A systems approach to rd investment

A SYSTEMS APPROACH TO R&D INVESTMENT







NG CHU NGAH







NATIONAL UNIVERSITY OF SINGAPORE


2007



A SYSTEMS APPROACH TO R&D INVESTMENT

NG CHU NGAH
B.Eng.(Hons.), NUS





A THESIS SUBMITTED

FOR THE DEGREE OF MASTERS OF ENGINEERING

DEPARTMENT OF INDUSTRIAL AND SYSTEMS
ENGINEERING

NATIONAL UNIVERSITY OF SINGAPORE

i

Summary

R&D activities are increasingly recognised as the engine for corporate growth, yet they
remain a challenge when it comes to valuation and selection. R&D projects carry huge
risks which make the potential high payoffs illusive, but these payoffs are precisely the
incentive for the examination of project selection methodologies. While there is no
hard and fast rule to compare and select R&D projects, this report aims to propose
possible improvements to the selection and management process.

Our work builds on the concept of the strategy-monetary division of the Organisational
Decision Support System (ODSS) and customises it into three segments:
(1) ONline R&D focus selection
(2) OFFline project valuation, and
(3) ONline portfolio selection.

At the online level, Real Options (ROs) thinking is incorporated to specifically deal
with the option to defer and its tradeoff of gaining competitive advantage. We reason
that this RO should make sense only if considered at the strategic level because of the
existence of tradeoffs.

Zeroing in on the individual projects at the offline stage, we separate risks and payoffs
with a new perspective—the “within firm” and “beyond firm” distinction. Decision
Analysis (DA) is then brought in for the modelling of sequential decisions. Since real

Summary

ii
projects are not as flexible as financial options when it comes to opportunities to exit,
decision trees are sufficient in capturing the option to abandon.

However, DA also has its shortcomings. It depends largely on subjective expert
opinions, and these are costly to obtain yet not reliable particularly at project outset as
studies have shown. Nonetheless, judgemental methods are unavoidable for R&D
projects due to decreased liquidity (Drzik, 1996). We thus propose the borrowing of
financial market data to replace, or at least complement, the subjective probabilities
used in DA, especially at the initial project selection stage. This recommendation relies
on the assumption that the eventual value of R&D projects would be reflected in the
shareholder returns in financial markets, following the launch of the new products or
technologies.

Our scope is hence limited to five industries as identified by Foster and Kaplan (2001),
where there appear to be a positive correlation between R&D investment and
shareholder return. They are namely pharmaceutical, pulp and paper, commodity and
specialty chemicals, aerospace and defence, as well as oil extraction.

For the treatment of the data, we extend the idea of collapsing the leaf-values into bi-
extremal values using the simplification rule, and adopted a suitable common financial
risk measure—the Value at Risk (VaR). However, VaR has, in recent years, been
discredited as an incoherent risk measure (Artzner et al., 1999). A similar risk measure
called the Expected Tail Loss (ETL)—the expectation of losses beyond VaR—turned
out to be a possible remedy. The purpose of both is the same and the calculation of

Summary

iii
ETL an extension from VaR. Hence, we continue the use of VaR but note that the
subsequent discussion makes reference to both.

VaR and ETL are boundary quantiles and should provide more information be it for
initial project selection or ongoing budget control: the point to exercise the option to
abandon becomes more guided.

Comparing VaR with simulation, we note that VaR is like integrating real options into
simulation. As pointed out by scholars, simulation is useful but probably the extent of
usefulness is limited to the central 80% of the information due to the consideration of
options and management flexibility. Thus, the interval between the VaRs allows us to
focus on the essential information.

On the technical side, the recent use of Extreme Value Distributions (EVDs) and

Generalised Pareto Distributions (GPDs) to approximate VaRs is appealing in our
study as they would allow direct simulation of the boundary quantiles. Our results
show that the GPD method is preferred over the parametric method for both the upper
and lower-bound VaR. This method would thus enable us to calculate a baseline for
the payoff/loss estimation, while allowing decision makers to see the maximum
potential of particular projects, thereby setting an investment limit before abandonment
should be exercised.


iv

Acknowledgement

This thesis extends my B.Eng honours project. Along the entire journey, I am grateful
to many people around me for their help, guidance, encouragement, and concern.

