Research_Report_Hudson_Real Options_final

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THE APPLICATION OF REAL OPTIONS ANALYSIS IN
THE MINING SECTOR
Jonathan Hamilton Knight Hudson
A Research Report submitted to the Faculty of Commerce, Law and Management,
University of the Witwatersrand, Johannesburg, in partial fulfillment of the
requirements for the degree of Master of Business Administration
Johannesburg
January, 2005

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ABSTRACT
Academic literature portrays real options analysis as a new paradigm that will
replace traditional valuation techniques over the next ten years. As yet, mining
organisations appear reluctant to adopt real options analysis to evaluate
investments. This aim of this study is to identify the key factors that influence
management acceptance of real options analysis in the mining sector. In depth,
semi-structured interviews are used to obtain the perceptions of consultants
servicing and the technical and financial management within global mining
organisations that have experience with or knowledge of the use of real options
analysis. Convenience sampling is used to draw comparisons between these two
groups of respondents. The research method interestingly highlighted that there are
differences of opinion between the two groups of respondents on the relative
importance of the factors that lead to management acceptance of real options
analysis. The main findings of the research are that skilled and influential internal
champions with support from executive and senior management are necessary to
pioneer the real options analysis method. The road to acceptance requires real
options analysis to be marketed as a complementary tool rather than a replacement
to traditional discounted cash flow methods. An ongoing training and education
program for senior and executive managers on the application of real options
analysis is required due to the current lack of “in house” knowledge. Pilot projects
and case studies are considered important to market the real options analysis
concept and reinforce the learning and acceptance process. In addition, the
development of a simple but sophisticated, interactive decision tree type software
application is considered necessary to improve understanding. In the long term,
there is a need to incorporate the software application into the firms mine planning
systems. To conclude, management must decide if the benefits of having a
sophisticated valuation tool which can improve investment decision making is worth
the costs and effort to get it in place.

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DECLARATION
I, Jonathan Hamilton Knight Hudson declare that this research report is my own,
unaided work. It is submitted in partial fulfillment of the requirements for the degree
of Master of Business Administration in the University of the Witswatersrand,
Johannesburg. It has not been submitted before for any degree or examination in
this or any other university.
Jonathan Hamilton Knight Hudson
23 January 2005

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DEDICATION
To Carla, without your love and support, this research would never have been
possible.

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ACKNOWLEDGEMENTS
I would like to express my sincere thanks to:
• Frank Durand, my supervisor, for his guidance and assistance.
• Louise Whitaker and Anthony Stacey for their help with the research
methodology.
• Trevor Raymond for his knowledge and advice.

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TABLE OF CONTENTS
Page no.
ABSTRACT ...............................................................................................................II
DECLARATION ........................................................................................................ III
DEDICATION............................................................................................................IV
ACKNOWLEDGEMENTS..........................................................................................V
TABLE OF CONTENTS ............................................................................................1
LIST OF TABLES......................................................................................................3
LIST OF FIGURES.....................................................................................................4
LIST OF APPENDICES .............................................................................................5
1. INTRODUCTION..............................................................................................6
1.1 BACKGROUND ......................................................................................................................6
1.2 THE RESEARCH PROBLEM .....................................................................................................7
1.3 RATIONALE FOR THE RESEARCH............................................................................................8
1.4 SUBSEQUENT CHAPTERS OF THE RESEARCH REPORT .............................................................8
2. LITERATURE REVIEW...................................................................................8
2.1 REAL OPTIONS DEFINED .......................................................................................................9
2.2 DISCOUNTED CASH FLOW AND REAL OPTIONS ANALYSIS .......................................................12
2.2.1 Limitations of Discounted Cash FlowTechniques................................................13
2.2.2 Benefits of Real Options Analysis.........................................................................14
2.3 EXAMPLES OF REAL OPTIONS ANALYSIS IN THE MINING SECTOR..............................................16
2.4 NUMERICAL METHODS USED TO EVALUATE REAL OPTIONS .....................................................20
2.5 FRAMEWORKS AND ROAD MAPS...........................................................................................24
2.6 STRATEGY AND COMPETITION..............................................................................................29
2.7 THE CHANGE PROCESS ......................................................................................................31
2.8 LESSONS LEARNED BY REAL OPTIONS PRACTITIONERS ..........................................................36
2.9 RESEARCH PROPOSITIONS ..................................................................................................39
3. RESEARCH METHODOLOGY......................................................................41
3.1 THE RESEARCH POPULATION...............................................................................................42
3.2 SAMPLE SIZE AND SAMPLING METHODOLOGY..........................................................................43
3.3 DATA COLLECTION .............................................................................................................45
3.4 RELIABILITY AND VALIDITY....................................................................................................47
3.5 THE PILOT STUDY ..............................................................................................................48
3.6 DATA ANALYSIS..................................................................................................................48
4. PRESENTATION OF RESULTS....................................................................50
4.1 PRESENTATION OF RESULTS FOR CONSULTANT RESPONDENTS ................................................50
4.2 PRESENTATION OF THE RESULTS FROM BUSINESS RESPONDENTS ............................................52
4.3 PRESENTATION OF THE RESULTS FROM ALL RESPONDENTS......................................................54

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5. INTERPRETATION OF RESULTS ................................................................56
5.1 INTERPRETATION OF THE RESULTS FROM CONSULTANT RESPONDENTS .....................................56
5.1.1 Key factors confirmed by consultant respondents from the literature ...............56
5.1.2 Key factors identified by consultant respondents................................................63
5.2 INTERPRETATION OF THE RESULTS FROM BUSINESS RESPONDENTS ..........................................66
5.2.1 Key factors confirmed by business respondents from the literature ..................66
5.2.2 Key factors identified by business respondents ..................................................70
5.3 SUMMARY..........................................................................................................................73
6. CONCLUSIONS AND RECOMMENDATIONS ..............................................74
6.1 STRENGTH OF SUPPORT FOR RESEARCH PROPOSITION 1 ........................................................74
6.2 STRENGTH OF SUPPORT FOR RESEARCH PROPOSITION 2 ........................................................78
6.3 LIMITATIONS OF THE RESEARCH ...........................................................................................78
6.4 RECOMMENDATIONS TO MANAGEMENT ..................................................................................79
6.5 SUGGESTIONS FOR FURTHER RESEARCH..............................................................................81
6.5.1 An evaluation of the critical success factors for gaining management
acceptance of real options analysis in the mining sector...................................................81
6.5.2 A case study on the application of real options analysis in the mining sector..81
REFERENCES.........................................................................................................81

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LIST OF TABLES
Page no.
Table 2.1 Taxonomy of real options……………………………………….…...….….6
Table 2.2 Disadvantages of DCF (Assumptions versus Realities)………….……..8
Table 2.3 Examples of real options in the mining sector……………..……………11
Table 2.4 Common misconceptions about real options………………………….. 27
Table 3.5 Respondents interviewed……………………………..…………………..37
Table 4.6 Colour coding for tier ranking of factors.………………...…….……..…43
Table 4.7 Frequency count of factors confirmed by consultant respondents
from the literature………………………………………………………….44
Table 4.8 Frequency count of the key factors identified by consultant
respondents ………………………………………………………………..45
Table 4.9 Frequency count of factors confirmed by business respondents
from the literature………………………………………………………….46
Table 4.10 Frequency count of the key factors identified by business
respondents ………………………………………………………………..47
Table 4.11 Frequency count of factors confirmed by all respondents from the
literature…………………………………………………………………….48
Table 4.12 Frequency count of the key factors identified by all respondents ……49

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LIST OF FIGURES
Page no.
Figure 2.1 The Banff taxonomy of real asset valuation methods……….….…......16
Figure 2.2 Example of a decision tree………………………………………………..17
Figure 2.3 Real options identification framework…………………..…….….……...19
Figure 2.4 Framework for the application of real options analysis………………..21
Figure 2.5 The tomato garden as a portfolio of real options……………....………24
Figure 3.6 The components of data analysis for a qualitative study……………...35
Figure 3.7 Road map to manage the data analysis ……………………………….42

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LIST OF APPENDICES
Page no.
APPENDIX 1………………………………………………………………………….….…80
A hypothetical case example using different numerical techniques……….…80
APPENDIX 2………………………………………………………………………………..93
Consistency Matrix….………………………………………………….…............93
APPENDIX 3………………………………………………………………………………..97
Interview process……………………………………………………….…............97
APPENDIX 4……………………………………………………………………………...101
Factor code used for part 2 of the interview process…………………..........101

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1. INTRODUCTION
1.1 Background
“The application of option concepts to value real assets has been an important and
exciting growth area in the theory and practice of finance. It has revolutionised the
way academics and practitioners think about investment projects by explicitly
incorporating management flexibility into the analysis.” (Schwartz & Trigeorgis,
2001:1).
Consultants and academics often state that real options analysis improves
investment decision-making (D’Souza, 2002). Some practitioners even predict that
the real options approach to valuation will have a significant impact in the practice of
finance and strategy over the next 5-10 years (Schwartz & Trigeorgis, 2001). For
example, Copeland & Antikarov (2001) stated that in ten years real options analysis
will replace Net Present Value (NPV) as the central paradigm for investment
decisions.
The origin of real options analysis took place over three decades ago. The
breakthrough in real options was made in 1970 with the development of an
analytical solution commonly known as the Black-Merton-Scholes model, used to
determine the value of derivatives (Jarrow, 1999). This memorable event was finally
recognised in 1997 where Robert Merton, Myron Scholes, in collaboration with the
late Fischer Black received the Nobel Prize in economics for the development of the
pioneering formula for the valuation of stock options (Jarrow, 1999). This work
paved the way for economic valuations in many areas, generated new types of
financial instruments and facilitated more efficient risk management in society
(Jarrow, 1999).

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The success in the use of option pricing theory to value derivatives in the market
place has led to the application of option concepts to value real assets. For
example, real options analysis can be used to value a company through its strategic
business options and as a strategic business tool in capital investment decisions
(Mun, 2002). In this regard, resource and commodity based companies are
frequently mentioned as ideal candidates for the application of real options analysis
(Borison, 2003). In addition, there are examples in the literature that illustrate the
practical application of real options analysis in the mining industry (Schwartz &
Trigeorgis, 2001).
At the “University of Maryland Roundtable on Real Options and Corporate Practice”
held at College Park, Maryland, USA in April 19, 2002, four practitioners of real
options with a moderator, Borison, Eapen, Maubossin, McCormack & Triantis
(2003:8), explored the following questions:
“Where is real options today in terms of the theory and practice?”
“What is the value added that corporations are getting from using real options?”
“What are the success stories?”
“What are the remaining barriers to applying real options in practice, and how can
the barriers be overcome?”
The consensus of the discussion was that despite successes in some sectors (e.g.
the exploration and development of the oil and gas sector), there was still a lot of
resistance and confusion getting in the way of corporate adoption of real options
(Borison et al, 2003).
1.2 The Research Problem
The purpose of the research is to establish the key factors that influence
management acceptance of real options analysis in the mining sector.

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1.3 Rationale for the Research
The study will help to ascertain how real options analysis can be applied to
organisations in the mining sector. The determination of the key factors for
management acceptance of real options will be valuable information, which can then
be used for focusing company resources or for conducting further research in this
field. Proactive interventions applying the factors will enhance the likelihood of more
widespread application of real options analysis to investment decision-making.
Consulting companies will benefit from this study. Given the knowledge of the key
factors, consultants can take appropriate measures before real options analyses are
conducted/ implemented within a particular firm. An identification of these factors
would also contribute significantly to the academic field and stimulate further
research relating to the application of real options analysis. Based on the above
discussion, academics, practitioners and facilitators would be interested in the
outcome of such research.
1.4 Subsequent Chapters of the Research Report
The structure of the subsequent chapters of this research report is as follows.
Chapter 2 consists of a literature review focusing on themes around the factors that
influence management acceptance of real options analysis in the mining sector,
leading to the formulation of research propositions. Chapter 3 describes the
research method used to answer the research problem and propositions. Chapters 4
and 5 present and interpret the results from the research. Finally, conclusions and
recommendations are drawn in Chapter 6.
2. LITERATURE REVIEW
The purpose of this chapter is to determine the proposition/s against which the

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research will be conducted. The chapter commences with the definition of real
options analysis and its application to the mining sector. Real options examples in
the mining sector are used to illustrate the theory. The proceeding sub sections of
the chapter discuss themes around the research problem that aim to explore the
factors influencing management acceptance of real options analysis in the mining
sector.
2.1 Real Options Defined
A definition provided for real options analysis is “the application of financial options,
decision sciences, corporate finance, and statistics to evaluating real or physical
assets as opposed to financial assets” (Mun, 2002:30).
There are many definitions of a real option, perhaps the simplest is “A real option
exists if we have the right to take a decision at one or more points in the future (e.g.
to invest or not to invest, or to sell out or not to sell out). Between now and the time
of the decision, market conditions will change unpredictably, making one or other of
the available decisions better for us, and we will have the right to take whatever
decision will suit us best at that time” (Howell et al, 2001:2).
The process of real options analysis helps management to decide the amount of
money required to acquire an economic opportunity and when (if ever) they should
commit to one of the available decisions (Howell et al, 2001).
Using the mining sector as an example, Brennan & Schwartz (1985a) stated that a
mine is a complex option on the resources contained in the ground. Furthermore,
they explained that similar to the holder of a stock option, the owner of a mine has
the right to acquire the output of a mine at a fixed exercise price equal to the
variable cost of production. Therefore, the mine can be valued using the option
pricing approach pioneered by Black-Scholes and Merton (Brennan & Schwartz,
1985a).
The value of a real option, like the value of financial option in the stock market using

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option pricing theory, depends on six basic variables (Copeland & Antikarov, 2001):
1) The value of the underlying asset. In the case of real options, this might be a
project, investment, or acquisition.
2) The exercise price. The amount of money invested to exercise the option.
3) The time to expiration of the option. The value of the option increases with
time.
4) The volatility (standard deviation) of the value of the underlying asset. The
value of the option increases with higher volatility due to the greater
likelihood that value of the underlying asset will exceed its exercise price.
5) The risk free rate of the interest over the life of the option. The value of the
option increases as the risk free rate goes up.
6) The dividends that might be paid out by the underlying asset over its life.
Perlitz, Peske, & Schrank (1999) distinguished between six different kinds of real
options, namely:
1) The option to defer a project is where one has the right to delay the start of
the investment.
2) The time to build option or option to extend the life of a project by paying
more to scale up the operations.
3) The option to abandon an investment project for a fixed price.
4) The option to contract, expand, or temporarily shut down an investment by
selling a fraction of it for a fixed price.
5) The options to switch input or output are portfolios of options that allow the
owner to switch at a fixed cost (or costs) between two modes of operation.
6) The growth option or option to expand (ability to scale on other operations).
In addition, there are options on options, called “Compound options” (Copeland &
Antikarov, 2001). Phased investment projects fit into this category with each phase
being an option contingent on the earlier exercise of previous options (e.g. an option
on an option).

