A Risk-based Real Options Framework for Flexible Technology
Planning
Juite Wang
1
, Chuan-Hung Cheng
1
, Yung-I Lin
2
and Chih-Hsin Chang
3
1
Graduate Institute of Technology Management, National Chung-Hsing University,
250 KuoKuang Road, Taichung 402, Taiwan, Republic of China
2
Aeronautical Systems Research Division, National Chung-Shan Institute of Science and Technology,
P.O. Box 90008-11-21, Taichung, Taiwan, Republic of China
3
Department of Information Management, National United University, 1 Lienda, Miaoli 36003, Taiwan, Republic of China
Keywords: R&D, Risk Management, Real Options Analysis, Managerial Flexibility.
Abstract: Although the importance of R&D is well understood by technology-based firms, with the increasing
uncertainty in technology development and market trends in recent years, managing uncertain R&D projects
to enhance competitive technological position is still a major challenge for those firms. This research
develops a technology planning framework that integrates R&D risk management with real options analysis
enables technology-based firms to allocate their limited R&D resources with managerial flexibility for
maximizing the expected market value of R&D project under uncertainty. The proposed technology
planning framework consists of four stages: technology roadmapping, risk identification, risk response
planning, and flexible plan optimization. Since technology development usually involves great uncertainty
at early R&D stages, the Monte-Carlo simulation optimization technique is applied to evaluate and select
optimal technology plans under different scenarios. The developed methodology is illustrated with a case
study of ASIC power module technology development project in Taiwan.
1 INTRODUCTION
Technology is an important asset that enables a
technology-based firm to develop future products
and the manufacturing processes supporting these
products. It is very important for those firms to
invest on adequate R&D projects to retain their
competitive advantages. Although the importance of
R&D is well understood by technology-based firms,
with the increasing uncertainty in technology
development and market trends in recent years,
managing uncertain R&D projects to enhance
competitive technological position is still a major
challenge for those firms (Pich et al., 2002; Song et
al., 2007). The presence of tremendous uncertainty
leads to many failures in their R&D projects.
Therefore, how to develop a technology plan and
effectively manage uncertainty in an R&D project
over time to enhance its success rate has become a
very important issue for technology-based firms
(Wang et al., 2015).
Traditionally, companies often use Net Present
Value (NPV) as a method of evaluating R&D
investments. However, the NPV approach neither
takes risks into account, nor includes managerial
flexibility in the investment decision-making
process. It is assumed that investment decisions once
determined at the initial stage, decision-maker will
be unable to make any changes for the investment
process. So the project has failed to effectively carry
out risk control and flexibility planning (Trigeorgis,
1996). Real options analysis (ROA) is a good way to
remedy these issues for R&D projects with great
uncertainty and high risk. It incorporates managerial
flexibility in the evaluation process in order to play
the hedging role on R&D investment and find the
adequate value for R&D project (Dixit and Pindyck,
1994; Trigeorgis, 1996; Huchzermeier and Loch,
2001).
This research develops a technology planning
framework based on the real options theory that
enables technology-based firms to allocate their
limited R&D resources with managerial flexibility
for maximizing the expected market value of R&D
project under uncertainty. The proposed technology
planning framework integrating R&D risk
Wang, J., Cheng, C-H., Lin, Y-I. and Chang, C-H.
A Risk-based Real Options Framework for Flexible Technology Planning.
DOI: 10.5220/0005736702790286
In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems (ICORES 2016), pages 279-286
ISBN: 978-989-758-171-7
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
279
management with real options analysis consists of
four stages: technology roadmapping, risk
identification, risk response planning, and flexible
plan optimization. Since technology development
usually involves great uncertainty at early R&D
stages, uncertain parameters involved in technology
planning and development are characterized by
appropriate probability distributions. The Monte-
Carlo simulation optimization technique is applied
to evaluate and select optimal technology plans
under different scenarios. The developed
methodology is illustrated with an ASIC technology
development project in Taiwan’s power module
industry.
The structure of the paper is described below.
Section 2 reviews the related literature for technical
planning and real options analysis methods. The
proposed risk-based real options framework for
flexible technology planning is developed in Section
3. Section 4 presents the case study on the ASIC
power module development. Section 5 concludes the
paper and describes the future research.
2 THEORETICAL
BACKGROUND
2.1 Technology Planning
Technology planning aims to planning the technical
evolution of a technology or product to achieve the
vision of a technology-based firm. Technology
roadmapping is a technology planning tool, which is
widely used in the industry for the development of
long term technology plans (Petrick and Echols,
2004a). Kappel (2001) presented two types of
technology roadmapping: scientific-oriented
roadmapping which predict the direction and trend
of technology from the industry point of view and
product-oriented roadmapping which is a business
perspective to assess the technology decision. Phaal
et al., (2004) proposed the Fast-Start technology
roadmapping method that provides a practical guide
to a fast application of the technology roadmapping.
