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THE APPROPRIATENESS OF THE HYPOTHESES 42

2.1 ADAPTING TO CHANGE – THE NAME OF THE GAME..................................................... 442.1.1 The Process of Change: Two Opposing Views ..................................................442.1.2 Evolution: A Process of Adapting to Change........ ........................... .................. 452.1.3 Variation and Selection: The Unit Events in Evolution.................................. ..... 462.1.4 Product and Process ‘equals’ Phenotype and Genotype........... ........................... 472.1.5 Stabilizing Products and Varying Processes: Industrial Practices ....................... 472.1.6 The Evolutionary Approach in the Realm of Engineering Design ....................... 48

2.2 THE EVOLUTIONARY APPROACH VERSUS THE RULING PARADIGM.......................... 49

2.2.1 Typological Thinking: Foundational to the Ruling Paradigm.............................. 492.2.2 Developing Products According to the Ruling Paradigm........................... ......... 512.2.3 Population Thinking: Foundational to the Evolutionary Approach...................... 522.2.4 Developing Products According to the Evolutionary Approach .......................... 54

2.3 CURRENT TRENDS – AN ARGUMENT FOR THE EVOLUTIONARY APPROACH ........... 55

2.3.1 Engineering Design: Discord with the Ruling Paradigm....................... .............. 552.3.2 The Evolutionary Approach: Its Standing in Engineering Design ....................... 562.3.3 The Evolutionary Approach: Its Standing in Social Sciences.............................. 582.3.4 The Evolutionary Approach: Its Standing in Economics ........................... ......... 592.3.5 The Evolutionary Approach: A Summary..........................................................60

2.4 “STABILIZING THE PRODUCT” – AN ARGUMENT FOR NUMERICALTAXONOMY........................................................................................................................... 62

2.4.1 Design a HPP Based on Commonalities: Recapping Chapter I ........................... 622.4.2 Group Technology (GT): A Formal Approach to Standardization.... .................. 622.4.3 Numerical Taxonomy: An Integrator................................................................. 65

2.5 “CHANGING THE PROCESS” – AN ARGUMENT FOR ADDRESSING LEARNING .......... 69

2.5.1 Improve the Process Through Change: A Recap................................................692.5.2 Technology Diffusion: Modeling the Effect of Learning................... .................. 702.1.3 From Mutation to Innovation: Mapping of Variation Mechanisms ..................... 71

2.6 SYNTHESIZING PRODUCT AND PROCESS – AN ARGUMENT FOR THECOMPROMISE DSP.... ............. ............. ............. .............. ............. ............. ............. ............. ... 74

2.7 A LOOK BACK AND A LOOK AHEAD................................................................................. 75


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LIST OF FIGURES

Figure 2-1 The Darwinian Logic in a Population of Self-Replicators (Berra 1990) ...................... 46

Figure 2-2 (a) Numerical Taxonomy in Biological Classification (b) Numerical Taxonomyused to Define HPPs .......................... ........................... ........................... .............. 67

LIST OF TABLES

Table 2-1 Introduction of Genetic Variations: A Mapping to Engineering Design............... ......... 72


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HYPOTHESES

NumTax TechDiff c-DSP HPPRM Demonstrating applicability of

• Evolutionary approach

• Numerical Taxonomy

• Technology Diffusion

• c-DSPX X X x

xxx xxx

The principal objective in this chapter is to substantiate the choice of hypotheses that we

postulate in Section 1.5.1. The approach we follow is first to substantiate the fundamental hypothesis,

then to substantiate the derived hypotheses.

§ In order to support the Fundamental Hypothesis we demonstrate that an “Evolutionary Approach”increases adaptability, and that it is gaining ground on the mechanistic approach (shows generality).

§ In order to substantiate Hypothesis 1, we demonstrate the applicability of “Numerical Taxonomy” toidentify the potential for standardization in an existing product portfolio.

§ In order to substantiate Hypothesis 2, we demonstrate how “Technology Diffusion” captures the effectof reduced performance while learning during organizational implementation of new processes.

§ In order to substantiate Hypothesis 3, we demonstrate that the compromise DSP is aimed at flexibleand robust solutions.

Based on this chapter we assert that the postulated hypotheses are appropriate in context of this

research, hence, testing these hypotheses represents validating the HPPRM.

“There never was in the w or ld two opin ions al ike, no m ore than two h airs or two gra ins ; the most universa l qual i ty is d ivers ity”

- Michel de Montaigne


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2.1 ADAPTING TO CHANGE –

THE NAME OF THE GAME

The objective in this section is to add to the substantiation of the fundamental hypothesis by

tying product development to the evolution of biological organisms. We do this by presenting two

opposing views in Section 2.1.1 of explaining how manmade system changes over time. Based on our

observations presented in Section 1.2 the evolutionary model seems to fit better, and in Section 2.1.2 we

present some key evolutionary principles. And finally, in Section 2.1.3 we tie these principles to product

development in general, and product platform design in particular.

2.1.1 The Process of Change: Two Opposing Views

In Section 1.1.3 we link reuse to “affordable adaptation” of existing products; in Section 1.2.1

we link “affordable adaptation” to “improving product adaptability”; and in Section 1.2.2 we link

“improved product adaptability” to economic success. It all boils down to (1) that the market changes

constantly and unpredictably, and (2) that success (i.e., survival) is linked to how well a product is

adapted to these changes. Hence, product development is very much linked to the processes that change

the market.

If we define the market as a system, and the state of this system in terms of the values taken at

a particular moment by its state variables, then it follows that the paths of the state variables through time

describe changes in the state of the system. To say how the system changes, we must explain how the

sequence of states arises, which ultimately is an explanation of the process of change. In (Hall 1994) two

opposing views of explaining this process are presented. One, taken from physics, views changes in the

state of systems as being “determined by externally given natural laws”; a mechanistic view. The other,

taken from the biological sciences, views system changes as being “generated by forces working within the

evolutionary view.

A Mechanistic View on the Process of Change

From a mechanistic perspective, complex reality is simplified by assuming that basic units of analysis are either identical, or that some description of a single unit can be adopted which captures the

essence of all. In this view, variation is merely a nuisance, attributed to the “imperfect manifestation of the

underlying essences, a distraction from the typical in which clues to the laws of the universe are to be

found” (Mayr 1982). The laws referred to are natural laws, which are viewed as timeless in the sense that

they hold in all places at all times. Hence, uncovering these laws would facilitate prediction of systems,


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which would make them deterministic. Developing products according to this view implies that we seek

equilibrium solutions that are optimal regardless of time or place .

An Evolutionary View on the Process of Change

From an evolutionary perspective, variation is not seen as a nuisance, but the central means of

explaining how system changes. Rather than seeking general principles of unknown origin about how a

system functions, evolutionists delve into how observed states come into being and give rise to other

states. These are questions about creative forces rather than pure functioning. In this endeavor, the

diversity of experience is viewed as central to understanding the process of change. The notion of diversity

is obviously relevant to engineering design once it is recognized that no two firms in an industry deliver

identical products in term of operational performance, cost or quality. Developing products according to

this view implies that we seek robust solutions for a changing world .

The Opposing Views in Context of Our Philosophical Anchoring

In Section 1.6 we position ourselves in the relativistic school of epistemology, which clearly

opposes the mechanistic view with its constancy and universal laws. In this context, ‘optimal’ solutions

are based on the assumption that the criteria for optimality are possible to derive and articulate. This

again, is based on the assumption that our knowledge is not only correct but complete. Adhering to the

relativistic school of epistemology, we do not believe that these assumptions are valid. We believe that the

world is constantly changing, hence, we believe in products that are changeable in an affordably way, or

are able to function for a range of conditions. In contrast to the mechanistic view, the evolutionary view

on the ‘process of change’ does not consider solutions as optimal or non-optimal. The direction of change

is based on the solutions that are present, and which of them are perceived as better than others in context

of the environment. We think the latter describe the observed reality in product development better, hence,

we adhere to the evolutionary view and proceed to further tie it to product development.