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In many circumstances, uncertainties within projects do not get resolved unless an
investment is made to learn more about the conditions of the project. This
management flexibility to make this investment is termed a “Learning option”
(Copeland & Antikarov, 2001).
There are two basic types of options are defined (Hull, 1998):
! A call option gives the holder the right to buy an asset by a certain date for a
certain price.
! A put option gives the holder the right to sell an asset by a certain date for a
certain price.
Options can either be European or American, a distinction that has nothing to do
with geographical location (Hull, 1998). American options can be exercised at any
time up to the expiration date, whereas European options can be exercised only on
the expiration date itself (Hull, 1998). A taxonomy of real options with examples of
management flexibility for a project to illustrate each case is shown in Table 2.1.
Table 2.1 Taxonomy of real options
Type of Option Derivative Instrument Management Flexibility
Option to defer American call Right to delay the start of a project.
Option to extend American call Right to extend the life of a project.
Option to abandon American put Right to abandon a project.
Option to contract American put Right to contract (scale back), or temporarily
shut down a project.
Option to switch Portfolio of options -
American call/ put
Right to switch at a fixed price between two
modes of operation.
Option to expand American call Right to expand a project by paying a fixed
amount to scale up the operations.
Compound options Portfolio of options -
American call/ put
Options on options. Each phase is an option
that is contingent on the earlier exercise of
other options.

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Type of Option Derivative Instrument Management Flexibility
Learning options Portfolio of options -
American call/ put
Right to learn more about the conditions of a
project and reduce the uncertainty.
2.2 Discounted Cash Flow and Real Options Analysis
Real options analysis is often marketed as a complementary valuation tool to
Discounted Cash Flow (Kemna, 1993; Jarrow, 1999; Borison et al, 2003). Finance
textbooks correctly teach that the selection of projects requiring investment should
be based on maximizing the Net Present Value (NPV) of the firm and that real
options analysis adds the recognition that most investment projects have options
embedded within them (Jarrow, 1999).
The following major insights were gained from real options analysis practitioners on
the comparison between real options analysis and Discounted Cash Flow (DCF)
(Kemna, 1993):
! The same fundamental principles underlie both DCF analysis and real
options analysis.
! DCF is a simplified technique, which is appropriate for a broad range of
problems under passive management.
! DCF and real options analysis are complementary rather than competing
techniques.
! Real options analysis is rather like an appropriate combination of
discounting and decision tree analysis – useful for phasing a series of
investments.
! DCF undervalues benefits of waiting.
! These techniques do not replace the need for strategic thinking and
judgment in the generation and examination of business alternatives.
Copeland & Antikarov (2001) stated that NPV is the single most widely used tool to
value investments made by corporations. Interestingly, surveys done over the years

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showed that it took over two decades for NPV to be widely accepted by
management (Copeland & Antikarov, 2001).
2.2.1 Limitations of Discounted Cash FlowTechniques
Real options practitioners often highlight the limitations of traditional DCF
techniques to evaluate projects with flexibility and uncertainty (Copeland &
Antikarov, 2001; Schwartz & Trigeorgis, 2001).
A key barrier to the management acceptance of real options is the inherent mistrust
by management of new analytical methods when old methods like DCF have done
the job in the past (Mun, 2002). The main approach to alleviate management’s
concerns is to show that real options methodology is principally not far off the
conventions of traditional financial analysis (Mun, 2002). For example, the
assumptions versus realities regarding the disadvantages of DCF can be
highlighted to management as shown in Table 2.2 (Mun, 2002).
Table 2.2 Disadvantages of DCF (Assumptions versus Realities)
DCF Assumptions Realities
Decisions are made now, and cash flow
streams are fixed for the future.
Uncertainty and variability in future
outcomes. Not all decisions are made
today, as some may be deferred to the
future, when uncertainty becomes
resolved.
Once launched, all projects are passively
managed.
Projects are usually actively managed
through project life cycle, including
checkpoints, decision options, budget
constraints, and so forth.
Future free cash flow streams are highly
predictable and deterministic.
It may be difficult to estimate future cash
flows as they are usually stochastic and

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DCF Assumptions Realities
risky in nature.
Project discount rate used is the
opportunity cost of capital, which is
proportional to non- diversifiable risk.
There are multiple sources of business
risks with different characteristics, and
some are diversifiable across projects or
time.
All risks are completely accounted for by
the discount rate.
Firm and project risk can change during
the course of a project.
Unknown, intangible or immeasurable
factors are valued at zero.
Many of the important benefits are
intangible assets or qualitative strategic
positions.
Source: Adapted from Mun (2002:59)
There are many examples from literature that illustrate the limitations of traditional
DCF valuation methods for valuing investments with uncertainty and flexibility
(Herath, 2002; Perlitz et al, 1999; D’Souza, 2002; Copeland & Antikarov, 2001;
Borison et al, 2003; McCarthy & Monkhouse, 2003; Samis & Laughton & Poulin &
Davis, 2003; Brennan & Schwartz, 1985a).
2.2.2 Benefits of Real Options Analysis
Real options practitioners often market the prognosis that real options analysis can
be used to evaluate projects with flexibility and uncertainty. The areas deemed
useful for the real options application are corporate and operational decision-
making, capital budgeting, and company acquisition valuations (Herath & Park,
2002; Kemna, 1993; Schwartz & Trigeorgis, 2001; Perlitz et al, 1999). The following
examples from literature provide insights into the benefits of using real options
analysis in the mining sector.

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Brennan & Schwartz (1985a) explained that it is not uncommon for natural resource
investments to be sold under long-term contracts that fix the price of the commodity
outputs. They therefore recommended an alternative approach to overcome the
problems of price uncertainties by using futures prices, discounting at the risk free
rate and treating a mine as an option on the underlying commodity.
Brennan & Schwartz (1985b) described a mine as a complex option on the
resources contained in the mine as in practice the owner of a mine generally has the
right to choose the optimal output rate, to close the mine, to re-open it, or even to
abandon it as circumstances dictated. The value of a mine therefore depended upon
whether it is currently open and producing or closed and incurring maintenance
costs (Brennan & Schwartz, 1985b). Hence, the optimal mine operating policy could
be derived from forming boundary conditions along the following input variables
(Brennan & Schwartz, 1985b):
a. Unexploited inventory remaining in the mine
b. Current spot price of the commodity
c. Mine Operating Policy
d. Calendar time
An alternative approach to valuing commodity based investments, for which futures
contracts exist, is to make an adjustment for risk to the cash flows and to discount
the certainty-equivalent cash flows, instead of the expected cash flows at the risk
free rate of interest (Schwartz & Trigeorgis, 2001). Certainty equivalent cash flows
can be used since they can be obtained from future (or forward) prices (Brennan &
Schwartz, 1985b). This obviates the need to obtain subjective forecasts of future
spot prices of the commodity, which were highly volatile (Schwartz & Trigeorgis,
2001). The risk-neutral environment is an appropriate and convenient environment
for option pricing with three major advantages (Schwartz & Trigeorgis, 2001).
Namely;

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• All the flexibilities (options) that a project might have are taken into account.
• All the information contained in market prices (future prices) is used where such
prices exist.
• It allows the use of powerful analytical tools developed in contingent claims
analysis to determine both the value of the investment project and its optimal
operating policy.
2.3 Examples of Real Options Analysis in the Mining Sector
There are examples, which illustrate the application of real options analysis in the
mining sector – see Table 2.3. This sub chapter provides a brief summary of each
example.
Table 2.3 Examples of real options in the mining sector
Type of Option Examples Reference
Option to defer Right to delay or defer the start of a
mining project or investment to
incorporate more favourable economic
conditions.
Trigeorgis (1990)
McCarthy & Monkhouse (2003)
Pindyck (1991)
Dixit & Pindyk (1995)
Gilbert & Moel (2002)
Option to extend Right to construct and develop a new
underground mine with sequential
investment outlays.
Majd & Pindyck (1987)
Option to abandon Right to abandon a shaft, mining
investment or project under sub
economic conditions.
Trigeorgis (1990)
Tapper (2001)

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Option to contract Right to contract (scale back) mine
operations by selling a fraction of it for
a fixed price under uncertain
Trigeorgis (1993a)
Option to switch Right to close a mining operation that
is currently open by paying a fixed
shutdown cost and to open it later for
a different fixed cost.
Kulatilkaka & Trigeorgis (1994)
Brennan & Schwartz (1985b)
Moel and Tufano (2000)
Option to expand Right to expand a mining project by
paying more to scale up the
operations.
Trigeorgis (1990)
Compound options Right to implement an R&D
technology project in the mining
industry.
Tapper (2001)
Learning options Right to implement an R&D
industry.
Tapper (2001)
Trigeorgis (1990) identified and evaluated the following four types of operating
flexibility in a natural resource investment for a multinational company, which
influenced the management to proceed with the project despite having a negative
NPV. This example illustrated the following types of options:
! Cancellation during construction (option to extend)
o Mineral prices turn unfavourable.
! Expansion (option to expand)
o Potential for expansion of production capacity in the future.
! Abandonment for salvage (option to abandon)
o Project can be abandoned at any time – repositioned into an
alternative case.
! Deferral (option to defer)
o Project Initiation can be deferred without any adverse consequences.

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McCarthy & Monkhouse (2003) used real options analysis to examine the following
management decisions for a higher-cost copper mine that had ceased operations,
but was not yet permanently closed due to the uncertainty in the copper price
(option to switch):
! Continue with annual care and maintenance so that the mine can be
reopened if the copper price increases.
! Permanently close down the mine but incur a large up front environmental
rehabilitation cost.
! Reopen mine and commence operations.
! Sell the mine.
Pindyck (1991) stated that labour intensive firms (e.g. South African mining
companies) face the high costs of hiring, training and sometimes firing workers and
that if future values were uncertain, it may be better to defer the initial project
investment (option to defer). Dixit & Pindyk (1995) discussed that an embedded
option in the purchase of land leases could lead to the exploitation of mineral
reserves (option to defer). Real options analysis was also used to guide the
bidding strategy for the right to develop a copper and zinc deposit in Chile (Gilbert &
Moel, 2002) (option to defer).
Majd & Pindyck (1987) described that optimal investment rules can be determined
for such projects according to sequential investment outlays which gave
management the ability to adjust the pattern of capital expenditure as new
information arrives. They used an example of the construction of a new underground
mine that had a long investment lead time of around 5 to 6 years with uncertainty
regarding the commodity price and the ore reserves (option to extend). It was
explained that investment projects of this kind had the following characteristics:
! Investment decisions and cash outlays occur sequentially over time.
! There is a maximum rate at which outlays and construction can proceed.
! The project yields no cash return until it is completed.

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Brennan & Schwartz (1985b) showed that during periods of low prices managers
often continue to operate unprofitable mines that had been opened when the prices
were high and at other times managers failed to reopen seemingly profitable ones
that had been closed when prices were low. They concurred that the investment
decision to start or close down a mine project or switch operating mode is aligned to
the firm’s optimal operating policy around a critical commodity price (option to
switch). In a similar vein, Moel and Tufano (2000) studied the annual opening and
closing decisions of 285 developed North American gold mines during the period
1988-1997. They found strong evidence to support the hypothesis that the real
options model was useful for explaining the opening and closing decisions (option
to switch). Kulatilkaka & Trigeorgis (1994) used the opportunity to invest in a mine
as an example of a real life project, which allowed switching (option to switch)
between more than just two operating modes during its lifetime (e.g. the
management could collectively utilise the following operating modes: wait to invest,
expand, contract production, shut down, re-open or collectively abandon). They
stated further that the valuation of a flexible project such as this must be determined
simultaneously with an optimal operating policy in mind due to the presence of
asymmetric switching costs, which can compound interactions.
In the literature, (Howell et al, 2001) described an investment in Research and
Development as payment for a call option to invest in future production and sales.
Tapper (2001) used this approach in the mining sector with the use of real options
analysis and decision trees to evaluate and prioritise the technology acquisition
processes at DebTech (compound, learning options).
Trigeorgis (1993a) described a situation in natural resource industries where market
conditions being less favourable than expected, brought about reduction in the scale
of operations (option to contract).

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2.4 Numerical Methods used to evaluate Real Options
Management’s flexibility to adapt its future actions necessitates the use of an
expanded NPV rule, which reflects both components of the opportunities value, the
traditional (static or passive) NPV of direct cash flows and a premium for the
flexibility inherent in its operating options (Trigeorgis, 1988). For example,
Expanded NPV = Static (Passive) NPV + Option Premium
Trigeorgis (1993b) stressed the importance of valuing firm projects with collections
of real options and quantifying the interactions amongst these options. He stated
further that the combined flexibility that they afford management might be as
economically significant as the value of the project’s expected cash flows. He
explained the importance of getting a feel for the value of flexibility to various factors
through sensitivity analysis.
There are a variety of techniques that can be used to evaluate the embedded
options within an investment. The following methods are most widely used to
evaluate real options (Borison et al, 2003):
! Black-Scholes
! Binomial Lattice
! Monte Carlo Simulation
A hypothetical case example on the decision to restart an abandoned sub economic
shaft belonging to a gold mining company was used to illustrate the theory and
application of real option analysis compared to DCF - see Appendix 1. The
company has an embedded option in that the management has the flexibility to open
up the abandoned shaft at a higher gold price. The resulting option value was
calculated using Microsoft EXCEL for all three methods. The data and assumptions
made for this exercise are conceptual and should therefore not be used or repeated
to calculate the option value of real life examples, which have added complexities.

- 21 -
A closed form solution like the Black-Scholes model is exact, quick and easy to
implement but difficult to explain to management because of the highly technical
stochastic calculus mathematics (Mun, 2002). While binomial lattices, in contrast,
are easy to implement and explain but require significant computing power and time
steps to obtain good approximations (Mun, 2002). Therefore the results from the
closed form solutions are used in conjunction with the binomial lattice approach to
present management with a complete real options solution (Mun, 2002).
The need to educate management on all the numerical techniques available for real
asset valuation is regarded as an important factor for gaining acceptance of real
options analysis (Laughton, 2004).The Banff taxonomy of real asset valuation
methods shown in Figure 2.1 was one of the key outputs from an organised
workshop held in Banff, Canada in 2003 on “The Theory and Art of Asset Valuation:
Building a case for change – applying to the oil and gas industry what finance has
learned”. The taxonomy can be used to explain all the valuation methods available
to management and the assumptions involved with each method (Laughton, 2004).
Management can use this taxonomy to decide on an organisational strategy to move
onto more sophisticated numerical techniques like real options analysis (Laughton,
2004). For example, a mining company using simple DCF scenarios can move up
the modeling uncertainty axis of the taxonomy and across to real options analysis
(Laughton, 2004). Another example which a mining company can use to get to the
same result is to move across the taxonomy to risk discounting with forward pricing
and then up to real options analysis (Laughton, 2004). Obviously the benefits of
moving to real options analysis or any other method would need to be quantified
before making the decision to use new numerical methods (Laughton, 2004). The
Marketed Asset Disclaimer (MAD) in MAD real options analysis refers to the
assumption that the present value of the cash flows of a project without flexibility
(traditional NPV) is the best unbiased proxy for the market value of the project were
it a traded asset (Copeland & Antikarov, 2001).