Walsh (2004) modified the traditional technology
roadmaps for disruptive technologies. Petrick and
Echols (2004b) recommended that companies must
share and extend the product and technology
planning with their supply chain partners to make
better planning and decision-making. In addition,
technology roadmapping is able to integrate with
other management tools, such as scenario planning
(Saritas and Aylen, 2010), morphology analysis
(Yoon et al., 2008), and patent analysis (Lee et al.,
2009).
2.2 Real Options Analysis
Real option has been widely used in many different
areas, such as operations management, supply chain
management, R&D management, and strategy
planning. There are two main evaluation methods in
R&D project evaluation: the Black-Scholes method
(Black and Scholes, 1973) and the binomial tree
method (Cox et al., 1979) which improve restrictions
of the Black-Scholes model that can only assess
single option value with restricted time to maturity.
Huchzermeier and Loch (2001) developed a real
R&D options model that not only considered market
uncertainty but also technological uncertainty.
Brandao et al., (2005) proposed an approach that
integrates the real options theory with decision tree
to assess the value of R&D project. A dynamic
programming model was applied for R&D project
evaluation. Wang and Yang (2012) extend their
model for flexible R&D planning of drug
development project in the pharmaceutical industry.
Although there have been several real options
research in R&D project valuation, few studies
really consider risk management and real options
analysis at the same time for technology planning.
Linking risk management with real options analysis
allows decision makers to select appropriate
contingent actions and to respond effectively to the
various risks faced by the project for maximize
project value while reducing risks.
3 METHODOLOGY
In this study, a technology planning framework that
integrates risk management with the real options
analysis is proposed to help technology-based firms
develop a new technology for maximizing the
probability of positive return or expected project
profitability (see Figure 1). The proposed framework
consists of 4 stages presented as follows.
Step 1: Technology Roadmapping
Technology roadmapping is a systematic
methodology that integrates the concepts of market
pull and technology push, aligning target market
requirements, products, technology capabilities, and
R&D resources (see Figure 2). Its main advantage is
to show the linkages of customer demands, products
performances, technology capabilities, and R&D
resources with timeframe to clearly present
ICORES 2016 - 5th International Conference on Operations Research and Enterprise Systems
280
Figure 1: The framework of flexible technology planning.
and communicate and corporate strategic planning
(Phaal et al., 2003).
Step 2: Risk Identification
The innovative R&D project usually has great
market and technological uncertainties, which may
lead to project failure. The purpose of this step is to
identify and understand critical R&D risks that may
affect the success rate of an R&D project. Risk
identification distinguishes potential risks that may
affect project goals and outcomes, and information
about potential R&D risks may be collected from
survey, interview, best practice, brainstorming, etc.
(Chapman and Ward, 2003; Wang et al., 2010).
Based on the information collected in development
phases, different scenario alternatives are analyzed
to identify potential risks for the R&D project.
Figure 2: Example of technology roadmapping.
Step 3: Risk Response Planning
According to the critical risks identified in the
second step, this step maps the risks that must be
managed to appropriate managerial actions or
options. This research summarizes the following
R&D investment options that have been used to
enhance managerial flexibility as follows
(Trigeorgis, 1996; Huchzermeier and Loch, 2001):
Continue/Abandonment Option: This type of
option is usually used in every stage of a R&D
development project. A project may be abandoned
because its performance can’t be satisfied to the
market requirement or it fails to obtain regulation
approval.
Expansion/Contraction Option: This type of
option represents the possibility of adjusting the
amount of investment, depending on product
performance and market conditions.
Deferral Option: The firm may invest in an R&D
project until the market has emerged. Deferral
option allows firms to wait for full investment
commitment in more uncertain market situations
until more useful information is available or market
opportunity is clear.
Switching Option: A switching option provides the
right and ability but not the obligation to switch
among different sets of technologies, markets, or
products based on the progress of technology
development and market condition (Trigeorgis,
1996).
Outsourcing Option: The benefits of outsourcing
include reducing cost, gaining extra capacity, and
utilizing a vendor’s special expertise to enhance the
competitiveness of developing products. Alongside
these benefits, however, this option also has the
drawback that the output quality of the outsourcing
partner might vary due to inappropriate process
compatibility, coordination policy, and cultural fit
between the two organizations (Piachaud, 2002).
License-in Option: This option provides a direct
way to access the advanced technologies and
methods of knowledge to get the ability. This option
can provide immediate access to more advanced
technologies that might enhance the technology
capabilities of a firm, while saving R&D lead-time
and cost. However, prior to the successful
technology transfer into the firm, there might be
higher uncertainty regarding the technology’s
achievable product performance in later R&D stages.
