an investigation into the commercial avenues for the
TRANSCRIPT
7123019
An investigation into the commercial avenues for the Resource
Valuation and Optimisation Model
A dissertation submitted to the University of Manchester for the degree of Master of
Enterprise in the Faculty of Humanities
2011
Charlotte Thompson
Manchester Business School
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Contents
Executive Summary .................................................................................................................... 1
1 RVOM Resource Valuation and Optimisation Model ............................................................. 6
1.1 Project Background .......................................................................................................... 6
1.2 Solving a problem ............................................................................................................ 8
1.3 How others have solved these problems before ............................................................. 9
1.4 Using the RVOM ............................................................................................................. 10
1.5 Intellectual Property ...................................................................................................... 11
1.6 RVOM Unique Selling Points .......................................................................................... 12
1.7 Competitive Advantage ................................................................................................. 13
1.7.1 The RVOM’s competitive advantage ...................................................................... 13
1.7.2 Competitive advantage to the end user ................................................................. 14
1.8 Market analysis for RVOM in mining industry ............................................................... 15
2.0 Opportunity Analysis ......................................................................................................... 16
2.1 Real Options ................................................................................................................... 16
2.2 Use of Real Options ........................................................................................................ 16
2.3 Competing and existing methods .................................................................................. 19
2.3.1 Net Present Value ................................................................................................... 19
2.3.2 Black-Scholes ........................................................................................................... 19
2.3.3 Binomial Lattice ...................................................................................................... 19
2.3.4 Monte Carlo simulation .......................................................................................... 20
2.3.5 Risk-adjusted decision trees ................................................................................... 20
2.3.6 Partial Differential equations .................................................................................. 20
2.4 Use of Real Options technique in practice .................................................................... 21
2.5 Competing and existing products .................................................................................. 21
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2.5.1 Oracle Crystal Ball ................................................................................................... 22
2.5.2 @Risk ...................................................................................................................... 22
2.5.3 Real Options Valuation ........................................................................................... 23
2.5.4 Comparison ............................................................................................................. 23
2.6 Decision-making software package ............................................................................... 23
3.0 Applications ....................................................................................................................... 25
3.1 Energy Industry; Oil, Gas, Nuclear Power and Renewable Energy ................................ 28
3.1.1 Oil and Gas Industry ................................................................................................ 29
3.1.2 Nuclear Power Industry .......................................................................................... 31
3.1.3 Renewable Energy Industry .................................................................................... 32
3.1.4 Energy Industry PEST .............................................................................................. 33
3.2 Utilities ........................................................................................................................... 35
3.2.1 Electricity Industry .................................................................................................. 35
3.2.2 Water Industry ........................................................................................................ 36
3.3 Pharmaceutical Industry ................................................................................................ 39
3.4 Biopharmaceutical Industry ........................................................................................... 44
3.5 IT Industry ...................................................................................................................... 45
3.6 Financial Services ........................................................................................................... 47
3.7 Target Industries ............................................................................................................ 48
4.0 Business Model Introduction ............................................................................................. 52
4.1 Business Models defined ............................................................................................... 52
4.2 Relevance of Business Models ....................................................................................... 54
4.2.1 Challenges and Opportunities ................................................................................ 54
4.2.2 A well-defined business model ............................................................................... 54
4.2.3 Business model innovation ..................................................................................... 55
4.3 Addressing a need .......................................................................................................... 55
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5.0 Software Business Models’ ................................................................................................ 57
5.1 Open Source ................................................................................................................... 58
5.1.1 Open Source Defined .............................................................................................. 59
5.1.2 Intellectual Property ............................................................................................... 60
5.1.3 Open Source and Intellectual Property working together ..................................... 61
5.1.4 Open Source Licenses ............................................................................................. 62
5.1.5 The Advantages and Disadvantages of Open Source ............................................. 65
5.2 Open Source and Proprietary Business Models’ ........................................................... 69
5.2.1 Subscription Model ................................................................................................. 69
5.2.2 Service Business ...................................................................................................... 73
5.2.3 Hybrid System ......................................................................................................... 75
5.2.4 Dual-Licensing ......................................................................................................... 78
5.2.5 Time-limited hybrid licensing .................................................................................. 81
5.2.6 Application Service Provider ASP Model ................................................................ 82
5.2.7 Consultancy ............................................................................................................. 84
5.3 Evaluation of Open Source for the RVOM ..................................................................... 87
5.4 Proposed Business Model .............................................................................................. 88
6.0 Strategies ........................................................................................................................... 90
6.1 Route to Market ............................................................................................................. 90
6.2 General Strategy ............................................................................................................ 91
6.3 Positioning...................................................................................................................... 91
6.4 Marketing Plan ............................................................................................................... 92
6.5 Pricing ............................................................................................................................. 93
7.0 Financial Analysis ............................................................................................................... 93
7.1 Scenarios ........................................................................................................................ 94
7.1.2 Scenario 1 Run RVOM alongside work at the university ........................................ 94
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7.1.2 Scenario 2 Run RVOM from home .......................................................................... 94
7.1.3 Scenario 3 Run RVOM from an office ..................................................................... 95
7.2 Overall assumptions ....................................................................................................... 96
7.3 Fixed Assets .................................................................................................................... 96
7.4 Gemcom proposed agreement ...................................................................................... 96
7.4.1 Planned sales with Gemcom licensing agreement ................................................. 96
7.5 Overall Planned Sales ..................................................................................................... 98
7.5.1 Assumptions ............................................................................................................ 98
7.5.2 Forecasted revenue for RVOM ............................................................................... 98
7.6 Sensitivity Analysis ......................................................................................................... 99
8.0 Conclusion .......................................................................................................................... 99
9.0 References ....................................................................................................................... 102
10.0 Appendix ........................................................................................................................ 110
10.1 Real Options Survey ................................................................................................... 110
10.2 SWOT Analysis............................................................................................................ 111
10.3 “Oomph-lib lunch” meeting notes 6/6/2011............................................................. 112
10.4 NAG John Holden email correspondence 26/7/2011 ................................................ 114
10.5 Wai Lau Interview notes 7/12/2010 .......................................................................... 115
10.6 Clare Arkwright UMIP notes 9/12/2010 .................................................................... 116
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Table of Figures
Figure 1 Application of Real Options (Block, 2007)…………………………………………….……………... 17
Figure 2 Reasons for not using Real Options ………………………………………………….………………....18
Figure 3 Competing and existing methods ………………………………………………….……………………..21
Figure 4 R&D Expenditure by sector (2006)….………………………………………….……......................26
Figure 5 Pharmaceutical R&D stages …………………………...........................................................40
Figure 6 Osterwalder template ............................................................................................ …..53
Figure 7 Technology Adoption Lifecycle ................................................................................... 56
Figure 8 Sun Microsystems Community Source License .......................................................... 64
Figure 9 Subscription Business.................................................................................................. 69
Figure 10 RVOM Subscription model ........................................................................................ 72
Figure 11 Service Business ........................................................................................................ 73
Figure 12 RVOM Service model ................................................................................................ 74
Figure 13 Hybrid System ........................................................................................................... 75
Figure 14 RVOM Hybrid system ................................................................................................ 76
Figure 15 Dual-Licensing ........................................................................................................... 78
Figure 16 RVOM Dual-Licensing ................................................................................................ 80
Figure 17 Time-limited hybrid licensing .................................................................................... 81
Figure 18 ASP Model ................................................................................................................. 82
Figure 19 RVOM ASP model ...................................................................................................... 83
Figure 20 RVOM Consultancy ................................................................................................... 86
Figure 21 RVOM Business Model .............................................................................................. 88
Figure 22 Bowman's Clock ……………………………………………………………………………………………….…92
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Table of Tables
Table 1 Using Real Options …………………………………………………………………………………….…..……..17
Table 2 Electricity generated ……………………………………………………………………………….………..…..35
Table 3 Use of Real Options in Pharmaceutical Industry …………………………………………………...41
Table 4 Top ten Pharmaceutical and Biotechnology companies by R&D expenditure in the UK...………………………………………………………………………………………………………………………………..….42
Table 5 Projected costs of running a business from a small office………………………………………95
Table 6 Planned sales with Gemcom licensing agreement………………………………………………….97
Table 7 Forecasted revenue for the RVOM………………………………………………………………………...98
Table 8 Revenue projections………………………………………………………………………………………….….101
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Abstract
The Resource Valuation and Optimisation Model (RVOM) is a Real Options software
package, which helps to optimally plan their operations, understand project risks and make
defensible valuations. It is currently in the final stages of development and being tested for
market with a leading mining software company, Gemcom. This is through an exclusive
licensing agreement with future royalties from the sale of the RVOM.
The RVOM has the potential to be applied to a number of other industries. A study into Real
Options software packages and Real Options in industry looks to find out which industries
should be targeted and an analysis of software business models’ aims to find a suitable
business model which achieves the most value for the RVOM. Overall the objective is to
evaluate the RVOM’s commercial prospects and highlight some of the challenges and
opportunities that may arise.
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Declaration
No portion of the work referred to in the dissertation has been submitted in support of an
application for another degree or qualification of this or any other university or other
institute of learning.
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III
Copyright Statement
i. The author of this dissertation (including any appendices and/or schedules to this
dissertation) owns any copyright in it (the “Copyright”) and s/he has given The University of
Manchester the right to use such Copyright for any administrative, promotional, educational
and/or teaching purposes.
ii. Copies of this dissertation, either in full or in extracts, may be made only in accordance
with the regulations of the John Ryland’s University Library of Manchester. Details of these
regulations may be obtained from the Librarian. This page must form part of any such copies
made.
iii. The ownership of any patents, designs, trademarks and any and all other intellectual
property rights except for the Copyright (the “Intellectual Property Rights”) and any
reproductions of copyright works, for example graphs and tables (“Reproductions”), which
may be described in this dissertation, may not be owned by the author and may be owned
by third parties. Such Intellectual Property Rights and Reproductions cannot and must not
be made available for use without the prior written permission of the owner(s) of the
relevant Intellectual Property Rights and/or Reproductions.
iv. Further information on the conditions under which disclosure, publication and
exploitation of this dissertation, the Copyright and any Intellectual Property Rights and/or
Reproductions described in it may take place is available from the Head of School of
Manchester Business School.
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Executive Summary
The Resource Valuation and Optimisation Model RVOM helps mine owners plan operations
optimally, understand the risks behind a project and make defensible valuations (Evatt et al,
2011). It is a Real Options computer software package. Real Options is an area of study
which looks at how to make business decisions under uncertainty.
The RVOM team saw the commercial potential of their work and so contacted the University
of Manchester Intellectual Property who made enquiries with leading mining software
providers to gauge commercial interest. Of those interested, Gemcom who has the largest
market share was chosen for an industrial partner. The RVOM has been developed to be
part of the Gemcom Whittle. It is now in the final stages of development and is being
prototyped with one of Gemcom’s clients.
The RVOM addresses a need of mining companies because prior to extraction of a mine,
they produce feasible extraction plans to evaluate the cost effectiveness of operation (Evatt
et al, 2010). By optimising on this plan, it can help mining companies achieve greater profit.
It distinguishes itself against other Real Options packages in its use of partial differential
equations, which allow for a far broader number of problems to be faced under multiple
uncertainties in comparison to other methods. Equally its ability to calculate the probability
of project completion or expected duration is the most innovative aspect of this software
package, as it has not been previously considered.
The RVOM isolates economic uncertainty, which has not been looked at extensively for the
mining industry. Therefore there are few rival software products, which look at this area.
The intellectual property protecting the RVOM is a patent for the algorithms. This is in the
examination stages in the US and Australia. Equally there are few with the expertise to solve
the valuation problems using partial differential equations and therefore the RVOM is
protected by know-how.
There is potential for the RVOM to be developed further for the mining industry to tackle
related problems. But the greatest gains will be had from diversifying into other industries.
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By considering how Real Options is already used in other industries, future routes to market
and growth strategies are suggested.
Real Options tend to be used by international firms as they are more likely to deal with
market uncertainty and quality improvements. Equally they have the sufficient resources to
run the analytics.
The most dominant industries using Real Options are Technology, Energy and Utilities. The
main uses of Real Options are in new product development, research & development and
mergers & acquisitions.
As a tool, Real Options has not shown significant adoption in industry. Existing techniques
haven’t been able to solve Real Options problems with the same accuracy and efficiency as
the RVOM. Therefore in selling the RVOM, it will have to tackle both the adoption of Real
Options and how improvements in mathematics can now help solve Real Options related
industrial problems.
Existing methods most prominently used in industry are Binomial Lattice, Monte Carlo
Simulation and Risk-adjusted decision trees. The choice between existing techniques is
typically dependent on the expertise of the user, the type of project, and the amount of
time it takes to use these methods.
It is difficult to know what products companies are using and how they conduct their
analysis. However there are some generic products used on the market. Oracle Crystal Ball,
@Risk and Real Options Valuation provide packages that deal with Real Options. They offer
varying degrees of functionality, had a large pool of industries they sold their products too
and they supported their products with maintenance, training and documentation. There is
the potential for the RVOM to be developed into a generic decision-making tool and this
could act as a “cash-cow” for further developments of the RVOM to specific applications.
In considering which industries the RVOM can diversify into, it must fit the conditions
necessary for Real Options: uncertainty over payoffs, irreversibility in project costs and
managerial flexibility. They also must be able to supply sufficient R&D data to make
assumptions about the uncertainties in Real Options analysis. This is true for the mining, oil
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and gas industry, pharmaceutical and biotechnology industry; but has also been used in the
automotive, aerospace and high technology industries.
The RVOM is a tool that can be used regardless if there is an upturn or downturn in the
market. Indeed recently it has been shown that more optimisation tools were sold in the
face of a downturn. Therefore looking at the health of an industry is not a clear indicator of
how well the RVOM would do. Instead, some of the prominent industries are looked at to
see how the RVOM can be applied and which ones would be best adopted.
As Real Options is mostly conducted in the natural resource sector, which is supported by
both academia and surveys, it offers the lowest risk strategy to encourage the adoption of a
Real Options software package. Within this sector, the renewable energy industry is
showing the most growth and is a hot political topic. Equally the oil and gas industry would
be a natural progression from working with the mining industry.
On the other hand industries that represent a high risk strategy are the pharmaceutical &
biopharmaceutical industry, IT and financial services. They offer lots of potential for high
returns but because they represent different sets of problems to the natural resource sector
and is a tangent to the work already developed they would require significant development
and networking. Therefore these industries should be addressed in the future after a secure
base of customers and work has been established with the lower risk strategy.
A well-defined business model can enhance the prospect for the RVOM, as technological
innovation on its own, is not a guaranteed means of achieving success. By analysing
business models found in the software industry, a suitable model is suggested for the
RVOM. This business model looks to improve the RVOM’s profitability and competitive
advantage.
For software business models to work effectively there must exist a need in the market for a
customer to be willing to pay for that need to be sufficed. Traditional software business
models are based on selling per user licenses and upgrades of their software. Yet only 30%
of software is ever sold, with most being developed directly for the customer by employees
or by consultants. Therefore it’s important to consider more “out of the box” models, which
could lead to greater revenue streams. In the literature, open source business models
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proved innovative as many were trying to generate revenue from a “free” product. Hence
both proprietary and open source business models are considered.
Open source is an attractive proposition because it will improve the RVOM’s distribution
and maximise market entry at minimal cost. However because of the advanced mathematics
used in the RVOM, it is unlikely to benefit from external developers and a wide community.
Although open source is not an appropriate avenue for the RVOM it has given insight into a
broad number of business models. Different aspects of both proprietary and open source
business models are suitable for the RVOM and incorporating these into one model leads to
multiple revenue streams.
It became apparent that selling the RVOM as a product is unlikely to maximise adoption and
revenue. Instead by adopting a consultancy model, the RVOM would be used as part of a
bespoke service. This would look to creating software as a solution, addressing customers’
particular problems. By working towards creating a full package for the customer, there is
the opportunity to offer on-going support, upgrades and add-ins after implementation to
generate further revenue.
The downside to this model is that it is labour and time consuming and therefore does not
have significant economies of scale. This is because its unique selling point lies with the
expertise of the team. However from the business models analysed a consultancy model is
perceived to offer the best prospects for the RVOM given its strengths and current position.
Although large companies are best equipped to deal with Real Options analysis, in practice
the RVOM team have found that medium businesses are more willing to put in time to
develop and learn how Real Options will benefit them. Large companies are proving more
difficult to communicate and develop with. Equally it is preferred to work with companies
with a base in the North West so that personal contact with the customer can be made.
The proposed general strategy for the RVOM is to offer a standard software package as a
“cash-cow”, which would help generate a stable revenue stream to support consultancy
work. Bespoke software will be developed for the customer through a consultancy model.
This would aim to offer software as a solution to individual customer problems.
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The RVOM is a superior product and has few rival products. It can therefore charge a
premium price to its customers. The generic software would be priced around £1,000, but
the bespoke software would have to be on a case-by-case basis.