- 22 -
Figure 2.1 The Banff taxonomy of real asset valuation methods
Source: Laughton (2004)
The mechanics of real options analysis is often simplified to a decision tree type
analysis, which can be used as a simple road map for management decision-making
and complement the real options analysis concept. For example, Trigeorgis &
Mason (1987) demonstrated that the options based technique of contingent claims
analysis is a special, economically adjusted version of decision tree analysis that
recognised market opportunities to trade and borrow.
The decision tree approach is supported by (D’Souza, 2002) who used this method
to map out options and help coordinate decisions more closely with unfolding
opportunities. The decision-tree framework is well suited to many of the
contingencies that arise over the course of a project (D’Souza, 2002). Thus
providing managers with a visual representation of how to allocate resources, when

- 23 -
to scale up or delay investments, and when to exit a project (D’Souza, 2002). In
addition, the uncertainties relating to the management of a project can be aligned
with milestone dates or nodes in the decision tree (D’Souza, 2002). Figure 2.2
highlights an example of a decision tree used to map out and evaluate the uncertain
process involved with the application of a new technology project. Borison et al
(2003) explained that real options and decision analysis were not so much
competing techniques as two important complementary elements in the overall
approach. Tapper (2001) in his research report highlighted the value of combining
an option value framework with decision trees to evaluate and prioritise research
projects. The following conclusions were drawn from his report:
! Decision tree methodology was found to be a practical tool that allowed easy
implementation of option valuation for value based research projects.
! Project decision trees were a useful communications framework for
management and researchers to focus their efforts on key issues that will
promote value.
Figure 2.2 Example of a decision tree
However, decision analysis is not without its implementation problems (D’Souza,
2002). For example, both business and technical managers need to be involved in
the mapping out of the decision tree to ensure unbiased decision-making, improved

- 24 -
buy-in of the real options concept and to avoid disputes on the probabilities of
failure and success for each stage of the project (D’Souza, 2002). Another key issue
relating to the implementation of real options is that company incentives should be
aligned to “value add” so that project members get rewarded for stopping a non
value adding project early and are not driven to reach set milestones (D’Souza,
2002).
To conclude, decision scientists agree that conventional valuation techniques are
often wrongly applied and propose the use of simulation and decision tree analysis
to capture the value of future operating flexibility associated with many projects
(Schwartz & Trigeorgis, 2001).
2.5 Frameworks and Road Maps
There are many examples given by practitioners in the literature of frameworks and
road maps that can be used to improve management acceptance of real options
analysis. For example, a key aspect to real options analysis is management’s ability
to identify the real options available to the organisation (Borison et al, 2003). To
illustrate this, the framework in Figure 2.3 is a useful tool for identifying the real
options within an organisation (Gilbert & Moel, 2002).

- 25 -
Figure 2.3 Real Options Identification Framework
Source: Adapted from Gilbert & Moel (2002:2)
In addition, Borison & Triantis (2001) identified and categorised the following
approaches used by a variety of firms that applied real options:
! Real options as a way of thinking – The firm/s uses real options primarily
as a language that frames and communicates decision problems qualitatively.
! Real options as an analytical tool – The firm/s uses real options and option
pricing models to value projects with known, well-specified option
characteristics.
! Real options as an organisational process – The firm/s uses real options
as part of a broader process and management tool to identify and exploit
options.
Trigeorgis (1988) described a general conceptual framework for analysing
investment opportunities seen as a collection of options on real assets. His

- 26 -
framework offers a unifying evaluation approach for all real investment decisions by
integrating capital budgeting and strategic planning under the single roof of value
maximization.
Kester (1984) discussed a growth option framework highlighting the difference
between the total market value of a company’s equity and the capitalised value of its
current earnings stream being an estimate of the value of the company’s growth
options. He stated further that companies should classify projects more accurately
according to their growth option characteristics.
Luehrman (1998) explained that option pricing should complement and not be a
substitute for existing capital budgeting systems. He suggested the following seven
steps for the application of real options analysis within organisations:
1. Recognise the option and describe it.
2. Map the projects characteristics onto call option variables.
3. Rearrange the DCF projections: to separate phases/ isolate exercise
prices.
4. Establish a benchmark for the option value based on the rearranged DCF
analysis.
5. Attach values to the option pricing variables.
6. Combine the five option pricing variables (Present value, Exercise price,
time period, Risk free rate and volatility) into two option - value metrics NPVq
and σ√T to compare and rank projects:
a. NPVq = S/ PV(X) is ratio of the present value of an asset divided by
the present value of the investment or exercise price that can
exercised over the set time period.
b. σ√T is the volatility of the asset multiplied by the square root of the
time period.
7. Compare the call value as a percentage of asset value to rank and
compare investments.

- 27 -
Copeland & Antikarov (2001) described a simple framework shown below in Figure
2.4, which uses four basic steps to calculate the value of real options using decision
trees and Monte Carlo simulation.
Figure 2.4 Framework for the application of real options analysis
Source: Copeland & Antikarov (2001)
Step 1: The DCF value
The first step is to calculate the DCF value of the project without flexibility using
Microsoft EXCEL.
Step 2: Model variable uncertainties
The second step models the causal uncertainties and feeds them into a Monte Carlo
simulation model based on the original NPV analysis. From the simulation we get an
expected volatility of the project’s value. The volatility is then used to build a value
based event tree.
Step 3: The decision tree
The third step is to identify the real options that management can exercise, their
effect on the remaining present value, their exercise prices, and their timing.
Step 4: Real options analysis
By starting at the end of the tree, the maximum value of the project after paying out
free cash flow is the maximum of its intrinsic value and the values of the embedded
options. The real option value can also be calculated using Monte Carlo simulation.
Compute base
case present
value using DCF
valuation model
Model the
uncertainty
using Monte
Carlo simulation
and event trees
Identify and
incorporate
managerial
flexibilities
creating a
decision tree
Calculate real
option value
(ROA)

- 28 -
All the steps involved with this framework must follow a change process and ensure
direct involvement with all the management decision makers and major stakeholders
(Copeland & Antikarov, 2001).
Mun (2002) illustrated a different approach of how to apply real options analysis
within an organisation through the following eight simple steps. He stated that a
thorough understanding of the process flow would make management more
comfortable in accepting the results of the analysis.
1. Qualitative management screening
a. Management to decide which projects are viable for further analysis
in accordance with the firm’s mission, vision, goal or overall
business strategy.
2. Base case net present value analysis
a. A discounted cash flow model is developed for each project that
passes the initial qualitative screening.
3. Monte Carlo simulation
a. Monte Carlo simulation may be employed to better estimate the
actual value of a particular project, using the most sensitive
precedent variables.
4. Real options problem framing
a. The strategic optionalities are identified and mapped out for each
project.
5. Real options modeling and analysis
a. The real option value of the project can now be modeled. The
implied volatility of the project can be calculated through the results
of the Monte Carlo simulation.
6. Portfolio and resource optimisation
a. This analysis will provide the optimal allocation of investments
across multiple projects.
7. Reporting
a. Results are presented to management in a clear, concise format.
8. Update Analysis

- 29 -
a. The analysis should be revisited on a regular basis once risks
become known.
Amran & Howe (2003) described five success factors as a simple road map to gain
management acceptance of real options analysis. They explained that the
combination of a strong story and a short set of calculations could assist
management acceptance of real options analysis:
1) Define and value the mature business model
2) Don’t get creative
3) Tell the story
4) Do one page calculations
5) Think one map of value
2.6 Strategy and Competition
There are numerous examples in literature that illustrate the alignment of real
options analysis with strategy (Schwartz & Trigeorgis, 2001; Myers, 1984;
Luehrman, 1998). For example, Schwartz & Trigeorgis (2001) stated that
sustainable competitive advantages (e.g. patents, proprietary technologies,
ownership of natural resources, managerial capital, reputation and brand name,
scale and market power) empower organisations with valuable options to respond
more effectively to unexpected adversity or opportunities in a changing
technological and competitive business environment. Myers (1984) discussed that
when time series links between projects are important, it’s better to think of strategy
as managing the firm’s portfolio of real options. He stated further that the process of
financial planning should involve the following:
! Acquiring options (e.g. investing directly in R&D, product design, cost of
quality improvements, and so forth, or as a by product of direct capital
investment).

- 30 -
! Abandoning options that are too far “out of the money” to pay to keep.
! Exercising valuable options at the right time (e.g. buying the cash producing
assets that ultimately produce positive net present value).
Luehrman, (1998) explained that the application of real options analysis was closely
aligned to strategic thinking and management decision-making. What is more he
stated that the application of real options analysis was all about practice and
recommended starting by drawing simple combinations of projects to learn some
common forms. He stressed the importance of picturing strategy in option space and
comparing with competitors strategies side by side. Furthermore, he described the
analogy that a business strategy was much more like a series of options than static
cash flows and that executing strategy almost always involved making a sequence
of decisions. Luehrman (1998) developed a framework shown below in Figure 2.5,
described as the tomato garden, which divided option space into regions and
provided a way to incorporate strategic options visually and quantitatively into option
value. He stated that by building option pricing into a framework financial insight can
be brought in earlier rather than later to the creative work of strategy.
Figure 2.5 The tomato garden as a portfolio of real options
Source: Luehrman (1998)
Region 6- rotten tomatoes
INVEST NEVER
Region 1 - ripe tomatoes
INVEST NOW
Region 2 - Imperfect
but edible tomatoes
MAYBE NOW
Region 3 - Inedible but
very promising tomatoes
PROBABLY LATER
Region 4 - less promising
green tomatoes
MAYBE LATER
Region 5 – late blossoms and
small green tomatoes
PROBABLY NEVER
Region 6- rotten tomatoes
INVEST NEVER
Region 1 - ripe tomatoes
INVEST NOW
Region 2 - Imperfect
but edible tomatoes
MAYBE NOW
Region 3 - Inedible but
very promising tomatoes
PROBABLY LATER
Region 4 - less promising
green tomatoes
MAYBE LATER
Region 5 – late blossoms and
small green tomatoes
PROBABLY NEVER

- 31 -
2.7 The Change Process
Many real options practitioners regard the use of a change process as a critical
success factor for gaining organisational acceptance of real options (Copeland &
Antikarov, 2001; Mun, 2002; Eapen, 2003).
Rogers (1995) described five attributes of innovation that affect the rate of adoption
of a change initiative:
1. Superior idea
o Provides better results
o Intuitive
o Logical
2. Compatible
o Includes current approach as a special case
o Congruent with culture
3. Low Complexity
o Easy to understand
o East to implement
4. Triability
o Can be experimented with in a limited way
o Results of an experiment can be easily generalized
o Low cost to implement
5. Observability
o Benefits easily observed
o Easy to communicate
Kotter (1995) learned that the more successful cases involving a change process go
through a series of phases that, in total, usually require a considerable length of

- 32 -
time. He stated further that skipping steps creates only the illusion of speed and
never produces satisfying results and critical mistakes in any of the phases can
have a devastating impact. He ascribed the following eight phases to transforming
an organisation:
1. Establishing a sense of urgency
2. Forming a powerful guiding coalition
3. Creating a vision
4. Communicating a vision
5. Empowering others to act on the vision
6. Planning for a creating short term wins
7. Consolidating improvements and producing still more change
8. Institutionalising new approaches
Change management specialists have found the following criteria need to be met
before a paradigm shift in thinking is found to be acceptable (Mun, 2002):
! The models and processes must have applicability to the problem at hand
and not merely an academic exercise.
! The process and methodology has to be consistent, accurate, and
replicable.
! The method must provide a compelling value-added proposition.
! The new methodology must be easy to explain.
Lack of understanding is considered by many practitioners to be a major barrier for
gaining management acceptance of real options analysis. To illustrate this, Eapen
(2003) having spent much time talking with academics, consultants, and corporate
executives about how to provide insights into making better decisions highlighted
the common misconceptions and counter responses about real options in Table 2.4.
The misconceptions highlighted the misunderstanding shown by many organisations
regarding the application of real options and the need for a change process to
ensure management acceptance (Eapen, 2003).

- 33 -
Table 2.4 Common misconceptions about real options
Misconception Counter response
Real options simply do not
work.
The real options framework provides a generalised
asset pricing methodology, of which the more
conventional techniques like DCF are special,
simplified cases. In situations with little variability in
expected outcomes and no flexibility in future decision
choices, conventional techniques like DCF are
adequate. When technical risks dominate market
risks, leading to lack of management flexibility, real
options analysis can be difficult to apply. However,
technical risks are treated the same for DCF and real
options analysis, it is only the market risks that are
treated differently.
There is no empirical
evidence that the market
uses real options to price
assets.
The market is capable of appreciating value beyond
what can be assessed using traditional DCF
techniques. For example, Gold mining companies
have market values higher than the DCF valuation due
to the optionality created by a fluctuating gold price.
Real options are
complicated and difficult to
calculate.
Complicated calculations in engineering require
complex modeling techniques. There are many ways
of making real options models more user friendly, but
such models will never become as generic as an
EXCEL formula.

- 34 -
Real options are difficult to
explain to senior managers.
Good managers often think about delaying,
abandoning or expanding aspects of a project
investment over time. Until real options, corporate
finance had not provided a way to structure such
thinking and quantify the value of these strategic
alternatives.
The data requirements for
real options analysis are
extensive and the analysis
itself is time consuming.
Real options analyses tend to be less data intensive
as the mean underlying value is used from the DCF
analysis together with an estimate of volatility. The
issue surrounding traditional DCF analysis is that after
exerting considerable effort in collecting data, most of
the information associated with the data is ignored
and the NPV is calculated by discounting at the cost of
capital or an unstructured risk adjusted discount rate.
Real options analysis
requires a measure of
volatility, which is not
always readily available.
Volatility although sometimes difficult to calculate –
includes a lot more real life information than an easy
to use discount rate. A project’s value is generally
more sensitive to discount rate than to the volatility
estimate.
Real options analysis uses
complex mathematics and
is difficult to understand.
Many decision makers don’t apply the same rigour to
the underlying assumptions of the capital asset pricing
model.
Real options are just
decision trees.
Decision trees are merely pictorial representations of
DCF calculations with technical risk represented in the
branches. Decision trees are a good way to frame the
decision problem and are good communication
vehicles but do not provide the full benefits of real
options analysis.

- 35 -
Real options techniques are
just a way to increase value
to make a project more
attractive.
Discount rates for a specific project or decision are not
generally observable in the market and neglecting
empirical market data is a missed opportunity.
Real options caused the
technology bubble and
recent crash.
Reliance on rules of thumb and precedent may have
contributed to the overvaluation of the tech stocks. If
real options were more mainstream today, it might
have prevented the overvaluation that led to the
technology bubble.
Source: Adapted from Eapen (2003)
To conclude, the following lessons on change management were highlighted by
John Stonier (The Marketing Director, Airbus Industrie) in a documented case study
on the successful implementation of real options analysis at Airbus Industrie
(Copeland & Antikarov, 2001):
! Use an application where clear evidence of the benefits of the real options
analysis can be seen.
! Find a sponsor at the highest level of the organisation.
! Ensure that there is an atmosphere of change within the company.
! Use an external advisor or consultant to provide a neutral and nonpolitical
expert coupled with an internal champion.
! Use the model to confirm and support intuition where possible.
! Don’t develop a black box model that no one understands.
! Reward managers for risk taking beyond what the company is used to.

- 36 -
! Ensure support from the corporate finance and treasury departments. Search
out and develop a real options champion within the treasury department.
! Focus on the big picture and not singly on the results of the model.
2.8 Lessons Learned by Real Options Practitioners
This sub section is a collation of issues and factors taken from literature that
describe real options practitioners’ perceptions of the key factors for the successful
application of real options analysis. Most of the factors identified in this sub section
are considered to be generic to most industries. The applicability of the factors and
issues to the mining industry would need to be tested through research.
Borison et al (2003) explained that exploiting real option value has everything to do
with the company’s managerial and operational competence. Notably, the following
issues were deemed to be important for the successful application of real options
analysis (Borison et al, 2003):
! Companies need to ensure that the business processes and decision rights
frameworks allow and promote a more risk averse and entrepreneurial culture
so that managers have the freedom to exercise and abandon strategic
options.
! Managers need to understand that although the application of real options is
a new language, the underlying concept is nothing new.
! Companies need to build a sound economic model for the business that can
be used to compare strategic options.
! The management team must be able to identify, create and then exercise
options.
! The use of simple graphical software allows a manager to formulate a
problem using a decision tree framework and then quantify the real option
value using Monte Carlo simulation.
! Companies with strong market positions often find themselves with more
options.