First and foremost, I would like to express my sincere appreciation for my mentor, A/P
Poh Kim Leng, who is always so patient and willing to take time off his hectic
schedule to give me invaluable advice—work or non-work related alike—and to
explain concepts foreign to me. I thank him also for granting me the freedom and
independence to explore possible research areas which I have interest in. Having such
a supportive supervisor is indeed a blessing.

Secondly, my heartfelt gratitude also goes to my internship supervisor, Mr. See Chuen
Teck, for his inputs and tips. Equipped with practical experience and industrial
knowledge, he is a great source of information and ideas. Indeed, a discussion with
him beats all blind research.

Thirdly, a special mention and acknowledgement for Dr. François Longin who had
guided me through my first research training at ESSEC Business School in France in

the year 2004. This invaluable experience gave me a peek into the vast world of
Finance, and introduced me to the concept of Value-at Risk which serves as one of the
preliminary sources of inspiration for this thesis.


Acknowledgement

v
In addition, I would also like to express my appreciation to the department and the
university for giving me the chance to fulfill my third and fourth year of my
undergraduate studies at the Ecole Nationale des Ponts et Chaussées (ENPC) in France,
as well as the opportunity to present my work at the Asia Pacific Industrial
Engineering Management (APIEMS) conference 2006 at Bangkok. The various
programs and experiences have been highly enriching.

On this note, my sincere thanks also go to DSTA who supported and financed my
studies in France and a return trip for the presentation of my internship at DSTA which
required my return to ENPC during my research period; as well as the NUS Graduate
Office for coordinating my return to the department.

Finally, I dedicate this thesis as a gesture of thanks to my parents and brothers for their
unyielding support and advice; to my friends particularly Zhili and Zhiyun for their
concern for my progress and adaptation back to the NUS culture; and to my lab-mates
who helped make my integration into the community a smooth and pleasant one.


vi

Table of Contents

Summary ______________________________________________________ i

Acknowledgement ______________________________________________ iv
Table of Contents ______________________________________________ vi
Terms and Abbreviations_________________________________________ ix
List of Figures _________________________________________________ x
List of Tables __________________________________________________xii

Chapter 1 Introduction 1
1.1 Motivation—Real Options ______________________________________ 2

1.2 Proposed Improvements _______________________________________ 2
1.3 Organisation of Thesis_________________________________________ 7

Chapter 2 R&D Projects 8
2.1 Benefits of R&D______________________________________________ 9

2.2 R&D Landscape _____________________________________________ 9
2.3 General Considerations_______________________________________ 11
2.4 R&D Project Lifecycle ________________________________________ 12
2.5 R&D Project Risks___________________________________________ 14
2.6 Conclusion_________________________________________________ 16

Chapter 3 R&D Project Valuation Tools 17
3.1 Discounted Cash Flow________________________________________ 17

3.2 Decision Analysis ___________________________________________ 19
3.3 Simulation _________________________________________________ 23
3.4 Real Options Approach _______________________________________ 24
3.5 Conclusion_________________________________________________ 30

Chapter 4 R&D Project Selection Cycle and ODSS 31
4.1 Offline: Individual Project Analysis ______________________________ 32

4.2 Online: R&D Capital Allocation _________________________________ 37
4.3 Conclusion_________________________________________________ 40

Table of Contents

vii

Chapter 5 Framework 41
5.1 A novel view of the R&D Project Lifecycle_________________________ 41

5.2 Project Selection: ODSS modified_______________________________ 42
5.3 Project Management: Online Decisions __________________________ 47
5.4 Consolidated framework ______________________________________ 49

Chapter 6 Offline Project Valuation Considerations 50
6.1 Role of Real Options _________________________________________ 50

6.2 Inconveniences of DA ________________________________________ 51
6.3 Assumption ________________________________________________ 53
6.4 Risk Measure: Value-at-Risk (VaR)______________________________ 55

Chapter 7 Value-at-Risk (VaR) 61
7.1 Calculation of the VaR by the Historic Method _____________________ 61

7.2 Calculation of the VaR by the Parametric Method___________________ 62
7.3 Calculation of the VaR by the Classical EV Method _________________ 63

7.4 Calculation of the VaR by the Modern EV Method __________________ 70
7.5 How VaR Adds Value ________________________________________ 73

Chapter 8 Case Example: 40 stocks from the NYSE 74
8.1 Context ___________________________________________________ 74