Improvement Option: The firm is allowed to invest
more resources on an R&D project for improving
product performance to meet higher market
requirements and obtain greater market return
(Huchzermeier and Loch, 2001).
Step 4: Flexible Planning Optimization
This research extends the real options model
developed by Huchzermeier and Loch (2001) with
Monte Carlo simulation to determine the best
decision path that maximizes the expected project
profitability or the probability of positive return.
Assume that there are T review stages: t = 0, 1,
2,…T-1, and the new product is launched to the
market at stage T. R&D uncertainty can be
represented by performance improvement and
Risk
identification
Risk response
planning
Flexible plan
optimization
Technology
roadmapping
A Risk-based Real Options Framework for Flexible Technology Planning
281
deterioration spread over possible performance
states. The state of the project at review stage t is
characterized by the expected product performance
X = (x
1
, x
2
, …, x
n
), where x
k
is the individual product
performance, k = 1, …, n. Let d be the management
decision (e.g., continuation, abandonment, etc.) at
stage t. It is assumed that whenever the system is in
state x
i
and decision d is made at stage t, the system
moves to a new state x
j
with transition probability
)(dp
t
ij
, development cost c
t
(d), and development
duration h
t
(d).
Let Y be the total product life cycle, S
t
be the
market size at year t, δ be the market share of a firm,
α be the average contribution rate of the product
using the technology, β be the product sharing ratio
of the technology, and r be the discount rate. The
potential profit margin M of the technology is
calculated as:
M
=
Y
t
t
t
r
S
1
)1(
(1)
When the project is launched at stage T with a
performance state X, it will generate an expected
market payoff (X):
(X) =
M
+ F(X)(
M
-
M
)
(2)
where F(X) = Prob(X R) represents the probability
that product performance X exceeds the market
requirement R,
M
is a maximum profit margin as
the realized product performance meets or exceeds
R, and
M
is a minimum profit margin if the project
misses the target market requirement R.
The total technology development project costs
C(D) is
C
(
D
) =
T
t
t
t
r
dc
1
)1(
)(
(3)
where D is the set of actions d selected at each stage
to mitigate the risks encountered. Then the project
profit can be calculated:
V
(
X
) = (X) -
C
(
D
)
(4)
Given the required input data, the above model
integrated with Monte Carlo simulation (Rubinstein
and Kroese, 2007) can be used to estimate the
probability distribution of R&D project profit for
every candidate solution and to determine the
optimal decision path using the simulation
optimization technique (Fu, 2002).
4 CASE STUDY OF AN ASIC
POWER MODULE
DEVELOPMENT PROJECT
DC/DC converter module is an important element in
power electronics and its purpose is to realize power
conversion from an electrical source to an electrical
load in an efficient, reliable, and cost-effective way
(Owen et al., 2008). There has been a great and
diverse demand for DC/DC converter modules,
ranging from consumer electronics (such as laptop
computers and cellular phones) requiring smaller
size and lower-cost power converters to large
industrial systems (e.g., telecommunication,
medical, mass transportation, etc.) demanding highly
reliable power converters. With growing demand of
energy-efficiency and miniature size for electronic
appliances in recent years, there has been an
explosion in demand for smaller and lighter, more
efficient, and less expensive power converters.
Increasing power requirements on energy efficiency
and need for reliable power drive demand for DC-
DC converters. Innovations in segments such as
telecommunications, medical, and technology
promote demand for newer products. It was
projected that the total worldwide revenue market
for DC-DC converter modules will reach
approximately $5.0 billion in 2019, a compounded
annual growth rate (CAGR) of 4.9%.
The case study was conducted at a leading power
module company in Taiwan (called M company in
this research). Facing the great competition
challenges, Company M conducted technology
roadmapping to identify market demands, product
features, and required technologies to fulfill the
market demands. The company decided to initiate
the new R&D project to develop the ASIC
technology that is able to integrate functional
circuits into a chip for making the overall decrease
in the number of components and increasing product
life, stability, and power density as well as product
size miniaturization, leading to better product
differentiation and product niche. Meanwhile, ASIC
technology can also improve manufacturing yield
and reduce manufacturing costs, while greatly
enhancing R&D of and product design quality. The
linkage between market drivers, product features,
and technologies are shown in Figure 3.
ICORES 2016 - 5th International Conference on Operations Research and Enterprise Systems
282
Figure 3: Technology roadmapping for Company M.
The ASIC power module R&D project can be
divided into four stages: defining ASIC specification
and identifying collaboration partners, collaborative
design for ASIC architecture, ASIC testing and
integration verification, and lot purchasing and
production quality verification. The firm identified
potential technical challenges and corresponding risk
response actions for each stage of ASIC R&D
project. The risks and corresponding options are
identified in Figure 4.
Figure 4: Risks and corresponding options for ASIC R&D
project.