2.1.2 Evolution: A Process of Adapting to Change

As we mentioned in Section 1.4.2 the leading theory when it comes to explaining evolution aswe observe it, is neo-Darwinism. Neo-Darwinism is a synthesis of Darwin’s idea of gradual evolutionary

changes (Darwin 1859) and Mendel’s discovery of genetic stability (Mendel 1865). The gradual

evolutionary change mechanism – also known as Darwinian Logic, see Figure 2-1 – is based on the

assumption that any organism’s primary goal is to reproduce as many times as possible. Thus, populations

tend to increase in an environment with finite supply of resources, creating competition for the resources.


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All individuals have different traits and those traits favored in competition will come to dominate the

population – survival of the fittest through natural selection. Since traits appear as a product of genes and

environment, and some traits are heritable, a population’s gene-pool will reflect the dominating traits.

7. Permanent change ingenetic composition of

population: evolution

7. Permanent change ingenetic composition of

population: evolution

1. Organisms tend toincrease in numbersexponentially

1. Organisms tend toincrease in numbersexponentially

2. There is a finitesupply of resources

2. There is a finitesupply of resources

3. There is competitionfor resources: the struggle for exi stence

3. There is competitionfor resources: the struggle for existence

4. All characters vary,including those that

affect competition

4. All characters vary,including those that

affect competition

5. Favored variantsincrease in frequency:natur al selection

5. Favored variantsincrease in frequency:natur al selection

6. Some part of variation is heritable

6. Some part of variation is heritable

Figure 2-1

The Darwinian Logic in a Population of Self-Replicators (Berra 1990)

2.1.3 Variation and Selection: The Unit Events in Evolution

At the finest scale, evolution is a coupling of an episode of genetic variation with an episode of phenetic selection during a single generation, hence, it is referred to as the unit event in evolution (Bell

1997). Based on this we infer that variation is the key to adaptation, and moreover, the amount of

variation determines the speed with which a population adapts. Variation is very prerequisite for any

change, development or progress, not only in biology, but in any domain, at any time. It seems like the

closest we come a universal principle. Selection, on the other hand, is not the key to adaptation, but the

‘turner of the key’. By selecting upon favorable variants, selection effectuates the adaptation of the

population, and hence, provides direction to the change.

In Nature, variation appears at a genetic level, whereas selection appears at a phenetic level. In

other words, Nature adapts by constantly changing the genotype and stabilizing the phenotype around

the individuals most fit for a given condition. We believe that where the variation and selection appear is

significant, hence, we propose a mapping of genotype and phenotype into the realm of engineering.


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2.1.4 Product and Process ‘equals’ Phenotype and Genotype

genotype, which remains unchanged over time, gives a description of the

complete set of alleles inherited. However, the term genotype is normally used on a subset of alleles for

classification. Hence, the definition of genotype used in this dissertation is “the class of which an

organism is a member, based upon the postulated state of its alleles” (Lewontin 1992).

The genotype provides the potential and will together with the environment determine the

actual state of the characters, referred to as the phenotype. As for genotype, the term phenotype is

normally used on a subset of characters for classification. Hence, the definition used in this dissertation is

“the class of which an organism is a member, based upon the observable physical qualities of the

organism including its morphology, physiology, and behavior at all levels of description” (Lewontin

1992).Taking this into the engineering domain, we suggest that genotype corresponds to the class of

which a product is a member, based upon the technology and processes that are actually used in realizing

it . Similarly, we suggest that phenotype corresponds to the class of which a product is a member, based

upon the observable characters of the product including its form, function, processes, material, and

performance at all levels of description. Included in performance is total cost and time to market.

In short, we define the processes (everything included) needed to realize a product as

analogous to genotype, and the product itself as analogous to phenotype. Following up on the analogy,

stabilizing the phenotype implies stabilizing the product, and changing the genotype implies changing

the processes.

2.1.5 Stabilizing Products and Varying Processes: Industrial Practices

Based on the previous we suggest that adaptation to changes, i.e., to become more in tune with

the market requirements, is best done by stabilizing the product and changing the processes. In other

words, the products that meet the requirements and provides the most leverage to existing designs are

favored, and costs / quality / time to market are addressed by continuously improving the realization

processes. How well does this jibe with the industrial reality?. To answer this question we look at two

industrial successes.

First we look to Volvo. In 1966 the 140 series was introduced and was refined and developed

within the same envelope for 8 years before the successor – the 240 series – was introduced in 1974. The

240 series was honed and refined for an impressive 18 years before discontinued in 1992. The 760 series,

introduced in 1982, was refined and honed for ‘only’ 9 years before the 850 series was introduced in 1991.

In all these cases, the outer appearance for each series virtually didn’t change; the changes were small


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from year to year, allowing all problems to be addressed in turn. This resulted in an exceptional high

quality for the later models within each series. In contrast to the stable products, Volvo has changed

dramatically in the way they realize their cars, and has been forerunners in many areas concerningorganization of the work-place; they introduced self organized working cells and a very flat organizational

hierarchy. Toyota has a similar history with their Corolla model, which they have honed and refined for

over thirty years. Its quality and value for money have made it the best selling car – world wide – for a

number of years.

Another example is Boeing. As we already have mentioned in Section 1.3.2 Boeing has

virtually not changed the 747 since they introduced it in 1969. Nor have they changed the other families

that are currently delivered. (They do develop new families, though.) And similarly to Volvo and Toyota,

they invest tremendous amount of resources into improving their processes. From their web-site

www.boeing.com we get to know that “beginning in early 1994, The Boeing Company initiated a process

improvement activity called Define and Control Airplane Configuration / Manufacturing Resource

Management (DCAC/MRM). This "breakthrough" initiative will improve the processes the company uses

to produce airplanes and is a significant opportunity to further reduce costs, cycle time and defects.

DCAC/MRM will substantially enhance the company's ability to deliver more value to its customers”.

Having linked Boeing’s product development strategy to the design of product platforms,

brings us to believe that designing product platforms per se is an evolutionary act in its own right; it’s a

specific instantiation of stabilizing the product. However, as we have pointed out, stabilizing the product

should be done in parallel with changing, i.e., continuously improving, the realization processes. Wefurther believe that this strategy improves adaptability to change in a very fundamental way; adapting a

product through adapting its realization process. From this perspective, the offshore way of saving time

and money by introducing new products – realized in the same old way – is clearly ‘wrong’. And the track

record of virtually all offshore developments may give support to this claim. This is elaborated further in

Section 2.2.2. Having to some degree demonstrated that the evolutionary approach show some merits in

industry, we proceed to place the evolutionary approach in an engineering design context.

2.1.6 The Evolutionary Approach in the Realm of Engineering Design

Biological evolution, or neo-Darwinism, has come to be a general theory that binds the

biological sciences together. Theodosius Dobzhansky is famous for having said that “nothing in biology

can be understood except in the light of evolution.” A general, unifying theory has meant a lot to the

biological sciences; just imagine the importance of being able to tie research at a micro-biological level to

research at a population level. In many other sciences this importance is understood, and they search for a


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unifying, general theory similar to evolution. In physics they seek to tie research at a quantum-mechanics

level to research at a cosmic level (Hawking 1988); in economics they seek to tie research at a house-hold

level to research at a global economy level (Hall 1994); in social sciences they seek to tie research at anindividual level to research at a society level (Sober 1995). In contrast, engineering designers are still

discussing whether a general, unifying theory is of any value at all (Eder 1987; Dixon 1988; Staley and

Vora 1990; Warfield 1990; Hoover and Jones 1991; Cross 1992; Sargent and Road 1994; Hubka and Eder

1996). We believe it is, and anchored in the discussion provided in the remainder of this Chapter we

assert that an evolutionary approach is a good starting point for a ‘general theory of engineering design’.