8.2 Historic Approach ___________________________________________ 78
8.3 Parametric – Normal _________________________________________ 78
8.4 GEV Approach _____________________________________________ 79
8.5 GPD Approach _____________________________________________ 85
8.6 Conclusion_________________________________________________ 90

Chapter 9 Conclusion and Future Work 91
9.1 Implications of Findings_______________________________________ 91

9.2 Limitations _________________________________________________ 92
9.3 Future Work________________________________________________ 93
9.4 Final note__________________________________________________ 94

List of References 96


Table of Contents

viii

Appendix A Financial Option Pricing 101
A.1 Option Pricing Models - Samuelson (1965)_______________________ 101

A.2 Option Pricing Models - Black-Scholes model (1973) _______________ 101

A.3 Option Pricing Models – Merton (1973)__________________________ 107
A.4 Option Pricing Models - Cox, Ross, & Rubinstein (1979) ____________ 108

Appendix B Fitting Extreme Value Distributions 116
B.1 Parameter Estimation by Maximum likelihood Method ______________ 116

B.2 Standard Error_____________________________________________ 118

Appendix C Calculating VaR using BestFit data 120
C.1 Gumbel __________________________________________________ 120

C.2 Weibull___________________________________________________ 121

Appendix D P-P plots for GEV and GPD fitting 122


Appendix E 5 * 8 Companies selected for study 134
E.1 Medical Laboratories and Pharmaceuticals_______________________ 134

E.2 Pulp and Paper ____________________________________________ 138
E.3 Commodity and Specialty Chemicals ___________________________ 141
E.4 Aerospace and Defence _____________________________________ 144
E.5 Oil Extraction ______________________________________________ 147

Appendix F Results from Case Study –VaR Max 149


Appendix G Results from Case Study –VaR Min 152

ix

Terms and Abbreviations

DA Decision Analysis
DCF Discounted Cash Flow
EBM Expectation-Based Management™
ETL / ES Expected Tail Loss / Expected Shortfall
EV(T/D) Extreme Value (Theorem/ Distribution)
GEV Generalised Extreme Value Distribution
GPD Generalised Pareto Distribution
LEAPS Long-Term Equity Anticipation Securities
NPV Net Present Value
ODSS Organisational Decision Support System
POT Peaks Over Threshold
R&D Research and Development
RO Real Options
Simulation Monté Carlo Simulation
VaR Value at Risk



x

List of Figures

Figure 2-1: Types of Innovation: Ritcher scale of innovation 8
Figure 2-2: Risk-payoff matrix showing position of R&D 11
Figure 2-3: Lifecycle of an R&D Project as proposed by Jensen & Warren (2001) 12

Figure 2-4: Decision criteria of R&D projects 14
Figure 2-5: Technical uncertainties arising at different stages of the R&D project 15
Figure 2-6: Uncertainty in different phases of R&D project 16
Figure 3-1: Abstraction of DA process 19
Figure 3-2: Decision factors of R&D projects 20
Figure 3-3: Decision Tree differentiating risk probabilities and risk impacts 21
Figure 3-4: Uncertainty “dissected” 25
Figure 3-5: Identification of the option zone 28
Figure 5-1: R&D Project 42
Figure 5-2: Framework for R&D capital allocation 45
Figure 5-3: Efficient Frontier and AHP focused on clusters 46
Figure 5-4: Reversal to Online Capital Allocation during Project Life 48
Figure 5-5: Lifecycle of R&D projects: From selection to management 49
Figure 6-1: Consideration of extremes using Substitution Rule 52
Figure 6-2: Comparison of VaR and simulation 57
Figure 7-1: Illustration of the calculation of the VaR by the historic method 62
Figure 7-2: Schema illustrating the use of classical EV method to calculate the VaR
(Longin, 1998) 64
Figure 7-3: Graphs of extreme value distributions 67
Figure 7-4: Illustration of the calculation of the VaR by the EV method 69
Figure 7-5: Contrasting between GEV (left) and POT (right) 70
Figure 8-1: Skeleton of Computations for a Single Company 77
Figure 8-2: Upper and lower VaRs as calculated using EVT 79
Figure 8-3: BLOCK-MAXIMA-of-20 Probability Plots – GEV Model vs Empirical 83
Figure 8-4: BLOCK-MINIMA-of-20 Probability Plots – GEV Model vs Empirical 84
List of Figures

xi
Figure 8-5: Peaks-over-(95%)Threshold MAXIMUM Probability Plots – GPD Model
vs Empirical 88