There is a barrier of mistrust between a new supplier and clients. Reviews,
recommendations and high profile early adopters can help break down these barriers. In
marketing the RVOM, methods used will be through networking events, word of mouth,
industry fairs and study groups. These would be the most effective methods as mass
marketing would be an ineffective tool for bespoke solutions.
A financial analysis of the RVOM has considered the future revenue stream. These were
based on predicted sales relative to other Gemcom Whittle modules. It also gave the
assumption that there would be an additional client in year 2 of sales and a further 2 in year
3. The following table gives a summary of the forecasted revenue.
2012 Sales
2012 Revenue
2013 Sales
2013 Revenue
2014 Sales
2014 Revenue
Bespoke Solution
£
£
£
Gemcom 10 23,212 15 36,052 25 61,353
Bespoke solution (1)
- 10 20,000 15 35,000
Bespoke solution (2)
-
- 10 20,000
Bespoke solution (3)
-
- 10 20,000
Standard software
- 10 10,000 30 30,000
Total
23,212
66,052
166,353
The revenue projection was then used to help evaluate whethere it was viable to launch the
RVOM independent of the university. It was concluded that the best scenario and lowest
risk option was to run the RVOM alongside work at the university until more clients and
sales were established.
The RVOM has shown to have commercial potential. The proposed strategies and business
model have considered how this potential can be put into practice.
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1 RVOM Resource Valuation and Optimisation Model
The RVOM is a Real Options computer software package, which helps mine owners plan
operations optimally, understand the risks behind a project and make defensible valuations
(Evatt et al, 2011).
Real Options is an area of study which looks at how to make business decisions under
uncertainty. While Real Options has the potential to be of use to mining companies, the
complexities and diverse problems in mining have previously halted its application (Evatt et
al, 2011). Recent advances in mathematics are starting to be able to solve these problems.
The RVOM uses the latest advances in financial mathematics to create an accurate Real
Options analysis to a broad number of mining operational problems.
1.1 Project Background
The RVOM initiated in early 2008 when a model for valuing a finite resource in relation to
valuing uranium stocks was needed in a UK government sponsored investigation into
nuclear power sustainability. Through the teams research they found a lack of literature in
order to solve this fundamental problem. With a strong background developed over the
previous few years suited to tackling this problem, they found that partial differential
equations had rarely been used to solve the problems surrounding finite resource valuation
(University of Manchester, 2009).
The team contacted The University of Manchester Intellectual Property (UMIP), who equally
saw the commercial and industrial potential of their work. UMIP made enquiries with
leading mining software providers to gauge commercial interest.
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UMIP
The University of Manchester Intellectual Property is a limited company, solely owned by
the University. UMIP is concerned with the protection and commercialisation of the
Universities expertise and intellectual property (IP). The most common routes for IP to
be commercialised is through sales, licenses or spin-out companies. They hold the
Universities patent budget, manage its proof of principle awards and its seed corn
investment funds. Their level of knowledge and extensive networks with industry
specialists is an asset to have behind the RVOM.
UMIP and the team found an industrial partner, Gemcom. They have provided ore-grade
data and advice on mining operations in helping develop RVOM for the mining industry.
Gemcom
Gemcom delivers comprehensive solutions to all major mining companies in more than
130 countries. Gemcom addresses clients' business goals and mining challenges, aiming
to achieve efficiency and profitability in mining operations (Gemcom, 2011).
The RVOM has been made compatible with an existing package, Gemcom Whittle. This
software package gives insight into the ordering of extraction to produce the optimum
pit shape (Evatt G. , Johnson, Duck, Howell, & Tonkin, 2011). It is well complemented to
the RVOM, as the RVOM is not radically changing existing methods. It is a detailed and
accurate tool, which can use the block by block processing schedule produced by the
Gemcom Whittle to make comparisons for any schedule (Evatt G. , Johnson, Duck,
Howell, & Tonkin, 2011).
At present the RVOM is in the final stages of development and is being prototyped with
one of Gemcom’s clients.
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1.2 Solving a problem
A mining company, prior to extraction of a mine, produces feasible extraction plans to
evaluate the cost-effectiveness of operation (Evatt et al, 2010). By optimising on this plan, it
can help mining companies achieve greater profit. However in calculating this, it must
consider both physical and economic uncertainty simultaneously.
The financial constraints and uncertainties feed into the extraction schedule (Evatt et al,
2010). But combining both these uncertainties into calculations is complex. It is not a
common problem studied in finance and process optimisation and is therefore challenging
to solve.
The RVOM tackles this problem by drawing from areas in mathematics, probability theory,
economics, computer science and mine engineering constraints (Evatt et al, 2011).
The probability of project completion is valuable to a commercial venture. This is especially
the case for the mining industry where large up-front capital investments are required. The
mining industry faces a broad number of uncertainties from political risk to labour market
changes (Evatt et al, 2010). The most vital however is the financial uncertainty found in
variations in commodity price and errors in the estimated ore grade, as these two generate
the cash-flow for a mining project (Evatt et al, 2010). Fluctuations in the commodity price
can help produce large profits, while at the other end of the spectrum, can cause a mine to
abandon operations.
The RVOM uses partial differential equations (PDE’s) to capture the probability of the mine
staying open through the whole planned extraction process. Although PDE’s are challenging
to use, they are a powerful technique, which are fast and easily adaptable.
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1.3 How others have solved these problems before
Earlier papers have been written in determining how to maximise the value of a mine by
managing the extraction plan under uncertainty; many of which come from the area of Real
Options. One example is that by Brennan and Schwartz (1985), who showed how to
optimally calculate when to abandon or mothball an extraction process, assuming
uncertainty in future price and the switching cost of capital (Evatt et al, 2011). To determine
the optimal process they used partial differential equations, but this was with a fixed
extraction schedule and ore-grade. Other work to have followed Brennan and Schwartz, has
shown the same underlying principles within Real Option theory (Evatt et al, 2011).
Alternative methods which have helped solve the problem of mine scheduling have been
developed. The Lerchs-Grossman algorithm used graph theory to find the optimal pit-shell
shapes for an open mine, without uncertainty being present (Evatt et al, 2011). This
algorithm is now incorporated into the Gemcom whittle software package, which the RVOM
has been developed with. The Lerchs-Grossman algorithm was then extended to include
variable slope constraints. This allowed for different geologies within the same mine.
The inclusion of discounting on the ordering of block extraction was introduced by Tolwinski
and Underwood (1996), through the use of dynamic programming (Evatt et al, 2011). The
addition of discounting however, made the Lerchs-Grossman algorithm no longer optimal
(Evatt el al, 2011). Therefore Tolwinski and Underwood proposed an approximate method
using dynamic programming to deal with the increased complexity.
Incorporating geological uncertainty into mine scheduling has been previously studied.
These looked at how to reduce the probability of a downside, for example by trying to
minimise periods of extracting poor ore quality. Ore-grade uncertainty has been examined
by Menabde et al (2004), Jewbali & Dimitrakopoulos (2009) and Martinez (2009), who
solved their models using a Monte Carlo method (Evatt et al, 2011). Monte Carlo approach
takes a long time to compute and is difficult to extract model sensitivities. Evatt et al (2010)
show how ore-grade uncertainty can be solved using partial differential equations, which
allow for fast and accurate computations. Ore-grade uncertainty proved minor in
comparison to price uncertainty, which the RVOM equally uses PDE’s to solve.
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Real Options is not a guarantee of solving mining problems in their entirety. For instance, it
does not give the ordering of extraction from the mine. But when the right problem permits,
Real Options is an effective tool to help maximise value and manage uncertainty.
1.4 Using the RVOM
RVOM can be applied to a broad number of mining situations, where different minerals will
have varying economic and physical uncertainties. Although it has been designed to work
with open pit mines, using the outputs from the Gemcom Whittle software package, it could
also be applied to underground mining processes. On the same lines it utilises the same
inputs, which include processing capacity constraints, and differing processing and
extraction costs for different block extractions and multiple commodities.
To use the RVOM, values for the stochastic uncertainties and remaining inputs are entered
into the computer package. The output can be single values, 2D or 3D graphs, which give a
range of valuations in relation to variables such as price and grade quality (University of
Manchester, 2009).
Key inputs for the RVOM are the degree of uncertainty, available extraction rates and the
order in which mining will be processed.
Key outputs are project valuation, the optimal price for when to take each decision and the
most novel aspect, the probability of having to make a specific decision.
Project valuation: The RVOMs ability to make Real Options based valuations gives
managers an expected net present value. This requires the RVOM to calculate the
optimal price for when to take all available decisions.
Optimal price: By being able to take decisions at the optimal point in an extraction
process, the potential of high cash-flow increases in the long run.
Probability of an event occurring: this is the most unique aspect of the RVOM. It
allows mine owners to evaluate the risk behind each option. This aspect is not only
useful to mine owners, but also to regulators or contract sellers who need to
conduct a risk assessment of the economic, environmental and sociological impact of
a proposed mine. (Evatt et al, 2011)
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Additionally, the RVOM can calculate the optimal strategy for making changes between
operational states (Evatt, Johnson & Moriarty, 2011). These operational states include
abandoned, mothballed, enter operation, normal operation and increased extraction rate.
The mining operator can specify which transitions between states are and are not possible.
For example some decisions will be irreversible (Evatt, Johnson, & Moriarty, 2011).
Importantly, the RVOM can take account of the capital costs of making each transition.
1.5 Intellectual Property
Intellectual property provides incentives and financing for innovation, by preventing the
copying or gaining from an inventor’s creativity and investment (International Chamber of
Commerce, 2005).
For the RVOM a patent for the algorithms is in the examination stages in the US and
Australia. These countries have the largest sales for Gemcom’s mining software.
There are few people in the world with the level of expertise and experience required in
order to solve these valuation problems using partial differential equations, therefore this
protects RVOM's position. This is an example of know-how. Know-how is the practical
knowledge of how to get something done (Wikipedia, 2011). The know-how is enhanced by
its tacit knowledge, as it is difficult to explain the mathematics behind it.
There are problems however in protecting the RVOM as it would be difficult to know if
someone was selling or using the mathematics behind it. This is because it is hard to
determine whether the decisions made by a firm have been enhanced by the use of a Real
Options software package and that the package used utilises the RVOM’s mathematics.
Equally as the aim would be to sell internationally, some countries do not hold the same
respect for intellectual property and therefore it would be challenging to defend on an
international stage. In a 1987 study of corporate innovation, patents were found to be one
of the weakest methods for capturing returns on innovation (Cottrell & Sick, 2002).
Consequently, the RVOM’s main form of protection is in know-how.
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1.6 RVOM Unique Selling Points
The RVOM’s ability to calculate the probability of project completion or expected duration is
the most innovative aspect of this software package, as it has not been previously
considered. The probability is of value to a company because it gives a quantitative measure
of a project’s risk (Evatt et al, 2010).
It distinguishes itself from other Real Options packages in its use of PDE’s to calculate the
results. PDE’s, in comparison to other methods, can tackle a far broader number of
problems when faced with multiple uncertainties. Equally it does this in a fast manner,
allowing for experiments to be repeatedly easily.
The RVOM isolates economic uncertainty. This is an area, which has not been looked into
extensively for the mining industry. The emphasis has been more on geophysical
uncertainty. Therefore there are few rival software products which look at managing
economic uncertainty.
The expertise used to solve these valuation problems using partial differential equations is
the strongest asset behind the RVOM.
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1.7 Competitive Advantage
“If all competitors fight with the same weapons, the natural result is
commoditization and declining profit margins” (Gouillart & Sturdivant, 1993)
1.7.1 The RVOM’s competitive advantage
The vast majority of companies and their shareholders seek to maximise their profit and
turnover. This is in an environment where firms are competing over their customers and
suppliers. But what makes one organisation strive over another, is its competitive
advantage. John Kay (1995) writes that this competitive advantage lies with matching the
distinctive capabilities of an organisation and the challenges it faces. These ‘distinctive
capabilities’ are identified as innovation, reputation, the architecture of relationships and
strategic assets (Kay, 1995). These four also need be sustainable and appropriate for a
competitive advantage to continue in the long run.
The RVOM is an incremental innovation. It improves upon existing methods in the market.
Innovation is a less sustainable capability because if it becomes a successful innovation it
will be quickly imitated by competitors (Kay, 1995). Innovation is one of the key
competences for the RVOM; hence stakeholders are working to maintain its advantage with
know-how and patent protection.
It is with launching an innovation that a firm is faced with the technology adoption lifecycle
from lead-users, early adopters, late adopters and laggards, as discussed later in addressing
a need. Moore suggested that there was a chasm between early adopters and early majority
where most technologies fail (Moore, 1999). For the RVOM to “cross the chasm” and
succeed it must be able to encourage the use of Real Options in general, as well as the
benefits of the RVOM.
Reputation is especially important for the RVOM because the value of the product cannot
be evaluated until after it has been “consumed”. Therefore by developing a reputation it
provides a guarantee of quality. The RVOM has already established a reputation by being
associated with The University of Manchester. However it can enhance this reputation with
organisations from industry. Its initial success with the mining industry has helped with this,
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especially as it has been developed with the leading provider in the field. Further clients will
increase the RVOM’s reputation and therefore improve the RVOM’s competitive advantage.
Architecture includes the internal relationships among employees, the external
relationships with customers & suppliers and networks between groups of organisations
(Kay, 1995). The internal team for the RVOM is the academics behind it and UMIP. As the
RVOM is at the start of a value chain it is not dealing with suppliers. It has established its
first customer GEMCOM and is spreading the word of the RVOM with potential customers
to come. Its networks are enhanced by being connected with the university and the
companies it is associated with. Equally it can utilise the connections of academics who have
worked previously for employers outside academia.
Strategic assets are based on an organisations market position or dominance. Hence it can
only be established over time and is not easily imitated (Kay, 1995). The RVOM has started
to develop its strategic position with the intellectual property it holds in patents and tacit
knowledge. With time it can strengthen its position by developing the RVOM and products
& services associated with it. Equally it will begin to gather a customer database of those
they have worked with and those who have shown interest. These strategic assets will help
the RVOM form a ‘barrier to entry’ (Kay, 1995).
1.7.2 Competitive advantage to the end user
The use of Real options is seen as an “evolutionary process” (Triantis, 2001). By this, Real
Options enhance a firm’s prospect by improving the valuation of investments and the
allocation of resources. Although there is no immediate need for Real Options, in the long
term it can develop better decision making and therefore provide a competitive advantage.
As the RVOM uses leading mathematics to help in making decisions under uncertainty, it can
improve a firm’s competitive advantage against other firms using existing methods.
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1.8 Market analysis for RVOM in mining industry
Prominent consultancy companies in the mining sector were approached at the time of
developing the RVOM. Snowden Group provides consulting services and technological
solutions to mining and related sectors and Maptek provide mining solutions with products
and services. It was decided however to go with Gemcom. Gemcom was the ideal company
to work with as it is the market leader with an estimated 70% market share. It follows a
premium pricing strategy, as the product offers trusted results, broad functionality and ease
of use.
The RVOM is a sophisticated tool, but many mines do not buy the advanced modules,
because the complexity of these packages is not required for all levels of mine planning and
operations. Therefore although the Gemcom Whittle was the optimum package to develop
the RVOM with, it has not offered a large market size.
An initial client for the RVOM has provided a strong example for the RVOM’s applicability.
Not only has it developed a potential long term customer, but it will be a good case study
when marketing the RVOM to other companies.
The opportunity for the RVOM to be developed further with Gemcom to solve other mining
problems with add-in features and consultancy will generate additional revenue. But the
greatest gains will be had from diversifying into other industries.
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2.0 Opportunity Analysis
The RVOM can be applied and developed to other industries. By considering how Real
Options is already used and in which industries these are present, potential routes to
market and growth strategies for the RVOM can be put forward.
2.1 Real options
It is important for a firm to make the right judgement with their investments, as they are
often partially or completely irreversible and costly to reverse. This is difficult to achieve in
the current economic climate, where it is frequently changing and fraught with uncertainty.
Investment evaluation tools have become increasingly important for companies in order to
manage this environment. Managers know intuitively that they have options, such as to
stage, abandon or expand a project (Real Options Group, 2011). Traditional capital
budgeting techniques fail to take account of this managerial flexibility. To help alleviate this
problem Real Options have risen as an approach to challenge traditional capital budgeting
techniques (Triantis, 2001).
Real Options is an effective tool to deal with market uncertainties. It recognises that new
information will be obtained after the start of the project which will have an effect on the
decisions a firm should make and what value is to be achieved.
2.2 Use of Real Options
Real Options tend to be used by international firms, as they are more likely to use tools
designed to deal with market uncertainty and quality improvements (Bain and Company,
2001). It is also found in the technology, energy and utility industries, as shown in table 1,
where top management are more likely to have engineering or technological backgrounds
(Block, 2007).