2.2 THE EVOLUTIONARY APPROACH

VERSUS THE RULING PARADIGM

The objective in this section is to add further to the substantiation of the fundamental

hypothesis by demonstrating that the evolutionary approach provides a better basis for adapting to change

than the ruling paradigm. We do this by presenting, in Section 2.2.1, the foundation for the ruling

paradigm. In Section 2.2.2 we present the ramifications of developing products based on the ruling

paradigm. In Section 2.2.3 we present the foundation for the evolutionary approach. And finally, in

Section 2.2.4 we present the ramifications for developing products based on an evolutionary approach.

2.2.1 Typological Thinking: Foundational to the Ruling Paradigm

The existing approach to engineering design is anchored in the ruling paradigm of science,

namely, reductionism / formalism / foundationalism, see Section 1.6.1. This paradigm is based on

Typological Thinking where the “types are real and any variation is an illusion” (Mayr 1995). Typological

Thinking is anchored in the pre-Socratic ‘process versus object’ discussion. Heraclitus represents a process

view by claiming that “there is nothing but change” whereas Parmenides represents an object view by

claiming that “change is an illusion”. Attempts to bridge these radical views were made by Democritus

who claimed that “unchangeable particles [atoms] make up an ever changing world”, and Plato who

eidos [types] from the ideal and real world underlie the observed variability inthe illusionary world of our senses” (Honderich 1995; Mayr 1995). Over the course of time, however, the

‘basic building blocks’ of Democritus and the ‘ideal types’ of Plato prevailed. These thoughts were taken

further by Aristotle, Descartes, Sir Newton, and Kant, just to name a few, and can be summarized in three

dogmas; the Dogma of Objects, the Dogma of Categorization, and the Dogma of Scientific Knowledge

(Emblemsvåg 1999).


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The Dogma of Objects emphasizes the importance of objects over processes. This dogma is

primarily anchored in the static world view advocated by both Democritus and Plato; what is real and

intrinsic does not change (types and atoms), whereas the observable changes are due to imperfect world processes (illusionary shadows and collision of atoms)(Honderich 1995). The focus on objects derives a

need for organization, which leads to the next dogma.

The Dogma of Categorization advocates that the whole is approximately the sum of a reduced

set of parts that are decoupled. This dogma is primarily anchored in the Platonic world view; a limited

number of fixed, distinct, and unchangeable types underlie the observed variability (Mayr 1995). Plato

may be the father of categorization but Aristotle, who himself rejected the Platonic types, has probably

contributed the most to sustaining and manifesting the dogma of categorization. In his scientific project,

the scale of being , groups of physical objects sharing the same typical features are placed relative to each

other based on their ‘value’. Interesting to note is that species was introduced to divide the organic world,

a division used even today (Honderich 1995). Even if Aristotle himself viewed the world as a continuum

(the distinctions between groups are blurred (Sober 1995)), the legacy to science is that the world consists

of ‘discontinuous, distinct, and unchanging types’ . This concept combined with the concept of ‘basic

building blocks’ has resulted in the notion that a whole can be treated as a collection of parts. The logical

consequence of this view is that the properties of the whole are viewed as a sum of the properties of the

parts. Moreover, the properties of some parts are more influential on the whole than others. Hence, in

order to gain knowledge about a whole, it is partitioned into its most important parts where each part in

the reduced model is analyzed separately. Then a synthesis of the whole is based on putting together pieces of knowledge from the reduced model. This is the basis for reductionism, an approach that has been

very successful, especially within biology and physics in the western world (Honderich 1995). When

combining the dogma of objects with the dogma of categorization, the world becomes a set of fixed,

distinct objects that are more or less decoupled. This world view rises the question: how can we acquire

knowledge about the objects and how can we organize them?, which leads to the next dogma.

The Dogma of Scientific Knowledge asserts knowledge to be measurable evidence supporting

the ‘truths’ of the ruling paradigm. Worth noting is the public notion that “this time the scientists really

got it – the truths we have now are final”. This dogma is also primarily anchored in the Platonic world

view; the type is the only thing that is real. Hence, the only philosophical and scientific inquiry considered

valid, is inquiry directed towards unveiling reality. The inquiry itself, which is the process of acquiring

knowledge, is based on logic and mathematics stemming from ancient Greek tradition and refined by

Galileo. He introduced the ‘scientific method’ stating that research should be done in one of two ways. (1)

Observe nature and describe the studied phenomena using logic and mathematics; (2) derive some

properties about some phenomena using logic and mathematics, then observe nature to see if the derived


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results fit the empirical results. This forms the basis for the foundationalist / reductionist / formalist school

of epistemology in the seventeenth century, where Descartes was the most prominent figure Having

elaborated on each of the dogmas of Typological Thinking we take a look at the ramifications of using thisapproach in engineering design.

2.2.2 Developing Products According to the Ruling Paradigm

We assert that there is a constant gap between what the market asks for and what the industry

can offer, i.e., there is always room for improvements. This implies that the ultimate objective of

developing products is to close this gap. i.e., to adapt to changes when they occur, as well as constantly

improving cost and quality. What are the ramifications of adapting products according to Typological

Thinking?

First we look at the ramifications of applying the dogma of objects. Viewing the world as fixed

objects has resulted in a search for the optimal solution . This implies searching for some static

equilibrium, an approach commonly seen in engineering design. This approach is supported by many of

our most prominent and fundamental scientific theories, such as the first and second laws of

thermodynamics. According to the second law of thermodynamics, any system progresses from high

towards low order to find its thermodynamic equilibrium. However, systems operating far away from their

thermodynamic equilibrium, such as life, cannot be described adequately by the laws of thermodynamics;

evolution show that life goes from low towards higher complexity. This apparent conflict may be seen as

the ultimate manifestation of the product versus process discussion (Capra 1996). As an effort to reduce

the adversary impacts of optimization, the optimality criteria are constantly being broadened; designing

for X, designing for manufacturing, Integrated Product and Processes Design, etc. Nevertheless, the

search for the optimal product and a fixed world view is still prevalent, resulting in processes still being

viewed through the product.

Secondly, we look at the ramifications of applying the dogma of categorization. Viewing the

whole as approximately the sum of a reduced set of decoupled parts, has resulted in a wide spread practice

of sub-optimization . When designers are faced with a complex problem, it is divided into manageable

sub-problems. This division has traditionally been along discipline lines to centralize discipline

knowledge. The centralization of discipline knowledge, however, has created an introvert focus; each

discipline seeks its optimal way to meet its specification. This is traditionally done by freezing inter-

disciplinary input to each discipline model. This results in a model only capable of optimizing variables

for a particular discipline for one fixed state of all other disciplines. Hence, reduced discipline-based

models can only give qualitative information (at the best ) about effects on the total system performance.


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Thirdly, we look at the ramifications of applying the dogma of scientific knowledge. Viewing

scientific knowledge as measurable evidence supporting the ‘truths’ of the ruling paradigm, has lead to

the notion that what we ‘ know’ is all there is . Besides providing the two other dogmas with validity andcredibility (amplifying their effects), this notion has resulted in an extreme confidence in technology and

its ability to solve all problems. This notion grows stronger as science progresses and we are able to

acquire more and more rational knowledge. As our notion of knowing all there is to know grows stronger,

our caution grows weaker. This is seen frequently as new products are released and then recalled because

of unanticipated effects. Classical in this context is the story about the ‘scientifically proven fact’ that

Formula is better for infants than breast milk. As researchers are looking into this matter they reveal an

incredible complexity regarding breast milk composition and infant needs. Similarly, the notion that all

we know is all there is has also created problems in the offshore oil and gas industry. Novel concepts are

introduced all the time to address particularly costly and / or time consuming activities. However, by being

novel new problems are introduced that cannot be fully anticipated in the early design stages. Frequently,

the magnitude of the new problems is underestimated grossly, and we suspect this to be mainly due to the

dogma of scientific knowledge.

By adapting and improving products according to Typological Thinking , we design for a fixed

world with limited knowledge ending up with products that are sub-optimized, yet we claim that we have

the optimal solution. The overall ramifications are perhaps best seen in the growing environmental

problems we are facing. This is elaborated in (Emblemsvåg 1999) where the solution is seen as designing

by deliberately violating the three dogmas. In the following we introduce the Evolutionary Approach anddemonstrate how it violates all three dogmas of Typological Thinking .

