An integrated decision model for strategic evaluation of the viability of new technologies

Diploma Thesis 2000 115 Pages

Engineering - General, Basics


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1.1 Acknowledgments

This thesis would be incomplete without mentioning those who made it possible.

I benefited tremendously from the support of my thesis advisor Dipl.-Ing. Armin P. Schulz from the Institute of Astronautics at the Technical University of Munich, who gave me the opportunity to conduct my thesis research at the Massachusetts Institute of Technology (MIT), an inspiring and motivating environment I never experienced before.

I am also extremely grateful to Professor Don P. Clausing as my advisor at the Center for Innovation in Product Development, who offered me not only to work at MIT but also gave me outstanding support throughout the entire research with his critical and vivid comments.

I would also like to acknowledge the support of Professor Cliff Whitcomb who provided me with insight that goes well beyond what I have been able to express in this thesis. I am also very grateful to Professor Kevin Otto who inspired me with through numerous discussions.

Additionally, I highly appreciate the support of Professor Dr.-Ing. Eduard Igenbergs from the Institute of Astronautics at the Technical University of Munich during the organizational preparation of my thesis in Munich.

The ‘Professor Dr.-Ing. Erich Müller Stiftung’ provided generous financial support, without I would not have been able to accomplish this research.

For all the days and nights spent in the office without loosing confidence and humor, I would like to thank all students in the Engineering Design Research Laboratory at the Center for Innovation in Product Development. Especially Javier Gonzalez-Zugasti and Kan Ota made my time at MIT a very special one.

Lastly, I would like to acknowledge the support of my parents, setting me on the right track to guide me where I am today.

1.2 The CIPD and its objectives

The Center for Innovation in Product Development (CIPD, www.mit.edu/cipd) is one of the National Science Foundation's Engineering Research Centers dedicated to finding the new knowledge, processes, tools, and educational programs that will enable industry to increase the effectiveness of its investment in product development. The CIPD supports an interdisciplinary program between MIT's School of Engineering (www.mit.edu/engineering) and Sloan School of Management (www.mit.edu/sloan). It is dedicated to combining the best ideas and experience of industry and academia in its research, education, deployment, and outreach programs and exploring the entire product development system: the end-to-end business process to conceive, plan, define, develop, demonstrate, deliver, and support families of products and services.

Center research seeks to provide the new knowledge, engage in pilot tests of the new tools and methods, and develop the foundation for commercial products and services that enhance an organization's product development capabilities.

Four key areas comprise the core program of the Center for Innovation in Product Development:

Product Portfolio Definition

Where is technology going and how can the firm affect technology evolution, internally or through external access or influences? What processes will help industry best identify high-value products that truly meet customer needs? The goal of this research is to strengthen the ability of companies not only to define outstanding products, but to ensure long-term success and profitability with those products.

Information-Based Product Development

What does the information revolution mean for product development? How can the power of information be harnessed to optimize the work of product developers? By creating more effective information-based tools to support product development activities, the Center helps practitioners do their work more effectively and with greater efficiency.

Enterprise Strategy

What are the relationships among the market environment, corporate capabilities, and product strategy? How can an understanding of those relationships enhance product development? The Center’s researchers explore potential improvements in enterprise strategy that can strengthen the product development process.

Effective Enterprise Learning

How can companies best benefit from research in product development? How can a network of learning communities be created, one that spans academia and industry? The Center’s researchers study and develop an integrated system of strategies and technologies, enabling companies to absorb best practices – both established and emerging – with greater speed and efficiency.

1.3 Guide to the thesis

Chapter 2 introduces the environmental background – global changes and problems impacting product and technology development – the thesis is developed under. It also gives an overview of the institution and its objectives this thesis is written at. Furthermore, this section discusses the objectives of this project, which have to be set to solve the problems discussed above.

Chapter 3 presents the approach, which will be developed in the thesis, in short. The aim and the interrelations of the three submodels will be presented as well as the essentials of successful product and technology development.

Chapters 4 through 6 are intended to provide the basis for the modeling of the decision framework. They cover existing approaches being the base of the model’s subsystems. These approaches are then expanded and evaluated for their applicability in the Integrated Decision Model.