Figure 8-6: Peaks-over-(95%)Threshold MINIMUM Probability Plots – GPD Model
vs Empirical 89

Figure A-1: PDF of lognormal distribution 105
Figure A-2: Binomial lattice 109
Figure D-1: BLOCK-MAXIMA-of-15 Probability Plots – GEV Model vs Empirical122
Figure D-2: BLOCK-MINIMA-of-15 Probability Plots – GEV Model vs Empirical .123
Figure D-3: BLOCK-MAXIMA-of-10 Probability Plots – GEV Model vs Empirical124
Figure D-4: BLOCK-MINIMA-of-10 Probability Plots – GEV Model vs Empirical .125
Figure D-5: BLOCK-MAXIMA-of-5 Probability Plots – GEV Model vs Empirical 126
Figure D-6: BLOCK-MINIMA-of-5 Probability Plots – GEV Model vs Empirical 127
Figure D-7: Peaks-over-(97.5%)Threshold MAXIMUM Probability Plots – GPD
Model vs Empirical 128
Figure D-8: Peaks-over-(97.5%)Threshold MINIMUM Probability Plots – GPD Model
vs Empirical 129
Figure D-9: Peaks-over-(92.5%)Threshold MAXIMUM Probability Plots – GPD
Model vs Empirical 130
Figure D-10: Peaks-over-(92.5%)Threshold MINIMUM Probability Plots – GPD
Model vs Empirical 131
Figure D-11: Peaks-over-(90%)Threshold MAXIMUM Probability Plots – GPD
Model vs Empirical 132
Figure D-12: Peaks-over-(90%)Threshold MINIMUM Probability Plots – GPD Model
vs Empirical 133



xii

List of Tables

Table 1-1: Critical review of traditional approaches 5
Table 1-2: Benefits of the VaR approach 6
Table 1-3: Thesis organisation 7
Table 2-1: Companies with the highest R&D intensity in the FT Global 500 10
Table 4-1: Five-phase framework for project selection propsed by Ghasemzadeh &
Archer (2000) 31
Table 5-1: Modified ODSS framework for initial investment into R&D projects 43
Table 6-1: Overview of Project Valuation Considerations 50
Table 8-1: List of 40 companies chosen 75
Table 8-2: Comparisons of GEV VaR calculations with parametric approach 81
Table 8-3: Comparisons of GPD VaR calculations with parametric approach 86


1

Chapter 1 Introduction

“In the current risk management world, the hunt for risk takes three
general forms. One is the hunt for treasure - combing through
markets for the next stellar investment management opportunity.
Second is the hunt for hazard - identifying unwanted risks in asset
and liability portfolios through techniques such as simulation,
stress-testing, and backtesting. Third is the hunt for knowledge -
forensic investigation of unexpected gains and losses.”
Tanya Styblo Beder. The Great Risk Hunt
The Journal of Portfolio Management Special Issue, May, 1999, p. 29.


Corporate financial strategy points out that it is not only the earnings that sustain a firm,
but also, indispensably, the growth which helps it thrive. R&D activities are

increasingly recognised as the engine for growth, but there remains no hard and fast
rule for the allocation of budget on R&D. Neither is there a definite way to compare
and select R&D projects which essentially carry huge uncertainty. It is also not clear
how the uncertainty should be managed, particularly how to decide whether to
abandon the project to prevent huge losses.

The purpose of this work is thus to propose an overall selection and management
framework that incorporates various tools and ideas, yet remaining logical and easy to
understand. This is in line with the observation by Cooper (1999) that the best
companies use several complementary methods at the same time.

Chapter 1 Introduction

2
1.1 Motivation—Real Options

The Real Options (RO) approach is theoretically attractive with its lofty ambition to
give a numerical value to management flexibility. Unfortunately, it is mathematically
complex as fundamental issues like volatility remain unresolved; and arguably
lopsided because it fails to recognise the tradeoff between having a business option and
gaining strategic competitive advantage.

Despite being greatly motivated by real options which exist in the entire project
lifecycle, we realise that real options are vastly different from financial options, which
explains why a direct use of option pricing is not pragmatic, and thus the real option
methodology cannot stand as a valuation tool.

