Oztoprak & Scholtes (2008): The Economics of Uncertainty in Technology Development
The Economics of Uncertainty in
Technology Development A Spreadsheet Teaching Case*
Niyazi Oztoprak & Stefan Scholtes
Judge Business School
University of Cambridge
May 2008
Introduction
Research and development of new and potentially disruptive technologies requires a concerted
effort of investors, companies, government, and research institutions. All stakeholders are
concerned with the creation of future value. But the realisation and magnitude of that value is
highly uncertain, driven by technological, commercial and political risks and opportunities. A
solid understanding and effective communication of these uncertainties and their effects on
development plans is paramount for an efficient allocation of effort and funds, relative to
alternative opportunities, and the design of effective development strategies that take account
of unfolding uncertainties and maximise the value of R&D projects over time.
Investments in R&D activities have a number of important characteristics in relation to
uncertainty. Firstly, R&D projects are typically staged, often with significant increase of
investment as development stages are successfully passed. The specifics of the staging depends
on the technology; an example could be
i)
Discovery is concerned with fundamental science and typically not directly
associated with a particular product idea; this phase is typically pre-patenting
ii)
Exploration – discoveries are associated with a product; tests are conducted to
establish proof of concept; a patent is filed
iii)
Prototyping – a prototype is built to confirm the proof of concept on a lab- scale
iv)
Industrial development of processes for large-scale production, followed by the
launch of the product.
The staging of R&D activity lends itself naturally to the development of contingency plans,
dynamic development strategies, which allow mitigation of risk and exploitation of opportunity.
A second feature of R&D projects is that they typically display an interesting dichotomy between
technical uncertainties and commercial uncertainties. In their early phases the focus is largely on
technological success. Once past proof-of-concept, the emphasis shifts to manufacturing costs.
Both of these uncertainties are largely driven by complex science and engineering
considerations and their understanding are a primary concern of any science-driven project
team. The commercial success, however, (revenues, market share or other benefits...), is not
only driven by technological success but also by market phenomena, consumer behaviour,
*
Teaching and solution spreadsheets as well as a teaching note can be downloaded at
http://www.eng.cam.ac.uk/~ss248/ikc
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Oztoprak & Scholtes (2008): The Economics of Uncertainty in Technology Development
competitors, and the development of complementing technology. These variables are outside
the control, and often outside the immediate perception of the engineers and scientists.
Commercial considerations become increasingly important as the R&D project moves
downstream through the R&D phases, closer to market, not least because the required
investments become much larger and investors require more rigorous demonstration of “value
for money”.
Traditional economic evaluation
Commercial value is traditionally estimated using discounted cash flow (DCF) models. The gold
standard is the net present value (NPV), obtained by deducting the upfront investment costs
from the discounted future cash flows generated by the project. NPV analyses, however, are
based on projections of the future, both in terms of exogenous variables, such as market size, as
well as endogenous controls, such as partnering, abandoning, etc. They do not communicate the
risk and opportunity, nor do they account for the interplay between uncertainties and future
decisions. NPV is appropriate for “cash cow” investments, where efficiency gains are primary
drivers of value. It is much less appropriate for R&D investments, where the creation of new
value propositions, as opposed to improvement of existing value, is the goal. In this case study,
we will illustrate valuation mechanisms that allow the articulation of uncertainty and the
assessment of the value effects of risk-mitigating and opportunity-exploiting dynamic
development strategies. The valuation processes will take account of the two characteristics
mentioned above: The staging of R&D and the interplay between technological and commercial
uncertainties.
IKC- ALPS Micro-projector Development Case
Several companies are investing in the development of micro-projectors. These are coin sized
projection devices that may ultimately be placed on laptops and cell phones. Companies are
working on different technologies for the same market. They are forming partnerships with
universities, to stay in touch with the most recent advances in research, as well as with other
companies with complementary capabilities, to ensure fast development and higher market
penetration once their product is launched. The total market for a particular technology,
provided it is technically successful, is typically highly uncertain. The uncertainty is not only
driven by the technical characteristics of the product or technology that is being developed, but
equally by the success of competing technologies or potential substitution products, regulatory
changes and highly unpredictable consumer behaviour.
A key figure in any economic evaluation is the projection of future revenues generated from the
technology. As time passes these forecasts will be updated, in particular at times when new
investments need to be made to push the development into its next phase. The revenue
projection is likely to change, in light of changing market conditions and actions taken by key
players such as competitors or governments. In the light of the updated revenue projection,
investors will take decisions on the fate of the product. Modelling these decisions, in a simple
form, is important if one wishes to understand the economic value of a project. In this teaching
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Oztoprak & Scholtes (2008): The Economics of Uncertainty in Technology Development
case we will build a model for the joint dynamic development of micro-projectors by the
Cambridge Integrated Knowledge Centre (IKC) and ALPS Electric Co.