Chapter 7, the core of this thesis, develops the integrated decision model. It introduces a step by step solution process for the investment decision in new product or technologies. The framework presents a generic model, which can be expanded for the application of additional methods or tailored to a specific field of applications.

Chapter 8 summarizes the results and ideas of the research. A review of the research is performed, lessons learned are addressed and an outlook for further work is given.

Chapter 9 contains the bibliography and references found in the internet.

The appendix introduces a pure real option analysis decision process, which can be used as a short ‘back of an envelope-calculation’ for making investment decisions in new technologies or products.

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Figure 1.1 : Thesis outline

2 Introduction

2.1 Changed markets and business environment

In the past years, there was a tremendous shift in the world, making it act in it more and more complex in almost every aspect. Especially in product development, companies have to cope with very diverse and rapidly changing conditions in globally oriented markets and increasing competition. Several major changes are (see Negele, Fricke et al. 1997, Schulz 1998):

Increasing globalization and worldwide competition offer customers more opportunities to fulfill their desires, they become more demanding and they ask for innovative, customized products. To meet these customer requirements, companies have to offer more variety and flexibility within their product programs. This increased product capability forces a trend to complexity and integrity. Furthermore, demanding customers and worldwide competition lead to highly dynamic markets. These can be only addressed by shorter product development cycles and increased corporate flexibility.

Thus, to fulfil all these aspects, business value considerations drive every aspect of product development. Failures in any area have a significant impact. This results in a tight error margin in decision making and therefore a great need for optimization, risk quantification and management.

2.2 Problems within Product- and Technology Development

The changes mentioned above are directly influencing product and technology development, leading to very interrelated development systems that have to cope with many problems. Four major problems are summarized:

The existence of a technology push with a lack of a pull leads to inconsistent strengths in technology development causing clever technologies, which do not satisfy customer needs, further causing strong customer needs with a lack of related technology generation correlation, and finally causing good concepts followed by an inadequate transfer into product development (Clausing, 1994).

Another shortcoming in today’s product and technology development is the disregard of the voice of the customer. Not enough attention is given to this area, so new products and new technologies are often not developed for the market due to a lack of respect to customer needs. This is caused by development divisions using extremely sophisticated technology for products without having a customer demand.

Furthermore, in most development processes a lack of systems thinki ng is obvious. In highly interrelated systems this way of thinking is especially significant in increasing performance on an overall system level. The lack of systems thinking occurs especially in ‘isolated approaches to implement methodologies or to reengineer processes’ (Fricke and Negele, 1997).

Technology development, as a part of product development being a major success factor for product development, suffers from a low suitability of the applied processes, which do not result in superior and robust technologies (Clausing, 1994).

Based on these insights, it seems important to specify a systems thinking approach and to apply it to the development process of a new product to keep the balance between technology-push and market-pull during the development of a technology.

2.3 Objectives of this project

This project is a continuation of the Total Technology Development (TTD) Framework which has been derived by Schulz (1998). The TTD is divided into four phases:

a) Integrated Technology Strategy (Defining next generation technologies)
b) Concept Generation and Enhancement (Overcoming psychological inertia)
c) Robustness Development and Analysis
d) Technology Selection, Transfer and Integration

The Integrated Decision Model (IDM) is partially going on with the ideas of the phases a), b) and d) of the TTD. It proposes and derives new methodologies for the evaluation and selection of new concepts. Furthermore, it considers – in contrast to the TTD – the development process as a continuous process which has to be evaluated continuously as well. That is, the attributes and influencing factors of the concepts are often changing during the development process which makes competing concepts possibly more valuable and attractive for the developer or changes the value of the concept to be developed.

Improvement in standards of living depend to a remarkable degree on the success of industrial innovations, but the odds of any one idea becoming an economic success are so low that many ideas are needed.

Across most industries, it appears to require 3.000 raw ideas to produce one substantially new commercially successful industrial product. This is illustrated with a logarithmic plot of the number of new project ideas that advance to the next step of development (figure 2.1). (Stevens, 1997)

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Figure 2.1: 3.000 Raw Ideas

(adapted from Stevens, 1997)

While this curve reflects the reality of most industries, there are segments of industry that have different success curves, i. e. drug companies that need a higher number of starting ideas.