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Table 1: Using Real Options (Block, 2007)
Industry Using Real Options
Survey participants
Technology 13 36
Energy 11 25
Utilities 6 22
Health care 4 26
Manufacturing 3 57
Finance 2 31
Transportation 1 12
Total 37 209
In these industries Real Options is mostly used in helping to make decisions in research and
development (R&D), new product introductions, and mergers & acquisitions (Block, 2007).
Figure 1 Application of Real Options (Block, 2007)
The amount Real Options is used however ranks quite low as a capital-budgeting tool. In
2000 Bain and company conducted a survey of 451 senior executives across more than 30
industries regarding their use of 25 management tools. Just 9% used Real Options (Bain and
Company, 2001), which was a decline in useage in comparison to previous years. This is
36.2
27.8
22.1
9.6 4.3
New product development
Research and development
Mergers and acquisitions
Foreign Investment
Other
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supported by a survey of 205 Fortune 1,000 CFO’s in 2002, that found Real Options as one
of the least used tools, with net present value topping the list with 96% (Teach, 2003).
Reasons for this lack of acceptance has been given to the satisfaction of existing methods,
lack of acceptance from decision makers, lack of transparency and knowledge of Real
Options and its complexity (Hartmann & Hassan, 2006). This was supported in a survey
conducted by Block (2007), as shown in figure 2.
Figure 2 Reasons for not using Real Options (Block, 2007)
It will be a challenge for the RVOM to break down these barriers as it uses complex
mathematics and has only begun in its launch into industry.
However there are those who argue that it took decades for net present value to be widely
accepted in industry. With Real Options being a more sophisticated tool, it is likely to take at
least as long (Teach, 2003).
John Graham and Campbell Harvey (2001) found in a survey of 4,000 CFOs that 27% of
respondents used an options approach almost always in evaluating and making decisions on
growth opportunities. Therefore the thinking behind Real Options is already being used in a
marginal way (Copeland & Tufano, 2004).
Existing techniques used in industry haven’t been able to solve Real Options problems with
the same accuracy and efficiency as the RVOM. The mathematics used in the RVOM are
better at tackling industrial problems. Therefore although the use of Real Options isn’t yet
widely accepted, this is from the use of existing methods.
42.7
25.6
19.5
12.2 Lack of top management support
Proven methods
Too sophisticated
Encourages too much risk taking
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2.3 Competing and existing methods
2.3.1 Net Present Value
Net Present Value is the difference between the present value of cash inflows and the
present value of cash outflow (Investopedia, 2011). It is based on the assumption that
investment is reversible, or if irreversible, investment is now or never (Dixit & Pindyck,
1993). Net Present Value is calculated using the following formula:
The sum of all inflow and outflow discounted back to the present
Since net present value does not incorporate flexibility into its calculations, a net present
value approach neglects to consider dynamic decisions. Options are what net present value
overlooks, yet options can have considerable value (Teach, 2003). It is possible for there to
be a negative net present value, but by taking into account the flexibility of a project, net
present value may undervalue the potential rewards of a project.
Real Options recognises that managers obtain valuable information after a project is
launched, and that these informed decisions can make a big difference (Teach, 2003).
2.3.2 Black-Scholes
Black-Scholes is a quick method to find the value of a financial option, but is only accurate
under certain conditions. For example, it can only be used for making a single investment
decision at a pre-determined time in the future. Black-Scholes is commonly mistaken and
used as a Real Options tool, when it only provides a single equation solution to a partial
differential equation.
2.3.3 Binomial Lattice
Binomial Lattice model allows for decisions to be made at the optimal point in time. It is
Binomial in that at each stage of the model, the option can either be exercised then or the
firm can hold the option until the next period. This is calculated “iteratively” from the latest
date the option can be exercised to present (Triantis, 2001). Therefore because it allows for
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decisions to be made at the optimum point, Binomial Lattice models can have wider
applications in comparison to Black-Scholes.
2.3.4 Monte Carlo simulation
The Binomial Lattice model fails to handle problems with multiple uncertainties. Monte
Carlo simulation has far greater flexibility in being able to manage a number of
uncertainties. By creating probability distributions for the uncertainties, all possible
scenarios are generated for the project. A Real Options valuation “is then calculated for
each of these scenarios, and the average of these values is discounted back to the present”
(Triantis, 2001). Monte Carlo Simulation is useful when the project is path-dependant, which
is only when a project has made prior decisions (Siddiqui, Marnay, & Wiser, March 2005).
2.3.5 Risk-adjusted decision trees
Risk-adjusted decision trees allow for multiple uncertainties and decisions, but do not offer
the complexity for more detailed modelling. For example in the pharmaceutical industry,
uncertainty over the discovery of a new drug must be modelled separately to other
variables as it will affect decisions made in the different investment stages (Triantis, 2001).
2.3.6 Partial Differential equations
The RVOM uses Partial Differential equations (PDE’s). These are easily adaptable and give a
thorough understanding of how a system behaves, perfect for optimising dynamic systems
(Evatt G. , Johnson, Moriarty, Duck, & Tonkin, 2011). They are also fast in calculating and
succeed at dealing with multiple uncertainties. The downside is that PDE’s can be difficult to
use and rely on advanced computational techniques. Overall, however, the result is very
powerful and is well suited to solve Real Options problems.
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2.4 Use of Real Options technique in practice
Figure 3 Competing and existing methods (Block, 2007)
Binomial Lattices is most frequently used, but is seen as simpler than Risk-adjusted decision
trees or Monte Carlo simulation (Block, 2007). Black-Scholes requires inputs for five
variables which aren’t always available for Real Options analysis. For example the expiration
date for a financial option is easily determined, but for Real Options the closing date can
change depending on how a project is progressing (Block, 2007). The limitations of Black-
Scholes have resulted in greater reliance of the top three methods (Triantis, 2001).
The choice between these leading techniques is mostly dependant on the expertise of the
user, the amount of time it takes to use these methods and the type of project being
examined (Triantis, 2001).
2.5 Competing and existing products
In determining which companies are using Real Options, to what extent and what products
they use to conduct the analysis, it is only when asking companies directly do we know. Real
Options help enhance decision making under uncertainty and are therefore intangible when
observing from the outside. There are however some generic Real Option products on the
market that can be evaluated:
40
30
22.5
2.5
5 Binomial Lattices
Risk-adjusted decision trees
Monte Carlo simulation
Black-Scholes option pricing model
Other
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2.5.1 Oracle Crystal Ball
Oracle Crystal Ball delivers services, training and consultancy in risk analysis and decision
making (Bloomberg, 2011).
Oracle Crystal Ball offers a spreadsheet application for predictive modelling, forecasting,
Monte Carlo simulation and optimization. It takes into account the factors most affected by
risk, aiming to help make better decisions and gain a competitive edge (Oracle, 2011).
Oracle claims to have customers in 85% of the Fortune 500 companies. These are in
industries such as aerospace, manufacturing, oil and gas, pharmaceuticals, financial services
and utilities (Oracle, 2011). It is also used in universities for teaching risk analysis (Oracle,
2011). It serves customers in Europe, the Middle East and Africa (Bloomberg, 2011).
Additionally, Oracle Crystal Ball has applications for financial risk analysis, valuation,
portfolio allocation, cost estimation and project management (Oracle, 2011).
2.5.2 @Risk
@RISK performs risk analysis using Monte Carlo simulation to show multiple outcomes.
These outcomes are also assessed for their likelihood of occurring and can therefore help in
making decisions under uncertainty (Palisade, 2011).
@RISK is delivered as an add-in in to Microsoft Excel (Palisade, 2011). It is available through
different licensing options (corporate, network and academic licenses) and is supported
with books, training and consultancy. The standard package is £845 (Palisade, 2011).
Industries that @RISK is present in are finance and securities, insurance, oil/gas/energy,
quality analysis, manufacturing, pharmaceuticals/medical/healthcare, environment,
government and defence, aerospace and transportation (Palisade, 2011).
Case example: @RISK can be used in the pharmaceutical industry for new product analysis,
research and development estimates and disease infection estimates. In practice it was used
to model blood screening safety (Palisade, 2011).
Case example: @RISK can be applied to the oil and gas industry for production, capital
project estimates, pricing, exploration, oil reserve estimates and regulation compliance
(Palisade, 2011).
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2.5.3 Real Options Valuation
The company Real Options Valuation offers software, consulting and training services to
deal with identifying and valuing risks and decisions (Mun, 2011). It uses Monte Carlo
simulation, advanced forecasting techniques, portfolio optimization and stochastic
optimization to tackle risk and Real Options analysis. Available is a modelling toolkit with
over 1100 models, tools and functions (Mun, 2011).
Their main product for dealing with Real Option issues is the Real Options SLS, which can be
a stand-alone product or an add-in to Microsoft Excel. It allows you to analyse using
American, Bermudan, European option or own customised option, where you can make the
decision to abandon, contract, expand etc. This package is available for $1,495 as a
perpetual price per user license (Mun, 2011) and can be tried before you buy. Additionally,
they provide certified risk management training courses at $4,995 per person.
2.5.4 Comparison
Real Options Valuation advertised a more comprehensive package against competing
products, which could explain its premium price. They all had a large pool of industries in
which they sold their products and services. The companies never sold just products, but
were supported by maintenance, training and documentation. Equally they had add-in
functionality with spreadsheet software. Prices varied significantly between their basic
products, which will be partly dependant on the variation of offerings and modelling
capabilities.
2.6 Decision-making software package
For the RVOM to be developed into a generic decision-making tool, it would be less time
consuming and require less support than with a bespoke piece of software. It would not be
possible to charge as high price, but could be sold to a broader number of users. This is
because standard solutions are hard to differentiate against competitors and are therefore
difficult to compete against other than on price. By producing for this market it could act as
a “cash cow” for further developments of the RVOM to specific applications.
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IBM SPSS Decision tree
IBM SPSS Decision tree is an example of a decision making software package. It helps
identify groups, relationships between them and predict future events. The decision tree
can be used for segmentation, stratification, prediction, data reduction, variable
screening, category merging and discretizing continuous variables.
There is the choice of four tree-growing algorithms to best model the data.
This decision tree package can be applied to database marketing, market research, credit
risk scoring, program targeting and marketing.
It can be purchased in standard, professional or premium. There is also a mixture of
student versions available. Most is sold as client-only software, but for better
performance and scalability, a server-based version is available.
Price for SPSS Decision tree standard package is $618.95 and the Graduate Pack Base is
$85. To run the decision tree you’re required to have the SPSS statistic package. This
would be an additional charge.
The RVOM would not be in competition with IBM SPSS Decision trees, but would be
complementary. The RVOM could help broaden the number of applications IBM has to
offer for decision making by taking account of uncertainty.
The IBM SPSS Decision tree is one of many examples where it is sold as an add-in to a
statistical program, by offering increased functionality and applications to a basic
package.
It would be an effective way for the RVOM to offer a generic decision-making tool by
being an add-in to an existing statistical package, such as to SPSS or Excel.
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3.0 Applications
When looking into areas for the RVOM to diversify into, it must take into consideration the
conditions necessary for Real Options: uncertainty over net payoffs, irreversibility in project
costs and managerial flexibility in how a project is structured (Dixit & Pindyck, 1993).
Real Options is best suited for Industries characterised by large corporate investments,
uncertainty and flexibility. This is especially the case for the mining, oil and gas industry,
pharmaceutical and biotechnology industry; but has also been used in the automotive,
aerospace and high technology industries (Triantis, 2001). Companies in these industries
tend to hold a large share in the market and have plenty of R&D data needed to make
assumptions about uncertainties in Real Options analysis.
They are in industries that tend to be more engineering orientated, where complex
mathematical tools have been long established (Teach, 2003; Triantis, 2001). Equally Real
Options can be seen in companies which have undergone significant structural change;
often making the use of traditional valuation techniques redundant, as shown in the electric
power industry (Triantis, 2001).
There is also the opportunity to use Real Options without being industry specific. The RVOM
could be developed to deal with supply-chain management, inventory, built-to-order
models, flexible assembly, contract manufacturing and procurement contracts (Teach,
2003). This method could lead to a broader number of uers.
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Figure 4 R&D Expenditure by Sector (2006) (Department for Innovation Universities and
Skills, 2007)
In a survey conducted by the Department for Innovation, Universities and Skills of the UK
top 850 R&D companies, the most dominant research and development sectors are the
pharmaceutical and technology equipment industries. The top five industries accounting for
almost two-thirds of R&D expenditure.
Although the top companies for R&D expenditure tend to be large corporations, it is the
smaller companies that tend to be more research intensive, especially in the high
technology sector. Large firms with high absolute R&D numbers, for example in the banking,
oil and gas industry, have a smaller percentage of R&D expenditure as a proportion of sales.
More research intensive companies tend to have sales between £5m-£50m, investing on
average 4.8% of sales, whereas companies of sales above £5b invested 1.5% (Department
for Innovation Universities and Skills, 2007).
19.4%
17.7%
16.8% 7.4%
7.3%
31.6%
Pharmaceutical & biotechnology
Technology hardware & equipment
Automobiles & parts
Electronic & electrical equipment
Software & computer services
Other
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Real Options Group ROG
ROG aims to source the academics and practitioners in the area of Real Options. With
links to the best academic institutions and a global network of those in business and
industry. The Real Options Group have expertise in biotechnology & pharmaceuticals,
telecommunications & media, high technology & the internet and the energy & natural
resource sector.
In the Real Options Annual conference, sponsored by ROG, management and academic
papers with practical applications were predominately in the areas of valuating natural
resources and environmental protection. Other key applications were in the transport
and pharmaceutical industry.
This highlights some of the main areas currently being focused on by experts in both
industry and academia.
The RVOM is a tool that can be used regardless if there is an upturn or downturn in the
market. Indeed, Gemcom found that in 2008 it was selling more optimisation tools in the
face of a downturn. Therefore looking at the health of an industry is not a clear indicator of
how well the RVOM would do.
Some of the prominent industries will now be considered in more detail. This will look at
how the RVOM could be applied, how feasible this would be and which companies are
dominant in these industries.
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3.1 Energy Industry; Oil, Gas, Nuclear Power and Renewable Energy
The Energy Industry is highly fragmented by markets and services, deregulation and price
volatility in the international arena (Real Options Group, 2011). Those involved need to
embrace this uncertainty to maximise shareholder value (Real Options Group, 2011).
A major issue in the energy industry is global warming. The UK government are being
stringent on industry to reduce their greenhouse emissions to slow down climate change
(Keynote, 2010). For example the UK’s coal fired power stations are set to cease operation
in 2015 to meet the terms of the EU emissions legislation. Equally a commercial-scale
carbon capture and storage (CCS) is being developed to remove carbon emissions from fossil
fuel power stations (Keynote, 2010). Therefore the production of different fuels is changing
with increased regulation.
The UK coal industry is a fraction of the size it used to be, producing 6.4% of all UK primary
fuels (Keynote, 2010). However there is still high demand for coal. The UK demand for coal
was 15.9% in 2008 (Keynote, 2008). This means the UK has become more reliant on imports.
This has led to increased popularity of the renewable energy sector. Not only does it
produce little carbon emissions, but it is not dependant on imports.
This has raised some of the issues facing the supply and demand of primary fuels.
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3.1.1 Oil and Gas Industry
Real Options is often applied to the Oil and Gas Industry. This is because it embraces a lot of
characteristics suited for Real Option valuation. These include:
Large capital investment
Uncertain revenue streams
Long lead times before any cash flow
There is uncertainty over the amount of potential production
At each stage of development there are multiple technical options
Political risk and market exposure also affect price and supply
(Bailey, 2005)
In its simplest form, extraction can be broken down into exploration and appraisal;
development, production and abandonment. Real Options can be applied to any of these
stages.
There is a growing need for Real Options in the Oil and Gas Industry as companies are
having to look more at extracting from inaccessible areas and the deepest seas (Bailey,
2005). This means investment costs are rising and that these investments are being affected
by larger uncertainties.
Route to Market
The stage in the value chain in which to direct the RVOM is between selling to oil and gas
suppliers directly or to software and consultancy companies already working in that
industry. By going with the former, the RVOM goes into direct competition with companies
already in the field. Equally not all oil and gas companies develop software inhouse, but
instead outsource their work. Therefore it is worth looking into selling and developing the
RVOM to service providers already in the industry. This way the RVOM can leverage their
networks and portfolio of companies.
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Companies’ close-up
Schlumberger
Schlumberger is the leading oilfield service provider. They optimise the performance of
reservoirs for customers in the oil and gas industry. Some of the products and services on
offer are formation evaluation through directional drilling, well completions and
productivity, well cementing and stimulation, consulting, software, information
management and IT infrastructure services (Schlumberger, 2011).
The success of Schlumberger is heavily dependant on revenue from oil and gas rigs (90%)
and are therefore reliant on oil and gas production (AOL, 2011).
Schlumberger has the largest market share with 108,000 employees over 80 countries.
There are however other large companies working in this field:
Baker Hughes Inc. deliver solutions to oil and gas operators to make the most of
reservoirs
Halliburton provide broad services and technologies to upstream oil and gas
customers
Weatherford International delivers oilfield products and services. One of their
products, Tactical Technology, is designed to maximise the value of oil and gas
assets.