2.2.3 Population Thinking: Foundational to the Evolutionary Approach

The Evolutionary Approach is based on Population Thinking where “variations are real and

types are illusions” (Mayr 1995). Population Thinking is anchored in neo-Darwinism and based on the

assumption that all organic phenomena are composed of unique features and can be described collectively

only in statistical terms. In Population Thinking the key to adaptation is (1) having a population, and (2)

having variation in the population. Hence, when external conditions changes, there is always somebody

that is better adapted, thus showing the preferred direction. From this we see that without variation, there

would be no adaptation. Hence, in Population Thinking variation is the key to progress. Having a

population of different variants implies competition for the limited resources. In this competition some

variants do better than others, which points to a very important aspect of Population Thinking ; relative

performance. This means that evolution operates on a relative rather than absolute scale. Hence, an object


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is ‘measured’ on a scale provided by its context, which implies that objects cannot readily be decoupled;

they all seem to be depending on each other.

From this it can be inferred that the relativistic aspect of Population Thinking supports a searchfor satisficing2-1 solutions rather than optimum solutions, which undermines the dogma of objects.

Further, it can be inferred that the relativistic aspect supports a holistic rather than reductionistic view,

which undermines the dogma of categorization. Finally, it can be inferred that the relativistic aspect

supports relative rather than absolute truths, which undermines the dogma of knowledge.

The competition between variants in a population is the basis for natural selection. In biology

natural selection refers to the rate of deaths or sterility that is directly linked to an organism’s genotype.

Note that this may be only a fraction of the total mortality in a population. Hence, the individuals best

adapted to their environment have the highest probability of survival and therefore the highest probability

of leaving most offspring, which leaves us with a selection that is probabilistic. The industrial analogy to

probabilistic selection is that decisions are based on limited information, hence, based on an object’s

perceived performance rather than its actual performance. From this it can be inferred that the

probabilistic aspect of Population Thinking supports cautious decision making and inclusion of more than

just rational knowledge (i.e., recognizing that the information is limited), which undermines the dogma of

knowledge.

When faced with changes in living conditions, some individuals do better in the competition

due to some unique combination of traits. This winning combination is preserved in the population

through heritage; the offspring is a slight variation of its parents. Hence, a population is very much a product of its history, which implies that nature works with what it got and is not so much concerned

about what it could have had. From this it can be inferred that the historical aspect of Population Thinking

supports our observation about path-dependence introduced in Section 1.2.2. It further enhances the

relativistic aspect by focusing on ‘what is present’ and not on ‘what could have been’, and hence,

undermines optimized solutions and thus the dogma of objects and scientific knowledge.

In addition to preserving the winning combinations, they are also ‘shared’ with the rest of the

population. The individuals having the winning combination (i.e., the fittest) leaves more offspring; these

offspring will have variants of the winning combination; they will fare better leaving even more offspring,

and so on. Over generations the winning combination disseminates through the population and we say

that evolution has occurred. Hence, evolution is a relative process of adapting to change and it rests on

three principles, population, heredity and selection. These three principles are characterized by variation,

path-dependence, and probabilistics, which also characterize product development as found in Section


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1.2.2. From this it can be inferred that the evolutionary approach supports Heraclitus’ claim that “there is

nothing but change”, hence, supporting the notion that we have to shift our focus from the object to the

processes shaping the objects in order to increase adaptability. This certainly undermines the veryfoundation of Typological Thinking ; the world is not constituted of fixed objects – it is a continuous

process of change.

2.2.4 Developing Products According to the Evolutionary Approach

Adapting and improving products according to Population Thinking implies (1) designing for

a changing world; (2) using all available knowledge; (3) showing caution by recognizing our limited

ability to understand the wholeness; (4) utilizing available assets to reduce design changes and improve

processes; (5) aiming at competitive solutions rather than optimal. We firmly believe that this approach

violate all three dogmas of Typological Thinking making it a suitable approach for realizing Hierarchical

Product Platforms. This belief is further substantiated by looking at some current trends in engineering

design.

2-1 Satisfacing solutions are solutions that are ‘good enough’ but not necessarily the ‘best’ (Simon 1996).


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2.3 CURRENT TRENDS –

AN ARGUMENT FOR THE EVOLUTIONARY APPROACH

The objective in this section is to substantiate our claim that the evolutionary approach is

suitable for engineering design in general, and for realizing Hierarchical Product Platforms in particular.

We do this by presenting in Section 2.3.1 some opposing views to the ruling paradigm that have recently

surfaced in engineering design. This is followed by a presentation in of some evolutionary concepts used

in engineering (Section 2.3.2); in the social sciences (Section 2.3.3); and in economic (Section 2.3.4).

Finally, in Section 2.3.5 we identify how this work is an extension of what has been done.

2.3.1 Engineering Design: Discord with the Ruling Paradigm

There are efforts in engineering design to move from a static to a dynamic world view. One

such effort is the introduction of Open Engineering Systems (OES). OES are defined as “ systems of

industrial products, services, and or processes that are readily adaptable to changes in their environment

and enable producers to remain competitive in a global marketplace through continuous improvement

and indefinite growth of an existing base” (Simpson, Lautenschlager et al. 1997). This definition

explicitly acknowledges that the world is changing and that products offered to this changing world has to

be adaptable in order to stay competitive. One particular strategy to achieve adaptability is to move from

optimal solutions to satisficing solutions – i.e., from solutions for a point to solutions for a range. This

strategy was initially introduced to reduce the impact of inaccurate information in the early stages of

design (Mistree, F. et al. 1993), however, it shows good merit for realizing Open Engineering Systems as

well (Simpson, Lautenschlager et al. 1997).

There are also concerns regarding the reductionist mode we use to handle our increasingly

complex world. Reductionism rests on the major assumption that there are no intrinsic properties of the

whole that cannot be found in the parts, an assumption supported by the dogma of objects. This

assumption normally holds for smaller and less complex manmade systems. However, as systems become

more complex, and as manmade systems interact on a larger scale with a living environment, this

assumption does not seem to hold. As a consequence, systems thinking and holism has been introduced

(Honderich 1995). These new thoughts emphasize that the properties of the whole lies in the relations

between part, i.e., in the way the parts are organized. One common argument is that life cannot be created

just by adding all the ingredients – the properties of life lies rather in the organization and sequence of

organization; i.e., a process view indeed (Capra 1996). The fact that the problem – and hence the solution

– lies in the relations between parts is seen all to often in the offshore oil and gas industry. In fact


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interfacing problems have lead to a new practice of interdisciplinary design teams trying to resolve inter-

disciplinary conflicts as early in the design process as possible. Other efforts have been to expand the

computerized engineering models in step with the increased computing capabilities, to capture morerelations between sub-systems and between the system and its surroundings.

Finally, the foundationalist / formalist / reductionist school of epistemology is under heavy

attack on the basis that ‘truths’ are observed to change over time. As a result, the holistic / social /

relativistic school of epistemology emerged in the beginning of this century, and has since then gained

growing support in the scientific community. Even the notion that only rational knowledge is valid is in

retreat, at least in industry. According to head-hunting firms senior personnel are now wanted more than

ever in managing positions in order to draw on their intuitive knowledge – their wisdom – to apply the

rational knowledge in more fruitful ways.

Having said this, we proceed to discussing some applications of natural analogies and

evolutionary thinking in engineering design and in other humanistic domains.

2.3.2 The Evolutionary Approach: Its Standing in Engineering Design

The unit events in evolution – variation and selection – are implemented in all the design

methods we have reviewed. When ever a solution to an engineering problem is sought – regardless of

system level – there is always a period where alternatives are generated and a period where one or more

alternatives are selected for further work (French 1985; Muster and Mistree 1988; Erichsen 1989; Mistree,

F. et al. 1990; Bras and Mistree 1991; Pahl and Beitz 1993; Lewis and Mistree 1995; Suh 1995; Hubka

and Eder 1996; Pugh 1996). In this respect they all support the evolutionary approach. However, they all

have a bias towards the product. The reason why is that design is invariably viewed as realizing a product

that meets the specified requirements (functional and product). All the methods reviewed boils down to

presenting (slightly) different ways of converging to a product that meets a set of criteria for good designs.