It is shown that it takes about 3000 raw ideas (stage 1) to come up with 300 ideas on which the idea generator is willing to take minimal action (such as discussing them with the management, stage 2). Approximately 125 ideas advance to stage 3 to become a small project, having a high probability of receiving a patent and approximately nine projects survive to stage 4 developing into significant projects. Four of these advance to stage 5 to become major development efforts and only 1.7 of these are commercially launched (stage 6). Of 1.7 projects commercially launched, only one is typically commercially successful (stage 7).

‘Commercially successful’ does not mean that somebody is buying the product or concept, but that the concept is providing economic profit to the company. That means that the money returned is greater that all the money invested in creating the product, including the cost of capital, raw material, manpower etc. used throughout the entire project.

Furthermore, determination of the percent of funding that is effectively spent on innovation is needed, so that the potential value of improvements can be calculated.

In addition, the definition of a ‘new’ product is also debatable. Are ‘new and improved’ products with minimal changes really ‘new’ – or should ‘new’ be reserved for products with significant change? In this case ‘new’ refers to products with a significant change that are substantially new.

A major shortcoming of the success curve is that it ignores the fact that ideas and concepts eliminated in the first stages may have become successful products if developers had more knowledge on the concept.

Recapitulating, the aim of good R&D is to reduce the area A under the success curve; this can be either done in increasing the number of successful generated products or in decreasing the number of ideas needed for generating products.

The Integrated Decision Model – developed in this paper – is a tool which helps reducing the area under the success curve by providing a better selection process. Thus, through the focus only on ideas or projects with a high probability of success the curve will be steeper with a lower beginning (see figure 2.1).

But how can the effort put in reducing the area under the success curve be measured? In other words, how can the success of the Integrated Decision Model be evaluated?

A good approach is provided by the R&D Effectiveness Index (McGrath 1994).

The R&D effectiveness index (EI) is an aggregate measure of the overall success of a company’s product development efforts; it quantifies the productivity of a company’s product development process and the success of its product strategy. The R&D effectiveness index compares the profit from new products to the investment in new product development, using the following formula:

(adapted from Stevens, 1997) (all % are stated as a percentage of current revenue)

As a simple interpretation, the index computes the ratio of increased profits from new products divided by the investment in product development.

When the index is above 1.0, the return from new products is running at a greater rate than the investment. An index value of less than 1.0 implies that a company may not be getting a sufficient return on its investment in R&D.

Computing new product profit presumes a definition of ‘new’ products. In the R&D effectiveness index, new products are defined as those that are still in the first half of their product life-cycle (McGrath, 1995). For example, a product that typically has a four-year life-cycle would be considered new for the first two years.

2.4 Essentials of successful product- and technology selection

R&D has become critical to the long-term survival of most companies but it is difficult to value because it is an uncertain and a complex undertaking. R&D generally aims to create technical capabilities to enable product features that are customer-driven. However, uncertainty makes it hard to envision the exact sequence of steps and costs that will translate an idea into a final technology or product. To compound the problem, technology often yields sets of technical and economic benefits. The revenues that an innovator generates from a technology depends on how end-users value these benefits along with a variety of competitiveness issues. The difficulty of valuing R&D is most apparent when potential uses of a technology are not obvious. Therefore, it is not clear if specific investigations could yield anything of value. As a result, it is hard to gauge if a technology is promising.

Because of a growing complexity and uncertainty the current set of valuation and decision-making tools just doesn’t work for the new business realities:

- strategic investments with lots of uncertainty and huge capital requirements,
- projects that must adapt to evolving conditions,
- complex asset structures through partnerships, licenses and joint ventures,
- and the relentless pressure from the financial markets for value-creating strategy.