(Wikinvest, 2011)
Wood MacKenzie
Wood MacKenzie gives commercial insight for the global energy, metals and mining industry
from discovery to delivery. They evaluate economic indicators, market supply & demand
and price trends to assess and value assets and companies around the world. Wood
MacKenzie also offer bespoke consulting services (Wood MacKenzie, 2011).
Industry sectors they specialise in are coal, corporate, deep water, gas and power, liquified
natural gas, metals, refining and marketing (Wood MacKenzie, 2011).
They provide analysis for assets and their valuation, company analysis, country and regional
analysis, fiscal and regulatory analysis, GIS/mapping, industry trends, market analysis and
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pricing, opportunity screening, portfolio analysis, scenarios and strategic advice (Wood
MacKenzie, 2011).
Clients of Wood MacKenzie incluse BP, Tital and Pemex (AOL, 2011). Furthermore, they have
650 professionals over 20 offices worldwide (Wood MacKenzie, 2011).
Care would have to be taken with Wood MacKenzie that a licensing agreement would not
overlap and infringe on the exclusivity agreement made with GEMCOM in the mining
industry, as this is an area Wood MacKenzie also work in.
The RVOM with some development could fit into these companies product and service
offerings.
3.1.2 Nuclear Power Industry
Nuclear power provides a steady output of energy at a high cost. To sustain the use of
nuclear power there needs to be a reliable source of uranium in the future. Sources of
uranium are found in Australia (1/3), Kazakhstan (12%), Canada (9%) (World Nuclear
Association, 2011). Over 14% of the world’s electricity is generated by uranium in nuclear
reactors. There are 440 nuclear reactors in 30 countries, with 60 under construction and 150
planned.
The UK has 18 reactors generating about 18% of electricity. All of these are to retire by 2023
and are to be replaced with 19 new-generation plants from 2018. These plants are to make
up a significant proportion of what the government predicts is required of net generating
capacity by 2025 (World Nuclear Association, 2011). On both a national and international
stage, the nuclear industry is growing in importance.
With the growing use of nuclear power, comes concern over radioactive management.
Governments have progressively introduced legal frameworks to manage nuclear waste.
The best option for nuclear waste is deep geological disposal facilities, although none exist
yet (Ionescu & Spaeter, 2011). Instead it is stored and accumulated under close monitoring.
This makes it easier to retrieve if necessary.
There is a need to maintain flexibility with nuclear waste as new technologies and methods
may result in making nuclear waste safer. Flexibility is improved by the ability to reverse
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waste disposal. Reversibility is ensured by multiple disposal stages, changing degrees of
retrieve ability and passive safety (Ionescu & Spaeter, 2011). Real Options helps address
uncertainty over the market value of radioactive material contained in waste packages and
the uncertain evolution of technological progress in nuclear waste management (Ionescu &
Spaeter, 2011).
Work on the valuation of uranium stocks for the sustainability of nuclear power, has been
previously studied by the RVOM team and proved a challenging problem to solve. It would
not be a straightforward industry for the RVOM.
3.1.3 Renewable Energy Industry
Climate change, energy insecurity and pollution are among the most important current
problems and addressing these will require major changes to energy infrastructures
(Siddiqui, Marnay, & Wiser, March 2005). Equally as demand is set to outstrip supply by
2016 (REN21, 2011), renewable energy is offering an answer to provide customers with
stable and secure energy supplies (Davis & Owens, 2003).
Developments are being made to increase the diversity in the number of energy sources. A
key component of the UK governments energy strategy aims by 2020 to have renewable
energy contributing 30% to electricity, 12% to heat and 10% to transport energy usage by
2020 (Keynote, 2010).
Main types of renewable energy are biofuels and waste, windpower, wave power,
hydropower and combined heat & power. Windpower added the most new capacity in
2010, followed by hydropower and solar PV (REN21, 2011).
In a working paper on small hydropower plants by Fleten, Heggadal, Linderud, it looked at
the uncertainty created by shifts in climate change policy. This affects the uncertainty in
electricity prices and uncertainty over the introduction of green certificates. They compared
this with the timing decisions of investment in small hydropower plants in Norway in 2001-
2010 and found that it was affected. To calculate this they had used Real Options theory,
translating the climate change policy uncertainty into investment uncertainty. Other
uncertainties which were raised were the rate of return from small hydropower plants, the
affect of taxation legislation and the total investment cost of the project (Fleten, Heggadal,
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& Linnerud, 2011). It is an illustration of how Real Options theory can been applied in the
renewable energy industry.
3.1.4 Energy Industry PEST
Political parties are expected to hold a stance on environmental issues; global warming,
rising energy prices, greater demand for energy especially from developing economies
and increased competition for energy suppliers.
UK energy supplies, especially gas, is dependent on imports from regions with political
instability and/or from countries with different political regimes. There is the threat that
this could lead to disruptions in supply.
Problems with the banking sector and government support that has been needed for
some of the major UK banks, means there has been a reluctance of financial institutions
to provide credit. This has an impact on the energy sector, which is highly dependant on
large investment funding. The result could mean a delay in the initiation of projects.
Investment in energy’s infrastructure includes replacing coal-fire generation, nuclear
power stations, renewable energy and the rollout of smart meters.
Rising energy prices will be a problem for the consumer. Reasons for this rise in price is
from maintaining and renewing energy infrastructure and the higher cost of renewable
energy.
Energy saving measures are becoming increasingly popular; better insulation, efficient
boilers, low energy lighting and smart meters.
Microgeneration: producing energy at a local level through solar and wind energy.
(transfer excess energy onto the electricity network.)
Renewable energy industry represents a number of challenges and opportunities in the
Technological
Political
Economic
Social
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development of technology. Smart meters are an example of a technology, which is
being currently developed and launched.
Smart meters give consumers information on tariffs and the rate of energy being
consumed (Keynote, 2010). This is important as the UK is pushing to meet its carbon
emissions target in 2020. To reduce the carbon emissions being produced, domestic
demand for energy has to be managed more effectively. The aim will be to reduce total
demand and have demand shaped by increasingly variable supply. Smart Grid projects
are helping consumers save 10% on bills and reduce peak demand by 15% (IBM, 2011).
The PEST analysis has highlighted some key uncertainties in the Energy industry that Real
Options can help address. The rising and variable cost of energy for the consumer and the
use of smart meters are a prime opportunity for the RVOM. Industries on the rise and
therefore could lead to some promising business are in the nuclear power industry and the
renewable energy industry. It also has highlighted that the end consumer as well as those
supplying energy could benefit from Real Option thinking.
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3.2 Utilities
3.2.1 Electricity Industry
The electricity industry makes use of multiple sources of energy; coal, gas, renewable
energy, nuclear power and CHP. Gas is the most dominant fuel used for electricity
generation. Its share of the market is expected to increase from 36.7% in 2005 to 53.1% in
2020 (Keynote, 2009)
Table 1 Electricity generated (World Nuclear Association, 2011)
Source of energy % Electricity
generated
Gas-fired 46.3%
Coal-fired 28.5%
Nuclear power 16.4%
Wind 2.7%
Hydro 0.95%
Other renewables (mainly biomass) 3.4%
Oil and ‘other’ 1.7%
Some areas that Real Options can help within the electricity industry include the clean air
act, which calls for a reduction in emissions of sulphur dioxide. Real options can aid in
addressing the need to minimise the cost of making these reductions. Another example in
which Real Options can be applied is the pay off between economies of scale and flexibility
(Dixit & Pindyck, 1993). It is much cheaper to build a large coal fired power plant than to
keep adding in small amounts. But they face considerable uncertainty over the rate in which
demand will grow. Forecasting in the electricity industry is becoming increasingly difficult
due to the economic downturn in the UK economy and factors such as high unemployment,
contractions in GDP and fluctuations in oil price, which are likely to affect market demand
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(Keynote, 2009). Therefore it may be more cost effective to invest in incremental amounts
than by building one large plant.
Company close up
National Grid PLC
Owns and operates the electricity and gas infrastructure networks in the UK and US. It also
owns and operates LNG storage facilities in the UK.
3.2.2 Water Industry
The water industry is undergoing similar problems as the Electricity Industry.
The opportunity cost of investment in the provision of public services has promoted the
involvement of private firms in water services (D'Alpaos & Moretto, June 2004). The aim is
to reduce inefficiencies and inject new financial resources into the industry. However
because of the technical and structural characteristics of the water sector, it means that
efficiency can’t be promoted through competition. Instead they compete to the entitlement
of a service contract, where they have the right to produce, operate and manage water
utilities in an area for a certain period of time. A condition to this contract is that prices are
low and justified to the consumer.
A case study on United Utilities looks to highlight some of the key areas that Real Options
can be applied:
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United Utilities
United Utilities regulates water and waste water networks in the UK’s North West. They
have to guarantee the security of water supply to almost one million people and that this
water is of a decent quality. Also they manage the catchment land around reservoirs,
safeguard raw water stored and collect wastewater from customers and surface water
from roads to treat as wastewater before returning to the environment.
OFWAT is the economic regulator of the water and sewage sector in England and Wales.
They see that providers keep bills low for consumers, monitor the services provided by
companies, examine companies’ costs and investment and encourage competition
where it will benefit the consumer. United Utilities has to therefore justify its price to the
consumer and ensure long term sustainability of water supply.
For United Utilities demand for water is quite predictable and remains relatively constant
other than in some dry periods. The need to maintain stable and low prices for the
consumer means the environmental impact of water supply is not reflected in the price
charged. There is however a high degree of uncertainty with the supply of water. The
biggest uncertainty being in the amount of rain. Therefore being able to take account of
uncertainty in supply is crucial to supplying sufficient levels of quality water to
consumers.
Real Options can help address problems with the uncertainty in investment prioritisation.
At present they currently use average net present cost. This is used instead of net
present value because no income is included. Income isn’t used because there can be no
gain in income when prices need to be kept low and stable. Net present cost does not
take account of uncertainty, so an alternative method needs to be used to capture this. A
Real Options method can manage this uncertainty and help optimise decisions in
investment prioritisation.
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Other areas Real Options can be applied to United Utilities is in
The valuing of R&D and investigation studies
Water storage where they can look to optimise on when to fill reservoirs outright
with electricity prices.
Valuing customer satisfaction: This is both in a monopoly situation and
competitive environment as there is currently competition in the non-household
market.
Value of drinking water quality to the customer: At the moment the quality of
water must lie between two band levels. The largest impact on the quality of
water is algae blooms.
Estimating the length and maintenance requirements of private sewers. This is a
new requirement for industry.
Value of water including environmental impacts.
Measuring environmental impact is important as United Utilities make projections 25
years ahead with head room. This head room is made up predominately of climate
change.
United Utilities has quite a lot of versatility in its operations and can therefore be flexible
in their decision-making. This helps the application of Real Options.
United Utilities have shown interest in applying Real Options to their company and are
entering into further discussions and projects with the University of Manchester and
RVOM.
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3.3 Pharmaceutical Industry
Over the last decade the pharmaceutical industry has seen double digit growth rates
(Hartmann & Hassan, 2006). To sustain this growth 4 new drug launches were required each
year from each of the large pharmaceutical companies (Bolton & De Gregorio, 2002 cited in
Hartmann & Hassan, 2006). However from 1996-2001 on average only one new drug was
launched. This decline in productivity is still evident. Pharmaceutical companies are having
to plow more into research and development, without the benefit of an increase in
launched products (Booth & Zemmel, 2004 cited in Hartmann & Hassan, 2006). Although
R&D expenditure has increased substantially in recent years, the number of new molecular
entities and biologics approved has been in decline (Department for Innovation Universities
and Skills, 2007).
Reasoning behind this is that treatments needed for remaining unconquered diseases are
far more complex than others previously tackled. These unconquered diseases include
cancer and neuro-degeneration diseases. Secondly, it has been difficult for the
pharmaceutical companies to incorporate emerging knowledge, e.g. genome information
into the research and development process. These methods are better established in
biotechnology firms. Finally, regulation on launching new drugs has become more stringent
on their safety and marketing requirements. Since 2005, a new pharmaceutical price
regulation scheme (PPRS) was introduced and the wider use of generic products have
bought reduction in per capita expenditure on medicines in the NHS (Keynote, 2008). This
has led to greater uncertainty on a drugs success rate and has contributed to rising research
and development costs.
Of concern to pharmaceutical companies is many of the blockbuster drugs are reaching their
patent expiry, meaning generics can be launched in competition, decreasing the revenue for
those who developed the drugs. This combined with the above strains means there is an
innovation gap, with a lack of drugs in the pipeline.
As a result there has been a rise in the number of pharmaceutical industries entering into
strategic alliances with biotechnology companies. For example UK Astra Zeneca acquired
Cambridge Antibody technology (CAT) to increase its strength in this area (Keynote, 2008).
Equally there has been a rise in in-license technology, use of therapeutic molecules that
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originate from the biotechnology sector and developing further applications for existing
drugs.
Moreover, the development of a drug is faced with multiple uncertainties. The process lasts
between 10-15years in a fixed sequence of research and development stages.
Phase I- involves testing on 20-80 humans to eliminate differences from animal testing. They
usually last 18 months and 70% tend to proceed to next stage.
Phase II- drug is tested on 100-300 volunteers who have the relevant medical conditions.
This is in order to provide evidence of its safety and efficacy. The stage lasts around 22
months and a third go on to phase III.
Phase III- is widespread testing of the drug. They are usually randomised and double blind to
prevent bias. This stage takes about 31.5 months and about 80% of drugs entering this stage
successfully complete (Hartmann & Hassan, 2006; PhRMA, 2011).
Once these phases have been completed, it is then filed with the relevant medical
regulatory authority. It can then be launched and marketed, but under close scrutiny to
detect any side effects that may not have been aparent in testing
(Keynote, 2008).
The technical success of a new drug is 8% (Gilbert et al, 2003 cited in Hartmann & Hassan,
2006). Equally once developed it must face market risk like any other product in an
unpredictable commerical market.
Real Options analysis takes place from the clinical phase and was shown to have no
significance in the research stage (Hartmann & Hassan, 2006). Black Scholes (interestingly
B/S does not capture the technical risk of R&D project directly), Binomial lattice and
expected net present value are the more common methods used to evaluate decisions.
Pre-Clinical
Phase
Chemical
Optimisation
Biological
Valuation
Clinical
Phase I-III
Registration
Figure 5 Pharmaceutical R&D stages
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The majority of Real Options analysis is seen in larger companies and marginally by smaller
biotechnology companies. Larger companies are more likely to use sophisticated methods
where they can have distinct departments for portfolio analysis. Various tools are used to
incorporate Real Options analysis. These include using software tools to structure and value
research and development projects, bioscience valuations and consultancies, e.g.
Pricewaterhousecoopers.
Table 2 Use of Real Options in Pharmaceutical Industry (Hartmann & Hassan, 2006)
Real Option use %
No knowledge of ROA 15
Knew it by name 15
RO introducted into
Routine
26
Use instrumental ROA 15
Applied in concept 11
Hartmann and Hassan (2006) have shown that Real Options is used in a marginal way. In a
survey to Astra Zeneca and Johnson Matthey, two of the largest pharmaceutical companies,
it revealed that they do not use Real Options and had no plans to in the near future. As
these companies are some of the largest in the pharmaceutical industry, this is not a
positive sign.
Therefore there is some evidence that suggests it is marginally used in the pharmaceutical
industry. This could be for a number of reasons, as indicated above, Real Options is still
growing in industry and the use of complex tools has yet to be fully adopted.
Real Options analysis in the pharmaceutical industry is well suited because of the staged
go/no-go investment decision process required in the development of a new drug (Real
Options Group, 2011).
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Real options can help in evaluating the value of a research and development project at any
given stage in the pipeline, determine the value of buying or selling a biotechnology firm
and model the impact of competitors responses (Real Options Group, 2011).
Route to Market
The pharmaceutical and biotechnology sector is the largest for R&D investment in the UK.
Of the 850 UK companies most active in R&D, they accounted for 35.5% of R&D expenditure
in 2006 (Department for Innovation Universities and Skills, 2007). GlaxoSmithKline and
Pfizer both have large expenditures in research and development relative to companies in
the UK and across the globe (Department for Innovation Universities and Skills, 2007).
Table 3 Top ten Pharmaceutical and Biotechnology companies by R&D expenditure in the
UK 2006/2007 (Department for Innovation Universities and Skills, 2007)
Expenditure
on R&D (£m)
% change
2005/2006-
2006/2007
Expenditure on
R&D as a % of
sales
GlaxoSmithKline 3,457 10 14.9
AstraZeneca 1,994 15 14.7
Pfizer 370 6 26.5
Shire Pharmaceuticals 154 5 16.8
Eli Lilly and company 110 47 7.7
Roche Products 97 -4 20.9
Merial 75 -4 7.2
Merck, Sharp and Dohme 74 -19 19.7
Novartis Pharmaceuticals 65 10 21.4
Aventis 60 119 8.4
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Companies’ close-up
GlaxoSmithKline
Largest single investors are GlaxoSmithKline and AstraZeneca. In 2006/7 GlaxoSmithKline
invested 46.6% of the sector total (Keynote, 2008) and are not only the largest investors in
the pharmaceutical industry, but the largest in the UK. GlaxoSmithKline has made alliances
with major players in the biotechnology industry. These include EPIX pharmaceuticals,
Galapagos, Domantis and ChemCentryx (Keynote, 2008).