- 37 -
! A need to communicate the resulting valuation to analysts.
! Ensure that technical (operational) and market risks are separated when
applying real options analysis.
! Develop a systematic way of framing the decision process.
! The more realistic treatment of uncertainty can correct basic mistakes made
with DCF.
Borison et al (2003) stressed that the resistance to corporate adoption of real
options analysis techniques today was mainly due to the fact that the management
science and finance communities were still not working together. Thus the approach
that successfully united these two disciplines towards the adoption of real options
analysis techniques would be very powerful and the ultimate winner in the corporate
world (Borison et al, 2003).
Mello & Pyo (2003) explained that many investments include both technical risks
and markets risks and option pricing techniques lost their advantage when private
risks were important. They explained further that a preference-based approach
should be employed to capture the real option’s sensitivity to the component of
private risk in the investment opportunity and clarify whether the private risk
enhances or diminishes the value of the real option.
In addition, Kemna (1993) identified the following organisational issues that were
deemed important for gaining management acceptance of real options analysis:
! Convince management that some proposals contain flexibility that cannot be
valued by using DCF analysis and must be valued using real options
analysis.
! Make a clear distinction between investment alternatives and options
embedded in these alternatives, because management often considers
options as alternatives, which leads to misinterpretation.
! Restrict the number of options to the most important ones; more options
increase complexity without necessarily adding much value.
! Restate the investment problem in the following sense: Can the costs of the

- 38 -
additional flexibility be justified by the benefits when the flexible alternative is
compared to the alternative without flexibility.
! Define properly the uncertainties that management faces and given these
uncertainties determine the valuable option.
! Whenever possible, incorporate the influence of competitors and other costs
that may affect the value of the option.
! Focus on the value of the project including the option and present sensitivity
analysis, especially for volatility.
Furthermore, Kester (1984) stressed the point that in order to link capital budgeting
with long range planning, a company should place them both under the supervision
of a single executive or an executive committee. Howell et al (2001) stated if in
house skills are insufficient that individual expert consultants can be used for the
following issues in the real options analysis process:
! Structuring the decision in general economic terms and in real option terms.
! Building the correct mathematical model.
! Solving the computations and doing sensitivity analysis and reality checks.
De Neufville, (2001) explained that in systems technology management, much of the
work in applying real options lies in the processes for determining when and how to
implement the options. Further, he stated that the process involved at least three
distinct phases:
1. Discovery – Identify the most interesting areas of uncertainty, which may
potentially offer the greatest rewards for options.
2. Selection – Evaluate the possible means of providing flexibility to the system,
and determine which of the options to implement.
3. Monitoring - Monitor the evolution of the uncertainties so that the organisation

- 39 -
will know when to implement or abandon the options that it has built into the system.
There are always barriers to acceptance. For example, Trigeorgis & Mason (1987)
found two negative reactions regarding the application of real options to value
managerial flexibility:
1. Professional managers found the concept of valuing managerial flexibility to
have intuitive appeal but thought the actual application of option-based
techniques to capital budgeting too complex for practical application.
2. Decision scientists preferred the use of traditional Decision Tree Analysis, a
technique that has existed for 20 years.
Also, Howell et al (2001) identified the following pitfalls of real options analysis:
! Using real options analysis when it is not applicable.
! Getting the real options model wrong.
! Getting the model right, but inserting data which is biased to the answer.
! Getting the model and data right, but miscalculating the solution.
To conclude, there are similarities and differences in the perceptions gained from
real options practitioners of what the key factors are for gaining management
acceptance of real options analysis.
2.9 Research Propositions
The following propositions are derived from the literature and are statements against
which the research was conducted. The combination of the problem statement with
the research propositions, factors and references in the research is shown in a
consistency matrix - see Appendix 2.
Proposition 1: The following factors may influence management acceptance of
real options analysis in the mining sector:

- 40 -
1. The highlighting of the flaws in the use of conventional DCF valuation
techniques to management.
2. The marketing of the benefits in the use of real options analysis to
management.
3. The presentation of real options analysis with decision tree maps.
.
4. The existence of a structured change process to influence the
organisational acceptance of real options analysis.
5. The availability of external consultants with the knowledge of real
options analysis in the mining sector to advise management.
6. The existence of an internal champion to facilitate and market the
application real options analysis within the organisation.
7. The degree of competence or lack thereof of the management team to
identify and evaluate real options.
8. The extent to which the organisational and business performance
metrics are adapted and aligned with the application of real options
analysis.
9. The existence of simple frameworks and road maps to improve
management understanding of real options analysis.
10. The extent to which real options analysis is used to quantify and justify
strategic decision making.
11. The degree and existence in the organisation of different valuation

- 41 -
techniques for sound economic analysis.
12. The application of real options analysis to decompose the private/
technical and market risks.
13. The availability of practical and proven examples of real options
Proposition 2: The factors identified in the research have varying relative
importance.
The purpose of this proposition is to measure the relative importance of the factors
identified in the research. The key factors to gaining management acceptance of
real options analysis in the mining industry can then be determined.
3. RESEARCH METHODOLOGY
The research method used a qualitative study to identify the key factors that lead to
management acceptance of real options analysis in the mining sector and involved
the following steps:
! A detailed literature review around the research problem to identify the
research questions and propositions.
! The completion of an interview process design with questionnaire that
addressed the findings of the literature review.
! The pilot testing and revising of the questionnaire.
! Semi structured in depth interviews with a sample of respondents taken from
research population.
! The collection of qualitative data from the recorded interviews into a
database.
! The decision on what data from the interviews is necessary to support or
demolish the propositions using content analysis.
! The drawing and verifying of conclusions.

- 42 -
The model shown in Figure 3.6 illustrates the components of data analysis involved
with a qualitative study of this nature.
Figure 3.6 The components of data analysis for a qualitative study
Source: Miles & Huberman (1984)
3.1 The Research Population
The population for this research included consultants servicing and the technical
and financial management within global mining and mineral resource organisations
that have experience with or knowledge of the use of real options analysis
practitioners. The scope of the research was to investigate the perceptions of the
following two groups of professionals namely;
1. External consultants that service the global mining sector.
2. Management, technical and financial staff that operate within the global
mining sector.
Group 1 included academics and consultants who were recognised practitioners of
real options analysis that serviced the global mining sector. Group 2 included
managers and technical and financial staff that operate within the global mining
sector and were real options practitioners or had knowledge of the application of
real options analysis. Comparisons were drawn between the perceptions of the two

- 43 -
groups. The sample was targeted largely in countries that had a developed mining
and minerals resource sector (e.g. South Africa, North America, Canada, and
Australia). The group 1 and group 2 interviewees are referred to throughout the
following chapters of the research as consultant and business respondents.
3.2 Sample size and sampling methodology
A convenience sample of fifteen professionals from each of the two groups of
professionals was taken. The sample design was purposive, in that the respondents
were purposefully selected through “word of mouth” to be able to answer the
research questions. The names of all the respondents interviewed are shown in
Table 3.5. Due to the shortage of respondents it was necessary to interview four
respondents from the petroleum industry, which has similarities with the mining
industry and has a reputation for using real options. One of the respondents did not
want to be interviewed but provided electronic responses to the interview questions.
Table 3.5 Respondents interviewed
Institution Country Name Designation Group
Monitor Group Hong Kong Alberto Moel Corporate Finance 1
University of Alberta
School of Business
David Laughton
Consulting Ltd.
Canada David Laughton Adjunt Professor 1
Chevron Texaco USA Frank Koch Decision Analysis
Practice Leader
2
Strategic Decisions
Group
USA Gardner Walker Partner 1
Strategic Decisions
Group
USA Rick Chamberlain Senior Engagement
Manager
1
Teck Cominco Limited Canada Greg Waller Director: Financial
Planning & Analysis
2
Charles River USA John Parsons Vice President 1

- 44 -
Associates
Petrobras Brazil Marco Dias Senior Consultant 1
BHP Billiton Australia Peter Monkhouse Vice President -
Business Strategy
2
University of Alberta Canada Samuel Frimpong Professor School of
Mining and
Petroleum Eng,
1
BP UK Simon Wooley Distinguished Advisor
- Financial Skills, BP
Finance
2
Impala Platinum South Africa Deon Janssen Corporate Finance 2
Resource Finance
Advisors
South Africa Dr Eric Lilford Director - Resource
Finance Advisors
1
UCT Business School South Africa Dr Evan Gilbert Lecturer-Corporate
Finance
1
Monitor Group South Africa Gareth Huckle Consultant 1
BHP Billiton South Africa Matt Mullins Project Development
Services
2
Anglogold Ashanti South Africa Paul Dennison Manager: Business
Development
2
Anglogold Ashanti South Africa Rob Croll Manager - Valuation 2
Anglogold Ashanti South Africa Mike Field Senior Divisional
Valuator: Business
Development
2
SWA Consulting Ltd UK Stephen Allport PMKN Network
Manager
1
De Beers Technical
Services
South Africa Staffan Tapper Research Manager 2
Anglo Platinum South Africa Trevor Raymond Senior Manager:
Investor Relations
2
AMEC Americas
Limited, Mining and
Metals Group
Canada Mike Samis Director of Financial
Services, Mining and
Metals
1

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Cerna, Ecole des Mines
de Paris
France Margaret
Armstrong
Professor 1
Anglo Platinum South Africa David Thomason Senior Manager
Business
Development &
Corporate Finance
2
Anglo Platinum South Africa Chris Jacobs Strategic Finance 2
De Beers South Africa Dave Fricker Consultant Mining
Engineer
2
De Beers South Africa Gary Hambidge Manager: scenario
planning / technical
investigations
2
Risk Capital USA David Shimko President 1
Colorado School of
Mines
Canada Graham Davis Associate Professor
Division of
Economics and
Business
1
3.3 Data Collection
An in depth semi structured interview technique was chosen as the measuring
instrument for the data collection. The rationale for choosing this technique was to
gain richer data through discussion around the research questions. An interview
process consisting of a questionnaire and open-ended questions was drawn up and
a pilot study was run to verify the reliability and validity of the measuring instrument.
The key objectives of the interview process were to:
! Confirm or disprove the factors identified from the literature;
! Understand the reasoning for the interviewee responses;
! Get the respondents to summarise opinion on the key factors leading to
management acceptance of real options analysis.

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The interview process shown in Appendix 3 comprises a questionnaire with a set of
probing and open-ended questions designed to yield richer information (Leedy &
Ormrod, 2001). The questionnaire used for the interview was based on the research
propositions drawn from the literature review. The interview was structured as
follows:
! Part 1 – The interviewer gained rapport with the interviewee and opened
proceedings with an overview of the rationale behind the research. The
interviewer provided examples of the application of real options analysis in
the mining sector from the literature review (Perry, 2001).
! Part 2 – The interviewee was asked to describe what factors were important
to gain management acceptance of real options analysis in the mining sector
and explain reasoning. The interviewer probed with statements relating to the
factors identified in the literature (Perry, 2001).
! Part 3 – The interviewee was asked to summarise opinion on the key factors
which lead to management acceptance of real options analysis in the mining
sector and describe a holistic approach to implement the identified factors in
the mining sector.
Each of the interviewees were contacted initially in person and interviewed on a
one-to-one basis. Face to face or telephonic recorded individual interviews were
held with interviewees. The reason for the use of telephonic interviews was to cater
for the overseas respondents and those individuals who were unable to be
interviewed face-to-face due to distance or time availability. The timing of the
interviews varied from 30 minutes up to 1 hour and 20 minutes.
The responses of the interviewees pertaining to the research questions in the
interview process were captured in a database. The data pertaining to parts 2 and 3
of the interview process were captured and analysed separately. The reason for this
was to compare the factors identified by respondents during part 3 of the interview
process with the respondents perceptions on the ranking and validity of the factors
drawn from the literature.

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Leedy & Ormrod (2001) highlighted the following suggestions for conducting a
productive interview which were adhered to during the interview process:
! Make sure the interviewees are representative of the group.
! Find a suitable location.
! Take a few minutes to establish rapport.
! Get written permission.
! Focus on the actual rather than on the abstract or hypothetical.
! Don’t put words in people’s mouths.
! Record responses verbatim.
! Keep your reactions to yourself.
! Always keep the responses of the participants as perceptions rather than
facts.
3.4 Reliability and Validity
The reliability of the measuring instrument refers to whether consistent and accurate
results can be drawn with repeated measurements on the same subject. Whereas
the validity of the measuring instrument is the extent to which the instrument
measures what it is supposed to measure (Leedy and Ormrod, 2001).
The fact that thirty interviewees were chosen for interview from a variety of countries
added external validity to the research. The rapport established between the
interviewer and the interviewee at the beginning of the interview process was
important to ensure face validity through the cooperation of the interviewees. For
example, any misinterpretation by the interviewee of the research questions was
easily resolved during the interview. The use of a validated standardised instrument
like content analysis also improved the internal validity of the research. The fact that
the interviewees were asked to summarise their own opinion at the end of the
interview (part 3) on what they perceived were the key factors for management
acceptance was an important validity check on the data from part 2. One main
reservation on the validity of the qualitative data was that it was based on the
perceptions of the interviewees and the interpretation of the interviewer. The

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interview process was followed in the same manner for all interviews taken because
the lack of standardised interviews can lead to unrepeatable results.
3.5 The Pilot Study
A trial run on the interview process was conducted to determine the respondents
understanding of the questions and provided the interviewer with an opportunity to
test the validity and reliability of the measuring instrument. One trial interview was
conducted with a volunteer who had knowledge of real options analysis. The
respondent gave honest and constructive feedback and some of the questions were
reworded to ensure better understanding.
3.6 Data Analysis
A content analysis was performed on the contents of the interview data. Two
methods were used to analyse the data. Firstly, the interviews were transcribed
verbatim and then statements and patterns containing constructs were highlighted
and coded. Descriptive and numerical coding was used to classify the words (Miles
& Huberman, 1984). The advantage of this method was that the relevant information
was structured in a database ready for analysis. A second method used to analyse
the data, comprised listening to the recording of an interview again while making
notes of the construct detail. The advantage of this method, even through the
information was not available on paper, was that the interviewer could relive the
emotions and feelings of the interview process. These analyses were also
conducted soon after the completion of the interviews to ensure that nothing was
forgotten. The notes from the second method were also analysed and statements
and patterns indicating constructs, were coded. The relevant constructs were then
grouped together and compared with the factors in the literature. The following data
analysis spiral in Figure 3.7 was used as a road map to manage the data analysis
process.

