A Risk-based Real Options Framework for Flexible Technology

Planning

Juite Wang

, Chuan-Hung Cheng

, Yung-I Lin

and Chih-Hsin Chang

Graduate Institute of Technology Management, National Chung-Hsing University,

250 KuoKuang Road, Taichung 402, Taiwan, Republic of China

Aeronautical Systems Research Division, National Chung-Shan Institute of Science and Technology,

P.O. Box 90008-11-21, Taichung, Taiwan, Republic of China

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

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

, x

, …, x

), where x

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

and decision d is made at stage t, the system

moves to a new state x

with transition probability

)(dp

, development cost c

(d), and development

duration h

(d).

Let Y be the total product life cycle, S

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:









)1(







(1)

When the project is launched at stage T with a

performance state X, it will generate an expected

market payoff  (X):

 (X) =

+ F(X)(

)

(2)

where F(X) = Prob(X ≥ R) represents the probability

that product performance X exceeds the market

requirement R,

is a maximum profit margin as

the realized product performance meets or exceeds

R, and

is a minimum profit margin if the project

misses the target market requirement R.

The total technology development project costs

C(D) is

(

) =







)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:

(

) =  (X) -

(

)

(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-

ICORES 2016 - 5th International Conference on Operations Research and Enterprise Systems

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.

REFERENCES

Black, F. and Scholes, M. 1973, The pricing of options

and corporate liabilities. Journal of Political Economy,

81, 637-654.

Black, F. and Scholes, M. 1973, The pricing of options

and corporate liabilities. Journal of Political Economy,

81, 637-654.

Brandao, L. E., Dyer, J. S. and Hahn, W. J. 2005, Using

binomial decision trees to solve real-option valuation

problems. Decision Analysis, 2, 69-88.

Chapman, C. and Ward, S. 2003, Project Risk

Management: Processes, Techniques and Insights:

Wiley.

Cox, J., Ross, S. and Rubinstein, M. 1979, Option pricing:

A simplified approach. Journal of Financial

Economics, 7, 229-264.

Dixit, A. K. and Pindyck, R. S. 1994, Investment under

Uncertainty. Princeton, N.J.: Princeton University

Press.

Fu, M. C. 2002, Optimization for Simulation: Theory vs.

Practice. INFORMS Journal on Computing, 14, 192-

215.

Huchzermeier, A. and Loch, C. H. 2001, Project

management under risk: Using the real options

approach to evaluate flexibility in R&D. Management

Science, 47, 85-101.

Kappel, T. A. 2001, Perspectives on roadmaps: how

organizations talk about the future. Journal of Product

Innovation Management, 18, 39-50.

Lee, S., Yoon, B., Lee, C. and Park, J. 2009, Business

planning based on technological capabilities: Patent

analysis for technology-driven roadmapping.

Technological Forecasting and Social Change, 76,

769-786.

Owen, T. G., Mitchell, S. D., Ncube, S. M. and Rashid, M.

H. 2008, Applications and Market Analysis of DC-DC

Converters International Conference on Electronic

and Computer Engineering. Dhaka: IEEE, 887-891.

Petrick, I. J. and Echols, A. E. 2004a, Technology

roadmapping in review: A tool for making sustainable

new product development decisions. Technological

Forecasting and Social Change, 71, 81-100.

Petrick, I. J. and Echols, A. E. 2004b, Technology

roadmapping in review: A tool for making sustainable

new product development decisions. Technological

Forecasting and Social Change, 71, 81-100.

Phaal, R., Farrukh, C. J. P. and Probert, D. R. 2004,

Technology roadmapping - a planning framework for

evolution and revolution. Technology Analysis &

Strategic Management, 71, 5-26.

Piachaud, B. S. 2002, Outsourcing in the pharmaceutical

manufacturing process: an examination of the CRO

experience. Technovation, 22, 81-90.

Pich, M. T., Loch, C. H. and De Meyer, A. 2002, On

uncertainty, ambiguity, and complexity in project

management. Management Science, 48, 1008-1023.

Rubinstein, R. Y. and Kroese, D. P. 2007, Simulation and

the Monte Carlo Method. New York: John Wiley &

Sons.

A Risk-based Real Options Framework for Flexible Technology Planning

285

Saritas, O. and Aylen, J. 2010, Using scenarios for

roadmapping: The case of clean production.

Technological Forecasting and Social Change, 77,

1061-1075.

Song, Y. I., Lee, D. H., Lee, Y. G. and Chung, Y. C. 2007,

Managing uncertainty and ambiguity in frontier R&D

projects: A Korean case study. Journal of Engineering

and Technology Management, 24, 231-250.

Trigeorgis, L. 1996, Real Options: Managerial Flexibility

and Strategy in Resource Allocation Cambridge, MA:

The MIT Press.

Walsh, S. T. 2004, Roadmapping a disruptive technology:

A case study: The emerging microsystems and top-

down nanosystems industry. Technological

Forecasting and Social Change, 71, 161-185.

Wang, J., Lin, W. and Huang, Y. H. 2010, A performance-

oriented risk management framework for innovative

R&D projects. Technovation, 30, 601-611.

Wang, J., Wang, C. Y. and Wu, C.-Y. 2015, A real options

framework for R&D planning in technology-based

firms. Journal of Engineering and Technology

Management, 35, 93-114.

Wang, J. and Yang, C.-Y. 2012, Flexibility planning for

managing R&D projects under risk. International

Journal of Production Economics, 135, 823-831.

Yoon, B., Phaal, R. and Probert, D. 2008, Morphology

analysis for technology roadmapping: application of

text mining. R&D Management, 38, 51-68.

ICORES 2016 - 5th International Conference on Operations Research and Enterprise Systems

286