AstraZeneca
One of the world’s largest pharmaceutical companies. Global sales of $3.9Bn in 2006 and
exports to over 100 contries (Keynote, 2008). It has 16 R&D centres in 6 countries, 5 based
in the UK. One of them is situated in Alderley Park, Cheshire with 4,500 people. This site
focuses on international sales and marketing. At this site they spend £400m/yr on R&D.
With Alderley Park being in close proximity to the University of Manchester, AstraZeneca
could be an ideal customer to develop a product with.
AstraZeneca has also been focusing its R&D efforts in biopharmaceutical treatments. In
2006, they acquired biotechnology company Cambridge Antibody Technology to strengthen
their position in the market.
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3.4 Biopharmaceutical Industry
The Biopharmaceutical industry is one of the most research intensive industries.
Biotech innovations account for 20% of all marketed medicines and 50% of all new
medicines in pipeline (Europabio, 2009). However, only 17% of biotechnology companies
intend to bring the medicines to market on their own (European Commission, Enterprise
and Industry, 2009).
There are a growing number of acquisitions with pharmaceutical companies due to
developments in monoclonal antibodies, genomics and proteomics.
An increasing problem in the biopharmaceutical industry is the limited amount of
investment from venture capitalists. Effective valuations could secure larger levels of
funding.
Leading companies within the biopharmaceutical industry are Amgen, Genetech and UCB. In
a survey to Amgen they indicated no use of Real Options.
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3.5 IT Industry
Uncertainty in the IT industry can be seen within the complexity and unpredictable
evolution of technologies, the integration of technologies across and within organisations
and the changeable nature of market appeal (Fichman, Keil, & Tiwana, 2005).
Flexibility in the diversity of business products and processes can be enhanced by producing
more generic, multi-purpose and scalable products. (Fichman, Keil, & Tiwana, 2005). The IT
industry is flexible in the way IT can be delivered. For instance in stage implementation;
simulations, prototypes and pilots. Equally, how IT is sourced, whether that be as it is,
modifying packages or developing bespoke solutions; all this either as a single or joint
venture and with or without the help of consultants (Fichman, Keil, & Tiwana, 2005).
Information technology is a cross industry field for applying Real Options. It consumes a
large part of corporate capital budgets and large software applications, which are
notoriously risky. Mark Jeffery assistant professor of technology at north-western university
Kellogg’s school of management states that Real Options can help optimise deployment and
minimise this risk (Teach, 2003).
Although there is widespread interest in taking a portfolio approach to managing IT
investments, few companies 24% optimise such portfolios. According to a recent survey of
130 senior IT executives conducted by Kellogg school, Diamond Cluster international and the
society for information management, none of the executives surveyed used Real Options
(Teach, 2003).
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Intel
Intel is training finance employees in Real Options valuation, and has used it to
analyse a number of capital projects (Teach, 2003).
Intel was faced with a strategic decision in 1996. The decision of whether, and
when, to invest in capital to ensure they would have the manufacturing capabilities
to deal with an influx in demand. Traditional forms of analysis would have
concluded that the expected benefit of excess production capacity was less than
that of having idle physical assets (Triantis, 2001). However Intel saw that traditional
methods failed to take account of managerial flexibility.
Intel’s analysts used a Binomial Lattice model with demand as the key uncertainty.
This Lattice used risk-adjusted probabilities to reflect the state of the economy as it
is intrinsically tied to demand. This method showed that the investment had clear
positive value.
A presentation to senior management of their findings highlighted the value of
considering the options available. In validating the options Black-Sholes and the
Binomial Lattice model were used. Black-Scholes given certain assumptions
produced a value similar to that of the Binomial Lattice model. A second test of
validity was a sensitivity analysis to test some of the key variables. This was to show
that the different valuations achieved through varying input values were in line with
their intuition.
The success of this initial analysis prompted further use of this technique to similar
problems. Awareness of this technique has been rolled-out using presentations and
seminars both to senior management and employees. Intel saw this technique being
spread across the company. This would require intensive training to those
employing the technique and a central group to advise and analyse valuations.
(Triantis, 2001)
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3.6 Financial Services
Financial Services continues to undergo significant changes with globalisation, deregulation
and intensified competition (Real Options Group, 2011). Real Options can help this industry
make better decisions in producing the right portfolio, mergers and acquisitions, evaluating
competitive responses and the effects of the internet economy.
Financial service firms regard Real Options analysis more positively with respect to the
importance of Real Options (Hartmann & Hassan, 2006). Yet according to Block (2007) 2 of
the 31 surveyed used Real Options.
Risk management is a top priority to the financial industry (Pafka & Kondor, 2001). This is a
key area in which Real Options can be applied and hence could be a potential avenue for the
RVOM.
One of the most popular and widespread tools for Risk management is RiskMetrics. This
uses Monte Carlo Simulation models to evaluate the value of assets over a range of time
horizon. RiskMetrics is owned by MSCI and run through a software-as-a-service business
model. This is hosted in two large data centres and delivered to customers over the
internet. Although RiskMetrics has proven to be beneficial in Risk Management, it has been
criticised for simplified assumptions (Pafka & Kondor, 2001; Ambrosio, 2007). The
mathematics behind the RVOM could offer more complexity and accuracy to RiskMetrics.
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3.7 Target Industries
Electricity Real Options can help with:
Issues facing the supply and
demand of primary fuels
Minimise reductions in cost for
sulphur dioxide emissions
Measuring the payoff between
economies of scale and flexibility
The dominant company in
the electricity industry is
National Grid PLC and there
are no other major players
Nuclear Power In the next decade, the nuclear industry
is facing large investment costs and will
contribute a more significant proportion
to our energy requirements than at
present
Need to maintain flexibility with nuclear
waste as new technologies and methods
are introduced
Previous work on solving
problems in the nuclear
industry have proven to be
challenging
Oil and Gas Stronger use of Real Options
The mathematics involved to solve
mining problems would be easier to
transfer to the mining industry in
comparison to other industries
Help in deciding when to invest in
production and development
Schlumberger would be an ideal partner
to work with developing and distributing
the RVOM
Negatives Positives Industries
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Renewable Energy
The fastest growing of the energy
industries and a “popular” topic to be
involved in.
There are lots of problems and
uncertainties in the renewable industry
to tackle
Pharmaceutical The largest R&D industry
Real Options can be used for:
staged go/no-go investment
decision process, which is
required in the development of a
new drug
Value research and development
projects at any stage
Determine value of buying or
selling companies
Model competitors responses
Real Options survey into the
use of Real Options in the
pharmaceutical industry
showed no signs of its use
Would be solving very
different problems to the
ones in the mining industry
and require a lot further
development and time than
some of the other industries
examined
Biopharmaceutical
Most research intensive industry
Not many companies intend to bring
products to market on their own
Growing number of acquisitions with
pharmaceutical companies
Limited amount of investment from
venture capitalists
Require significant
development of RVOM to
enter this sector
Little sign of Real Options
being used
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IT
Real Options can be applied to the
following issues:
Unpredictable evolution of
technologies
Dependency it imposes on a firms
future IT trajectory
Irreversibility
High costs in deployment and
high switching costs after
There has been little sign of
Real Options being applied
to the IT industry and would
therefore be a high risk
strategy
Financial
There are areas in which Real Options
can be applied. Potential to develop
better mathematics for RiskMetrics or
other risk management tool
Not a strong sign of Real
Options being used
Real Options is mostly conducted in the natural resource sector. This is supported from
academia and surveys in industry. It would be the lowest risk strategy to encourage the
adoption of a Real Options software package. Within this sector, the renewable energy
industry is showing the most growth and is a hot political topic. The renewable energy
industry has strong potential for investment and revenue for the RVOM. Additionally, by
moving towards the oil and gas industry, Schlumberger would be a natural progression after
working with the mining industry.
The pharmaceutical and biopharmaceutical industry would be a high risk strategy. It
represents a different set of problems to the natural resource sector and is a tangent to the
work already developed for the mining industry. However it would be an opportunity to
consider if more people become involved in the RVOM or in the future when the low risk
strategy has been exhausted. It has the potential to offer large returns for the RVOM and be
of significant value to pharmaceutical companies and therefore should be an industry to
pursue at a later date.
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The same applies to the financial services and IT industry. They don’t show as much use of
Real Options in comparison to other industries and should therefore be considered at a later
date as part of a balanced risk portfolio.
Digital Energy: Finding an Application
Digital Energy was formerly known as I-prophets. It was founded by Wai Lau after
developing some innovative technology during his masters. Initially this technology was
not applied to any industry. In trying to find the best avenue to take, Wai Lau looked at
current affairs and events to assess which markets and industries are most relevant. I-
prophets decided to focus on the energy industry. It compiled its selling proposition
while holding a stall at an energy fair. Through talking to those in the industry about
problems they were experiencing, by the end of the fair they were able to put together a
list of problems they were able to tackle (Lau, 2010). The technology is now being used
to measure buildings energy consumption, of which government buildings are by law
required to keep a logbook.
For the RVOM, generating work will be best achieved through discussing with companies
the problems they encounter to see if the mathematics can provide a solution. This is in
preference to producing software that seems to solve an industrial problem and then
trying to sell the software.
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4.0 Business Model Introduction
The success of an innovation is not solely associated to the product or service itself, but is
dependent on a number of factors; these include the economic conditions in which it is
launched, the people behind the innovation and the business model.
The importance of business models is often under-stated and undefined, yet as we will see
there are a number of case studies in which innovative business models have been the key
to success.
By looking into business models for the software package, the Resource Valuation and
Optimisation Model, it is hoped that this will enhance its profitability and competitive
advantage.
The effectiveness of the business models will depend on whether the narrative and the
numbers make sense (Magretta, 2002), that is does the story make sense and does the P&L
add up. The business model will need to align what the RVOM is offering and the needs of
the customers. Equally it will have to take account of the current state of the RVOM.
4.1 Business Models defined
There is not an agreed consensus on what a business model is. The term is “often stretched
to mean everything and ends up meaning nothing” (Magretta, 2002). In a paper by Zott,
Amit and Massa (2010), they note the lack of a clear definition; with more than a 1/3 of 103
business model publications reviewed having no definition, less than half notably define a
business model and of ones that did, there are slight differences in definition that can result
in multiple interpretations (Zott, Amit, & Massa, 2010). Hence there is a need to define what
a business model is from the outset.
A business model lays out the foundations of how a business creates value for its customers,
how it delivers this value to its customer and what value and profit can the company
capture (Innovation Intelligence Group, 2010).
Osterwalder conceptualised the essential components of a business model (Zott, Amit, &
Massa, 2010) into a visualisation tool.
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FIGURE 6 OSTERWALDER TEMPLATE
The Left side depicts the supply side, where it focuses on how the company delivers the
value proposition. The right side looks at demand and how value is created for both the
company and the customer.
Osterwalder’s business template is an effective means of illustrating the main features of a
business model. Hence aspects of this tool will be used when applying the RVOM to
business model examples.
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4.2 Relevance of Business Models
4.2.1 Challenges and Opportunities
Technological innovation is not a guaranteed means of achieving success. The environment
and technological landscape is continually evolving, creating new challenges and
opportunities in the process. Prominent issues on the international market are:
Global warming
The Internet
Dwindling resources and an increasing world population
Growing domestic markets in India and China
Oil
Technological change (Innovation Intelligence Group, 2010)
The consumer market has growing expectations and higher demands
A business model helps convert an innovative technology into economic value (Quick MBA,
2011). With each innovation or development, a business model should be shaped in order
to pursue its “go to market” and “capturing value” strategies (Teece, 2010). These need to
take into account the opportunities and uncertainties present in the market. Therefore a
new technology combined with a defined business model can be used to enhance the
prospects of a business (Innovation Intelligence Group, 2010).
4.2.2 A well-defined business model
A well-defined business model enables innovators to deliver and capture the value of their
innovations more effectively (Teece, 2010). For internet and software companies, the
definition of a business model is especially important. Often basic services are expected to
be free by customers, which mean a clear definition needs to be established of how revenue
is going to be generated (Teece, 2010).
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4.2.3 Business model innovation
The cost of creating and developing new products has risen. Also shortening product life
cycles (Real Options Group, 2011) mean that innovations no longer can be guaranteed to
earn a satisfactory profit level before it becomes commoditised. Therefore innovation can’t
be exclusive to products and services, but must include business model innovation
(Chesbrough, 2007).
4.3 Addressing a need
For any software business model to work effectively there must be a need in the market.
Equally to generate revenue there must exist pain for a customer to be willing to pay money
for that problem to be solved (Olson, 2006). Whether that is enough pain that they would
prefer to enter into a proprietary license than an open source one, or even enough pain that
they'd pay for technical support.
Geoffrey Moore who developed the model of technology adoption in 1991 showed the
adoption of technology over its life cycle from innovators, early adopters, early majority,
late majority and, lastly, laggards. This technology adoption life cycle formed a bell shape,
which suggested a business needed to tackle each group of users progressively, using each
captured group as an indicator for the next (Linowes, 2011). Moore advocated that there
was a chasm between early adopters and early majority where most high technologies fail.
A business can cross the chasm by focusing on a specific market, win domination over the
specific market and to then use this as a springboard to extend into adjacent markets
(Linowes, 2011).
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FIGURE 7 TECHNOLOGY ADOPTION LIFECYCLE (WIKIPEDIA, 2011)
One of the ways Moore suggested a business could cross this chasm and increase
technology adoption was by addressing the customer problem in its entirety. To do this a
business must provide the customer a "whole product solution" (Moore, 1999). The vendor
needs to offer a core value product proposition and then wrap around various product add-
ons and services to meet customers more defined needs (Walli, 2006). Essentially, trying to
solve the problems of various customer segments in what the customer perceives to be the
best solution in the market.
Therefore in evaluating these subsequent business models it's important to consider not the
product or service, but the problem in which it is trying to solve in its entirety.
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5.0 Software Business Models’
Software companies have a high fixed cost of supporting developers who build the product.
The traditional software business model’s revenue stream is based on selling per user
licenses and upgrades of their software.
“Around 30% of the software that is written is sold as software” (Koenig, 2004). Therefore a
large majority of software is never sold. Most software is developed directly for the
customer. This is either through customer’s employees or by consultants. Consultants will
work as a service, creating software as a solution rather than aiming to produce an end
product (Koenig, 2004). Therefore it is important to consider where revenue will be
generated, who the end customer will be and what they are willing to pay.
In the literature, based on software business models’, a large proportion of examples came
from the open source world. Their business models’ tend to be more “out of the box” in
their approach, especially as some aim to generate revenue from a “free” product. Hence
this section will begin with an introduction to open source followed by a look into both open
source and proprietary business models.
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5.1 Open Source
The typical commercial business model for proprietary software is a company selling its
software to a customer without fully transferring ownership of product and places
restrictions on its use. They sell licenses with different costs and terms and offers support,
consulting and system integrating systems to help get the software to function in what you
need it to do (Weber, 2004). Therefore the power often lies with the supplier and not the
customer. Suppliers build software to meet the demands of the customer, but in a market
fragmented and rapidly changing it is difficult to address each user's particular needs.
It is with good reason however that companies prefer to maintain ownership of their
software as it is the key to competitive advantage. In open source, the source code is made
available. By releasing the source code of their software they are relinquishing control on
what can be done with the product (Weber, 2004). Therefore it is important to consider
how a company can generate sustained economic returns even after the source code has
been made available. Problems that will need to be addressed are the issue of what can be
protected through legal restrictions for example branding and trademarks and the
accumulation of hard to communicate, tacit knowledge that is needed to turn source code
into a practical solution (Weber, 2004).
Tacit knowledge is hard to articulate and represented by the absence of an agreed upon
language (Smith, et al., 2009). The difficulty in articulating this knowledge makes it hard to
imitate and hence could lead to a competitive advantage (Kogut & Zander, 1992 cited in
Smith, et al., 2009, p43). For example Linus Torvalds' competitive advantage in Linux is that
he knows the code better than anyone else. Customising it for different settings, years of
experience and knowing how it works poses a barrier to competitors.
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5.1.1 Open Source Defined
The "Open Source Definition" is based on the Debian free software guidelines. It is used by
the open source initiative to determine whether a software license is open source. The main
points raised are that all source code must be distributed with the software. That anyone
may distribute the software for free, without royalties or licensing fee to the original author.