Traditional methodologies aren’t able to capture managerial flexibility to adapt and revise later decisions in response to unexpected market developments. They also cannot capture the strategic value resulting from providing a technology or capturing the impact of project interdependencies and competitive interaction. In a constantly changing and uncertain world marketplace, managerial operating flexibility and strategic adaptability have become crucial to capitalizing successfully on favorable future investment opportunities and to limiting losses from adverse market developments.

The Integrated Decision Model, which will be derived in this paper, shall help to compensate the se shortcomings of traditional selection methodologies.

2.5 Approach within this project

The approach taken in this thesis is based on a systems engineering process, which describes the step-by-step procedure of problem analysis and solution generation.

The question of why something should be changed (problem analysis and formulation) has been discussed in section 2.1 Changed markets and business environment and section 2.2 Problems within Product- and Technology Development.

A definition of the goals, in other words the question of what should be changed and achieved, is performed in sections 2.3 Objectives of this project and 2.4 Essentials of successful product- and technology selection.

The remainder of this thesis will discuss how to achieve these goals; the processes, methods and tools will be described as well as an integrated decision model to achieve this goal.

3 The decision framework

3.1 Aim of the model

This model aims to improve upon the current R&D valuation practice by addressing difficulties caused by uncertainty, the sequential nature of R&D, and the complexity of valuing project benefits. Most R&D projects, especially the development part in a project, can be seen as a sequential succession of steps. Such a sequence could be represented by a concept, a prototype, a feasibility, a final product development and an implementation phase. This succession of stages is also very common in the development process of a new drug by the pharmaceutical industry (see also Nichols, 1994). In contrast to the development, the research part, especially the basic research, cannot be seen sequentially, since this is a process of many iterations without a given path to follow.

The framework developed in this paper focuses on

- Evaluation of superior solution concepts, respective to technologies or products
- Valuing opportunities to revise R&D projects in response to uncertain resolution
- Better matching of valuation frameworks to project uncertainties (risks)
- Integrating methods for valuing benefits with financial valuation frameworks.

These issues help to optimize the R&D selection process by reducing the area under the success curve (chapter 2.3) and increasing the effectiveness of R&D (® R&D effectiveness index, chapter 2.3).

3.2 Block 1 to 3 in short

The integrated decision model is based on three major units: the technical capability valuation (adapted from a masters thesis by Michael Frauens [2000] at MIT), the assessment of market value and finally the investment evaluation model. Each of these sub-models has interfaces with the other sub-models (see figure 3.1).

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Figure 3.1: The Integrated Decision Model

Each of these sub-models will be now briefly discussed in the following paragraph:

The major goal of the technology-model is to generate a list of superior solution concepts for a new problem or a shortcoming of an existing product or technology. On a very generic basis, the process asks three questions: Where are we?, Where do we want to go? and How are we going to get where we want to go?

Translated into a more technical language this leads to the following questions framing the basic frame of the technology model (Frauens, 2000):

- What is the technological capability,
- what does the technological capability need to be and
- what technologies will be pursued to enable this capability.

Having a set of solutions for a problem, they will be assessed in a market model, which consists basically of six design stages: The first two stages relate to what and how a respondent will be asked about; the second two calculate a prioritization and weighting of the new technology’s attributes with a calculation of the overall utility function of each solution concept; the last two relate to whether the set of data needs to be reviewed, and a conversion in a real monetary benefit of the technology.

The investment model transforms all data derived by the previous two models into the final benefit of the solution concepts. It contains of three major phases: Framing of the application, implementation of the valuation model, and reviewing the results. The model uses a hybrid real options decision tree analysis approach, to include the uncertainty a project has and the flexibility which makes a project more valuable.

The final result of the integrated decision framework will be to show the present value of the solution concept respective the new technology or product.

4 Technology Model

4.1 Introduction

This chapter covers the formulation of a technology strategy in order to anticipate and generate a list of superior solution concepts. The content is mainly adopted from a CIPD Thrust 1 (see 1.2) research of Frauens (2000) and the author recommends this reference as further literature on this topic.

This model follows a step by step solution process framed by the following questions.

4.1.1 Questions to be answered

When considering what technical direction a firm must pursue there are three questions that should be answered. These questions are:

- What is our technological capability relative to alternative technologies and relative to our competitors?
- What does our technological capability need to be?
- What technology or technologies are we going to pursue to enable this capability?