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Figure 3.7 Road map to manage the data analysis
Source: Miles & Huberman (1984)
A computerised database was set up and the electronic recordings of the interviews
filed under each of the respondents names. An electronic copy of the notes taken
straight after the interview was completed was also combined with the name of the
respondent. The interviews were copied verbatim into Microsoft Word documents
and filed. The entire dataset was perused several times to get a sense of the
content and notes were taken regarding the interpretations found. The responses to
each of the research questions in part 2 and opinions in part 3 of the interview
process were then categorised and classified. The factors identified in the literature
and from the interviewee responses were tabulated and the results were ranked,
presented and interpreted in chapters 4 and 5. Recommendations and conclusions
were drawn from the results.
Comments from the interview transcripts have been used to support the
interpretation of the results. The comments have been quoted verbatim and no
grammatical errors were rectified. The names of the respondents have not been
The Raw Data
The Final Report
Organisation
•Filing
•Creating a computer database
•Breaking large units into smaller ones
Perusal
•Getting an overall sense of the data
•Jotting down preliminary interpretations
Classification
•Grouping the data into categories or themes
•Finding meaning in the data
Synthesis
•Offering hypothesis or propositions
•Constructing tables, diagrams, hierarchies
The Raw DataThe Raw Data
The Final ReportThe Final Report
Organisation
•Filing
Organisation
•Filing
Perusal
Perusal
Classification
Classification
Synthesis
Synthesis

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attributed to the comments for confidentiality reasons. However, a complete list of
quotes per respondent is available upon request. All quotes have been highlighted
in italics.
4. PRESENTATION OF RESULTS
The results are divided and presented in three parts. The first and second parts of
the chapter (sub sections 4.1 and 4.2) present the results of the perceptions of the
two groups of respondents. The final part of the chapter (sub section 4.3) combines
the perceptions of the consultant and business respondents. In each sub section
the factors from literature are ranked and comparisons made with the factors
identified by the respondents. A colour coding system shown in Table 4.6 was used
to tier rank the factors confirmed by the respondents in the research. The mandatory
and supportive factors (Tiers 1 and 2) are considered to be key factors. The factor
code used to categorise and classify the factors in part 2 of the interview process is
shown in Appendix 4.
Table 4.6 Colour coding for tier ranking of factors
Factor clusters Percentage Colour Code Meaning
Tier 1 +85% Mandatory
Tier 2 +70-85% Supportive
Tier 3 55-70% Optional
Tier 4 <55% Not relevant
4.1 Presentation of results for consultant respondents
The frequency count for the factors confirmed by consultant respondents is shown in
Table 4.7. The factors are tier ranked using the colour coding in Table 4.6.
Table 4.7 Frequency count of the factors confirmed by consultant respondents
from the literature

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Factor number Factor Code N=15 Percentage Rank
6 Internal 13 87% 1
7 Management 13 87% 1
1 Flaws 11 73% 3
10 Strategy 11 73% 3
11 Analysis 11 73% 3
4 Change 11 73% 3
8 Process 11 73% 3
3 Decision Trees 10 67% 8
13 Examples 9 60% 9
2 Benefits 8 53% 10
9 Roadmaps 7 47% 11
5 External 6 40% 12
12 Risks 6 40% 12
The frequency count for the key factors identified by consultant respondents is
shown in Table 4.8. The factors have been ranked in order of decreasing
importance.
Table 4.8 Frequency count of the key factors identified by consultant
respondents
Factors N=15 Percentage Rank
The need for pilot projects 9 60% 1
The development of a software application 5 33% 2
The need for an internal champion 4 27% 3

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Management issues 4 27% 3
Change process 4 27% 3
Training programs to educate management 4 27% 3
Presentation and marketing 3 20% 7
Real options analysis as a complementary tool 3 20% 7
4.2 Presentation of the results from business respondents
The frequency count for the factors confirmed by business respondents is shown in
Table 4.9. The factors are tier ranked using the colour coding in Table 4.6.
Table 4.9 Frequency count of the factors confirmed by business respondents
from the literature

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6 Internal 14 93% 1
12 Risks 14 93% 1
9 Roadmaps 10 67% 7
4 Change 9 60% 8
1 Flaws 6 40% 9
2 Benefits 6 40% 9
8 Process 6 40% 9
13 Examples 5 33% 12
5 External 4 27% 13
The frequency count for the key factors identified by business respondents is shown
in Table 4.10. The factors have been ranked in order of decreasing importance.
Table 4.10 Frequency count of the key factors identified by business
respondents

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4.3 Presentation of the results from all respondents
The frequency count for the factors confirmed by all respondents is shown in Table
4.11. The factors are tier ranked using the colour coding in Table 4.6.
Table 4.11 Frequency count of the factors confirmed by all respondents from

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the literature
6 Internal 27 90% 1
4 Change 20 67% 6
12 Risks 20 67% 6
1 Flaws 17 57% 8
8 Process 17 57% 8
9 Roadmaps 17 57% 8
2 Benefits 14 47% 11
13 Examples 14 47% 11
5 External 10 33% 13
The frequency count for the key factors identified by all respondents is shown in
Table 4.12. The factors have been ranked in order of decreasing importance.

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Table 4.12 Frequency count of the key factors identified by all respondents
Factors Identified N=30 Percentage Rank
5. INTERPRETATION OF RESULTS
The objective of this chapter is to interpret the results of the interviews. The first part
of the chapter (sub section 5.1) interprets the results of the consultant respondents
and the second part (sub section 5.2) interprets the results of the business
respondents. The sub section 5.3 summarises opinion on the results from the
chapter. For this analysis, only the key factors (tiers one and two) are considered
important and discussed.
5.1 Interpretation of the results from consultant respondents
5.1.1 Key factors confirmed by consultant respondents from the literature
The tier ranking of the following key factors is shown in Table 4.7.
Tier one factors

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The consultant respondents considered the tier one (mandatory factors) to be:
The need for an internal champion was considered to be fundamental for gaining
management acceptance. Although, it was stressed by the consultant respondents
that the internal champion would need to be senior enough or have sufficient
influence on senior management to be effective “Yes, it’s fundamental, it’s a
necessary condition. You need somebody who pushes, actually somebody senior
enough who says, you know what I don’t care whether you think this is good or not,
this is what we are doing”. The following quotes reinforce this point:
“Internal champions do not help much unless the push for real options comes from
the most senior managers.”
“Mid-level managers who champion real options tend to be rebuffed when they start
to introduce ideas that make senior managers uncomfortable.”
A potential solution to this problem mentioned by respondents was the combination
of an internal champion and a senior sponsor “latch onto a person in senior top level
management, catch his attention, help him or her to see the value of that
methodology … it will be an inroad into the company”.
It was indicated by one consultant respondent that the motivation for being an
internal champion was to further his/her career “One of the motivations for studying
new things is to advance their own career in some sort of way”. The example of the
introduction of geostatistics into the mining industry was used as an analogy to
where real options analysis should be heading “the companies that got geostatistics
to work really well and generate profits, turned out to be companies where they had

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one person, the hero, the person who pegged his life on this, who decided that his
career would go forward as the champion of geostatistics”.
The need for a competent management team that is keen to create shareholder
value was highlighted as being the other tier one factor by the consultant
respondents “My belief is that you need to have a management team that is
business focused and value focused ….they understand that their job is to create
shareholder value”. The need for an innovative management team was considered
important by consultant respondents “you need to have a management that is open
to and not afraid of new…if you have younger management that’s always helpful
because they are not set in their ways as much and the other thing is financial
people in key business positions.”
Many of the consultant respondents considered a lack of education and
understanding in finance theory to be a key barrier for gaining management
acceptance of real options analysis “Most managers do not have the background in
finance theory to fully understand the differences between DCF and real options and
further do not have the numerical skills to build effective valuation models on their
own.” It was stated by one consultant respondent that some managers didn’t
understand DCF and that most technical staff and management were not receiving
financial training “most senior management…don’t get commercial training”.
Therefore, structured training programs were posed as a solution to this problem
“The numerical skills and insights can be developed with appropriately structured
real options courses”. Notably, one consultant respondent stated that the lack of
understanding by management was due to the academic institutions “I think the
academic institutions…are definitely not providing adequate training…I’m just going
to put it on a table and say any valuation methodology, other than DCF”.
It was pointed out that it would be futile to teach senior management about the
mathematics behind real options analysis and option pricing, due to time availability
“I think that the expectation is not to have management understanding the technical
details of option pricing. It will be a lost cause”. A key issue was therefore to gain
management’s attention about the importance of the real options analysis through

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marketing so that resources are made available to do the real options calculations
“Get their interest and then let them go forward but the actual work of understanding
must still fall on the engineers and the economic evaluation team who are actually
going to use the method to solve the problems”.
To conclude, one consultant respondent voiced that the strategic intent of most
mining organisations to become lowest cost producers due to market forces was
contradictory to not aligned to the application of real options analysis “Get as much
of it as possible, get the lowest cost possible. I don’t need flexibility in that…..all
they will see is more hard work, more fancy maps and what’s the point, the point is
let’s get the stuff out the ground as cheaply as possible.”
Tier two factors
The consultant respondents considered the tier two (supportive) factors) to be:
1. The highlighting of the flaws in the use of conventional DCF valuation
techniques to management.
2. The existence of a structured change process to influence the
organisational acceptance of real options analysis.
3. The extent to which the organisational and business performance
metrics are adapted and aligned with the application of real options
analysis.
There was general consensus by most of the consultant respondents that
highlighting the flaws in DCF is a supportive factor for gaining management
acceptance of real options analysis. The fact that the market capitalisation of most

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gold mining companies are multiples higher than their respective net asset values
was highlighted as a major flaw in the use of DCF to evaluate mining assets. It was
mentioned that a compelling case to market real options analysis was to evaluate a
mining firm’s stock price (i.e. something which has a known value) using real options
analysis and show management that the method is more accurate than DCF.
The following quotes illustrate the limitations of DCF:
“Lets look at your historical stock performance and your industries performance in
investing shareholder money and returning value, my point would be that whatever
techniques you are using right now have done a terrible job of shareholder value
creation.”
“If DCF worked then there would be no need for a new valuation technique.”
“Well it’s very simple, if you go to a client or to somebody and explain, I mean
everyone knows what the flaws in DCF are, it’s not rocket science, then you provide
an alternative and you might mitigate some of those, there is interest in trying it out.”
However, there were some strong counter arguments from respondents regarding
the importance of this factor “just saying there are flaws is not going to convince
anybody”. It was mentioned that real options analysis should rather be treated as a
complementary rather than a competing technique to DCF.
“The approach I’m trying to make is to say that this is sort of adding on but it’s not
sort of something that’s going to replace DCF or that fundamentally conflicts with
DCF.”
“I always try to present a simplified version of the problem that can be done as a
DCF and then present the real options as just the big power plant, huge engine, for
valuing that simple problem when you do it in proper detail.”
It was stated by consultant respondents that some consultants and academics

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oversell real options as being a panacea instead of a complementary tool, invariably
doing more damage than good. As voiced by one respondent “Unfortunately, an
enormous number of people who are trying to sell real options do not do that, they
try very hard to say the thing that you know well, DCF, is stupid, it cannot solve the
problem, here I’ve got a black box which you can’t understand.“ Other consultant
respondents stated that efforts to highlight the flaws in such a tried and tested
technique like DCF could be detrimental to gaining management acceptance of real
options. One consultant respondent voiced that the embedded nature of the DCF
brand within most businesses was not to be underestimated “NPV has its own brand
… managers seem to be embarrassed that they do not understand it.”
The need for a change process was also considered by respondents to be an
important supportive factor “Certainly if they don’t have a structured change process
you are dead in the water and you have to be very careful how you design that, it’s
going to be idiosyncratic to the corporation involved depending on their particular
business and sources of expertise when they start”. There were many views that
stated a change management process was definitely necessary “you have to have
some organisational process in which you adopt real options because that polarises
it within your organisation as being important so people can’t avoid it and say well I
don’t want to learn about it because they don’t have enough time.” Top management
support for the change program was considered critical “the first person to go on the
training course will be the CEO so no one can turn around and say well I don’t have
time to go on it”.
It was mentioned again by one consultant respondent that real options analysis
should be marketed as a complementary technique and not need a structured
change program “My firm belief still stands that it’s an additive tool rather than a
unique tool that should be used at the expense of others”. Interestingly, a contrarian
view by one of the consultant respondents was that change processes were
detrimental to the adoption of real options analysis “Change costs money …detracts
from incentive to make the change in the firm.”
Consultant respondents agreed that the need to adapt the business process was an
important supportive factor. Many consultant respondents stated that a change of

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organisational behaviour was required to make real options analysis successful,
particularly in the planning and execution phases of investment projects.
”There has been talk in the different business magazines like Harvard Business
Review about rewarding management for shutting down projects earlier when there
are no hopers rather than hoping that some day in the future it will turn around and
be something great where they will get their reward”.
“I think it can be tremendously important in getting people to identify all of the options
that do exist for a typical project”.
One consultant respondent remarked that a key barrier to change was the way DCF
was hard coded into the organisational business processes “Corporations are
structured for conventional DCF valuation approaches. It is difficult to change this”.
However, organisations that were already looking at other valuation techniques
other than DCF were considered to have a clear advantage “you need a firm that
has at least enough of a framework or enough of a way of thinking that making the
step to real options is not that difficult”.
One consultant respondent remarked that smaller entrepreneurial companies would
find this factor less appealing “That’s probably true for big companies but if you look
at little entrepreneurial companies… they might effectively be going through
something very close to the what-if process we are thinking about, seeing that it’s
such a small team you’ve got a much better chance of them talking to each other”.
Consultant respondents were in agreement that the fact that real options analysis
can quantify and justify strategic decision making was an important supportive factor
“If your strategic analysis doesn’t show the sources of value and your financial
analysis doesn’t prove the sources of value then you’re not clear that you have
value, so that’s the way to think about it”. There was general consensus from
consultant respondents that the use of real options analysis assisted strategic
decision making “Real options, when you talk about strategic decisions, should we
go for higher cost projects verses lower cost projects, do we want to spend that

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premium on this type of project or not. If real options can provide you with better
information, better understanding of those decisions then it’s good.” One view from a
respondent was that senior management were not using real options analysis for
strategic analysis due to its complexity and lack of adaptability “If you want it to be
broadly used in particular by higher management for strategic decision-making that’s
not happening at all unless it becomes a more general user friendly and adaptable
tool”. Further, one consultant respondent stated that the use of real live examples
would help illustrate the importance of real options analysis to strategic decision
making “a real option experiment… will help people understand” .
Consultant respondents were in agreement that the degree of sound economic
analysis for management decision-making real options analysis within an
organisation was an important supportive factor. As voiced by one respondent “a
higher degree of sound economic analysis is going to generate more interest in use
of real options”. The knowledge and skills of the management team was considered
to be an important issue for management to shift to real options analysis “Managers
within an organisation must have the background to recognise what can be
considered a sound valuation model. Without reasonable valuation skills, it will be
difficult to explain why the move to real options is important and what benefits will be
realised”.
The need to improve management decision making was considered by consultant
respondents to be an important long term objective “Sound economic decisions are
essential for a good performance in the long run”. In order to achieve this, it was
considered necessary to build the organisations capability to do economic analysis.
In this regard, external consultants were considered necessary “External consultants
can help, but staff is necessary with sophisticated knowledge on economic analysis”.
5.1.2 Key factors identified by consultant respondents
The following factors were identified by the external consultants during the open
ended questions from part 3 of the interview process. The factors are ranked in
order of decreasing importance (see Table 4.8).