Anyone may modify the software or derive other work from it and then redistribute the
modified version under the same terms (Open Source Initiative, 2011). Therefore software
released at no cost is still considered proprietary if the license does not allow for
modifications or free redistribution (Fogel, 2010).
Source code is a list of instructions for a software package that a human can read and
understand. All software has source code (RedHat, 2011), however commercial software is
mostly released in machine language, binaries, where a machine can read it but a human
cannot. Therefore this is an effective way for proprietary software companies to control
what you can do with the software (Weber, 2004). Open source software reverts this, giving
the freedom for anyone to use, but not necessarily give it away for free. "Free software is a
matter of liberty, not price. To understand the concept, you should think of free as in free
speech, not as in free beer" Richard Stallman (GNU Operating Systems, 2011). Hence
freedom includes the right to run the program for any purpose, study how it works and
adapt it to their own needs, to redistribute copies and to share improvements with the
community for all to benefit.
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5.1.2 Intellectual Property
Most common form of intellectual property in open source software is copyright. Copyright
is established when an idea or innovation is expressed in permanent form. It prevents
software from being redistributed. When licensing to a user, the copyright holder specifies
permission for the redistribution of the software. These terms can be written in order to
fulfil certain rights (Weber, 2004). For example the General Public License (GPL) states that
redistribution is free, but must include all source code. Therefore ensuring that the open
source properties of the original license are passed on in redistribution.
Another means of protecting the intellectual property of software is through patenting.
However this posse's a number of problems as patents cover inventions and new processes,
not quite expressions. The US court of appeal for the federal court removed limitations on
the patenting of software in the 1998 State Street Bank decision, which overturned the
limits on patenting of mathematical algorithms and business methods (Weber, 2004). It is
considered justifiable to patent software because of the extensive capital and human
resources invested in producing and distributing an invention. Patents allow a temporary
monopoly that give the original authors time to reap all short term gains from their
invention.
There are those however that argue that software patenting should be prohibited, because
the software industry is fast moving and rapidly evolving. The software also tends to be a
creative combination of previous known ideas and knowledge (Weber, 2004). Equally the
overall cost of producing software and distributing is a lot easier and faster than traditional
manufacturing processes. Justifications need to be made when patenting software that the
mathematical techniques or algorithms are not incremental changes but significant leaps.
Software can also use Trademark protection. This ensures that the name of the original
software is not used in derivative works without permission. Equally that the "original
code's reputation be preserved and transmitted, but not tarnished by association" (Fogel,
2010).
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5.1.3 Open Source and Intellectual Property working together
One of the core aspects about intellectual property is about creating incentives for
innovation. Patents, copyrights, licensing schemes and other methods of protecting
knowledge are in place to ensure that economic rents are created and that a proportion of
these can be received by the innovator (Weber, 2004). If they were not protected, an
updated and improved version would be made available shortly after and the innovator
would have no defensible economic claim (Weber, 2004). Hence it is believed that without
forms of intellectual property there would be no rationale for innovation.
Contrary to belief, the open source community doesn't totally oppose intellectual property.
It is rare to find developers willing to put their software in the public domain forsaking all
copyright (Weber, 2004). Instead, the collaboration of open source depends on a specific
definition of intellectual property, which is explained through a series of licenses (Weber,
2004).
Open source is different in its assumptions and goals that are found in mainstream thinking
of intellectual property. Open source's main goal is to maximise the on-going use, growth,
development and distribution of free software. In this sense, the aim shifts from protecting
the author's objectives to a generation of users’ objectives (Weber, 2004). In its basic
assumption, people want to be creative and original and hence don't need additional
incentives to act in this manner. The only time when innovation will be "undersupplied" is
when access to raw materials and tools are restricted for them to work on (Weber, 2004).
Therefore the unrestricted distribution is considered essential to ensure continuous
innovation.
With patenting it is dependent upon the license. The GNU General Public License version 3
and the Apache License version 2 intend to prevent patents being used by taking away the
rights granted by the license (Fogel, 2010). If someone initiates patent litigation when using
software under these licenses, they lose all patent grants that would have been provided for
that work (Fogel, 2010).
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5.1.4 Open Source Licenses
It is rare for a customer to buy computer software; instead a person often purchases certain
rights to use a copy of it. To achieve this, the business sells a license to use the software, but
does not sell ownership. The Open Source Initiative (OSI) defines what is required for a
license to be open source. Their main requirement being that the source code is freely
available and the user can modify it for their own requirements.
Open source licenses make up the social structure for the open source process (Weber,
2004). This social structure ensures access to source code. The rights to use the source code
are passed on in a license. Typically an open source license gives the user the right to use,
modify, copy and redistribute. The author as copyright holder holds the right to enforce the
terms of such license (Weber, 2004) and can set conditions on use of the software within it
(Olson, 2006).
Software licensing revenue is an effective way of bringing in revenue, because the marginal
cost of producing an additional copy of software is minute. Equally, if a business is solely
dependent on selling services and support to generate revenue, a business must have the
capacity to meet the demand of every customer, which can lead to additional costs. With
open source, the consumer can often download the product off the internet for free; thus
establishing an effective distribution channel to a large number of users at little cost.
Another type of open source license is a reciprocal license whereby any recipient is
expected to share changes and additions made to the software. Reciprocal licenses are
coercive in that they enforce sharing (Olson, 2006). The General Public License (GPL) is the
most well-known example of this.
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General Public License
The GPL's main use is to restrict the terms in which derived works are distributed. So if a
company wants to incorporate GPL source code in its product, then any product which it
sells must include the source code. This source code must be made freely available. This
avoids the risk of competing with a proprietary modified version of your own work (GNU,
2011). The GPL does not require you to release the modified work, but if you do so then
it must be under the same terms (GNU, 2011). Hence in general GPL reduces the profit
potential of companies (Krishnamurthy, 2005) by protecting free software from those
taking full advantage of GPL code (Fogel, 2010).
The GPL and other well-known licensing agreements are useful for their familiarity. It is
likely to lead to less disagreement and will be easier to enforce.
Another example of licensing is the academic license, where often acknowledgement of
original author's work is enough to use the software, such as the Berkeley Software
distribution (Olson, 2006).
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Sun Microsystems Community Source License
FIGURE 8 SUN MICROSYSTEMS COMMUNITY SOURCE LICENSE
Sun Microsystems tried to combine the best of proprietary and open source models and
eliminate the disadvantages of both. They wanted to create an open source like
community of individuals and organisation who wanted to build upon the existing
common infrastructure, such as Java. Their solution was a three tiered licensing system.
Anyone can join the community and gain access to the source code for research use
with a click through agreement. If you agree in addition to conform to tests and
specifications that ensure compatibility with the standard core infrastructure, you move
onto an "internal development" license that allows limited distribution and testing of
application before a product launch. If you then want to market the product, you can
enter into a commercial license where you are required to pay a fee to Sun
Microsystems as they built the core infrastructure for your product. The expectation of
these agreements is that developers share bug fixes with the community. Any other
modifications such as platform adaptations and performance enhancements are not
required to share (Weber, 2004).
What is appealing about this licensing agreement is how customers have different terms
depending on how they use the core product and their level of feasibility.
Therefore just as proprietary software sets conditions for use of its software, so does open
source. These conditions can be different for different people. In both cases they grant the
use of software through licenses that protect and support the concept of intellectual
property and ownership.
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5.1.5 The Advantages and Disadvantages of Open Source
Advantages
By opening the product out to a large number of users, there is a source of developers able
to work on and better the product. A software company aims to maximise its profits and will
therefore seek ways in which to generate further revenue and reduce costs. It is becoming
more common for companies to seek open source as a business strategy in order to meet
both these objectives (Krishnamurthy, 2005).
A community of developers reduces the cost burden for the company. This is supported by a
survey conducted by Forrester (2008) where "87 percent of those surveyed realized the cost
savings they expected from open source" (Assay, 2008). The result is an increase in revenue
by generating a greater number of users through open source. This is where money can be
raised, as it can lead to an increase in demand for upgrades, maintenance, subscription and
product development (BBC Radio 4 InBusiness, 2007).
A business can take advantage of open source licensing to promote and distribute products
at much lower costs than its proprietary competitors (Olson, 2006). Open source
development and distribution reduces the cost of the core infrastructure for consumers
(Olson, 2006). This is because open source does not need to be shrink wrapped or sold in
retail stores, you are effectively downloading a product who’s underlying costs are zero
(BBC Radio4 InBusiness, 2009).
Disadvantages
Providing only support for open source software is harder to defend against competitors.
Although the original authors will have superior knowledge as to how it works, a competitor
can learn the internal mechanisms of the software and can too offer the same service
(Olson, 2006).
Differing opinions exist as to the incentives for innovation between proprietary and open
source projects. One stand point is that proprietary licensing allows and encourages
companies to invest in new developments by protecting their position from competitors and
ensuring they attain maximum returns.
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The complexity of the mathematics behind the RVOM would probably result in only a small
number of developers being able to contribute to its development. Therefore it wouldn’t
reap some of the benefits from using open source.
Open source has been most successful in mature and stable markets such as operating
systems and web servers (Olson, 2006). Although innovation has been seen within open
source, it has mostly dominated in markets with commoditized products (Olson, 2006). The
RVOM does not fit into this category of commoditized products nor has real options
software been long established.
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Oomph-Lib Object-oriented multi-physics finite-element library
Oomph-Lib provides an example of academic work from the mathematics department
at the University of Manchester, which has been developed through an open source
webpage.
Oomph-Lib provides the infrastructure for problem formulation and solutions of multi-
physics problems. It is not a mouse driven package. Users are required to write their
own C++ driver codes to specify their problem, using Oomph-Lib’s high-level objects.
It is free to download and use under a GNU public license (GPL). The GNU public
license is a free, copyleft license, where anyone is free to share and change any
version of the program. Any work developed from the Oomph-lib must be released
under the same terms. These terms also apply to commercial users. For those who
unhook from the system to develop the Oomph-Lib for custom changes, they do so
without the support of Oomph-Lib. Dedicated assistance required for a specific
project, can discuss the possibility of consultancy arrangements with the authors. The
benefits of using the GPL are that it is commonly used and avoids potential confusion
of the licenses terms.
Oomph-Lib is supported by extensive documentation; providing a top down
introduction to finite element methods, a bottom up discussion of Oomph-Lib’s overall
data structure and a not-so-quick-guide on how to create new instances in Oomph-
Lib’s fundamental objects: problems, meshes and elements. Additional support is
offered with an extensive page of example problems and frequently asked questions.
The creators, Matthias Heil and Andrew Hazel, are also available for discussion and
support of Oomph-Lib after all other options are exhausted. The documentation and
tutorials available, although useful to those using it outside the university, it is also
useful internally for developing new versions and thinking through problems.
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Oomph-Lib uses subversion to track changes to the program. It acts as a centralised
open source control system. This allows developers to see how Oomph-Lib is
developing and allows them to go back to previous versions, a bit like a decision-tree
diagram.
There is also an extensive self-test facility to ensure that new developments are
compatible with the Oomph-Lib. Reasons why Oomph-Lib launched their webpage
was to provide a cohesive base to work from. This was to ensure that there were less
overlaps and that everyone was working from the same page. For Oomph-Lib this
helped eradicate code differences and allowed work to be conducted more efficiently.
Oomph-Lib has received some commercial interest, and has received feedback and
developments from the outside community. Oomph-Lib estimates that they have had
growing interest in Oomph-Lib over the years, but exact numbers are unknown.
Estimates can be made when they release a new version, every 1-2 years, from the
number of subsequent downloads.
It has never been the intention of Oomph-Lib to generate revenue from their work.
Although this is a distinctively different objective to the RVOM, it has given an insight
into how academic material has been used in the open source world. The use of a
webpage, extensive documentation and a means of tracking changes in developments
with a programme such as subversive are valuable points to take away.
(Oomph-Lib, 2011) (Heil & Hazel, 2011)
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5.2 Open Source and Proprietary Business Models’
This section will look at different business models’ in relation to the RVOM.
5.2.1 Subscription Model
FIGURE 9 SUBSCRIPTION BUSINESS
A business model based on subscription offers the customer consulting and engineering
services to support versions of their open source software. For example subscription is
offered by Red Hat who supports Linux, Sun is offering Star Office and Lindows offers a large
selection of open source desktop applications for an annual fee.
This model is also used for proprietary software but the customer would often have to buy
the initial product.
Open Source
RVOM
Subscription
Business
Customer
Upgrades
£
Support
£
A year of
maintenance
£
Free Product Free Product
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Red Hat
Rather than developing a product from scratch, Red Hat relies on a community of
developers who voluntary build the product through open source. Red Hat realised that
companies wanted reliable, licensing fee-free software but were reluctant to adopt to
open source as no single legal entity would take responsibility for providing and
maintaining it (Osterwalder & Pigneur, 2009). Red Hat seized this opportunity by
offering tested and stable versions of open source software, especially Linux. Each Red
Hat launch is supported for seven years (Osterwalder & Pigneur, 2009). Customers
benefit by having both the cost and stability advantages. Red Hat benefits from
continuous improvements in its software through the community of developers, which
will reduce Red Hats development costs (Osterwalder & Pigneur, 2009).
Red Hat's revenue is achieved through a subscription service. Instead of selling updates
on its software Kernel, customers pay an annual subscription fee for unlimited access to
all Red Hat new releases, service support and interacting with a responsible body
(RedHat, 2011). Although it is possible to get multiple versions of free Linux, customers
and companies are willing to pay the fee because of the benefits which entail.
Red Hat has developed a successful business model that is showing a rapidly growing
community worldwide. It has advanced from 16,000 projects in 2001 to 150,000 in 2007
(RedHat, 2011).
The subscription model is an effective way of getting a consistent revenue stream and to
develop a base of customers. For the RVOM, selling through a subscription model would be
good to generate other revenue streams that aren’t solely dependent on selling a piece of
software. The subscription model may be one way in which this whole package solution can
be delivered.
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Manchester Economic Forecasting
Manchester Economic Forecasting (MEF) is a macroeconomic business forecasting
organisation, which specialises in risk and global scenario analysis (Manchester
Economic Forecasting, 2011). Their target audience are business economists who
need help in analysing UK and global economic developments and taking advantage
of possible business sales and outcomes (Manchester Economic Forecasting, 2011).
MEF is delivered predominately as a web-based service. Its business model is based
around online subscription to tools and services and offers consultancy through one
of its academic partners.
Products: Global macro forecast reports, DIY macroeconomic forecast service
MEF sell a quarterly global macroeconomic report as a one off purchase or under an
annual subscription.
Bespoke forecast subscribers can either have standard forecasts where you can input
forecast projections for the key inputs (commodity price, interest and exchange
rates) or have a full bespoke forecast which include the above and exogenous policy
projections for individual OECD countries. Also available is an online scenario service
whereby subscribers can access their forecasts online to run instant DIY
macroeconomic scenarios.
Consultancy: Tailored reports, bespoke forecasting models, planning forecasts
Tailored reports can either be stand-alone products or can be contributions to own
specialist reports. Bespoke forecasting models are economic forecasting models
developed specifically for the client. Planning forecasts provide long term projections
for example in sales and demand.
MEF provide a broad service offering with lots of choice for customers. This broad
offering increases the number of channels to generate revenue.
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Manchester Economic Forecasting offers a broader offering than a simple subscription
model. But the way in which the subscription model is delivered through a web-based
service is an interesting means to communicate with customers and potential customers.
This could be appropriate for the RVOM as it would be cost effective and easier to manage
with a small team. Equally it doesn’t require the business to be run out of an office, which
with RVOM being in the early stages, would lower overhead costs.
The subscription model applied to the RVOM using Osterwalder’s template:
FIGURE 10 RVOM SUBSCRIPTION MODEL
Highlighted areas indicate where this model differs to other business models that will be
later discussed.
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5.2.2 Service Business
FIGURE 11 SERVICE BUSINESS
In open source a common means of commercialising software is through developing a
service business. The "basic functionality costs nothing, and all the money is in
customization" (Shirky, 2011). A service business, therefore, typically offers consultancy and
support for open source users.
This model is also seen within proprietary software, where consultancy, training and
support are offered as a complement to selling software.
The service business is similar to the subscription model in that it offers alternative revenue
streams. However additional revenue is made through piece work rather than under an
annual or quarterly fee. In this case there isn’t the security of a regular income, but there is
the opportunity that customers may pay more overall for each of these separate services.
Open Source
RVOM
Service Business
Customer
Support
£
Consultancy
£
Development
£
Free Product Free Product
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Process Integration Ltd
Yin Scarlett Qiying developed a methodology, which was patented by the University of
Manchester and licensed exclusively to Process Integration Ltd. The service offered by
Process Integration was offered in tokens. The customer purchases and uses tokens
when services are required. A token could be for example half a day’s on-site work,
installation of software or even a combination of emails and phone contact (Qiying,
2010).
This could be a simplified means for customers to purchase support and allow different
levels of service to be packaged appropriately.