The following section discusses now each of these questions with it’s relevant process steps shown in figure 4.1 in turn.

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Figure 4.1: Technology model (adapted from Frauens, 2000)

4.2 What is Technological capability?

4.2.1 Determine the expected and actual level of invention associated with the technology.

This essentially involves developing an understanding of the “S-curve” (Betz, 1993):

S-curves are created by plotting the level of a performance parameter over time, which results in a S-shaped curve, such as in figure 4.2. This common historical pattern has been called the technology S-curve, and it has been used as a basis for extrapolative forecasts of technology change.

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Figure 4.2: The S-Curve

The S-curve implies increasing value to the user and can generally be broken down into three sections (Betz, 1993):

- The first of these sections is an early period of new invention. This is the point in time that discovery or “invention” occurs. The inventor is working to implement some new insight into a product framework. Once in a product implementation, the user can realize some benefit and the technology performance parameter increases. This is often a time of product uncertainty as a firm seeks to understand the long-term viability of the product or technology. The speed at which this new technology is adopted is often dependent on the state of the previous technology. (Altshuller, 1984)

- The middle section is the technology improvement period. During this time the corporation has typically started to realize financial gain from early product technology implementations. The firm works to improve the technology and differentiate itself from alternative suppliers of the product technology.
- The last of these sections is the mature technology period. It is at this point the physics and natural phenomena enabling the use of the technology impede further improvement. Money spent by the firm typically results in diminishing returns because the science being exploited is close to its theoretical limit.

In general, deciding which of these three sections a given technology is in, is enough resolution to make many subsequent strategy decisions. It is not necessary to be completely certain which point the firm is at within each of these regions.

The transition points between sections in the S-curve do take on added importance. It is at these points that technological paths forward require conscience shifts in the firms strategy. At the first transition, when technology begins to rapidly accelerate in its improvement of performance, the firm needs to move from an investigative frame of mind to vigorous commercialization. If it does not, it is likely to be left behind by firms that more rapidly implement improvements. At the second transition, when gains in the technology's performance begin to level out, the firm needs to actively search for new technological implementations. Continued investment in their core technology is becoming less efficient and they need to be aware of alternative technologies that may replace theirs.

Following the technique of fitting technology S-curves to technical data on the rate of progress of a technology is summarized (Betz, 1993):

1. Identify a key technical performance parameter.
2. Collect existing historical data on technical performance since the date of innovation of the technology, and plot them on a time graph.
3. Identify intrinsic factors in the underlying physical processes that will ultimately limit technical progress for the technology.
4. Estimate the magnitude of the natural limit on the performance parameter, and plot this asymptote on the graph of the historical data.
5. Estimate the time of two inflection points between the historical data and the asymptotic natural limit (first inflection from exponential to linear rate of progress and second inflection from linear to asymptotic region).
6. Expert forecasting of the exact times of inflection will likely be more unreliable than their anticipation of the research issues required to be addressed for inflection points to be reached.

Conventional techniques require the gathering of historical performance data and plotting it with time. Forecasting using the “level of invention” provides the expert with an alternative way to assess the current level of maturity on the S-curve, and build related insight on the technology's specifics.

These two techniques complement each other and provide the strategist with a way to check their suppositions regarding the development of the technology. Altshuller’s methodologies help characterize the inventive behavior to date and map the behavior to the performance expected. They do not inherently forecast the eventual level of performance, especially with technologies that are emerging or in the early growth phase. Therefore, the expert still needs to provide this function. The process for developing the related information associated with performance and ‘level of invention’[1] is enhanced by the existence of the World Wide Web. Commercial software packages that work with web accessible information helps track the citation history associated with developments of technology and speeds the analysis by the forecaster.