However, an important issue that real options highlight is the strategic concerns of
embarking on R&D projects. Indeed, as Torkkeli et al. (2003) pointed out, there is a
straightforward relationship between strategic orientation and R&D directions through

technology selection: technology selection is based on company’s strategic goals and
targets, and technologies selected draw new R&D directions for the company. This
means that strategic consideration is of paramount significance in R&D project
selection.

1.2 Proposed Improvements

Persuaded that strategy cannot be fully quantified but that RO has its merits, we adapt
the separation into online and offline segments as in the Organisational Decision
Chapter 1 Introduction

3
Support System (ODSS) for project portfolio selection (Ghasemzadeh & Archer, 2000;
Tian et al, 2002).

The customised ODSS framework consists of three segments:
(1) ONline R&D focus selection
(2) OFFline project valuation, and
(3) ONline portfolio selection,
which allow the separation of strategy from project valuation. This also enables a
proper consideration of the relevant ROs at appropriate levels.

At the online level, we incorporate real option thinking (Myers, 1977; Kester, 1984;
Faulkner, 1996; Kulatilaka and Perotti, 1998) into the framework. Specifically, the
option to defer should be considered at the management level in view of the existing or
potential competition.

Apart from the option to defer to be considered at the online stage, other relevant
options can effectively be examined with the aid of a decision tree during the offline
project valuation stage. An in-depth exploration of various project valuation models

accentuates the usefulness and clarity of modelling the project life with a decision tree.
In fact, it makes redundant the continuous hedging as proposed by RO and stands out
as a sufficient starting point for projects which cater for sequential decision nodes.

Nevertheless, Decision Tree Analysis, commonly referred to as DA, has its
shortcomings. In our study, we would prefer to distinguish the tool i.e. Decision Tree
from the methodology – DA, which depends on subjective expert opinions, and these
Chapter 1 Introduction

4
are costly to obtain in terms of time and money yet not reliable as studies have shown.
Nonetheless, judgemental methods are unavoidable for R&D projects due to the
decreased liquidity (Drzik, 1996).

We highlight the deliberate separation of risks and payoffs through the boundary of
“within firm” and “beyond firm”, and propose the borrowing of financial market data
to replace, or at least complement, the subjective probabilities used in DA, especially
at the initial project selection stage. This recommendation relies on the assumption that
the eventual value of R&D projects would be reflected as shareholder return in
financial markets, following the launch of the new products or technologies. These
values are conditional on the success of the R&D projects.

The above assumption scopes out five industries, as identified by Foster and Kaplan
(2001), where there appear to be a positive correlation between R&D investment and
shareholder return. The five industries are namely pharmaceutical, pulp and paper,
commodity and specialty chemicals, aerospace and defence, as well as oil extraction.

Chapter 1 Introduction

5

Table 1-1 summarises the tools and context at the traditional level, as well as the
proposed amelioration and paradigm shifts.

Table 1-1: Critical review of traditional approaches
Traditional approaches Paradigm shift & Improvements
Tools – Real Options
- the concept of risk neutrality can be
applied to take care of choice of
discount factor in DA

- the value of managerial flexibility is
explicitly quantified
- Real options are not financial options,
thus the assumptions not applicable.
i Existence of ‘twin’ security
ii Dynamic hedging
- Tradeoffs have to be considered as
well, and this at the strategic level.
Tools – Decision Analysis
DA depends on expert opinions for leaf
values and probabilities.

Expert opinions are usually flawed
(Meadows, 1968; Souder, 1978, 1969),
but might be remedied by the adoption of
the financial risk measure, VaR, to
quantify values of R&D projects in
particular industries identified.
Context – at the valuation level
- Consideration of R&D projects at year
0 and projecting into 3 stages (Jensen
and Warren, 2001).
- Perplexity in merging strategy with
valuation.
Distinct separation between “within firm”
expenditure and “beyond firm” earnings.
Earnings then serve as input to
consideration of project worthiness at the
strategic level
Context – at the strategic level
ODSS separates strategy and valuation
altogether
ODSS is customised and improved with
implicit consideration of ROs.