And here we are at the heart of the problem: how do we establish criteria for good design? To some

degree the criteria for good design are domain dependent. Nevertheless, there are some criteria that are

more ‘universal’ than others, and here is where we see the evolutionary approach. Stabilizing the product

while changing the process, i.e., to favor products that meet the requirements and provides the most

leverage to existing designs while addressing costs / quality / time to market by continuously improving

the realization processes, is viewed as one of the more ‘universal criteria. This is to some extend

supported by the new quality drive referred to as ‘Continuous Quality Improvement’, where improvements

are implemented continuously in small steps – however, on a product level.


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When it comes to using natural analogies directly, one person stands out; French. Being a

professor, his work is in context of teaching where he uses natural analogies to derive principles for good

designs. This is based on observing the ‘designs’ by nature, i.e., the products of nature, and hence, these principles are only used at a level of deriving working principles (i.e., a physical manifestation to meet a

set of functional requirements) (French 1994). The very principles leading to the “elegance in natural

designs”, however, is not touched upon. Hence, the process perspective is not included, and he does not

seem to believe that nature can provide a basis for a broad design theory; “science is the study of the

natural world; it is concerned with what is. Engineering design is considered with creating new thins; it

makes extensive use of science, but it is a different activity”.

Henry Petroski is a designer that is interesting from the perspective of ‘stabilizing the product’.

His work emphasizes on human aspects of engineering rather than the principles used to achieve designs.

These human aspects include the drive to make existing designs bigger or better; the drive to make

something unique with your stamp on it; and most importantly, the mistakes made during the whole

process. The latter is seen as imperative to future successes; “those who cannot remember the past are

condemned to repeat it”. In his many examples, failure is attributed to ‘extrapolation’, i.e., utilization of

technology / principles / etc. outside traditional ranges of application (Petroski 1985). One of the more

famous instances is the Tacoma Narrow Bridge outside of Seattle, WA. The suspension bridge principle

had not been used for so slender and long spans before, and the result we all know; disasters. His many

examples support our assertion about stabilizing the product; i.e., to favor satisficing solutions closer to

existing, well proven solutions. The amount of caution is of course dependent on the consequences of failure. Petroski is also concerned with the prevalent use of modern design tools. He fears that the

designers accept the results without questions (Petroski 1992). This jibes with the dogma of knowledge;

‘outputs from computer software represents the ultimate technological knowledge, hence, it cannot not

only be trusted, it gives us all the information we need’.

Koen is a designer that is interesting from a research validation perspective. He claims that

“everything is heuristics”; he uses Gödel’s proof to define arithmetic as a heuristic (Gödel 1931); he uses

Einstein’s Theory of Relativity to prove time as a heuristic; and he uses

Principle to prove position and energy as heuristics (Koen 1985). This support our definition of scientific

knowledge as ‘justifiable belied’. However, it follows from his thinking that the “universal method is to

use heuristics”. In other words, “as an engineer do what you think is the most appropriate and represents

best practice at the time you have to decide and come up with a solution” (Koen 1987). In the sense he

advocates satisfacing solutions over optimized solutions, we follow him. In the sense he advocates ‘ad-

Koen’s work supports a relativistic view on knowledge, and it supports robust,

satisficing solutions over optimized ones.


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Even though there are evolutionary elements in some of the engineering design processes and

there are initiatives to include fabrication processes, the very principles behind evolution are not

considered as being part of the engineering design process itself. However, designers seem to have no problems using these principles in other contexts, for example for a certain type of search and

optimization techniques referred to as evolutionary computations (IC). In their current form, evolutionary

computations are studied and applied in three standard formats, each distinguished by what level of

“granularity the algorithm models evolution”. Genetic Algorithm, the best known of the variants in the

USA, model evolution at the level of gene propagation. Evolution Strategies, an evolutionary computation

developed and studied chiefly in Germany, model how evolution optimizes individuals to better exploit

their particular environment. Evolutionary Programming, on of the earliest investigated evolutionary

computations, employs a model of evolution as it operates on multiple species competing for shared

resources (Angeline 1995). As Evolutionary Computation indicates, they operate on a population of

candidates in parallel. By manipulating a population of solutions simultaneously, evolutionary

computations can search several areas of a solutions space allocating appropriate numbers of population

members to search the various regions of the space. The allocation is determined by a fitness function used

to determine the ‘goodness’ of each individual solution. The best set of solutions is allowed to mate to give

rise to a new generation of solutions. The variation part of evolution is taken care of by ‘mutating’ the

new parent population so as to create small variations around the parental mean (Forrest 1996).

In other domains there seem to be less ‘resistance’ when it comes to applying the very

principles of evolution to explain and ‘control’ system changes. This is elaborated next.

2.3.3 The Evolutionary Approach: Its Standing in Social Sciences

Using Darwinian thinking in non-traditional areas like psychology and culture is relatively

new, a fact that many finds astounding in hindsight. The reason for the disconnect between Darwin and

other fields is by some believed to be an innate fear, even among many scientists, of letting Darwinian

thinking ‘leak’ into realms outside of biology. Applying evolutionary theory to other disciplines is seen by

many scholars as an unwelcome intrusion, because it might dramatically alter the fundamental notions

that underlie those fields of study (Dennet 1995). Still, the use of Darwinian thinking has expanded in

spite of efforts to ‘contain’ it. It has gone “from being a sideline for a few people to being a major,

(Clark, Cooper et al. 1997).

For example, psychologists are asking how and why natural selection has seen fit to leave

people with conditions like moodiness, jealousy and anxiety. What purpose could, say, jealousy serve in

aiding a person’s chances for survival and reproduction? Likewise, physicians have been using


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evolutionary principles to discover the purpose behind some diseases and other unwanted conditions.

“why is the body vulnerable to disease? Why hasn’t natural selection done a better job?” At closer

examination, however, it often turns out that what initially is perceived as being harmful, actually turnsout to be beneficial from a species perspective (Clark, Cooper et al. 1997).

Scholars have also used natural selection to explain cultural phenomena, relying on an idea

developed by Oxford University biologist Richard Dawkins. Dawkins has come up with the concept of

‘memes’ , which are important ideas, like Darwinian Evolution, Christianity or Postmodernism, that are

governed by the laws of natural selection. If society is an organism, memes are like genetic traits. Some

evolve and are passed on, other disappear. Put together, the memes that survive make up our culture and

society, just as each creature is the sum of its genes. Dawkins and others argue that natural selection can

help us understand why some memes survive and flourish, and others die (Dawkins 1986). Elliot Sober

follows this trend, and he points out that Darwin owed a depth to economy who showed him how order

can be produced without conscious design. The example was that “socially beneficial characteristics can

emerge in a society as if by an ‘invisible hand’; though each individual acts only in his or her narrow self-

interest, the result would still be a society of order, harmony and prosperity” (Sober 1995).

Even morality is being explained as being molded and constrained by epigenetic rules. For

example, incest is not always perceived as morally wrong, however, it turns out that children growing up

together very closely are unable to form strong sexual bonds during adolescence or later (Ruse and Wilson

1995).