4.2.2 Firm’s performance relative to competition and technology limits

Often a given level of performance can be attained by competitors with similar or alternative technologies. For the firm to have a realistic understanding of their position, their performance level may need to be plotted on alternative S-curves, independent of the underlying technology (see Frauens, 2000). Additionally, it is necessary to develop the ultimate performance level associated with alternative technologies. The gap in ultimate performance between technologies indicates which technology is likely to be superior, assuming equal harmful effects. In cases where harmful effects are not equal, the relevance of these harmful effects to the business and society must be considered. Additionally, gaps between achieved performance level and ultimate performance level suggest the strategies that respective firms should be operating under.

This section was intended to address the question, ”What is our technological capability relative to alternative technologies and relative to our competitors?” Use of the S-curve helps to assess the firm’s position relative to alternative technologies and their competitor’s technologies. Gaps in the firm’s capabilities inform and educate the larger corporate strategy as suggested in table 4.1. The process is represented by its inputs and outputs as shown below.

Table 4.1: Inputs and Outputs to Determine Technological Capability

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4.3 What does Technological Capability need to be?

The basis for this section is to provide the innovator with an understanding of their goal. The goal is presented in two forms. The first is identification of problems. The second is identification of opportunities. The construct being proposed presents these as two different possible goals with prioritization on problems versus opportunities driven by the previous technology and competitor gap assessment.

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Figure 4.3:Prioritization and selection process for defining technological capability

4.3.1 Problem Identification and Formulation

Problems are typically solved when innovative solutions are introduced at the subsystem level as incremental improvement. This is because a firm typically has complementary knowledge or organizational assets causing the products have a disincentive to make drastic changes. Radical innovations typically devalue these assets and therefore incremental improvements are more efficient.

Proper problem identification and statement is an important part of improving any system. Problem statements anchor the innovator's thoughts and can make it more difficult to envision solution states. Much of TRIZ, not discussed in this paper, helps the problem solver to properly envision the problem. Two insights to problem statement formulation are discussed below.

- Often designers resign themselves to trading off one useful performance characteristic for another. Fundamental to TRIZ is the identification and resolution of conflicts. TRIZ encourages identification of these areas of conflicts and encourages inventive practices to resolve these conflicts. As a result, tradeoffs are minimized, genuinely treating the problem as an opportunity for advantage.
- Additionally, especially with complex systems, it is increasingly difficult to identify the root cause of problems. Careful description of the system, including tracing of the flow of energy associated with harmful effects can help the problem solver identify the correct problem to solve. Too often, engineers are tasked with solving what is effectively a symptom of the root cause and not the root cause itself. Furthermore, firms concentrate on this approach, especially as technology matures. Solutions purely of a symptom curing focus do not result in increased ideality systems.

Therefore, careful identification of problems is critical to identifying inventive solutions.

4.3.2 Identify and Evaluate Future Opportunities

Opportunities, as discussed subsequently, come largely from consideration of the ‘Laws of Evolution’[2] and Marketing input. Introduction of technology to satisfy these opportunities may require more extensive repositioning of assets to execute design, development and manufacture of the product. Therefore, these events are less frequent for the mature company.

An opportunity map of evolving technology constructs can be envisioned using the Laws and Lines of evolution. They provide a vehicle to anticipate future product states because they are based on observations of societies behavior. Therefore, while some may view this simply as a mechanism to push technology, it is not. Instead, use of the Laws of Evolution represents a vehicle for anticipating future states.

4.3.3 Prioritization and Sequencing of Problems and Opportunities

At this point, the organization has a comprehensive list of problems, opportunities, and a solid understanding of their position relative to their competitors. It is likely that the problem faced by the organization is that they will have too many possible paths to pursue. Therefore, to determine which paths to develop and pursue a process must be put in place to rank the problems and opportunities logically, and decide on the most promising technologies to pursue. The use of scenarios may have eliminated some items from the list but additional measures can be taken to focus the selection efforts.

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Figure 4.4: Selecting process for problems and opportunities (Frauens, 2000)

The process steps detailed above need the following inputs and outputs to perform their task (see also Frauens, 2000):

Table 4.2: Inputs and Outputs to Determine Capability Need

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4.4 What Technologies will be pursued to enable this capability?

