Carlo Simulation. In International Encyclopedia of Human Geography. https://doi.org/10.1016/B978-008044910-4.00476-4
Rębiasz, B., Gaweł, B., & Skalna, I. (2019). Financial valuation of production diversification in the steel industry using real option theory. Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-3-319-99993-7_29
Reddy, K., & Clinton, V. (2016). Simulating stock prices using geometric Brownian motion: Evidence from Australian companies. Australasian Accounting, Business and Finance Journal. https://doi.org/10.14453/aabfj.v10i3.3
Riml, A., Dimitrakopoulos, R., & Gamache, M. (2020). A dynamic stochastic programming approach for open-pit mine planning with geological and commodity price uncertainty. Resources Policy. https://doi.org/10.1016/j.resourpol.2019.101570
Sabour, S. A. A., & Wood, G. (2009). Modelling financial risk in open pit mine projects: implications for strategic decision-making. Journal of the Southern African Institute of Mining and Metallurgy.
Sabour, S. A., & Poulins, R. (2010). Mine expansion decisions under uncertainty. International Journal of Mining, Reclamation and Environment. https://doi.org/10.1080/17480931003664991
Samavati, M., Essam, D., Nehring, M., & Sarker, R. (2018). A new methodology for the open-pit mine production scheduling problem. Omega (United Kingdom). https://doi.org/10.1016/j.omega.2017.10.008
Samis, M., & Davis, G. A. (2014). Using Monte Carlo simulation with DCF and real options risk pricing techniques to analyse a mine financing proposal. International Journal of Financial Engineering and Risk Management. https://doi.org/10.1504/ijferm.2014.058765
Sattarvand, J., Gilani, O., Shishvan, M. S., & Khan, A. (2015). Application of the metaheuristic approaches in open pit mine planning. Application of Computers and Operations Research in the Mineral Industry - Proceedings of the 37th International Symposium, APCOM 2015.
Savolainen, J. (2016a). Real options in metal mining project valuation: Review of literature. Resources Policy, 50, 49-65. https://doi.org/10.1016/j.resourpol.2016.08.007
Savolainen, J. (2016b). Real options in metal mining project valuation: Review of literature. Resources Policy. https://doi.org/10.1016/j.resourpol.2016.08.007
Savolainen, J., Collan, M., & Luukka, P. (2017). Analyzing operational real options in metal mining investments with a system dynamic model. Engineering Economist. https://doi.org/10.1080/0013791X.2016.1167988
Savolainen, J., Collan, M., Kyläheiko, K., & Luukka, P. (2017). On the trade-off between the leverage effect and real options thinking: A simulation-based model on metal mining investment. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2017.06.002
Siña, M., & Guzmán, J. I. (2019). Real option valuation of open pit mines with two processing methods. Journal of Commodity Markets. https://doi.org/10.1016/j.jcomm.2018.05.003
Singh, A., & Skibniewski, M. J. (1991). Development of flexible production systems for strip mining. Mining Science and Technology. https://doi.org/10.1016/0167-9031(91)90282-H
Slade, M. E. (2001). Valuing managerial flexibility: An application of real-option theory to mining investments. Journal of Environmental Economics and Management. https://doi.org/10.1006/jeem.2000.1139
Stange, W., & Cooper, B. (2008). Value options and flexibility in plant design. Australasian Institute of Mining and Metallurgy Publication Series.
Thompson, M., & Barr, D. (2014). Cut-off grade: A real options analysis. Resources Policy. https://doi.org/10.1016/j.resourpol.2014.10.003
Upadhyay, S. P., & Askari-Nasab, H. (2018). Simulation and optimization approach for uncertainty-based short-term planning in open pit mines. International Journal of Mining Science and Technology. https://doi.org/10.1016/j.ijmst.2017.12.003
Vargas, J. P., Koppe, J. C., & Prez, S. (2014). Monte Carlo simulation as a tool for tunneling planning. Tunnelling and Underground Space Technology. https://doi.org/10.1016/j.tust.2013.10.011
Vargas, J. P., Koppe, J. C., Perez, S., & Hurtado, J. P. (2015). Planning Tunnel Construction Using Markov Chain Monte Carlo (MCMC). Mathematical Problems in Engineering. https://doi.org/10.1155/2015/797953
Von Varendorff, R. (2003). Confidence in Mine Planning - The Role of Intelligent Technologies. Australasian Institute of Mining and Metallurgy Publication Series.
Wang, T., & Neufville, R. De. (2004). Building Real Options into Physical Systems with Stochastic Mixed-Integer Programming. Options.
Whittle, G., Stange, W., & Hanson, N. (2007). Optimising project value and robustness. Australasian Institute of Mining and Metallurgy Publication Series.
Wu, W., & Lin, B. (2020). Reducing Overcapacity in China's Coal Industry: A Real Option Approach. Computational Economics. https://doi.org/10.1007/s10614-018-9872-z
Yu, S., Gao, S., & sun, H. (2016). A dynamic programming model for environmental investment decision-making in coal mining. Applied Energy. https://doi.org/10.1016/j.apenergy.2015.09.099
Zhang, K., Nieto, A., & Kleit, A. N. (2015). The real option value of mining operations using mean-reverting commodity prices. Mineral Economics, 28(1-2), 11-22. https://doi.org/10.1007/s13563-014-0048-6
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