2.3.4 The Evolutionary Approach: Its Standing in Economics

Having borrowed from the economic domain the notion that there need not be consciousness

involved in creating order, economists seem to embrace evolutionary principles to a greater extent than

many other domains. Fisher’s equation for Natural Selection (see Equation 4-1), has been widely used to

model market behavior as a function of ‘fitness’ and market shares (Hall 1994). Evolutionary principles

are also used to explain why the market behaves the way it does. The long periods of stasis where minor

innovations ‘diffuse’ through the various industries resembles genes propagating in a gene-pool to yield

optimal adjustments, and sudden large changes occurring rapidly in periods of ‘revolution’ when ‘growth

rules’ resembles punctuated equilibrium’s. According to (Emblemsvag and Bras 1998) the periods of

stasis are seen as needed for allowing a continuous revenue to ‘keep the wheels spinning’ and to finance

research and development efforts. From time to time these research and development efforts result in

sufficiently large innovations to ‘rock’ the industry. These are periods where “a subset of the system’s old


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pieces, along with some new ones, can be put back together in a new configuration, which operates

according to a new set of rules” (Gersick 1991).

Another interesting story about technology that has left ‘home, traveled to new place, seen newthings, and returned home’, is game theory. Being published by von Neumann and Morgenstern in 1944,

the “Theory of Games in Economics” was developed within the realm of economics based on the

assumption of ideally rational players. This assumption works well for two-person zero-sum games where

the Nash-equilibrium’s are interchangeable. However, the equilibrium selection problem for more general

games has no such easy solution. This lead to the game theory hitting the ‘doldrums’ in the late 1980s,

and it was not until biologists directed the attention away from rationality that game theory came back as

being interesting. Biologist using game theory to predict the long term interactions within populations,

pointed out that insects hardly can be said to think at all, and so rationality cannot be so crucial if game

theory somehow manages to predict their behavior under appropriate conditions. Simultaneously the

advent of experimental economics brought home the fact that human subjects are no great shakes at

thinking either; trial and error is the standard mode of reaching an equilibrium. Hence, in economy as

elsewhere, attempts to model people as hyper rational players is giving way to “selecting an equilibrium

equilibriating’ process by means of which it is achieved” (Weibull 1997). This is also

pointed out when the prisoner’s dilemma is played many times with the same players; then the solutions

tend to be co-operative since this gives a better accumulated pay-off (Kreps 1990).

2.3.5 The Evolutionary Approach: A SummaryFrom the foundations of the evolutionary approach presented in Section 2.2.3 we have

substantiated a) path-dependence, b) that variation is the key to adaptation, and c) that decisions (i.e.,

selection) is human centered and probabilistic, see Section 1.2.2. In doing so, we have identified the

fallacies of d) the dogma of objects, e) the dogma of categorization and f) the dogma of knowledge. Hence,

by demonstrating that current thinking support a), b) or c); that it breaks with d), e) or f); that it does not

contradicts a) through f); and that there are some aspects of a) through f) that are not covered, we assert

that the evolutionary approach is appropriate, and that it is an extension of existing work.

First of all, none of the traditional thinking within the field of engineering design explicitly

contradicts a) through f). Secondly, Open Engineering Systems, see Section 2.3.1, breaks with the dogma

of objects (the world is viewed as changing) while supporting path-dependence and human centered /

probabilistic decision making (robust and adaptive designs). Thirdly, work in the social sciences and

economics support population thinking by demonstrating that the process of evolution can be used to

describe changes within non-organismic systems. In particular, game theory shows us the importance of


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path-dependence, population thinking, and probabilistic decisions. Specifically, game theory shows that

rational players are not only utopian, they are not needed nor wanted. Finally, we miss a holistic approach

to implement evolutionary principles to the very process of design so as to guide designers in selectingsolutions. We assert therefore, that the evolutionary approach represented by “stabilizing the product

while changing the processes” is a valid approach AND an extension of existing work within the field of

engineering design. Based on this we proceed to further elaborate on how to “stabilize the product” in

Section 2.4, and how to “change the processes” in Section 2.5.


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2.4 “STABILIZING THE PRODUCT” –

AN ARGUMENT FOR NUMERICAL TAXONOMY

The objective in this section is to substantiate the choice of Numerical Taxonomy as hypothesis

to address product platform definition. In Section 2.4.1 we recap where we left in Chapter I before we

move on to presenting Group Technology (GT) in Section 2.4.2 as our basis for standardization. Then, in

Section 2.4.3, we present Numerical Taxonomy as an integrator of GT formalism, clustering analysis and

product platform perspectives, and we assert Numerical Taxonomy to be an appropriate Hypothesis.

2.4.1 Design a HPP Based on Commonalities: Recapping Chapter I

Before we substantiate our choice of Numerical Taxonomy as hypothesis to define Hierarchical

Product Platforms, we briefly recap Chapter I. In Section 1.1.3 we define reuse for a range of applications

as our approach to develop marginal fields. In Section 1.2.1 we define that a solution has to be based on

existing commonalities in a product portfolio. In Section 1.2.3 we identify product platform design as our

choice of approach. In Section 1.3.1 we identify standardization as our choice of increasing existing

commonality. In Section 1.3.2 we provide a set of definitions related to product platform design. In

Section 1.3.3 we identify the Hierarchical Product Platform (HPP) to be an advancement to product

platform and product family design. And finally, in Section 1.5.1 and 1.5.2 we hypothesize Numerical

Taxonomy as a good framework for defining HPPs, and in the following we will substantiate this choice of

hypothesis.

From the previous we summarize the research objective to provide a method to find the level of

standardization that gives the best compromise between reduced operational performance (not wanted),

and reduced cost & time (wanted). Being focused on the product, standardization has to be considered

both from a design and a manufacturing perspective. By searching the literature we find that Group

Technology (GT) is maybe the philosophy that provides the most systematic approach to standardization,

both from a design and a manufacturing perspective.

2.4.2 Group Technology (GT): A Formal Approach to Standardization

Group technology (GT) has been used as a valuable tool in the ongoing effort to streamline the

design and manufacturing process. This is done by forming part families with members that are somewhat

different but uses similar processes (Hyde 1981; Suresh and Kay 1998). The formation of part families

and manufacturing cells is based on domain dependent classification and coding schemes, where existing

designs / parts are grouped in order to prevent design proliferation (design oriented grouping) and to


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identify potential for cellular manufacturing (manufacturing oriented grouping). The design oriented

groups are the basis for any standardization effort, and is hence of most interest to this research.

Coding and Classification: The Heart and Soul of GT

Group Technology (GT) is basing family formation on classification schemes and

corresponding coding schemes; in fact, S.P. Mitrofanov, widely credited with developing the scientific

basis for GT, has stated that the role of classification is the “basic problem (solution) on which group

(Mitrofanov 1966). The classification scheme is to bring together items by their

similarities and then segregate them by their essential differences. What similarities and differences to use

should express the ultimate objectives of the user. For design purposes geometric features, materials,

capacities, etc. are normally used. For manufacturing purposes functions, processes, etc. are normally used

(Ingram 1984). Having decided on a basis to group parts, i.e., a classification scheme, this must be

followed by a coding scheme to facilitate the classification. There are many different coding schemes,

however, similar to all is the qualitative nature due to being discrete; the parts are either in the group or

not. The similarity is based on the similarity in the coding structure, hence, there is nothing saying how

much you are into the group, i.e., there is no quantitative grouping (Eckert 1984; Ingram 1984; Dunlap

and Hirlinger 1987; Hyer and Wemmerlov 1987). This presupposes the existence of a grouping that

coincides with the proposed classification scheme, which may cause problems as we shall see next.

Unit Part Families Only? A Major Limitation to GT

Being anchored in the discrete parts manufacturing industry, GT traditionally has sought to

standardize single / unit parts and not whole structures that are assembled in a hierarchical way (unit

parts into sub-assemblies, sub-assemblies into assemblies, assemblies into modules, modules into products,

etc.). This is considered a weakness, since the cost and schedule benefits of standardizing assemblies seem

proportional with the level of assembly.

This is accounted for by focusing on the hierarchical manner ship structures are designed and

manufactured, and we have defined the Hierarchical Product Platform as “a hierarchical organization of

the entities to be standardized across the members in the product family”, see Section 1.3.3. (In fact, most

of products are designed and manufactured in a hierarchical manner.)