4.4.1 Creation of list of superior solution concepts and technologies

After this initial sorting and sequencing process, more specific understanding of technological alternatives is likely to be necessary. The process detailed to date has had a low level of effort expended on developing a specific understanding on how to solve the problems identified as conflicts or how to implement the opportunities. The innovator, having chosen which problems to solve and opportunities to exploit, must now determine what are the possible ways to solve them. Initially, this will create an expanded set of solutions that will need to be sorted and prioritized so that a final selection can be made.

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Figure 4.5: Creation process of solution concepts (adapted from Frauens, 2000)

4.4.2 Determination of ideality and risk associated with concept technologies

The proposed process is likely to generate alternative technological implementation states. Final selection is often based on a company’s familiarity with a given technology. However, with the advent of the internet and its implied access to knowledge and the global nature of business providing a variety of partner possibilities, this should not be a principle criterion.

4.4.3 Narrow the candidate list

At this point opportunities have been converted to problems and technical solutions for all problems have been identified. An initial assessment of different technology's promise has been made with an initial determination of the technology's ideality and associated risk. Now the best solutions, as mapped to the problems chosen, need to be pursued. Multiple solutions to the same problem should be evaluated and placed in a framework , the Integrated Decision Model, which will be introduced in the following chapters.

5 Market Model

5.1 Introduction

The next section is examined with calculating the benefit of the solution concepts derived in the previous chapter (see figure 5.1). For this purpose, several methodologies will be analyzed and later incorporated in a decision model (Fishburn, 1970; HBS, 1990; Mangin, 1993; Neely, 1998).

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Figure 5.1: Aim of the Market Model

5.2 Multi-attribute utility analysis

Multi-attribute utility analysis (MAUA) is a promising method for valuing R&D with complex benefits because it can lead to a dollar based estimate of value. MAUA provides a quantitative basis for valuing alternatives. It develops a scale, referred to as utility, that cradles each relevant attribute according to its importance to a set of users. Overall, a single metric of value results, where higher levels of utility are preferred to lower levels.

MAUA recognizes that individuals value sets of product attributes, and they make trade-offs between these attributes when deciding on alternatives. MAUA provides a basis for estimating how potential end-users value technical and economic product features. As a result, it can indicate which product is likely to be preferred by a given set of users. More importantly, it can rate how much specific improvements would improve the competitive position of a less favorable choice.

The basic output of utility analysis is an expression of user preferences. Preferences for price and performance as utility curves, which indicate combinations of these attributes that specific users value equally, are shown in figure 5.2. If the utility curve ‘moderate utility’ accurately reflects a user’s preferences, the user should be indifferent between any products that offer a combination of price and performance that lies along it.

Although the generalizations that lower price and higher performance are preferable, utility additionally provides a basis for estimating how much more valuable one alternative is than another. As a result, it provides a basis for identifying a preferred alternative in cases where clear dominance is not obvious.

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Figure 5.2: Utility curve of user attributes

Figure 5.2 shows how utility identifies a preferred product (A,B,C or D), for three possible sets of user preferences: price-sensitive, pay for performance and compromises. The steepest utility curve represents a price sensitive user because large performance gains are required to offset small price increases. Conversely, users who pay for performance tolerate large price increases in return for small performance gains.

The preferred product for each case lies to the left of the utility curve while all others lie on it or to the right. Thus, product A is the price sensitive choice, product C is the performance choice and product D is the best compromise. Product B is never preferred. The value (utility) of a product increases for combinations of price and performance that approach the upper left corner of the figure, since users should prefer low price and high performance. For this set of preferences, product D has the highest utility.

If an R&D project could improve the performance and cost (and thereby the price) of B to levels shown as B’ in figure 5.3, product B’ would be more valuable than the current leader (product D), even though B’ has lower performance. Utility can estimate the difference in value these products have. This is the additional amount an user should be willing to pay for B’ such that it has the same utility (or value) as product D.

This willingness to pay (WTP) shows the value of the R&D project benefits to the user (Fishburn, 1970). It represents the value that the innovator hopes to gain.


[1] For further literature, the author recommends Altshuller, 1984

[2] For further literature the author recommends Altshuller, 1984 and Frauens, 2000


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Title: An integrated decision model for strategic evaluation of the viability of new technologies