5 RESULTS AND DISCUSSIONS
5.1 Maximizing the Probability of
Positive Investment Return
Figure 5: Probability distribution of project profit for
ASIC R&D project.
The first scenario is to maximize the probability of
positive investment return. Using the method
developed in section 3, the optimal expected project
profit was NTD$ 5.952 million and the probability
of positive investment return was 73.6%. Please
refer to Figure 5 for the probability distribution of
project profit.
(a) Insufficient R&D risk
(b) Technical barrier risk
Figure 6: Optimal decision paths for maximizing the
probability of positive investment return.
Figure 6 depicts the optimal decision paths that
suggest an appropriate risk response action for every
potential risk. For example, in the first stage of
technology development project, if R&D budget is
insufficient to pay to the collaborated firm, then the
option of switching to alternative IC design firm is
suggested. In the second stage, if the specification
still cannot be satisfied, then the option to modify
for improving the ASIC design is suggested. On the
other hand, if technology barrier or IC design patent
infringement is encountered, then the option of
switching to alternative IC design firm is also
recommended. Next, if the target design
specification cannot be reached in the third stage,
then the option to modify the original design is
A Risk-based Real Options Framework for Flexible Technology Planning
283
suggested. If a major design flaws has been found,
then expanding R&D investment is suggested to
improve the ASIC design. At the last stage, the
option to expand the production capacity is
suggested for the ASIC inventory shortage problem,
while the option to modify design is recommended
for the production quality problem. Please refer to
Figure 6 for optimal decision paths regarding other
potential risks such as technical barriers, patent
infringement, and inappropriate economic benefits.
5.2 Maximizing R&D Project
Profitability
The second scenario is to maximize the expected
project profit and the optimal expected project profit
is about TWD$ 12.335 million with a probability of
70.53% having a positive return on investment. The
probability distribution of project profit is shown in
Figure 7.
Figure 7: Probability distribution of project profit for
ASIC R&D project.
The optimal decision paths are shown in Figure
8. For example, if technical barrier is encountered in
the first stage of technology development project
(such as the selected design firm cannot provide the
ASIC design satisfying the specification required),
the option of switching is suggested to be applied to
find another IC design firm with better IC design
competence.
In the second stage, if the specification still
cannot be satisfied, then the option to modify for
improving the ASIC design is suggested. Next, if a
design problem has been found in the third stage,
then the option to expand R&D investment is
suggested to improve the ASIC design. When there
is an ASIC inventory shortage problem in the third
stage, then the option to expand the production
capacity is suggested.
5.3 The Value of Managerial Flexibility
From the above results, the first scenario has a
higher probability of positive investment return, but
the second scenario has much better investment
profitability with a bit higher potential project
failure. The final selection decision is dependent on
the risk attitude of R&D manager. If R&D manager
is risk averse, then the objective of maximizing the
probability of positive return is recommended. On
the other hand, if R&D manager is toward to risk-
pro, then the objective of maximizing project profit
is suggested.
(a) Insufficient R&D risk
(b) Technical barrier risk
Figure 8: Optimal decision paths for maximizing ASIC
project prifitability.
We also applied the NPV method to calculate the
project profit and the expected NPV project profit is
TWD$ -49.527 million. The value of flexibility for
the first scenario is TWD$ 55.479 million, while the
first scenario is TWD$ 61.862 million. Therefore, it
is necessary to include managerial flexibility in the
process of project planning and valuation for
managing project uncertainty and risks to improve
the chance of project success. Failing to take
managerial flexibility into account would under-
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284
estimate project value and potential feasibility. The
probability distribution of three scenarios are shown
in Figure 9.
Figure 9: Comparison of three scenarios.
6 CONCLUSIONS AND FUTURE
RESEARCH
Technology planning is significant for technology-
based firms to enhance their competitive advantages
in today's rapidly changing and highly competitive
industry environment. This study developed a real-
option framework integrating with risk management
that helps R&D managers consider managerial
flexibility in their technology planning to maximize
project profitability, while enhancing project success
rate. The first stage used technology roadmapping
linking market requirements, product features, and
technology capabilities. The second and third stage
identified the risks and corresponding risk response
actions, respectively. The final stage evaluated and
constructed optional flexible technology plans. The
case of power module ASIC R&D project was used
to illustrate the developed methodology. The
obtained results show that the developed
methodology can not only mitigate the risks but also
enhance the profitability of technology investment.
This paper only consider two key performance
indicators: operating voltage and temperature for
illustrative purposes. Since the power module ASIC
is complex and has more than two critical
performance indicators, future research will take full
ASIC technology complexity into account for more
practical validation.
ACKNOWLEDGEMENTS
This research is partially supported by grant nos.
MOST 103-2221-E-005 -049 -MY2 from the
National Science Council of the Republic of China.
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