This definition implies that we are seeking to unveil commonalities at any level of assembly,

and it may seem like this is virgin land to traditional GT. Fortunately, there have been attempts to utilize

GT at a hierarchy of parts and processes. In (Dehoff 1990) one such attempt is described for implementing

GT at Boeing Helicopters during their design and fabrication of the V-22 Osprey helicopter. The V-22


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Osprey is fabricated almost entirely of graphite epoxy solid-laminates. These laminates are actually

designed from scratch to suit every application. The result was an enormous proliferation of ply-laminate

orientations and layers, ply-pack configurations, … you name it, which made the Bonding Assembly Rigquite extensive. GT was implemented to investigate the potential for standardization at the various levels

of design; from ply-laminates through ply-pack assemblies, see illustration. In doing so they devised a

classification scheme that accounted for

fabrication processes as well as part functions,

and they used the coding to investigate the

standardization potential in the current part

inventory. By doing this they were able to

reduce the number of single pliers (the unit

part) to a small set with standard laminate

configuration; the number of single ply-packs

to a small set with standard geometric features;

and so on. However, as we have pointed out,

the discrete coding presupposes that the parts

group in a particular way. The problems of presupposition are linked among other things to the actual

similarity within a group; it may turn out that some parts really should have belonged to another group,

and so on. The problem Boeing Helicopter faced strongly resembles our case; we are seeking to unveil

commonalities in order to investigate the potential for standardization. Hence, we seek to avoid any problems caused by presupposing that the parts are grouped in any particular way. One approach that may

seem a little more ‘scientific’ is cluster analysis, a method frequently used to investigate how elements

group based on some chosen features.

Cluster Analysis: Investigating The Potential For Part Family Formation

Cluster analysis separates numerical data sets into unique clusters of data, each with a unique

set of characteristics. These characteristics are related, by the groupings, to the control variables of the

data. The most common type of cluster analysis calculates either a similarity value or a dissimilarity value

for each pair of data elements and stores these values in a two-dimensional similarity array. These values

are then accessed by the clustering algorithm to group the data elements. A common practice using

dissimilarity values is to represent data elements as points in space and minimize the sum of distances of

grouped elements from the calculated centroids of their respective clusters. This reveals the closeness of

data in a quantitative way, hence, would be preferable in the formation of parts families. There have been

Geometric

features

(design oriented)

Ply-laminates

(process oriented)

Level of

aggregation

BONDING

ASSEMBLY

JIG

(BAJ)

SinglePliers

SinglePly Pack

(Multiple)Ply Packs

Ply Pack Assemblies

Pre-assembledPly Pack(s)

BONDING

ASSEMBLY

JIG (BAJ)

COMPOSITE

ASSEMBLIES COMPOSITE

ASSEMBLIES

DETAILED

COMPOSITE

PARTS

DETAILED COMPOSITE

PARTS

STANDARDIZATION

ATTEMPTS

STANDARDIZATION

ATTEMPTS


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attempts in GT to cluster the data prior to suggesting any classification (Gongaware and Ham 1984), but

this approach has not gained wide support mainly due to the difficulties associated with implementation;

there seem to be enough problems implementing ‘simple’ classification and coding schemes. In context of our problem, however, cluster analysis seems like an attractive approach, which is preferred.

Based on the previous, we assert that GT formalism expanded with product platform thinking

and cluster analysis is a good approach to define WHAT to include in a Hierarchical Product Platform.

However, this pose a new question: Are there any philosophies that effectively can integrate product

platform thinking, GT formalism and data clustering, in the event of developing HPPs? From the

literature we find that Numerical Taxonomy as given in (Sneath and Sokal 1973) is thought to be.

2.4.3 Numerical Taxonomy: An Integrator

Numerical Taxonomy, as given in (Sneath and Sokal 1973), is a stand-alone method primary

used to classify biological organisms based on overall similarity, with the intent to (1) create stable

phenetic groups (phenetic taxonomy) and (2) study phyletic lineages and evolution (phylogenetic

taxonomy. In Figure 2-2 (a), the relationship between clustering, phenetic taxonomy and phylogenetic

taxonomy is indicated, using classification of some of the big cats as an example. In clustering, the

resemblance between each sample is ‘measured’ based on their recorded character-states. When the

characters and samples are chosen (subjective activity), the clustering itself is fairly free of personal bias

(objective activity). Being applied in a scientific setting, this is considered a strength (Sneath and Sokal

1973). In creating a taxonomy, however, personal judgement is called for which requires insight into the

aims of the study, conventions in the particular group, convenience and esthetics. Thus, the creation of a

taxonomy represents insight and knowledge, while the clustering represents scientifically obtained

‘supporting evidence.’ For further reference, see Chapter IV where we give a more comprehensive

presentation of Numerical Taxonomy in terms of its ‘fundamental positioning’, clustering and creation of

taxa.

How do evolutionary principles and numerical taxonomy fit into development of Hierarchical

Product Platforms? According to evolutionary principles, all living organisms are stemming from the

same point of origin, hence, it becomes natural that convergence into forefathers along a timeline coincide

to a high degree with convergence into higher taxa on a hierarchical scale. According to Figure 2-2 (a),

big cats that are classified into the Panthera genus, are viewed to have the same forefather some 3 million

years back (a panthera species?), and all members of the Felidae family is viewed to have the same

forefather some 10 million years back (a felidae species?). Thus, each classificatory juncture in the

phenetic taxonomy, up to the rank of family, could be viewed as potential species-junctures in the


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phylogenetic taxonomy. Therefore, phenetic taxonomy is by some viewed as a good indicator of hereditary

relationships between species2 (Sneath and Sokal 1973; Berra 1990).

2 The genetic relationships inferred from biochemical and immunological techniques agree very nicely with thegenetic relationships based on morphology, i.e., form and structure (Berra 1990).


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species

Panthera

Animala

Mammalia

Cordata

T i m e

5 millyears

1 millyears

Acinonyx

Puma

Lynx

Panthera

T i g e r

L i o n

L e o p a r d

C l o u d e d

L e o p a r d

J a g u a r

S m a l l

C a t s

B o b c a t

C o u g a r

C h e e t a h

Acinonyx

Canidae

T i g e r

L i o n

L e o p a r d

C l o u d e d

L e o p a r d

J a g u a r

S m a l l

C a t s

B o b c a t

C o u g a r

C h e e t a h

Pantherinae Felinae Acinonychinae

Carnivora

Felidae

Neofelis LynxFelis Puma

PHENETIC TAXONOMY

(stable phenetic groups)CLUSTERING

...

R

e s e m b l a n c e

PHYLOGENETIC TAXONOMY

(phyletic lineages and evolution)

genus

family

order

class

phylum

kingdom

sub-family

(a)

ANALYSIS

Plate

Midship module

BulkheadShip Side

Girders

Stiffener

Bow

H i e r a r c h y o f A s s e m b l y

Webs Stringers

Total Vessel

Panels

H i e r a r c h i c a l P r o d u c t

P l a t f o r m

FPSO

FPSO / FSU

FPSO / FSU / Shuttle

ASSEMBLYTAXONOMY

(stable manufacturing

entities)

CLUSTERING(based on existing designs)

STANDARDIZATION TAXONOMY

(Hierarchical Product Platform)

...

R e s e m b l a n c e

Plate Stiffener

Coding

scheme

Potential

platforms

HierarchicalProduct

Platform

(b)

Figure 2-2

(a) Numerical Taxonomy in Biological Classification

(b) Numerical Taxonomy used to Define HPPs


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In the same way, a product platform taxonomy can be constructed where each junction

becomes a potential product platform, and where junctions at higher taxa represents platforms for an

increasing number of OTUs, i.e., a Hierarchical Product Platform. Consequently, the objective of theresearch pertaining to defining Hierarchical Product Platforms is to find a way to go create a taxonomy

that reveals the ‘contours’ of a Hierarchical Product Platform. In this effort taxonomy represents

knowledge and insight, and clustering represents scientifically obtained ‘supporting evidence,’ as for

biometrics. This is illustrated in Figure 2-2 (b), where the Assembly Taxonomy to the left (hierarchical

representation of construction assemblies) corresponds to Phenetic Taxonomy in Figure 2-2 (a), and the

Standardization Taxonomy (Hierarchical Product Platform) corresponds to the Phylogenetic Taxonomy in

Figure 2-2 (b). However similar, there is a difference in the sequence of events. The assembly taxonomy

becomes known when vessels are partitioned. This fact is utilized to code the parts and assemblies for

database manipulation. After all, we are investigating the potential for standardization at each level of

assembly, and need to run cluster analysis on corresponding data. Hence, the Assembly Taxonomy

becomes input to cluster analysis, rather than the outcome.