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Nine consultant respondents identified the need for pilot projects in real options
analysis as a key factor for getting management acceptance “Real applications of
real options in mining, I think that is what you need.” There was consensus that
management need to be shown the benefits of real options analysis through the use
of real life pilot projects “if you could find a single example where you could compare
a real options analysis with conventional and show the difference…X versus Y
…show practical examples of how people have used it and the benefits”. It was
mentioned that real options analysis would make no inroads until more successful
pilot projects have taken place, “until you get that it is an uphill battle”.
Five consultant respondents identified the need to develop and market an
interactive software application to industry “Interactive option pricing software must
be developed to show industry analysts its capabilities, without exposing them to its
mathematical rigor”. It was mentioned that one of the big complaints about real
options analysis is its complexity and lack of adaptability “If it is going to replace
DCF it has to be as adaptable as DCF”. It was stated that there are software
applications available in the market that can assist people with simple exercises
involving real options analysis “there are definitely some useful tools out there for
understanding where this fits in ... which get people to think beyond very simple mine
planning exercises.” Decision trees were mentioned as an ideal front end to the
software application due to its visual presentation and ease of understanding “I think
also to convince management they need to be shown user friendly, transparent
methods of evaluation tools and you pointed out decision trees…for management to
see the map and put it in front of him in black and white and they can see if they
make certain decisions what the outcome will be”.
Four consultant respondents identified the need for a change process to gain
management acceptance of real options analysis. It was however mentioned that
quality people would be required for the change process to be successful “make
sure that you have very high quality people involved, both internally and externally in
managing that change”. The need to combine a series of factors/ initiatives within an
overall change process was mentioned by respondents “internally there needs to be

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some organisational process where people receive the training …and then following
that up with examples to see how they would apply in their situation to whether you
are a mine planner or not or doing financing for a particular project”. One consultant
respondent stated that the answer to a few simple questions was useful to decide on
whether the use of real options analysis was appropriate to the problem in hand
“What I do…is look to see if the problem is appropriate to options”.
Four consultant respondents identified that a training and education program on real
options analysis was necessary for getting acceptance by management “One of the
barriers is education”. It was stated by one consultant respondent that the first step
for getting real options analysis accepted amongst companies was to provide a
training program for senior management “I think that a natural sequence could
be…….real options courses – mainly for top and intermediate managers.”
Four consultant respondents identified that the need for an internal champion to be
a key factor for gaining management acceptance of real options analysis “Unless
the organisation internalizes and takes on the thinking of their own – you can get as
many external consultants as you want”. Senior level support was reemphasised “It’s
important that you have a very senior level of acceptance first so everyone down
below accepts and realises that this is something that they have to get a grasp of
otherwise it becomes very adhoc in the way things will be accepted”.
Four consultant respondents identified the importance of management issues “A key
factor would be … to have management that are shareholder value focused”. This
theme was used to group a variety of management issues that were considered
important by the consultant respondents for real options to be successful. The
culture of the mining industry towards management intuition was mentioned as a key
issue that might prevent the acceptance of real options analysis “there is a culture in
the mining industry that intuition is better than any analysis”.
Three consultant respondents stated that the presentation and marketing of real
options analysis was a key factor. The need to present real options analysis from a
simple business perspective instead of an academic perspective was considered

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important “The hideous presentation by people who are primarily
mathematicians…inspired by the mathematics and not the business problem”.
Another view reinforced this perception “it needs to be shown to management that
the real option practitioners are not just from an academic background, that they
actually understand the business”.
To conclude, three consultant respondents considered real options analysis to be a
complementary tool “I think it will be an add-on, it will be an additional tool that
management will gain acceptability or comfort in using and hence become an
additive tool, additive to DCF analyses, so it’s a second look at a similar situation”.
5.2 Interpretation of the results from business respondents
5.2.1 Key factors confirmed by business respondents from the literature
The tier ranking of the following key factors is shown in Table 4.9.
Tier one factors
Business respondents considered the tier one (mandatory factors) to be:
2. The application of real options analysis to decompose the private/
technical and market risks.
The need for an internal champion to gain organisational acceptance of real options
analysis was reinforced by the business respondents. The need to have senior
management support was again mentioned “senior sponsor is critical”. It was stated
by one respondent that the internal champion would need to be senior enough to be

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able to influence the board of directors “If you can’t get the CEO get the CFO”. In
fact, it was considered critical for the internal champion to market real options
analysis amongst the analytic community using DCF to prevent a potential backlash
“You need to have internal champions … to champion it among the analytic
community where there has been a lot of rebellion. Some of the biggest foes of real
options are the experts in DCF and decision analysis who have made a career of
helping people make decisions around this … they are very resistant.” In that
regard, the CFO was again considered to be a critical stakeholder for the internal
champion to get on board. In addition, it was stated that a group of individuals from
the financial and technical departments acting as internal champions would be
beneficial to get real options analysis moving “Champions rather than champion” as
someone would need to do the mathematics and also market the tool to
management “probably want a team – corporate finance and technical”.
The fact that real options analysis facilitates the decomposition of risks into
technical and market risks was considered important by business respondents. The
need to model the individual risks involved with a mining investment project was
deemed to be a beneficial aspect of real options analysis to understand the true
value of an asset “I think it’s important for people to understand that all risks aren’t
created equal and that you get different clues about different risks”. The area of
Research and Development was considered to be a good example by one business
respondent where the risks and uncertainties can be mapped out and evaluated
using real options analysis “If you ask me that is a major attraction of real options
and the risk reduction process… for each Research and Development project we
have a risk assessment for technical and market risks.” A general consensus from
business respondents was that by splitting up the market and technical risks and
highlighting the assumptions made was helpful to gain management acceptance “by
splitting them up into those aspects and looking at them independently and
presenting them, building up to your final outcome is a good way of getting buy in so
that people understand the assumptions.”
The need for real options analysis to quantify and justify strategic decisions was
considered to be an important factor for gaining management acceptance by the

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business respondents. There was common agreement from most of the business
respondents that this factor was important and that real options can assist strategic
decision making. The case for long term mining expansion projects was mentioned
again, where it was felt by many of the business respondents that DCF, due to its
discounting nature, didn’t capture the true value of projects with flexibility and
choices “In fact I was looking at a project the other day where I thought that real
options could help on a decision on a mine, whether to go for a 30 or 50 year life… if
you look at the additional value creation in those extra 20 years, you must be able to
put a number to that.”
One business respondent was skeptical about the use of real options analysis to
support strategic decisions “Some people would feel strategic decisions would be
done with a financial tool or not.” There was a consensus that real options analysis
was another analytical tool to assist decision making and that using a suite of
analytical tools helps gain important insights into the value of an asset “I try to push
people to say by thinking about the real options approach gives you some additional
insights that makes this more attractive. Then do the opportunity not because the
different technique says it looks good but because by using the analytic technique
you’ve learned something about that asset that makes it more attractive.” However,
there was a risk mentioned by business respondents of certain managers looking for
the analytical technique which pushes their projects above the hurdle rate, rather
than looking at all the techniques and then making a decision “people want to pick
and choose the analytic technique no matter which technique gives them the number
they want. And its nuts in the long run because it gives people the wrong impression
of why we do analysis.”
Tier two factors
Business respondents considered the tier two (supportive factors) to be:
1. The presentation of real options analysis with decision tree maps.

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The ability to identify the flexibility and options in a project through the use of
decision trees was considered an important factor for gaining management
acceptance by the business respondents “where we can use decision tree model to
help people talk about optionality itself, the people tend to buy in”. The visual
presentation of the decision tree tool was considered to be a key aspect for gaining
management’s attention.
“The old adage a picture tells a thousand words is correct. Decision trees present
the results in pictorial format/ logical format...Decision trees can go a long way in
aiding the understanding with someone that doesn't understand real options.”
The ability to map out and identify the options or choices available in an investment
using decision trees was considered to be the most important part of the real options
analysis process “The decision tree model is helpful because management is used
to looking at it and so it is familiar. They understand how it works and they sort of
trust it”. The rigorous process of identifying real options was considered the major
value add for the road to acceptance “If you look at every avenue of the project
using decision trees… that is useful”.
The competence of the management team was considered to be an important factor
for gaining management acceptance by the business respondents. Some of the
business respondents felt that there was lack of knowledge on basic finance within
mining organisations “some managers do not understand DCF”. One view was that
many mining organisations have managers who have gone through the production
background and typically have given them no great exposure to financial issues “I
think this company’s got a pretty good management group but probably
characteristic of all resource companies, it’s fairly heavily influenced by the
engineering and technical side of the organisation and sometimes that’s a bit of a

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road block to getting advances in the modern financial method”. Training was
mentioned by one respondent as a solution “I think there is a component of training
the management and decision makers”. Financial people in key positions was given
as an example by one respondent as a means for getting organisational acceptance
“The fact that our CEO is a finance guy certainly helps, I think a lot in this
organisation, in getting acceptance for looking at things that maybe we didn’t make
as good a progress on ten years ago”.
Management culture was mentioned as an important barrier to acceptance by
respondents “I think it’s more fear of the unknown. It’s culture, it’s blinkers, it’s not
wishing to accept change. If you get a CFO that doesn’t understand what Black
Scholes is you haven’t got a hope so I think this is a very big barrier to entry”. To
conclude, it was felt by one of the business respondents that an internal champion
would need to build up the management competence in real options analysis “at the
moment that competence is lacking but I am confident that I can build it.”
The degree and existence in the organisation of sound economic analysis for
management decision-making was considered to be an important factor for gaining
management acceptance by the business respondents “It would be more difficult for
organisations to take on real options if they didn’t do more detailed economic
analysis”. The culture of the management team to accept new valuation methods
was mentioned as a barrier by one of the business respondents “I think that is more
of an indication of the culture of the management team whether they are more open
to looking at other ways of coming to their decision making”. Furthermore, the lack
of financial expertise within the management team was considered to be a major
barrier for accepting new valuation techniques.
5.2.2 Key factors identified by business respondents
The following factors identified by the business respondents during the open ended
questions from part 3 of the interview process. The factors are ranked in order of
decreasing importance (see Table 4.10).

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Training programs to educate management was considered by ten business
respondents to be the most important factor “I would say that the biggest thing
holding it back is just a lack of knowledge. People don’t know what it is and how to
use it”. It was stated that there was a total lack of understanding on the subject of
real options analysis “lots of misconceptions about what it actually is. Everyone has
an idea on what it does…..very few people have a good idea.” An education gap at
the senior management level was considered a key barrier “it is not going to make
any difference at the senior management until they know what it is about.” One
business respondent explained that by getting management to understand the
assumptions made when using different valuation methods assisted understanding
“it’s getting the conversation down to the right level, to say this is what you are
including and this is what you’re ignoring and so when you sit with the conventional
analysis and say here are all the simplifying assumptions you’ve made and here are
all the things you have chosen to ignore by saying let’s do DCF, people’s jaws drop”.
The presentation and marketing of real options was considered to be a better
approach by seven business respondents for gaining management acceptance “This
is really just marketing, selling the concept and if you can do this simply, then yes
you will get it across better”. It was stated by one business respondent that getting
real options accepted is all about not overselling it “Getting it done is just about not
overselling it, getting people instinctively to believe it”. As one business respondent
mentioned, it is sometimes beneficial not to use the word real options to market the
concept to management “I carefully stayed away and did not mention or whisper real
options… I presented scenarios that embodied the philosophy of real options…that
actually sold the project”. As stated by one business respondent, the analogy
between geostatistics and real options analysis can be used as a marketing ploy for
getting management acceptance “Now if you’re prepared to use probability as the
main component of your decision making on this mining business why on earth
wouldn’t you consider using the probability theory to understand metal price ore
costing”.
The use of an internal champion was considered to be an important factor by five
business respondents. The need for a senior sponsor was reiterated by two

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business respondents as being an important addition to the use of an internal
champion “senior sponsor is critical”. It was stressed by one business respondent
that there should be a group of internal champions operating at various levels of the
business “Yes, I would say without an internal champions, plural, in the right places,
you need some senior people who are willing to think this way and some techies
who will do it.” Another view was that the corporate finance or the department that
deals with investment valuation would be ideal candidates for the internal champion
position “the conclusion from that is it is a futile except for someone else but
corporate finance to try to sort of get into this kind of decision-making”.
Five business respondents considered real options analysis to be a complementary
tool. It was stated by one business respondent that real options analysis wouldn’t be
accepted if marketed and positioned to replace DCF “If you try and replace DCF, I
think that you will fail. It is an extra tool to present as one of a suite of options… one
of your results.” The need to provide management with additional information for
improved decision making was reiterated “what you’re doing is giving another tool
that says once you’ve done your DCF and you’ve got some answers, stop and then
take this methodology and overlay it and see whether the answers that come out of
that can enable you to better interpret what is there”.
Four business respondents considered the need for pilot projects to be an important
factor. It was stated by one of the respondents that external consultants require
proven case examples of real options analysis before marketing to business “I need
them to show me a project that works with real options”.
Three business respondents considered management issues and the development
of a software application to be key factors. The need for management support to
provide resources for the application of real options was considered important by
one respondent “need resource time to do this”. The understanding of organisational
culture was also deemed important by one business respondent “you have a 100%
chance better if the GM believes this”.
Three business respondents considered the development of a software application

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to be key factor. It was stated that there was an urgent need for advancements in
mine planning software “requires advancements in mine planning”.
Finally, two business respondents considered the change management process to
be important “somehow create a change management process … how are you
going to get it in? How you are going to address the resistance? How are you going
to demonstrate some tangible early successes to maintain the momentum?”.
5.3 Summary
From the results it is clear that there are key factors that influence management
acceptance of real options analysis in the mining sector. The interesting point was
that the two groups of respondents had differences of opinion on the relative
importance of the key factors.
The one mandatory (tier one) factor that both groups (consultant and business
respondents) agreed to was the need for an internal champion. Further, both
groups agreed that without a senior sponsor the internal champions would fall on
deaf ears. The other mandatory factors identified by the consultant and business
respondents were different. For example, consultant respondents preceived the
competence of management to be a mandatory factor, whereas business
respondents felt that the splitting of technical/ market risks and the justification of
strategic decisions were mandatory factors.
As far as the supportive (tier two) factors are concerned, both groups agreed that
the need for sound economic analysis within organisations was essential. The
differences of opinion were that the consultant respondents considered the
highlighting of flaws in DCF, the need for a structured change process and the
adaptation of the business processes to be supportive factors. Business
respondents, on the other hand, mentioned that the mapping out of real options
analysis with decision trees was a supportive factor.