Exploring common financial tools, we identified Value-at-Risk (VaR) and Expected
Taill Loss (ETL) as the remedy to the previous insufficiencies. Essentially, ETLs and
VaRs are boundary quantiles and should provide more information be it for initial
project selection or ongoing budget control. ETL is basically a coherent alternative to
VaR, but the purpose remains the same, and the calculation an extension of the latter.
Hence, subsequent discussions refer to both measures when VaR is mentioned.
Chapter 1 Introduction

6

Comparing VaR with simulation, which is another tool commonly used to generate a
more complete risk profile, we argue that VaR is like integrating ROs into simulation.
It has been suggested that for simulation, only the central 80% of the information is
more realistic with managerial responses considered. Thus, by concentrating on the
interval between the VaRs, one is indeed focusing on the useful information.

Our findings show that the Generalised Pareto Distribution (GPD) approach is
preferred over the parametric method for both the upper and lower bound VaR. This
method would thus enable us to calculate a baseline for the loss estimation, while
allowing decision makers to see the maximum potential of particular projects, thereby
setting an investment limit before abandonment should be exercised.

Table 1-2 summarises the advantages of VaR over the traditional approaches.

Table 1-2: Benefits of the VaR approach
Traditional approaches How VaR value-add
Calculate Expected Value of individual
projects (NPV, DA, RO) for comparison
Use VaR to identify interval of possible
returns and thus capture volatility
DA depends solely on expert opinions Financial data as captured by VaR as an
objective complement
VaR need not assume a return
distribution, yet also gives a fuller profile.
Direct simulation of boundary quantiles
possible.
Simulation to take into account fuller risk

profile
VaR is akin to RO incorporated into
simulation


Chapter 1 Introduction

7
1.3 Organisation of Thesis

The report is structured to give a logical flow from the conception to the selection and
finally to the management of R&D projects. The first three chapters aim to establish
the background and motivation for this project. Chapter 2 provides a comprehensive
literature review of R&D projects while Chapter 3 explores the existing R&D project
valuation tools to provide a better understanding of existing methodologies and
critically review their advantages and inconveniences. Chapter 4 describes the R&D
project selection cycle, adapting the offline and online distinction of ODSS.

Chapter 5 introduces our framework from project conception to selection and finally to
management.

Next, we shall then focus on the offline stage for the alternative treatment of individual
project valuation. Chapter 6 explains all the considerations taken into account. Chapter
7 dives into the various approaches to calculate the financial risk measure, VaR.
Chapter 8 gives a case example showing the calculation of VaRs from the New York
Stock Exchange, as well as a discussion of the results.

Finally, Chapter 9 concludes and states further work that might be worth exploring.
Table 1-3 illustrates the logical flow of the thesis by parts.

Table 1-3: Thesis organisation
Background (R&D projects)

Framework

Offline segment
Chapter 2: the projects
Chapter 3: valuation tools
Chapter 4: selection cycle
Chapter 5 Chapter 6: considerations
Chapter 7: VaR
Chapter 8: Case Example

8

Chapter 2 R&D Projects

“ a company may die a quick death if it does not manage its
critical risks, it will certainly die a slow death if it does not take
enough risks.”
James Lam.
Enterprise: Risk Management, Wiley (2003), p. 273


From a business point of view, innovation is beneficial to allow the renewal of the
Foster’s S-curve (1986) of industry earnings so as to maintain sustainable growth. One
basic type of innovation is product innovation, which involves the introduction of a
new good or service that has been substantially improved. According to Foster and
Kaplan (2001), innovation can be classified in increasing impact of wealth creation and
newness as incremental, substantial or transformational. (c.f. Figure 2-1). Different

level of innovation requires a different managerial treatment. In our study, we are
interested in the latter two types of innovation which usually stem from research and
development (R&D) activities.

Figure 2-1: Types of Innovation: Ritcher scale of innovation
Source: Foster, R.; Kaplan, S. (2001). Creative Destruction, p109.
Newness
Wealth Creation
Transformational
inn
ovat
i
o
n
Substantial
inn
ovat
i
o
n
Incremental
inn
ovat
i
o
n
1 10 100
100

10


1
Chapter 2 R&D Projects

9
2.1 Benefits of R&D

R&D activities refer to future-oriented, long term projects in science and technology
that aim for breakthrough innovations, and are crucial in ensuring competitiveness in
our ever-progressing society. International studies have consistently demonstrated the
positive correlation between R&D investment intensity and company performance
measures such as sales growth, wealth creation efficiency and market capitalisation in
the sectors where R&D is important. In particular industries like pharmaceutical, pulp
and paper, commodity and specialty chemicals, aerospace and defence, as well as oil
extraction, it appears that the companies which concentrate on sustained growth
through investment in R&D are most likely to achieve increased shareholder return.
1

(Foster and Kaplan, 2001).