Another difference is that evolutionists are trying to reveal historical connections whereas

‘standardizationists’ are trying to reveal future connections. Hence, basing HPPs solely on existing

designs may impose constraints that are historically anchored and not really applicable for contemporary /

future designs. Thus, the historical data has to be analyzed for its applicability for future designs. As an

example, we consider a hypothetical taxonomic distinction between offshore designs and regular ship

designs. The differences may be due to a non-interrupted operations requirement for offshore installations.This requirement prohibits docking of the vessel during its operational life (15-25 years)., which results in

fatigue contingencies (material amounts and quality) for offshore designs that typically makes them 20%

more expensive (Törmä 1998). For marginal fields, however, the operation time is between 2-6 years

which allows almost normal docking schedules – even for production units. Hence, the hypothetical

taxonomic differentiation between offshore and ship designs which is historically anchored, may not be

applicable to contemporary and / or future designs.

From the previous we argue that in evolutionary studies as well as in standardization efforts

the same objective is sought, namely, junctures of ‘lineages’, and that Numerical Taxonomy provides a

solid means for achieving this. Therefore, we assert that Numerical Taxonomy is an appropriate

hypothesis, and we propose to develop a method for defining HPPs by partitioning the vessels while

creating an Assembly Taxonomy for input to a Coding Scheme; obtain the states of the characteristics for

each partitioned part to be clustered; cluster the data to reveal taxonomic structures; analyze the clustering

based on current information; and finally, synthesize a Standardization Taxonomy for future designs.


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2.5 “CHANGING THE PROCESS” –

AN ARGUMENT FOR ADDRESSING LEARNING

The objective in this section is to substantiate the choice of “Technology Diffusion” as our

hypothesis to address the effects of learning when transferring from one process to another. In Section

2.5.1 we recap why a change in process is ‘required’ in the first place. Then, in Section 2.5.2, we present

Technology Diffusion as a means of modeling the effect of learning. In Section 2.5.3 we outline a

mapping of how to introduce change, and assert Technology Diffusion to be an appropriate Hypothesis. In

Chapter IV we elaborate more on Technology Diffusion in context of its origin and application.

2.5.1 Improve the Process Through Change: A Recap

In Section 2.3.5 we conclude that the evolutionary approach suggests “stabilizing the product”

and “changing the process”. In our terms this means to constantly look for ways to improve the ‘process’,

this vague term that encapsulates all activities leading to the realization of the product. Further, any

improvement implies change, which we have discussed at length in the beginning of this Chapter to be of

the essence of progress and development. From a ‘rational’ perspective, the parts of the process with the

highest potential for improvement should be addressed at each round of improvement. As for any

‘evolution’ there has to be alternatives upon which selection can act. Which alternatives to propose

depend on what is available and what seems to be most promising. However, the selection of which

process to proceed with has to be based on some evaluation of how much the new technology will

contribute to a firms overall competitiveness. Important in this context is the time frame considered for

pay-back; the longer time frame, the less contributions are required for a favorable decision. (In context of

developing marginal fields, the investment cost of new processes is preferred paid off over one contract, or

maximum over the realization of a whole family. This implies a time frame from 6 to 10 years, see

Chapter VII.)

Being a new process (or product) implies that a firm cannot fully utilize all its benefits until

the operational skills have developed adequately. In turn the acquisition rate of operational skills depends

on the new technology’s maturity in the industry, and the amount of transferable skills from current

technology.

Based on the previous, we seek a way to model these effects, and we find that “Technology

(Silverberg, Dosi et al. 1988; Silverberg 1991) and used in (Hall 1994) shows

promising merits.


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2.5.2 Technology Diffusion: Modeling the Effect of Learning

In their research, the decision to change technological ‘trajectory’ is based on evaluating how

long it will take for the economic benefits of a shift to outweigh its costs, see Equation [2-1]

b

X SKL

C C

P P ≤

⋅−

−

121

12 [period]

where

P 2 – P 1 = cost per unit of implementing new technology [$]C 1 = current unit operational cost of old technology [$/period]C 2 = current unit operational cost of new technology [$/period]SKL = skill level, see Equation [2-2] and [2-3] [ – ]

X = anticipation bonus; future potential beyond C 2 [ – ] B = required pay-back period [period]

[2-1]

In evaluating the economic benefits (denominator), two important factors are introduced,

namely, the effect of lacking skills (SKL ) and the effect X of each firm’s judgement of the future prospects

of the new technology. If X = 2 indicates that the new technology is viewed to have a potential for cutting

the current unit operational cost C 2 in half. The skill level represents an efficiency reduction factor and is

being modeled as a sigmoid learning function, given in Equation [2-2] as:

( )

)( 0

1/11

1)(

0

t t

SKL

t SKL−

−+

=σ

[2-2]

where SKL(t) is the organizational skill-level at time t , SKL 0 is the initial skill-level at time t 0 , σσ is the

learning rate, and t 0 is when the new technology is implemented

As we see in Equation [2-2], SKL(t) gives the skill-level at one particular time t , while the new

design cost shown in is accumulated cost over the whole project period. Hence, we average the skill-level

for the project period as defined in Equation [2-3], and the results are given in. How can we implement

SKL? We answer by giving an example.

∫

−

= t

t

dt t SKL

t t

SKL

0

)(

0[2-3]


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From Equation [2-1] through [2-3] we see that high learning rates (σ) favor first movers, being

able to reap the benefits of a new technology fast enough to not affect their market shares. Intermediate

learning rates favor later movers, having access to more start up information due to learning leaks fromfirst movers that struggled to make the new technology work. Low learning rates favor no change at all,

i.e., those who changed to the new technology changed back to the old in the simulation (or they ‘died’).

In any of these scenarios, the last movers were the inevitable losers. This emphasizes the importance of

the learning rate in a successful change from one technology to another.

The learning rate is viewed as dependent on the educational level within the part of the

organization that is affected by the change. Intuitively, higher education gives higher learning rate. This is

based on the assumption that people with higher education have ‘demonstrated’ their ability to acquire

new knowledge. In addition, the initial skill level affects the learning rate; the lower initial skill level, the

longer time to the steep part on the learning curve. This shows the double effect of initial skill level; it

affects the learning rate as well as the starting point, thus, it becomes an important parameter.

The factors we believe affects the initial learning skills are the leverage potential from current

processes. This emphasize the importance of not meandering too far away from what is known; with small

values of skill level SKL , the benefits of the new technology becomes ‘negative’ in the sense that it yields

a worse situation than the initial one. In Chapter IV we give a more comprehensive presentation of the

mathematical basis behind Technology Diffusion.

How is this concept applicable to evaluate new realization-technologies for the purpose of

getting a product better adapted to its requirements (more fit)? Even if the context of this research isdifferent (no mass production and the decision regarding transfer is based on more criteria than just cost),

the concept of reducing the benefits of a new technology to reflect the actual skill level is still applicable.

Based on the previous, we assert that Technology Diffusion is an appropriate hypothesis.

2.5.3 From Mutation to Innovation: Mapping of Variation Mechanisms

Having substantiated Technology Diffusion as our choice of modeling learning, we proceed to

look at some approaches to introduce variation. We keep it in the evolutionary spirit, and present a

mapping of strategies from the realm of biology to the realm of engineering design. as we have already pointed out in the beginning of this chapter, if variation is not introduced in new generations of organisms

/ products, evolution will eventually cease. In the following, mechanisms for

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