Company and Technology Background
ALPS Electric Co. is a Tokyo, Japan based company which invents and develops technologies for
the mobile, automotive and home markets. In October 2004 a research contract was signed
between ALPS and Cambridge University in order to cooperate with Cambridge Advanced
Photonics and Electronics (CAPE) and Cambridge Integrated Knowledge Centre in Advanced
Manufacturing Technologies for Photonics and Electronics (IKC).
The collaboration between Cambridge University and ALPS Electric Co. resulted in the
development of a novel holographic projection technology that generates an image on a small,
fast, high-definition liquid crystal over silicon (LCOS) panel.
As the name suggests, the most desirable property of micro-projectors is their size. The small
size of these projectors may very well allow them to be placed on devices such as laptops or cell
phones.
Nevertheless, these technologies can not yet provide the high level of resolution and screen size
that is demanded by most prominent users of projectors. Yet, there are consumers that are
excited about the mere idea of being able to project pictures or video to the back of a train seat.
This suggests that there is potential for the product to be launched in emerging niche markets
and that there is room for improving the product and moving into different market segments.
We now develop a methodology to evaluate such an investment opportunity.
Valuation Principles
In order to understand the value added to a project through the incorporation of dynamic
development strategies we first need to understand how projects are evaluated without such
contingency plans. To this end, we briefly review the traditional discounted cash flow (DCF)
analysis.
DCF is based on projections of cash inflows (revenues) and cash outflows (costs), which sum up
to a net cash flow profile over time, typically in annual periods. Cash flows will typically be
negative for an initial period and then become positive.
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Fig 1: Predicted Revenues and Costs if all R&D completed Successfully
The cash flow projection must be accompanied by a financing plan, detailing how any cash
shortfall will be financed and what return any cash surplus will achieve. DCF is based on a
concept that simplifies financing considerations. A DCF analysis essentially assumes that a
company finances its projects from a bank account with practically unlimited overdraft and will
also put all proceeds into this bank account. The interest rate applied to the bank account is the
company’s weighted average cost of capital of d% p.a.†. The beauty of this concept is that it
allows a comparison of cash flows at different points in time in a very simple manner: A cash
flow of $x in n years time, whether positive or negative, is worth the same to the company as a
discounted cash flow of $x/(1+d)n today. If x is negative then the company will need to inject $x
into the project in n years to keep it afloat. Do do this, the company can depost $x/(1+d)n now in
its bank account, which will grow to the required injection of $x over n years. If x is positive
then, instead of withdrawing $x from the project in n year’s time the company can withdraw
$x/(1+d)n from its bank account today and balance the account by putting the project’s cash
flow $x back in the bank account after n years. A DCF analysis amounts to estimating the cash
flows over time, discounting all future cash flows to today at the company’s cost of capital, and
then summing them up. This is called the present value of the future cash flows. A project will
typically have some initial set-up investments that the company commits to at the start. This
investment is subtracted from the present value to obtain the Net Present Value of the project.
†
WACC p.a. is defined as d = Expected return on equity (% p.a.) * Percentage equity of total capital + Cost
of debt (% p.a.) * Percentage debt of total capital.
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Taking technical uncertainty into account.
The projected cash flows in a technology development project will assume that the project is
developed to its end. This, of course, is often not the case. Quite frequently the technological
characteristics of project turn out to be insufficient to continue investing in the project. In other
words, all future cash flows have to be multiplied with the probability that they actually occur,
i.e. with the probability that the project is still alive at the time. When the discounted cash flows
are multiplied with their probability of occurrence before they are summed up, then the value
obtained is the expected Net Present Value, also called the Risk-adjusted Net Present Value
(rNPV) of the project.
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Oztoprak & Scholtes (2008): The Economics of Uncertainty in Technology Development
Task 1
Consider the investment in a micro-projector technology with the following parameters. The
product needs another two years to be complete. Until then two stages of investments need to
be made. Investment in the first stage to develop a working prototype will cost £1M and will be
successful with 70% probability. Investment in the second stage to develop the final product will
cost £10M and will be successful with 60% probability. We adopt a discount rate of 25% per
stage before the product is launched and a discount rate of 12% after the product is launched.
Manufacturing cost and revenue estimates once the product is launched (Stage 3) are given in
table 1 below.
Year
Costs
0
1
2
3
4
5
6
7
8
9
10
-190
-50
-50
-45
-45
-45
-40
-35
-35
-35
-30
Revenues Net Cash Flow
70
80
85
90
100
90
80
80
70
60
-190
20
30
40
45
55
50
45
45
35
30
Table 1: Costs and Revenues if Product Successful
A. Perform a discounted cash flow analysis of the project described above. Calculate the
risk adjusted net present value of the project.