The RVOM applied to template:
FIGURE 12 RVOM SERVICE MODEL
In comparison to the subscription model, the service model allows the customer to
purchase service when required. Yet the subscription model has the potential for a more
stable revenue stream, as the customer pays for services regardless if they use them.
Additionally the subscription model gives the opportunity to develop stronger ties with
customers as they work with them through the term of their subscription.
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5.2.3 Hybrid System
FIGURE 13 HYBRID SYSTEM
Another means of receiving a revenue stream through open source is by developing a hybrid
system, where the open source platform is distributed with proprietary add-ons (Olson,
2006). This delivers the basic package to a larger number of users with open source. It has
the potential to gather revenue from selling developments and further functionality for the
software.
The Hybrid model could also work solely as a proprietary model, where the customer pays
for differing levels of software capability.
Open Source
RVOM
Proprietary
add-ons
Proprietary
add-ons
Proprietary
add-ons
Proprietary
add-ons
£
£
£
£
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RVOM applied to template:
FIGURE 14 RVOM HYBRID SYSTEM
The RVOM could incorporate parts of this model with others, as an additional option for its
customers, such as with the service model. It will also attract a broader base of customers
who have differing requirements for the complexity and functionality of the software.
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NAG Numerical Algorithms Group
RVOM could be incorporated into a toolbar such as the one delivered by NAG. NAG is a
non-profit software company that produces high quality numerical computing software.
The NAG library has been well-documented and tested to ensure reliability and
robustness. It has been made compatible with a variety of environments, packages and
computer languages; Visual Basics, Excel, MATLAB, Scilab, Octave, LabView, Fortran, C,
Java, GPUs. NAG also runs under Windows and Linux.
In exploring this option for the RVOM, it was initially thought that this would be an
effective avenue to leverage its networks and be less time-consuming producing a
general options toolbar than developing bespoke solutions. In correspondence with the
Vice-president, sales of NAG, John Holden, code is typically donated and so there would
be no direct revenue generated. However it could increase client exposure and there is
the opportunity for joint marketing. NAG is well-known and trusted within the financial
mathematics community with a strong database and contacts in the financial services.
So there is definitely a strong option for the RVOM to be developed into an add-in
toolbar either solo or with NAG.
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5.2.4 Dual-Licensing
FIGURE 25 DUAL-LICENSING
Another strategy is dual-licensing. Under this method, a software company offers two
licenses for the same piece of software. One is available for free use with some limitations
and the other is offered for a fee under proprietary terms (Koenig, 2004). The dual-licensing
business model is illustrated by MySQL and Sleepycat. MySQL works on a per-server fee,
whereas Sleepycat’s commercial database license has an application only limit. Both operate
on a tiered system whereby they charge higher fees for greater functionality.
The open source license encourages sharing and collaboration, whereas the usual
proprietary license allows privacy and competition (Olson, 2006). Thus dual licensing gives
open source users the opportunity to use the product under open source terms, and for
paying customers receive it under proprietary terms.
A dual license strategy can capture a large user base (Koenig, 2004). This would be a good
strategy for distribution and marketing, because of the ease of circulation and familiarity. It
would also give customers more flexibility in what the product has to offer, increasing its
appeal.
One of the most common open source terms is reciprocity. This allows the community to
develop a better product than if one were to develop themselves. In addition, reciprocity
Dual-Licensing
RVOM
Under proprietary
terms
Open Source
RVOM
Sharing and
collaboration
Privacy and
competition
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means that when using an open source product to develop their own, this product must be
released as open source as well. To ensure this, a well-established open source license is
preferable (Olson, 2006). An example would be the GNU GPL. Customers would already be
familiar with the terms and would reduce the chance of mistakes if you were to draft your
own (Olson, 2006).
Because the open source software has certain conditions that restrict the format of
distribution, a company wishing to sell a developed open source product would opt for a
proprietary license (Koenig, 2004). Having to release the source code would put them at a
severe competitive disadvantage in many commercial situations (Olson, 2006). Therefore
they would be willing to pay for a proprietary license in order to withhold their source code.
Majority of open source licensed products are sold as they are. Such that the user takes on
the risk of using it and the licensee does not take responsibility for problems associated with
the software. Proprietary licenses on the other hand often come with a clear warranty
(Olson, 2006). This warranty can include various promises to the customer. As they are
paying customers, the proprietor can put aside some of the money to deal with the costs of
meeting these promises (Olson, 2006).
With dual-licensing it is important that the core product is produced by a single author,
otherwise there would be no single entity to negotiate licensing terms (Olson, 2006). Equally
the actual number of developers that contribute to any project is minuscule (Asay, 2006).
Dual-licensing does not work effectively as a development tool, but is most advantageous as
a distribution channel.
Importantly, a revenue stream is established with the proprietary licensing and a smaller
revenue stream created with technical support and services to both open source and
proprietary customers. The combination in revenue streams means the company can
manage slowdowns in either of its licensing or service offerings (Olson, 2006).
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RVOM applied to template:
FIGURE 3 RVOM DUAL-LICENSING
Dual-Licensing could be incorporated with other models, because it represents a means of
selling a product and increasing distribution channels. For example add-ons could be offered
under the same dual-licensing terms. Or with the service model, additional support could be
supplied at a cost to both proprietary and open source customers.
The possibility of having different customer being charged different fees is plausible.
For instance take Google. It generates revenue from only one customer segment,
advertisers, and offers Google for free to two other segments, web surfers and
content providers. The more web surfers that Google attracts the more advertising
revenue is generated (Osterwalder & Pigneur, 2009).
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5.2.5 Time-limited hybrid licensing
FIGURE 17 TIME-LIMITED HYBRID LICENSING
All the source code used in a particular build will be provided to the customer under the
same terms as the rest of the codebase. This, however, is not until the vendor has recouped
their investment under a proprietary license. The time limit in which the source code is
released will depend on the application and how fast the software evolves; typical time
frames range from 1-5 years. Only the closed-source patches for a previous build are
released and any new source code written during the time limit will not have to be released
until it has completed its own time frame. As a result, a time-limited hybrid source will only
have 50-80% of its source open at any one time. The closed patches help pay for further
developments, before being released as open source (Sprewell, 2010).
For this model to gather substantial returns there needs to be an immediate need for the
product so that the customer would be willing to pay for the product now rather than wait
for it to become open source. The RVOM helps a company to make better decisions under
uncertainty, but the immediate benefits of this product are hard to measure. Although an
interesting model, it wouldn’t be appropriate for the RVOM at present.
Released as
Open Source
Closed Source
Patches
Closed Source
Patches £
£ Released as
Open Source
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5.2.6 Application Service Provider ASP Model
FIGURE 18 ASP MODEL
Whether the software is released as open or closed source, it becomes irrelevant under the
ASP model.
Application Service Provider is software "delivered to the customer over the internet,
hosted on a central server by the vendor, with customers paying for the value they access
over the network" (Asay, 2006). In this model customers buy software as a service, rather
than a product. As such the business does not sell software but sells solutions to customers'
problems (Asay, 2006). Thus customers pay for the value of the software, offered through a
service (Asay, 2006).
Similar models include utility computing and on-demand computing.
RVOM
Hosted on central
server
Access via the
internet
(Open or closed
source)
£
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RVOM applied:
FIGURE 19 RVOM ASP MODEL
The idea of buying a solution to problems rather than a product is more in tune with the
RVOM’s offering. Equally, hosting this service with access via the internet would be an
effective distribution channel.
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5.2.7 Consultancy
A potential avenue for the team behind the RVOM is to use their expertise and the
experiences gained from working with GEMCOM to develop a consultancy business. It has
been previously mentioned that consultancy could be incorporated into some of the other
business models, but this part will look at consultancy solely.
Consulting is a service which is provided to organisations, based on the expertise and skills
of the consultant. Firms gain access to their knowledge and expertise, which they do not
have in-house.
Sometimes enough work is being generated for the consulting services to be launched into a
continuing business. It is common when launched from academia for these businesses to be
set up as a separate research unit, so as to be handled outside of mainstream research. In
these cases they may have dedicated staff to network with specialists and draw upon the
resources of the institution on an assignment by assignment basis (University of Manchester
Intellectual Property, 2010). The costs of operating this consultancy research unit would
have to be included in the client fee, so as not to undermine market rates with the
subsidised use of facilities and the university infrastructure. Consulting on top of academia
work would be dependent on the schools policy.
Another form of consulting is due diligence. This is where an expert provides advice on a
significant investment. Often this occurs when an investor, such as a venture capitalist, is
looking to invest in an early stage business or when a firm is looking to acquire another
(University of Manchester Intellectual Property, 2010). Due diligence could be one of the
main selling points for the RVOM. An academic paper regarding regulation has recently
been submitted to a law journal. This gives a mining example using the RVOM. Therefore
due diligence could be a potential avenue.
There is the question of whether this consultancy should be done alongside academic work
at the university or going solo and developing a business. It takes time to develop and
secure a consistent amount of work and consultants often have to gain access to the latest
resources. The support the university offers means that in the short run, consultancy work
would be best achieved while working for the university. If a strong clientele is developed
that is going to give consistent work then a business could be launched to run full-time.
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The fee that can be charged for consulting will grow with reputation, once a network of
firms is established through research success and your portfolio of enterprise activities
increases, with for example spin-out companies and licensing (University of Manchester
Intellectual Property, 2010). The fee will also vary by case and the cost base of whether it is
run as an independent firm (higher costs with premises, staff and to compensate for periods
of lower activity) or through a research institution (where costs will be subsidised).
The success of a consulting business will depend on how one can apply their skills to a broad
number of needs and having an extensive network.
Real Options Group
“Provide comprehensive real options based enterprise package solutions” (Real Options
Group, 2011).
The Real Options Group offer a broad service offering; management consultancy (project
design and valuation, product portfolio management and corporate strategy), Corporate
finance advisory (IPO and mergers & acquisitions valuation), equity research and market
analysis, training and conferences (Real Options Group, 2011).
Their main attribute is their superior knowledge in this area. This has been utilised to
offer an extensive package.
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The RVOM consultancy model utilises the expertise of the team behind it, rather than selling
the product. Therefore this time there are more distinct changes when using Osterwalder’s
template.
FIGURE 20 RVOM CONSULTANCY
The consultancy model addresses one of the RVOM’s unique selling points, the team.
Through sales of the RVOM the team can develop a portfolio of businesses and a reliable
clientele to work with. This is important to ensure a consistent level of work through the
year. This will take time to develop and would therefore be a model that the RVOM looks to
evolve into in the future.
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5.3 Evaluation of Open Source for the RVOM
Open Source is an attractive proposition, especially as it will enhance the RVOM’s
distribution and maximising market entry at minimal cost. Open source as a disruptive
innovation will not only allow start-up vendors to compete against established vendors in
established markets, but will help create new markets by competing against under-
consumption and non-consumption (Asay, 2006). With the need to spread the use of real
options in industry, open source could be an effective way to encourage its adoption.
The RVOM however, is mathematically advanced and its key advantage lies in the tacit
knowledge in which it was formed. By releasing it as open source it reduces the RVOM’s
main form of competitive advantage. Hence with open source it would be difficult to
contend against potential competitors.
There are few people in the world with the level of expertise and experience required in
order to solve valuation problems using partial differential equations, therefore the RVOM is
unlikely to benefit from a community of developers and it is doubtful that the RVOM will
retrieve the full costs of investment. This is supported with the research into Oomph-Lib,
who received some contributions and commercial interest but this wasn’t shown to be
significant.
Therefore open source is not the most appropriate avenue for the RVOM. At present it
poses a few benefits, yet these are outweighed by the disadvantages and perceived risk. The
option for open source should not be entirely ruled out for the future, depending on the
commercial development of the RVOM.
Open source business models have provided some useful insight into innovative software
models. By looking at different aspects of open source and proprietary models, a well-
developed model for the RVOM can be suggested.
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5.4 Proposed Business Model
A large majority of software is never sold, but instead is developed directly for the customer
either by its customer’s employees or by consultants. Selling the RVOM as a product is
unlikely to maximise adoption and revenue. By adopting a consultancy model, the RVOM
would offer a bespoke service, by creating software as a solution rather than a product. This
would involve working with firms directly and finding solutions to unique problems. It can
make use of the mathematics behind the RVOM to help in creating solutions.
As the RVOM is upstarting, it is relatively small in comparison to large corporations, it can
utilise this position to meet certain customer needs, which large corporations do not have
the man-power to support.
By working to create a full package for the customer, there is the opportunity to offer on-
going support and upgrades after implementation. Likewise by following up with the
customer on how the package is working and making them aware of new developments
that may be beneficial to their work, further work for add-ons and developing a recurring
customer base can be generated.
FIGURE 21 RVOM BUSINESS MODEL
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The RVOM team’s target market segments are companies with problems in real options and
research and development industries. As some companies outsource the development of
their software because of the level of expertise that they do not have in-house, the RVOM
team should also consider developing clients who sell industry specific software and
technical advice to companies. They can then utilise these software companies’ networks.
This model is time consuming and does not have economies of scale, because its unique
selling point lies with the expertise in the team. So the largest cost will be time and labour.
Initially RVOM should be developed alongside working with the university. It provides a
great deal of legal, IP, business expertise with UMIP and the university can provide the
resources to the latest papers, journal articles and an office. It is also good to be associated
with the university, because it has a strong reputation for research. If a sufficient base of
customers was established for there to be a consistent revenue stream and that this
revenue stream could support an external base, then running this consultancy business
independent of the university should be considered at a later date.
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6.0 Strategies
The following strategies look to make the most of the RVOM’s market potential.
6.1 Route to Market
Real Options is a relatively new technique for industry. The most common means for it
being introduced in an organisation is through middle management, where they have read
an article, attended a presentation or learnt it in education (Triantis, 2001). It was often
seen, however, that adoption was not from finance but management involved in strategic
planning, marketing or operations (Triantis, 2001).
From looking into applications for the RVOM, different companies pose different
opportunities and so cannot solely consider one route to market for every industry. One has
to find out whether companies outsource their expertise, to determine who to target.
Small companies would find it difficult to gather sufficient information necessary to value
Real Options. In practice, the best response to the RVOM has come from medium sized
businesses that are willing to put in the time to develop and learn how Real Options can
benefit them. Larger companies are less approachable by a small business and the RVOM
team have found large companies more difficult to communicate and develop with. Equally
working with companies which have a base in the North West means that personal contact
with customers can be made. This is a preferred means of communicating.
UMIP cellophane wrapped products
The University of Manchester Intellectual Property is developing an outlet for selling
products from the university. These products would be ready for purchase from a
website (Arkwright, 2010). UCL business, Edinburgh research services and Glasgow easy
access already offer “cellophane wrapped products” for market.
This could be a future outlet for the RVOM’s generic software package.
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Price
Perceived
added
value
Low High
High
6.2 General Strategy
Porter (1990) proposed that for a firm to become dominant in the market it must do so
either by competing on cost or by offering a superior product in comparison to its
competitors (Porter, 1990). The RVOM offers a superior product and has few packages to
compete against. Its main challenge will be getting this enterprise solution adopted by
companies.
The RVOM can offer a standard software package as a “cash cow” for research and
development, which will help provide a steady level of revenue. This is beneficial when the
bespoke software will not have the same volume and consistency of work. The standard
software package can be integrated as part of an add-in toolbar to an existing statistical
software package.
6.3 Positioning
Bowman’s Clock
Figure 22 Bowman’s clock
Low Price
Hybrid Differentiation
3
2
1
8
6
5
4 Focused Differentiation
“No Frills” Not sustainable Business models
7
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Differentiation
Differentiation with price premium is where added value is sufficient enough to charge a
higher price.
Differentiation without price premium is where it is perceived that the product or service
gives sufficient added value to the user to yield market share benefits.
The RVOM is positioned within differentiation with price premium. If there becomes a
threat from competitors, then the RVOM should reposition itself as differentiation without
price premium.
6.4 Marketing Plan
There is a barrier of mistrust between a new supplier and client. Reviews and
recommendations can help break down this barrier, especially when these come from
customers with strong reputations. Therefore in order to develop a consistent customer
base, RVOM should target high profile early adopters.
A method to catch early adopters is through seeding trials. This is where opinion leaders are
identified and then given the opportunity to be involved in a trial by having a say on the
future development or marketing of the product. The idea is to develop strong advocates
for the product or business.
Traditional marketing assumes that there are large measurable groups of customers that are
considered to behave and buy in similar ways. This would be an ineffective method for the
RVOM when offering bespoke solutions. Entrepreneurial marketing methods consider all
strategies and activities to focus on meeting customer needs. Attending networking events,
word of mouth, industry fairs and study groups would be the most effective means of
marketing the RVOM. The no.1 source of new customers for small firms and entrepreneurial
ventures is word of mouth recommendations (Barclays, 1997) These are time and energy
consuming methods, but would have low marketing costs compared to other methods,
which is important in the early stages of this business. By having direct interaction with the
client base, it allows key customer needs to be identified.
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Study Groups
The University of Manchester’s school of mathematics hold study groups where those in
industry are invited to talk to academics about industrial problems (Abrahams, 2011).
