There are numerous examples where RO has been strategically applied in business strategy and policy analysis. For instance, the RO method was used in the design and management of software engineering in the US Air Force (Olagbemiro, 2008). It was also used in the valuation of a drone development project by Boeing (Datar et al., 2007), in urban development (Morano et al., 2014), in real estate development, in research and development of pharmaceutical products, in manufacturing and in energy resources
(Nembabhard & Aktan, 2010). However, there are limited cases where it has been tactically applied at an
operational level.
The literature review has shown that the adaption of RO is being hampered by lack of synthesis of the RO process and computation challenges that are explained in the following sections.
2.7.1 Conceptive challenges impeding the adaptation of RO ‘on’ project
Conventionally, any good engineering design must minimise risk. Such a mindset is reactive to risk(Neufville, 2002). According to Samis et al. (2002), industry risk management has been focusing on an
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phases. The combined effect of globalisation, deregulation and reduced technology cycles result in managers facing very volatile environments in their strategic investment decisions, which in turn, limit the range of possible future actions (Fu, 2002).
The lack of a single, well-proven technique is a challenge to valuers and engineers alike (Lilford &
Minnitt, 2005; Kazakidis & Scoble, 2002). According to Copeland & Tufano (2004), business leaders
frequently use some sort of option approach in evaluating and deciding investment opportunities at the initial stages but many of the users of an option approach who have tried gave up due to technical grounds.
Others argued that Chief executive officers (CEOs) intuitively understand the value of flexibility but there is a disconnect with Chief Financial Officers (CFOs) that predominantly use static DCF analyses
(Portfolio Group, 2002). Trigeogis & Smit (2003)attributed a general implementation problem of option
pricing methodology to an insufficient set of market quotes of correlated financial instruments or data. According to Benaroch & Kauffman (1999),most people who use the DCF method are ill-equipped to use the option pricing model correctly due to a large number of varied information and assumptions required than are usually used in discount cash flow for NPV concepts. The real options valuation (ROV) and DCF methods differ fundamentally in the way they are discounted. Option pricing takes into account changes in revenue as time passes without parameter adjustment but, considers the asymmetric distribution of the expected revenue and their variability called volatility or the variance of the expected rate of return on the mining project (Haque et al., 2016).
Moreover, there is an overriding question on whether one can analyse non-trading assets using models (Black–Scholes and binomial) formulated to evaluate assets traded in a financial market. Both Black – Scholes and binomial models assume risk neutrality and are not immune to the analyst subjectivity that can lead to overestimation or underestimation of the NPV which can have significant impacts on investment decisions.
2.7.2 Computational challenges facing adaptation of RO ‘in’ project
The lack of corroborating evidence in existing literature showing how to apply RO valuation in practice, under multiple uncertainties and non-uniform conditions without oversimplifying the reality has also contributed to the low uptake of the RO technique. Wang (2005) studied the application of RO in engineering design, by focusing on option identification and analysis. He used screening and simulation models to identify options and also used stochastic mixed- integer programming to value options. As the author of the thesis, he acknowledged that any use of stochastic mix-integer programming reformulation to value options complicates the analysis and makes the approach less attractive and discourages potential users.
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Brandao et al. (2005)have focused on RO analysis techniques. They argue that the use of sophisticated
mathematical models to carry out RO analysis takes it onto the path of many other algorithms which turns out to be complicated, lacking intuition and under applied. They used a simple binomial tree and decision tree analysis that is simple and intuitive. It is important to emphasise that the technique applied by Brandao
et al. (2005)is pertinent to this research and it will be referred to as the Brandao – Dyer – Hahn approach.
To overcome users fear, Neufville (2002)presented a paper on approaching fundamental engineering issues. He suggested that the RO analysis method can provide a conceptual basis for defining optimal configurations by designing flexible engineering systems that can evolve optimally to meet new challenges and opportunities. However, improper differentiation between risk and uncertainty in the running of operations is impeding the use of RO methodology. It is not uncommon that risk and uncertainty are frequently confused and loosely used (Koleczko, 2012). Brammer & Smithson (2008)attempted to put forward a taxonomy for uncertainty by illustrating what is known and what is unknown, but most importantly, they acknowledge that there is a gap where current practices and perspectives cannot be easily mapped onto existing structures. Thus, there is no definitive guide to managing uncertainty that is currently established. Therefore, providing another, more appropriate and user-friendly guideline is a good justification for the importance of this research.
Even though their approach was aimed at the application of RO at the strategic level, Haque et al. (2014, 2015) made a genuine attempt to promote the use of RO in mine valuation. However, their methodology requires a reasonable understanding of calculus and stochastic processes as they utilised partial differential equations with Matlab simulation to solve RO. The system dynamics approach has also been used for policy development and decision making during feasibility studies (Inthavongsa et al., 2016). However, the
Inthavongsa et al. (2016) approach was flawed as the process mimics scenario planning where the option
is chosen and executed. Thus, there was no real managerial flexibility designed into the mine operations.