B. Is this a good project to invest in? What are some benefits and drawbacks to the
methodology employed in our analysis?
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Oztoprak & Scholtes (2008): The Economics of Uncertainty in Technology Development
Task 2
The fundamental concern of a more sophisticated valuation method should be an improved
incorporation of value uncertainty. R&D projects are, by their very nature, highly uncertain.
Clearly, technical uncertainty is at the forefront of consideration and can lead to outright failure
and the abandonment of the project before the first cash inflow has occurred. Technical
uncertainty will also largely determine the cost base for the new product. Revenue uncertainty
of new products is equally, and quite often even more important. Will the product successfully
substitute existing products? Will it efficiently scale and thereby further grow into substitution
markets? Will it trigger new applications and create new markets? Are there stable barriers to
entry in the case of success? Are there competing product developments that may set a
different standard? Such questions lead naturally to the development of scenarios, which is the
first step in the development of a mature relationship with uncertainty.
To conceptualise uncertainty, it helps to break the project up in several phases. For the sake of
simplicity, we will use three main phases:
1. Research, success establishes proof-of-concept
2. Development, success establishes working prototypes
3. Launch, establishes manufacturing capability and viable demand
Such phasing is conducive to contingency planning. Indeed, a transition from one phase to the
next requires a significant additional capital commitment and therefore a re-evaluation of the
project. We will assume, for simplicity, that within each phase, the activity plan decided on at
the beginning of the phase is largely unchanged and phase expenditure is committed but at the
transition points the project can be re-shaped and plans for the future phases can be changed in
the light of the new information gained during the given phase.
To illustrate how a DCF analysis can be improved to provide a more informed and more reliable
valuation of an R&D project, we improve the previous DCF analysis according to the following
guidelines.
1. We introduce a financing plan that is more realistic than the “company bank account”
concept that DCF relies on by allowing a range of possible financing parameters.
2. We acknowledge value uncertainty by allowing for a range of development scenarios of
project parameters.
3. We plan for contingencies by including action points at which management will check the
unfolding uncertainties, both on project and financing parameters, and will take action on
further development and financing of the project.
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To make the spreadsheet work without simulation software we have saved data sets comprising
20,000 scenarios for the change of the projected launch value of the product, as development
unfolds. The scenarios are saved as a Stochastic Library Unit with Relationships Preserved
(SLURP)‡. You can copy and paste other scenario lists into the designated area in the Task 2 tab.
The tab SLURPS contains five different SLURPs with increasing levels of variability.
A. What happens to the value increase due to dynamic development as volatility around
value at launch increases? Can you explain why?
‡
A scenario generator is available at http://www.eng.cam.ac.uk/~ss248/ikc . For details on stochastic
library units see Savage, Scholtes, Zweidler, Probability Management, Part I and II, OR/MS Today,
February and April 2007.
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Oztoprak & Scholtes (2008): The Economics of Uncertainty in Technology Development
Task 3
In the previous section we incorporated staging, uncertainty and decision making into our
valuation. An important aspect of financing a new venture is partnering. Different parties might
be involved in financing different parts of a project. While some partnerships are rewarding for
all parties involved, others don’t function as well and lead to failures.
Let’s take the investment opportunity discussed in our case study as an example. We will
assume that the inventor does not have any more funds to continue the project. A business
angel will finance stage I while a private equity firm will finance stage II. How should the shares
be split between the three parties?
In the Task 3 tab we adjust the model from task 2 to incorporate the three partners, their
investment decisions and their payoffs. Familiarise yourself with the formulation of this
worksheet by making use of the descriptive text boxes.
A. Copy and paste the SLURPS with varying levels of variability in the SLURPs tab into the
designated area in the Task 2 tab. Two cells in the Charts tab give the expected value
under dynamic development and partnering for the current simulation. Copy and paste
these values into the table above the two cells to create two graphs comparing the
value from the partnership to the value under dynamic development. What happens to
the difference between these two values as volatility increases? Can you explain why?
B. Can you find a way to ease the discrepancy between total value from partnership and
the value from dynamic development for the intermediate levels of volatility?
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Oztoprak & Scholtes (2008): The Economics of Uncertainty in Technology Development
Appendix A: Partnering Terms in Task 3
One way to model the shares, decisions and payoffs of each of the partners may be as follows:
Business Angel
Share = Value Invested / Expected Value at Launch in Stage 1
Decision: Invest when rNPV is positive.
Payoff: Share times realised value at launch.
Private Equity
Share = Value Invested / Expected Value at Launch in Stage 2
Decision: Invest whenever the required percentage of shares can be attained.
Payoff: Share times realised value at launch.
IP Holder
Share = Shares remaining if both partners have decided to invest - otherwise zero.
Payoff: Share times realised value at launch.
Please see spreadsheet for how the above rules are applied.
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