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The factors considered not to be important by consultant respondents but important
by business respondents were the need for road maps and frameworks to improve
management understanding. Whereas the factors considered not to be important by
business respondents but important by consultant respondents were the adaptation
of the business processes and the availability of proven examples in real options
analysis.
The key factors identified by respondents at the end of the interviews revealed some
interesting information. For example, the most important key factor identified by the
consultant respondents was the need for pilot projects to provide more proven
examples of the application of real options analysis. Whereas, the most important
key factor identified by business respondents was the need for training programs to
educate management.
This chapter has provided insights into the factors that influence management
acceptance of real options analysis in the mining sector. The conclusions and the
testing of the research propositions are discussed in Chapter 6.
6. CONCLUSIONS AND RECOMMENDATIONS
In the final chapter, the results from Chapters 4 and 5 (Presentation and
Interpretation of Results) are tested against the research propositions. The
limitations of the research are highlighted and recommendations made to
management on the lessons learned from the research. To conclude, suggestions
are made on potential areas for further research.
6.1 Strength of support for research proposition 1
From the presentation and interpretation of results in Chapters 4 and 5, it is clear
that research proposition 1 would have to be amended due to the differences of
opinion between the two groups of respondents. Also the bias of the research
problem (“to establish the key factors that influence management acceptance of real

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options analysis in the mining sector”) must take cognisance of the factors identified
by the respondents within business in a different light to the factors identified by
external consultants.
Proposition 1: The following factors may influence management acceptance of
real options analysis in the mining sector.
Proposition 1 (Amended): There are differences of opinion between
consultants servicing and management within the mining sector on the relative
importance of factors that influence management acceptance of real options
analysis.
Using the amended proposition, the research highlighted that there were differences
of opinion between the two groups of respondents (consultant and business) on the
key factors that influence management acceptance of real options analysis in the
mining sector. Both groups of respondents were however in agreement that the
need for an internal champion was a mandatory factor. Furthermore, it was stated
that the combination of a senior sponsor and internal champion would be necessary
to convince top executives and the board of directors. The differences of opinion
between the perceptions of the two groups of respondents were around the
positioning of the remaining factors. For example, consultant respondents believed
that the competence of the management team to identify and evaluate real options
was important and highlighted the training and knowledge gap that existed in
business. Interestingly, the need for training programs to educate management was
identified (specifically by the business respondents) as a key factor for gaining
acceptance of real options.
The mandatory factors confirmed by the business respondents were the need to

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decompose real options into technical and market risks and the fact that real options
analysis can quantify and justify strategic decisions. A general consensus from
respondents was that by splitting up the market and technical risks and highlighting
these assumptions was beneficial. Respondents also commented that real options
analysis was another analytical tool to assist decision making and that the use of a
suite of analytical tools would help gain more insights into the value of an asset for
improved decision making. A particular example given by respondents was the
valuation of long life mining assets with uncertainty and flexibility, which were
deemed to be undervalued by conventional DCF, due to the excessive discounting
of cash flows far in the future. The use of real options analysis as a complementary
tool was also identified by the business respondents as one of the key factors for
gaining management acceptance.
The differences of opinion with the supporting factors were that the consultant
respondents considered the flaws in DCF, the need for a structured change process
and the adaptation of the business processes to be important. It was perceived that
the consultant respondents understood and were more passionate about the flaws
in DCF than the business respondents. Also the fact that DCF undervalues most
mining companies was mentioned as another need to use more sophisticated
valuation methods like real options analysis. The general opinion of business
respondents was that due to the embedded nature of DCF in business, highlighting
the flaws wouldn’t work. In fact, it was stated that the word “flaws” was too strong
and that “limitations” or “shortcomings” would be a more appropriate description.
The use of real options analysis as a complementary tool was again mentioned as a
preferred solution for gaining management’s attention.
The need for a change process was also regarded by consultant respondents as
being necessary. Although senior management support was stated as being the
most critical part of the change process, a need for an internal champion to shift the
organisation's attention onto real options analysis through a series of initiatives was
considered important. In general, business respondents were against the use of a
change process probably due the abundance of change management programs that
have taken place over the years and its negative connotation within organisations.

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An approach mentioned by respondents was to use internal presentation and
marketing skills to gradually gain managements attention.
The adaptation of the organisational business processes was considered necessary
by consultant respondents but not business respondents. A critical area being that
the organisational business performance metrics were generally not aligned to the
adoption of real options analysis. For example, the need for organisations to reward
management for shutting down non value adding projects was deemed to be
lacking. Another issue linked to strategy was that the need to spend money to gain
production flexibility in the future was not aligned to being the lowest cost producer
of the mining sector. This is especially the case when the commodity price is on a
downward trend and companies are more prone to cutting costs as opposed to
investing money to gain strategic positioning.
Business respondents felt that the use of decision trees to map out and identify the
options available in an investment was a key supportive factor. In fact, many of the
respondents considered the simple visual representation of decision trees to be an
important tool for getting real options understood in an organisation. Many
consultant respondents confirmed successes involved in getting management to buy
into real options analysis concepts though the use of decision trees. It was also
mentioned by respondents that the development of a decision tree type software
application combined with the organisation’s planning software would be necessary
in the long term.
The fact that the business respondents felt that simple road maps and frameworks
were important to improve management understanding of real options analysis,
illustrated again the potential education and knowledge gap within business.
Interestingly, the consultant respondents considered the need for more proven
examples in real options and pilot projects to be important for getting real options
analysis applied. On the other hand, business respondents considered the need for
training programs to educate management to be important factor. This leads to a
conclusion that there is a lack of knowledge and understanding of real options
analysis within business compared to external consultants. Business respondents

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were more interested in gaining more knowledge on the subject, whereas
consultants, with the knowledge gained just wanted to get pilot projects going and
real options analysis proven. The need for discussion workshops between real
options practitioners from consultancies and business would therefore be beneficial
to both parties as a learning experience to plug the knowledge gap. The workshops
would also potentially provide a guiding coalition and pave the way for more
organisations to accept real options analysis.
6.2 Strength of support for research proposition 2
Proposition 2: The factors identified in the research have varying relative
importance.
This research proposition was proved correct through the tier ranking process which
highlighted that factors identified in the research had different relative importance.
The key factors that influence management acceptance of real options analysis
were established to answer the research problem. For example, the use of an
internal champion was established as a key factor and found to be relatively more
important than other factors identified in the research.
6.3 Limitations of the Research
The time constraints on the research left it incomplete. In that the factors identified
from the research using the qualitative study could not be tested again using a
quantitative study. The fact that the sample for the study was purposive meant that
respondents were chosen with the required experience to answer the research
question from the global population. Therefore, no attempt was made to ensure that
the sample was random and representative of the population. This study explored
the factors that influence management acceptance of real options analysis in the
mining sector. No effort was made to ascertain whether there is any relationship
between the factors. Another limitation to the research was the fact that most of
literature was derived from academic sources and might therefore not properly

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reflect the perceptions of the technical and financial management working within
mining organisations.
6.4 Recommendations to management
From the knowledge gained from this research, the following action plan is
recommended to management.
Real options analysis can add value to mining investment decisions with uncertainty
and flexibility. However, it is not the panacea and should still be used as a
complementary tool to other valuation methods (e.g. DCF, simulation and decision
trees). It will provide additional information about the valuation of a mining asset to
improve decision making. Due to the complexity of the calculations required to
perform real options analysis, a skilled internal champion is required with a senior
sponsor. The role of the internal champion is to facilitate acceptance and
implementation of real options analysis. The role of the senior sponsor is to ensure
availability of resources, executive buy-in and generally support the internal
champion with his endeavors.
Before commencing a program to implement real options analysis, it is important
that the internal champion and sponsor map out a change program of how to take
the organisation from the currently used valuation methods to using real options
analysis. The Banff taxonomy mentioned in the literature (Figure 2.1) would be a
useful tool to assist this process. The positioning of the current valuation methods
used by the organisation on the taxonomy would determine the strategy required to
get real options analysis accepted within the firm.
There are key issues that an internal champion will have to address in order to get
real options analysis accepted. Firstly, the depth of knowledge within the mining
organisation of different valuation techniques would need to be built up to a higher
level of understanding. In this regard, training programs from basic financial
valuation up to advanced financial valuation would be necessary to enhance the

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organisational capability to do real options analysis calculations. Specialist external
consultants that understand mining and real options analysis can be used (if
available) to assist with the knowledge transfer process.
A dedicated specialist team would need to be set up to do a pilot project involving
real options analysis. It is important to allocate sufficient time and resources to the
pilot project for success. The objectives of the pilot project being to promote the real
options analysis method and demonstrate to management the value add of using
this new valuation method. The use of decision trees methodology can assist
management to identify and map out the real options. Due to the complexities
involved with the application of real options analysis, it is important for the dedicated
specialist team to have a clear understanding of the assumptions made for the real
options analysis calculations and any other valuation method used. The need to
discuss the assumptions made for handling the technical and market risks is
important to reinforce management understanding. A simple framework highlighting
the process being followed is important to use in the initial stages for training and
educational purposes. Simple concepts attached to simple spreadsheets (e.g. Real
Options Analysis as an EXCEL add in) should be used to do the calculations. The
pilot project should be treated as a case study and all the lessons learned during the
project should be written up and presented back to management. Once proven real
options analysis can be tested on a broader scale and applied to other investment
decisions.
In the long term the development of a sophisticated in house software application
interfaced with the mine planning software will be required. The performance metrics
for the organisation will have to be adjusted to enable real options analysis thinking.
The need to share knowledge between organisations in a forum type environment is
considered to be an important step towards the long term acceptance of real options
analysis. The forum should include the overall resource sector as lessons can be
learned from cross pollination between the petroleum and mining industry. In this
regard, regional or international workshops and conferences between organisations
and external consultants would be ideal venues. Depending on the size and
capability of the organisation in question, the road to acceptance of real options

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analysis could take years. To conclude, management must decide if the benefits of
having a sophisticated valuation tool which can improve investment decision making
is worth the costs and effort to get it in place.
6.5 Suggestions for Further Research
The research presented in this report provides a broad view of real options analysis
in the mining sector. In this regard, there are a number of areas identified for further
research.
6.5.1 An evaluation of the critical success factors for gaining management
acceptance of real options analysis in the mining sector
A test on the factors identified in this research with a quantitative study would be
relevant. The quantitative nature of the research would be able to highlight more
specifically the critical success factors. The outcome of the research would be to
improve the strategy for gaining acceptance of real options analysis in the mining
sector.
6.5.2 A case study on the application of real options analysis in the mining
sector
Case studies using the factors identified in the literature could be used as an
experiment to test whether the factors identified in this research are relevant. The
lessons learned during this exercise would make an interesting conclusion to this
research.
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Trigeorgis, L. (1990): A Real Options Application in Natural Resource Investments,
Advance in Futures and Options Research, 4, 153-164.
Trigeorgis, L. (1993a): Real Options and Interactions with Financial Flexibility,
Financial Management, 22, 3, 202-224.
Trigeorgis, L. (1993b): The Nature of Option Interactions and the Valuation of
Investments with Multiple Real Options, Journal of Financial and Quantitative
Analysis, 28, 1, 1-20.
Trigeorgis, L. (1997): Real options: Managerial Flexibility and Strategy in Resource
Allocation, Cambridge MA: MIT press, 2
nd
edition.
Trigeorgis, L. & Mason, S.P. (1987): Valuing Managerial Flexibility, Midland
Corporate Finance, 5, 14-21.
Thurner, M-O. (2003): Are Real Options Dead? Considerations for Theory and
Practice, Doctoral Seminar in Corporate Finance, St Gallen: University of St. Gallen
Appendix 1: A hypothetical case on the decision to open an abandoned shaft
of a gold mining company using different numerical techniques
This hypothetical case illustrates the application of conventional DCF, DCF using a
forward curve approach and real options analysis on the decision to restart an

- 87 -
abandoned sub economic shaft of a gold mining company. The gold mining
company comprises the abandoned shaft and six other operational shafts.
The assumptions made for the conventional DCF calculation on the abandoned
shaft were as follows:
• The predetermined extraction plan for the shaft was optimal for any gold
price.
• The life of the shaft for the predetermined extraction plan was 10 years.
• An extrapolated 10 year future spot price of gold was used in the cash flow
calculation.
• Corporate WACC (Weighted Average Cost of Capital) of 16%.
• The capital expenditure required to open up the shaft and build up to full
production over a one year period was R100 million.
The present value of the cash flow to extract the gold was calculated to be R95
million, The resultant net present value of the decision using conventional DCF was
therefore -R5 million (The present value of the cash flow subtracted by the capital
expenditure to open the shaft and commence production).
The calculation of DCF using a forward curve approach differed from the
conventional DCF in that the revenue and cost streams were separated, risk
adjusted and discounted at the risk free rate of return.
The assumptions made for the DCF using the forward curve were as follows:
• The predetermined extraction plan for the shaft was optimal for any gold
price.
• The life of the shaft for the predetermined extraction plan was 10 years.
• An extrapolated 10 year forward price of gold
• A risk free rate of 13%.
• The capital expenditure required to open up the shaft and build up to full
production over a one year period was R100 million.

- 88 -
The present value of the revenue and cost stream was calculated to be R500 million
and R420 million respectively, resulting in an overall cash flow present value of R80
million. The resultant net present value of the decision using conventional DCF was
therefore –R20 million (The present value of the cash flow subtracted by the capital
expenditure to open the shaft and commence production). This calculation is
deemed to be more accurate than the conventional DCF technique and is referred to
as the project value with the real options turned off.
Using real options analysis, there was an option to defer the decision to operate the
shaft for 5 years under current mining legislation. This option to defer was a call
option on the underlying real asset (the present value of the cash flow of the
revenue stream).
The additional assumptions made for the real options calculation were as follows:
• The historical long term volatility of the underlying asset is 23% (market
proxy).
• The risk free rate is 13%.
• The time to exercise the option to reopen the shaft is 5 years.
• The annual shaft maintenance costs are R2.5 million/ annum (after tax).
The theory of Black-Scholes, Binomial trees and Monte Carlo simulation follows and
the value of the deferral option using these methods were calculated for the
hypothetical case.
Method 1: Black-Scholes
The Black-Scholes formula developed approximately 30 years ago by Fischer Black,
Myron Scholes and Robert Merton to value stock options can be applied to the
valuation of real options. When a quick analysis is required the use of the Black-

- 89 -
Scholes formula is often the best approach as it provides a methodology for framing
a problem and determining the variables that affect the valuation. European call and
put options are calculated using the following formula (Hull, 1998):
C = S.N (d1) – X.e
-rT
.N (d2)
P = X.e
-rT
.N (-d2) – SN (-d1)
Where
d1 = (ln(S/X)+(Rf + σ
2
/2 )T)/(σ √T)
d2 = d1 - σ √T
C = The value of the call option
P = The value of the put option
S = The price of the underlying (e.g. a share of common stock)
N(d1) = The cumulative normal probability of unit normal variable d1
N(d2) = The cumulative normal probability of unit normal variable d2
σ = Standard deviation (volatility)
X = The exercise price
T = The time to maturity
Rf = The risk free rate
e = The base of natural logarithms, constant = 2.1728 ….
Concerns by management were that the software that calculated the esoteric Black-
Scholes equations was a “black box” and the results intangible (D’Souza, 2002).
Copeland & Antikarov (2001) stated that it was important to understand the following
restrictive assumptions when using the Black Scholes model to understand its
limitations for use in real options analysis:
1. The option is a European option as it may only be exercised at
maturity.

- 90 -
2. Rainbow options cannot be applied as there is only one source of
uncertainty (e.g., the interest rate is assumed to be constant).
3. Compound options are ruled out as the option is contingent on a single
underlying risky asset.
4. The current market price and the stochastic process followed by the
underlying are known (observable).
5. The variance of return on the underlying is constant through time.
6. The exercise price is known and constant.
Case example solution 1:
The annual shaft maintenance costs was discounted back to present value and
subtracted from price of the underlying asset (S). The value of the deferral option in
this example was calculated using the Black-Scholes formulation in Microsoft
EXCEL. The value of the deferral option in this example was valued at R28.43
million - see Figure 1.
Figure 1 The valuation of the deferral option using Black-Scholes

- 91 -
Method 2: Binomial Trees
The binomial lattice framework allows an analyst to consider multiple underlying
variables and thus multiple sources of uncertainty. Herath & Park (2002) explained
that the binomial trees provided a greater modeling flexibility to analyse complex
real options that existed in the real world.
Binomial trees can be used to evaluate both American and European call and put
options.
A diagrammatical example of a one step binomial model is highlighted in Figure 2.