R&D results in valuable inventions, ideas and designs which can be sources of
potential value when it comes to gaining competitive advantage. A variety of
Intellectual Property Rights including patents and trademarks exists to help a company
protect these valuable assets.

2.2 R&D Landscape

In the 2005 R&D Scoreboard

2
, the US continues to score highly in "R&D intensity" –
the ratio of R&D to sales. American companies invested 4.5 % of sales revenues in
R&D, compared with 4.0 % for Japanese and 3.3 % for European companies.


1
2
nd
group witnesses no correlation: soaps and detergents; medical and surgical equipment;
telecommunications. 3
rd
group incredibly sees a negative correlation: computer hardware, software;
semiconductors.
2


Chapter 2 R&D Projects

10

Table 2-1 shows the top 15 companies ranked by R&D intensity. These firms are
amongst the 500 largest companies in the world by market capitalisation. As can be
inferred from the table, the US is strongly represented in the three big R&D-intensive
industry sectors: pharmaceuticals, IT hardware and software. In contrast, Europe is
relatively weak in IT and related fields, while Asia lacks a vibrant pharmaceutical
sector.

It should be noted that an R&D intensity of over 15% is considered remarkable and
companies under this category usually gain a reputation for being high technology

companies.

Generally, high-tech firms prosper in markets of extreme demands, such as medicine,
scientific instruments, safety-critical mechanisms (aircraft) or high technology military
armaments. The extreme needs justify the high risk of failure and consequently high
gross margins from 60% to 90% of revenues. Most industrial companies however get
only 40% of revenues.

Table 2-1: Companies with the highest R&D intensity in the FT Global 500
Company R&D
Intensity
R&D
£bn
Growth of
R&D(1 yr)
Sector Growth of
Market Cap
Country
1. Computer Associates(7)* 21.5% £0.4bn +8% S +9% USA
2. Electronic Arts, (20) 20.2% £0.3bn +24% S +22% USA
3. Analog Devices (6) 19.4% £0.3bn +14% H -8% USA
4. Eli Lilly(12) 19.4% £1.4bn +15% P%< -17% USA
5. Schering-Plough (17) 19.4% £0.8bn +9% P +13% USA
6. Amgen (10) 19.2% £1.1bn +23% P +38% USA
7. Adobe Systems (n) 18.7% £0.2bn +12% S +31% USA
8. Juniper Networks (n) 17.8% £0.1bn +35% H +9% USA
9. AstraZeneca (13) 17.7% £2.0bn +10% P +2% UK
10. Merck (34) 17.5% £2.1bn +26% P -34% USA
11. Genzyme (12) 17.2% £0.2bn +20% P +56% USA
12. Gilead Sciences (11) 16.9% £0.1bn +36% P +42% USA

Chapter 2 R&D Projects

11
13. Ericsson (4) 16.7% £1.7bn -25% H +37% Sweden
14. ST Microelectronics (24) 16.5% £0.8bn +25% H -8% Netherlands
15. Roche (28) 16.3% £2.3bn +7% P +42% Switzerland
* Position in the equivalent list from the 2004 R&D Scoreboard (n = not in FT Global 500).
† S = Software & computer services; H = IT hardware, P = Pharmaceuticals & biotechnology.

2.3 General Considerations

Technology investments are interesting both from the management and the financial
perspectives. If aligned with corporate strategy, R&D projects often grant the
possibility of pursuing an avenue in several months or a couple of years. Each
successful innovation may be used as a building block for further R&D efforts,
enabling creation of sustainable competitive advantage through a cohesive R&D
program that blends and builds upon previous results.

From the capital budgeting point of view however, the only certain aspect of R&D
projects is the investment sum which unfortunately can be rather significant. The
potential payoff may be high, but that is contingent on the combination of many factors,
including (internal) technical maturity, (external) market competition, and the extent of
innovation (radical/ incremental).

Figure 2-2 pinpoints the position of R&D in a risk-payoff matrix.

Figure 2-2: Risk-payoff matrix showing position of R&D
High
R&D
Risk
Low
Machine upgrades, product
improvements, etc.

Low High
Payoff