Academics equally discuss what they are working on. The hope being that industrial
problems can be matched with academic solutions.
It seems the most direct and effective way to get academia used in the environment and
industry. It raises issues that may not have been considered and meets specific market
needs.
For the RVOM to have discussions with those in industry, it could open up new avenues
for development and revenue by meeting customer needs. By delivering customized
solutions, the RVOM can charge a higher price than for a generic product and develop
relationships for further work.
6.5 Pricing
Generic software will be dictated by competitors already in the market. A competitive price
would be around £1,000. However when developing bespoke solutions, they can charge a
price up to but not exceeding the perceived value by customers (Mc Donald, 2002). One
would need to know the total value of the product for each individual customer, but this is
difficult to know. Pricing of bespoke solutions will have to be on a case-by-case basis.
7.0 Financial Analysis
The financial analysis will look at the best options for the RVOM from a financial
perspective. This includes an evaluation of where the RVOM should be run and a forecast of
sales from selling the RVOM with Gemcom and potential future clients. The forecasts are
calculated given a set of assumptions and the impact of these assumptions is considered.
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7.1 Scenarios
7.1.2 Scenario 1 Run RVOM alongside work at the university
The amount of money an academic charges on average for a day’s consultancy is £750-
£1000. Of this the university takes a 40% cut and academics are required to give an
employer contribution to national insurance. There is also a limit on the number of days’
consultancy an academic can do, but this is dependent on the department.
By running it alongside the university, the overheads of running the RVOM are minimised.
They appear as an indirect cost through the cut the university takes for the work and the
national insurance contribution. Arguably, 40% is a small price when we later consider the
cost of running a business from an office.
In the early stages, when clients are being developed and the RVOM can leverage the
resources and networks of the university, the RVOM is best suited to being run from the
university. It represents the lowest risk option and is the best scenario for the first few years
of the RVOM. The best scenario will have to be re-considered regularly as the business
grows.
7.1.2 Scenario 2 Run RVOM from home
Running the RVOM from home incurs low overheads. The largest cost arises from not having
access to academic resources and networks in order to develop the RVOM. The university
has a strong reputation which can help gather clients, so more proactive means of
marketing the RVOM will be required, which will come at a cost.
Equally there isn’t the consistent salary the university provides from being an academic at
the university. Therefore there isn’t the security of a regular salary. Once a more stable
client base has been developed, running the business from home could be a potential future
option.
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7.1.3 Scenario 3 Run RVOM from an office
Table 5 Projected costs of running a business from a small office
£
Office Rent
8400
Utilities
4200
Business Rates
2000
Office Supplies/Miscellaneous Expenses
350
Telephone
400
Advertising
150
Accounting
450
Computer Systems/Software
2000
Insurances
2000
Travel/Hotel/Meal Expenses
1500
Training/Education/Seminars
800
Bad Debts/Unforeseen Expenses
300
Total
22550
Given the average costing’s it would not be feasible for the RVOM to consider its own office
while making decent profit margins until year 3. This is using the forecasted revenue in table
5. Profit margins would have to exceed the salaries of academic work for this to be an
attractive opportunity. Therefore the prospect of an office should be considered at a later
date. Costing’s would have to be readdressed, as these figures would be subject to change
and variation.
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7.2 Overall assumptions
Business run from university in the first few years
No. of orders in first year operation is forecasted
Sales predicted to be 50% more in second year of trade
Pricing of development, time scales, consultancy and bespoke solutions will be dependent on the client. Therefore can only give an average approximation.
An evaluation of running the RVOM alongside academic work and how profitable it will be
to run the RVOM separately should be reconsidered after a couple of years sales.
7.3 Fixed Assets
no tangible assets
Intangible assets: IP patents £17,000 (£10,000 from funds, 50% paid by Gemcom, UMIP covering the rest until revenue comes in) (expect to pay £15,000 in patenting costs over the next five years)
No stock-only human resources
There are no salaries
Marketing is done through papers, word of mouth and networking at the moment.
7.4 Gemcom proposed agreement
£10,000 upfront payment to RVOM, then run prototypes and further development. With a
commercial launch, a second payment of £20,000 will be made to RVOM. From sales RVOM
will receive 20% software royalty and 15% from maintenance.
7.4.1 Planned sales with Gemcom licensing agreement
The price and planned sales are given by the number of active licenses sold for similar
Gemcom Whittle modules. An average price has been given because there is the potential
to sell or bundle the RVOM with other options.
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Table 6 Planned sales with Gemcom licensing agreement
Revenue analysis 2012 2013 2014
$ $ $
License
Planned sales of licenses 10 15 25
License average price 17,000 17,000 17,000
License Revenue 170,000 255,000 425,000
RVOM Licence Royalty 20% 20% 20%
Licence Royalty revenue 34,000 51,000 85,000
Maintenance
Maintenance Revenue 18,000 40,000 80,000
RVOM Maintenance Royalty 15% 15% 15%
Maintenance Royalty Revenue 2,700 6,000 12,000
Revenue to RVOM $ 36,700 57,000 97,000
2012 2013 2014
US $1= UK £0.64 £ £ £
Revenue to RVOM £ 23,212 36,052 61,353
There is a proposed end of year 3 minimum buy out valuation of £250,000. Otherwise 3rd
year buy out = 2. ( )
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7.5 Overall Planned Sales
With the plan to increase the number of clients for the RVOM, this next table gives a
projection of future sales of bespoke solutions and standard software. The purpose behind
this forecast is to give the overall potential revenue for the RVOM.
7.5.1 Assumptions
Bespoke solutions: pick up clients in the next couple of years with similar revenue
projector.
The forecast has been based on selling one bespoke solution in year 2 of sales and
two in year 3.
Produce standard unit of software for licensing: £1k average on market
7.5.2 Forecasted revenue for RVOM
Table 7 Forecasted revenue for the RVOM
2012 Sales
2012 Revenue
2013 Sales
2013 Revenue
2014 Sales
2014 Revenue
Bespoke Solution
£
£
£
Gemcom 10 23,212 15 36,052 25 61,353
Bespoke solution (1)
- 10 20,000 15 35,000
Bespoke solution (2)
-
- 10 20,000
Bespoke solution (3)
-
- 10 20,000
Standard software
- 10 10,000 30 30,000
Total
23,212
66,052
166,353
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7.6 Sensitivity Analysis
An adverse risk to the planned outcome for sales and revenue is the acceptance of Real
Options in industry. As has been previously addressed, although the RVOM uses the latest
advances in mathematics, previous adoption rates of industry using Real Options software
packages are poor. Equally as the monetary gains of using the RVOM are quite intangible
and where it is not a solution with immediate industrial need, the economic climate may
have an effect on industries acceptance of a new technique. The success of the RVOM will
be dependent on industries acceptance and willingness to adopt new methods. The
marketing strategies look to help with this.
Additionally, developing bespoke solutions to individual customer needs is firstly time
consuming, but is secondly reliant on those with the expertise in order to solve it. It is
therefore dependent on a small team of people to deliver a service.
As there have been no sales of the RVOM with Gemcom yet, the forecasted sales are based
on other packages of the Gemcom Whittle and the potential market size for the software
program. This is a significant assumption to make when the sales for the other bespoke
solutions have used the same projection.
8.0 Conclusion
By looking at Real Options software packages and Real Options adoption, it has raised
problems that might be encountered and opportunities that are best suited for the RVOM.
These all point to future commercial avenues and the RVOM’s potential success.
It has been highlighted how Real Options is an “evolutionary process” both in its acceptance
in industry and the long term competitive advantage it can give to the firm by developing
better decision making. As the RVOM uses leading mathematics in this area, it improves a
firm’s competitive advantage against other firms using existing methods.
The main way in which the RVOM has shown to distinguish itself from other methods is in
calculating project completion or expected duration, as it has not been previously
considered. This is of value to a company because it evaluates a project’s risk. It also
differentiates itself with the use of partial differential equations, which can tackle a broad
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range of problems under multiple uncertainties, quickly and accurately. Equally the mining
industry has not previously considered economic uncertainty extensively in their
calculations. Therefore there are few that can rival this product.
Real Options is a new technique to industry. It has been shown that Real Options is not yet
in significant use and this will be one of the biggest hurdles for the RVOM. Trying to
persuade those who have tried existing methods and improving the credibility of Real
Options will be a timely process.
There is lots of potential though, as there are extensive avenues for the RVOM to go down.
Real Options is best suited for Industries characterised by large corporate investments,
uncertainty and flexibility, and as identified, there are a number of industries which have
revealed these characteristics.
The natural resource sector has been ear-marked as the main focus in these earlier stages.
The main reasons being because of its natural progression from solving mining problems
and eases the need to encourage the RVOM’s use. Within this sector the most prospects is
seen within the renewable energy industry and oil and gas.
A higher risk strategy and one to be adopted once a portfolio is starting to be established
with the natural resource sector is to go for the pharmaceutical industry, who as the largest
R&D industry have the potential to offer the highest returns to the RVOM. Other industries
that should also be considered at a later date are the IT industry and financial services.
A medium sized business was identified as the best to target. These were deemed most
approachable and still suited to using Real Options. The ideal company would have a base in
the North West so that the team could have direct contact with the customer.
The proposed general strategy would be to develop a standard Real Options software
package to be a “cash cow” for research and development, which will help provide a steady
level of revenue. The standard software package can be integrated as part of an add-in
toolbar to an existing statistical software package. This is while bespoke software is being
developed to individual customer needs. In the initial stages the bespoke software will not
have the same volume and consistency of work. The bespoke solutions will be sold under a
consultancy model, where the aim is to sell the RVOM as a solution rather than a product.
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The main methods for selling this solution will be through reviews and recommendations,
word of mouth and networking. These use more entrepreneurial methods of marketing.
Although they are more time consuming, they are lower cost methods. As the RVOM aims
to sell bespoke solutions, having direct contact with potential customers will help address
particular needs. These marketing methods will be better at achieving this.
The price charged for bespoke solutions will be dependant on the client. However from
looking at generic Real Options software packages, the RVOM should look to launch its
standard software at a £1,000.
The financial analysis has looked to make projections based on the predicted sales of the
Gemcom Whittle module. This is based on the assumption that the RVOM will pick up
clients with similar revenue projections, selling an additional bespoke solution in year 2 of
sales and two in year 3. The following table gives a summary of the overall forecasted
revenue.
TABLE 8 REVENUE PROJECTIONS
2012 2013 2014
£ £ £
Revenue 23,212 66,052 166,353
The revenue projection was used to help make a decision on whether it was viable to launch
the RVOM independent of the university. It was concluded that this should be considered at
a future date contingent on more clients and sales. The lowest risk option and the best
scenario for the next few years would be to run the RVOM alongside work at the university.
By looking at how Real Options is being used in industry and how the RVOM compares to
existing methods and products, the RVOM has shown to have commerical potential. The
business model and proposed strategies have considered how to enhance this potential.
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10.0 Appendix
10.1 Real Options Survey
1) What does “Real Options” mean to you?
2) How did you hear about Real Options?
3) Who in your firm has expressed the most interest in Real Options?
4) What made your firm interested in Real Options?
5) Where have your firm applied Real Options?
6) How have your firm applied Real Options?
7) How has it been applied analytically? i.e. Black-Scholes, Binomial Lattice, Monte Carlo,
decision trees
8) How has your firm responded to Real Options?
9) Do you expect the use of Real Options to grow for your firm?
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10.2 SWOT Analysis
Know-how- leading academically
Fast and accurate valuation of reserves in the presence of multiple economic and geological uncertainties
Supported by UMIP
Latest techniques
University reputation
IP- patent being examined in US and Australia (main countries GEMCOM trade in)
Applications to multiple industries; mostly used in technology, energy, utilities, health care, and manufacturing
Sell as service not software- avenues for consultancy, add-ons, development
Can be turned into a bespoke product or incorporated into a pre-existing package
Economic uncertainty is a considerable factor in current climate
Lack of business knowledge?
Labour and knowledge intensive (time)
(Can sufficient data be supplied?)
Patent infringement
Patent only covered in US
Adoption of real option thinking
Complexity of mathematics- focus on large companies
Discounted cash flow a proven method
Encourages too much risk taking
Threats Weaknesses
Opportunities Strengths
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10.3 “Oomph-lib lunch” meeting notes 6/6/2011
1) What was the motive to develop the webpage?
Cohesive base, no overlaps with each other’s work, working from the same page
(not having to re-read and work out others codes, when they are effectively the
same thing).Wanted to share work
Documentation and tutorials not only useful to outsiders, but internally, allow
problems to be thought through and broken down
2) Why open source?
Allow people to use, easy way to distribute, no guarantee or expectations to users.
3) Have you received comments/developments which have helped improve Oomph-
lib?
All changes to code have to be tested to see if they work- quality control
Have received questions. These over the years have become more technical.
4) a. Do you know how many people visit the site?
New releases every 1-2 years. Downloads recently after a new release is the only
indicator of how many are using it. User’s personal details can be given when
downloading, but this is not compulsory. (although you can see the number of
downloads, they do not know whether they go on to use it)
Have heard from people who have given technically useful changes to Oomph-lib-
so know being used and getting feedback.
b. Do you use search optimisation? No
5) Is track changes in word similar to subversion?
Subversion is like a tree diagram. An extensive version of word’s track changes
6) GNU GPL a. why this license?
Well used, no confusion
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b. What if this license was infringed?
Wouldn’t know
7) Do you promote Oomph-lib in any other way than the webpage?
No, except through university
8) Have you found it an effective way to disseminate knowledge?
Yes
9) Have you considered generating revenue from Oomph-lib?
Don’t intend to generate revenue from it. Those who wanted custom changes or
wanted to commercialise would have to unhook from system. They couldn’t
provide support, unless separate consultation to be agreed.
10) Have you had any commercial interest?
Greater commercial interest over the years
An underwater systems company has shown interest
The industry study days that the university holds has shown some interest in
Oomph-lib
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10.4 NAG John Holden email correspondence 26/7/2011
1) Do you have any real options in the NAG library?
We have some basic close form solutions for things like Black Scholes, Heston,....
hidden in our Special Functions chapter. These were initially implemented for
academics rather than practitioners, but we may extend/improve further for
practitioner usage. We also have a Black Scholes solver in our PDE chapter - this
too may need an update. Most financial institutions use NAG Library components
as "building blocks" for their own solvers so use things like FFTs, Linear Algebra,
Quadrature, Interpolation, Optimisation....
2) How do people contribute to NAG?
Typically code is donated, NAG then improved it (this might be with code tuning,
documentation, new research ...).
3) What are the benefits/ revenue of doing so?
mentioned above [further code tuning, new research,..], very strong technical
support, possible joint marketing (NAG is well known, used and trusted in the
financial maths community - most banks have licences for NAG + database of
3000+ contacts in Financial Services), people like Ser-Huang Poon, Nick Higham
have been given more client exposure so we have helped bridge the gap between
academia and industry. We have given them exposure at events like
http://www.nag.co.uk/nag-quant-event-london-2011
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10.5 Wai Lau Interview notes 7/12/2010
Background: Masters of Physics - Specialist area was in the modelling of nuclear decay using
neutral network techniques. Masters of Enterprise – worked on business concept to deliver
software for information management in energy services. This eventually became
Information Prophets Ltd in 2003 and later, Digital Energy.
Questions to consider in own work: What do you want out of it? Where do you want to be?
What others want out of the project/business/idea? Where would you sit in a board room?
Only way to know if someone is interested in your product is to just sell. The same applies
when trying to find an application.
Wai knew he wanted to launch a business with the technology developed. He didn’t mind
which industry it was applied to.
Wai gave an example of where he and a person of commercial experience held a stall at a
fair. They discussed with those there problems in industry and applied this to the
technology. At the end of the fair they had put together an attractive proposition.
A good way to find a profitable application is to look at the news and current events for
important and relevant topics.
Consider why people would invest. Investors tend to consider the team rather than the idea.
Open source needs a large base of end users that you can directly apply.
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10.6 Clare Arkwright UMIP notes 9/12/2010
UMIP
Knowledge transfer, partnerships KTP
Develop R&D contract
Risk for university to support commercialised research and therefore they take a small share of the pie
Proof of principle funds helps raise investment
UMIP and IP
Due diligence
Software copyright
Know how
Trademarks
Patent search: ensure vigorous search, only as good as large search, PTO
E-commerce
Cellophane wrapped products
E.g. UCL business, Edinburgh research services, Glasgow easy access
Gemcom License
Trial for free, no source code
Free evaluation license, 3 month option agreement, 6 month option to exercise license or not
Can’t reverse engineer or sell on (patent further protection)
License
License: how you transfer IP
Options: license and royalty, annual fee, prepaid license (license for life), university maintain IP, receive some of the upsides of the sales
License types: exclusive- pay patent costs, for so many years, meet certain criteria, performance targets, all in order to stop them sitting on it and keeping it away from competitors
License type: some exclusivity- not all industries, in field/area/market sector