- 92 -
Figure 2 Example of a one step Binomial model
The analysis can be generalised by considering a future price of an underlying stock
that starts at S and is anticipated to rise to Su or move down to Sd over the time
period T. Now consider a stock option of the underlying stock, which matures at
Time T and has a payoff of fu if the future prices move up and fd if it moves down
(Hull, 1998). This situation is shown diagrammatically in Figure 2:
To be riskless, Su ∆ - fu = Sd ∆ - fd
Therefore ∆ = (fu – fd) / (S (u-d)) ……(A)
Hull (1998) stated that the delta (∆) of a stock option is therefore the ratio of the
change in the price of the stock option to the change in the price of the underlying
stock. Now denote the risk free rate as Rf, the present value (PV) of the underlying
stock at time T is:
(Su ∆ -fu).e
-Rf. ∆T
, which must equal the starting value of the stock
Thus, S∆ - f = (Su∆ - fu). e
-Rf. ∆T
Thus, f = S∆ - (Su∆ - fu). e
-Rf. ∆T

- 93 -
= e
-Rf. ∆T
(p.fu + (1-p).fd), having substituted (A)
where p = (e Rf. ∆T
– u)/ (u-d), the risk neutral probability of an up movement in the
stock price.
Putting binomial trees into practice, the life of an option is typically divided into more
steps. In each time step there is a binomial stock price movement. The value of u
and d are determined from the stock price volatility. There are a number of different
ways to make the determination (Hull,1998). Now define ∆t as the length of the one
time step: -
u = eσ√(∆T)
and
d = 1/u
The complete set of equations defining the binomial tree is then (Hull,1998):
u = eσ√(∆T)
p = (e
Rf.∆T
– d) / (u-d)
f = (p.fu + (1-p).fd).e
-Rf. ∆T
S = The price of the underlying (erg a share of common stock)
T = The number of timeslices
Rf = The risk free rate
u = Proportional up movement
d = Proportional down movement

- 94 -
p, (1-p) = The risk neutral probabilities that the upper and lower nodes are reached
r = The risk free interest rate
The use of binomial trees can be generalised through the addition of more steps.
The option price is considered to always be equal to the expected payoff in a risk
neutral world, discounted at the risk-free interest rate.
The binomial formula is therefore given by:
Σ Max(0, Su
T-t
d
t
–X) - Option pay off value
x p
T-t
(1-p)
t
- Probability level of this node
x T!/(T-t)t! - Number of ways of getting to this node
x e
-rT(∆T)
- Discount back to present value
S = The price of the underlying asset (e.g. an investment project)
T = The number of timeslices
t = Time at node
p, (1-p) = The risk neutral probabilities that the upper and lower nodes are reached
r = The risk free interest rate
In summary, lattice models represent a discrete-time approximation of a continuous-
time process assumed to characterise the behaviour of the underlying asset
(Thurner, 2003). Copeland & Antikarov (2001) considered lattice trees to be more
flexible than Black Scholes and able to incorporate multiple options, complex option
payoffs and downstream decisions

- 95 -
Using the application of Binomial trees for the same case the deferral option was
valued at R27.11 million - see Figure 8. The annual shaft maintenance costs were
again discounted back to present value and subtracted from price of the underlying
asset (S).
Figure 3 The valuation of the deferral option using Binomial trees
Method 3: Monte Carlo Simulation
Monte Carlo simulation can be used to solve real option problems. Its main
advantage is its ability to handle models of increased complexity and that it is a
more practical solution if the problems involve more variables and /or is path

- 96 -
dependent (Schwartz & Trigeorgis, 2001).
Monte Carlo simulation is also used to model uncertainty and formulate the volatility
of the underlying, the determination of which is a key input to the option value
calculation (Copeland & Antikarov, 2001). For example, the Monte Carlo simulation
function can also be used to model the causal uncertainties of a project on the
original NPV analysis. The simulation creates an estimate for the expected volatility
of the project’s value, which is used to build a value based event tree to calculate
the option value as shown in Figure 4.
Figure 4 The use of Monte Carlo simulation to calculate volatility of the
underlying asset
Hull (1998:374) noted that Monte Carlo Simulation could in conjunction with
Binomial trees be used for valuing derivatives and therefore real options. For
example, a decision tree can be constructed and random paths simulated through it.
The only difference now being that instead of calculating the option value working
backward from the decision tree to the beginning, the tree is worked forward.
A random number is firstly sampled at the first node. The upper branch is taken if
the number lies between 0 and p and the lower branch taken if the number lies
between p and 1. This procedure is then repeated at the following node and at all

- 97 -
subsequent nodes until the end of the tree is reached. The payoff on the option for
the particular path sampled is then calculated. The simulation repeats this process
creating additional payoffs. The value of the option is the arithmetic average of the
payoffs from all the trials discounted at the risk-free interest rate.
The Monte Carlo simulation using the NORMSINV function on EXCEL which returns
the inverse of the standard normal cumulative distribution (has a mean of zero and
standard deviation of 1) can be used to evaluate the option value of an underlying
asset through the following formula:
S(t + ∆t) = S(t) .e
[(µ - σ2/2) ∆t + σ √ ∆t.N (0,1)
Where:
S = the price of the underlying asset (e.g. an investment project)
µ = Risk free rate
σ = Volatility of the underlying asset
N (0,1) = Random sample from a normal distribution with mean zero and standard
deviation of 1.
Thurner (2003) stated that the Monte Carlo method was the most widely used
simulation technique. He stated further that the method simulates the stochastic
process that generates the returns of the underlying asset and several sources of
uncertainty can be used. The resulting simulated terminal option values are
discounted using risk- neutral valuation and the payoff of possible random paths for
the underlying stochastic variable is calculated and discounted at the risk free rate
(Thurner, 2003). The arithmetic average of the discounted payoffs is the value of the
option.
The application of Monte Carlo simulation results in the value of the call option to be

- 98 -
R28.85 million - see Figure 5. The mean forward price of the underlying asset, the
shaft, is R147.42 million using a simulation of 10000 iterations.
Figure 5 The valuation of the deferral option using Monte Carlo Simulation
The probability graph in Figure 6 showed that the mean forward price of the shaft
was greater than the strike price. The option value being the difference between the
mean forward price and the strike price discounted back over the 5 year period at
the risk free rate.
Figure 6 The forward price of the shaft - probability graph
Forward Price of Shaft
Probability graph
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
-200 0 200 400 600 800 1000 1200
Price (R million)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Frequency Mean forward price Strike Price Cumulative
Strike price =
R100 mil
Mean forw ard
price = R147 mil

- 99 -
Presentation of Results:
The results from this hypothetical case illustrated that the expanded NPVs using the
three real options analysis methods are positive and “in the money” when compared
to the static NPV of the conventional and forward curve DCF methods which were
both “out of the money” – see Table 1. This highlights the additional value added
through having the flexibility to defer the decision to open the abandoned shaft.
Table 1 Results for the case example (R million)
Valuation Method Static NPV PV of Maintenance
Costs for 5 years
Option
Premium
Expanded
NPV
Conventional DCF (5.0) NA NA NA
Forward Curve DCF (15.0) NA NA NA
Black-Scholes (15.0) (8.8) 28.4 4.6
Binomial Trees (15.0) (8.8) 27.1 3.3
Monte Carlo Simulation (15.0) (8.8) 28.9 5.1
Conclusions:
For this hypothetical case, the recommendation using the conventional DCF method
is that the shaft be abandoned. Using the three real options analysis methods (Black
Scholes, Binomial Trees and Monte Carlo simulation) it was found that there was
additional value in deferring the decision to open/abandon the shaft for the 5 year
period. The decision not to abandon the shaft would obviously depend on whether
the project met the company’s hurdle rate and strategic objectives. This case
highlights the limitations of conventional DCF to capture the value of management
flexibility to defer the decision to open an abandoned shaft.

- 100 -
Appendix 2: Consistency matrix
Research Problem: To establish the factors that influence management acceptance of real options
Proposition Factor
number
Factor Source of Factors Source of data Analysis
1
The highlighting of the flaws in
the use of conventional DCF
valuation techniques to
management.
Herath (2002); McCarthy &
Monkhouse (2003); D’Souza
(2002); Perlitz et al (1999);
Borison et al (2003); Samis et
al (2003); Brennan & Schwartz
(1985a).
Qualitative in-depth
semi-structured
individual interviews
Content
analysis on
qualitative
data
2
The marketing of the benefits in
the use of real options analysis to
management.
Jarrow (1999); Herath & Park
(2002); Schwartz & Trigeorgis
(2001); Schwartz & Trigeorgis
(2001); Kemna (1993);
Brennan & Schwartz (1985b);
Copeland & Antikarov (2001).
semi-structured
individual interview
Content
analysis on
qualitative
data
3
The presentation of real options
analysis with decision tree maps.
D’Souza,(2002); Herath &
Park (2001); Borison et al
(2003); Tapper (2001).
semi-structured
Content
analysis on
qualitative
data
4
The existence of a structured
change process to influence the
organisational acceptance of real
options analysis.
Copeland & Antikarov (2001);
Borison et al (2003); D’Souza
(2002)
semi-structured
Content
analysis on
qualitative
data
1
Thefollowingfactorsidentifiedintheliteratureinfluencemanagementacceptanceofrealoptions
analysisintheminingsector
5
The availability of external
consultants with the knowledge
of real options analysis in the
mining sector to advise
management.
Copeland & Antikarov (2001);
Howell et al (2001)
semi-structured
Content
analysis on
qualitative
data

- 101 -
Proposition Factor
number
6
The existence of an internal
champion to facilitate and market
the application real options
analysis within the organisation.
Copeland & Antikarov, (2001);
Howell et al (2001)
semi-structured
Content
analysis on
qualitative
data
7
The degree of competence or
lack thereof of the management
team to identify and evaluate real
options.
Borison et al (2003); Eapen
(2003)
semi-structured
Content
analysis on
qualitative
data
8
The extent to which the
organisational and business
performance metrics are adapted
and aligned with the application
of real options analysis.
Borison et al (2003); Copeland
& Antikarov (2001); Amran &
Howe (2003)
semi-structured
Content
analysis on
qualitative
data
9
The existence of simple
frameworks and road maps to
improve management
understanding of real options
analysis.
Amran & Howe (2003);
Borison et al (2003);
Luehrman (1998); Trigeorgis &
Mason (1987).
semi-structured
Content
analysis on
qualitative
data

- 102 -
Proposition Factor
number
10
The extent to which real options
analysis is used to quantify and
justify strategic decision making.
Borison et al (2003);
Luehrman (1998)
semi-structured
Content
analysis on
qualitative
data
11
The degree and existence in the
organisation of different valuation
techniques for sound economic
analysis.
Borison et al (2003); Amran &
Howe (2003).
semi-structured
Content
analysis on
qualitative
data
12
The application of real options
analysis to decompose the
private/ technical and market
risks.
Eapen (2003); Borison et al
(2003); Samis et al (2003)
semi-structured
Content
analysis on
qualitative
data
13
The availability of practical and
proven examples of real options
Trigeorgis (1990); McCarthy &
Monkhouse (2003); Pindyck
(1991); Dixit & Pindyk (1995);
Majd & Pindyck (1987);
Tapper (2001); Trigeorgis
(1993a); Copeland & Antikarov
(2001); Kulatilkaka &
Trigeorgis (1994); Brennan &
Schwartz (1985b); Moel and
Tufano (2000); Trigeorgis
(1993a)
semi-structured
Content
analysis on
qualitative
data

- 103 -
Proposition Factor
number
2
Thefactorsidentifiedin
theresearchareequally
important
semi-structured
Content
analysis on
qualitative
data

- 104 -
Appendix 3: Interview process
Part 1
Proceedings will be opened with an overview of the literature review regarding
the application of real options analysis in the mining sector.
Consultants and academics have been touting real options analysis as a means of
improving capital investment decision-making. Some argue that in ten years real
options analysis will replace NPV as the central paradigm for investment decisions.
However, general consensus on the application of real options is that despite
successes in some sectors (e.g. the exploration and development of the oil and gas
sector), there was still a lot of resistance and confusion getting in the way of
corporate adoption of real options.
Resource and commodity based mining companies are frequently mentioned as
ideal candidates for the application of real options analysis. There is evidence to
support the hypothesis that the real options model can be applied to the mining
sector. There are numerous worked examples developed by real options
practitioners that illustrate the application of real options analysis in the mining
sector see Table 1.

- 105 -
Table 1 Examples of real options in the mining sector
Option to defer Right to delay or defer the start of a
mining project or investment to
incorporate more favourable economic
conditions.
Trigeorgis (1990)
Pindyck (1991)
Dixit & Pindyk (1995)
Gilbert & Moel (2002)
Option to extend Right to construct and develop a new
underground mine with sequential
investment outlays.
Majd & Pindyck (1987)
Option to abandon Right to abandon a shaft, mining
investment or project under sub
Trigeorgis (1990)
Tapper (2001)
Option to contract Right to contract (scale back) mine
operations by selling a fraction of it for
a fixed price under uncertain
Trigeorgis (1993a)
Option to switch Right to close a mining operation that
is currently open by paying a fixed
shutdown cost and to open it later for
a different fixed cost.
Kulatilkaka & Trigeorgis (1994)
Brennan & Schwartz (1985b)
Moel and Tufano (2000)
Option to expand Right to expand a mining project by
paying more to scale up the
operations.
Trigeorgis (1990)
Compound options Right to implement an R&D
industry.
Tapper (2001)
Learning options Right to implement an R&D
industry.
Tapper (2001)

- 106 -
A discussion will then take place on the research problem and the rationale
for the research.
The purpose of this research is to identify those factors that influence management
acceptance of real options analysis in the mining sector. The study will help to
ascertain how real options analysis can be successfully applied to the South African
mining sector. The determination of the factors for management acceptance of real
options will be valuable information, which can then be used for focusing company
resources or for conducting further research in this field. Proactive interventions
applying the critical success factors could enhance the likelihood of the successful
application of real options analysis to investment decision-making.
Part 2
The interviewee is then invited to describe what factors are important to gain
management’s acceptance of real options analysis in the mining sector and
explain reasoning? The interviewer to ask a response and probe around the
following statements linked to the factors identified in the research:
1. There are flaws in the use of conventional DCF valuation techniques to
evaluate mining investments with uncertainty and flexibility.
2. There are proven benefits in the use of real options analysis to evaluate
mining investments with uncertainty and flexibility.
3. Real options can be mapped out using decision trees.
4. Structured change processes can be used to gain organisational acceptance
5. External consultants are available to advise management on the application

- 107 -
6. The use of an internal champion gains management acceptance of real
options analysis.
8. The extent to which organisational business processes are adapted to the
application of real options analysis.
9. Simple frameworks and road maps improve management understanding of
real options analysis.
10.Real options analysis can quantify and justify strategic decisions.
11.The degree and existence in the organisation of sound economic analysis for
management decision-making.
12. Real options can be decomposed into technical and market risks.
13. Practical and proven examples of real options analysis in the mining sector
are available.
Part 3
The interviewee is then asked to summarise opinion on the key factors that
lead to management acceptance of real options analysis in the mining sector
and discuss a holistic approach to implement the identified factors in the
mining sector.

- 108 -
Appendix 4: Factor code used for part 2 of the interview process
Factor
number
Factor Factor code
1
The highlighting of the flaws in the use of
conventional DCF valuation techniques to
management.
Flaws
2
The marketing of the benefits in the use of real
options analysis to management.
Benefits
3
The presentation of real options analysis with
decision tree maps.
Decision Trees
4
The existence of a structured change process
to influence the organisational acceptance of
real options analysis.
Change
5
The availability of external consultants with the
knowledge of real options analysis in the
mining sector to advise management.
External
6
The existence of an internal champion to
facilitate and market the application real
options analysis within the organisation.
Internal
7
The degree of competence or lack thereof of
the management team to identify and evaluate
real options.
Management
8
The extent to which the organisational and
business performance metrics are adapted
and aligned with the application of real options
analysis.
Process

- 109 -
Factor
number
Factor Factor code
9
The existence of simple frameworks and road
maps to improve management
understanding of real options analysis.
Roadmaps
10
The extent to which real options analysis is
used to quantify and justify strategic decision
making.
Strategy
11
The degree and existence in the organisation
of different valuation techniques for sound
economic analysis.
Analysis
12
The application of real options analysis to
decompose the private/ technical and market
risks.
Risks
13
The availability of practical and proven
examples of real options analysis in the mining
sector.
Examples

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