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CHAPTER VIII.

CLOSURE 288

8.1 DEVELOPING THE HPPRM – ADDING NEW KNOWLEDGE?...........................................290

8.1.1 Revisiting the Research Questions and Hypotheses.......................................... 290

8.1.2 Validating New Scientific Knowledge – an Overview ......................................292

8.1.3 Testing the Theoretical Structural Validity (TSV) ...........................................295

8.1.4 Testing the Empirical Structural Validity (ESV)..............................................298

8.1.5 Testing the Empirical Performance Validity (EPV).......................................... 301

8.1.6 Testing the Theoretical Performance Validity (TPV) ....................................... 303

8.2 DEVELOPING THE HPPRM – ACHIEVEMENTS AND CONTRIBUTIONS........................305

8.2.1 Contributions to the Field of Engineering Design.............................................305

8.2.2 Original and Significant: Completing the Criteria for a Ph.D. .......................... 305

8.3 CRITICAL ANALYSIS – RESEARCH LIMITATIONS..........................................................309

8.3.1 The HPPRM – Limitations to its Constructs and Framework........................... 309

8.3.2 The HPPRM – Its Robustness ........................................................................ 311

8.3.3 The HPPRM – Limitations to the Validation Process ......................................312

8.3.4 The HPPRM – Limitations to Implementation................................................. 313

8.4 RECOMMENDATIONS – AVENUES FOR FUTURE WORK ...............................................315

8.4.1 Industrializing the HPPRM – the Industrial Avenue.........................................315

8.4.2 Improving the HPPRM Constructs – the Academic Avenue............................. 318

8.5 CONCLUDING REMARKS....................................................................................................320


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LIST OF FIGURES

FIGURE 8-1 HOW THE RESEARCH QUESTIONS AND HYPOTHESES CONNECT TO THE HPPRM

................................................................................................................................................... 291FIGURE 8-2 HYPOTHESES TESTING GUIDE: WHERE THE ANSWERS ARE PROVIDED........... 294

FIGURE 8-3 THE THEORETICAL STRUCTURAL VALIDATION PROCESS – A PICTORIAL

REPRESENTATION .................................................................................................................. 297

FIGURE 8-4 THE EMPIRICAL STRUCTURAL VALIDATION PROCESS– A PICTORIAL

REPRESENTATION .................................................................................................................. 300

FIGURE 8-5 THE EMPIRICAL PERFORMANCE VALIDATION PROCESS – A PICTORIAL

REPRESENTATION .................................................................................................................. 302

FIGURE 8-6 THE THEORETICAL PERFORMANCE VALIDATION PROCESS – A PICTORIAL

REPRESENTATION .................................................................................................................. 304

FIGURE 8-7 THE CONTRIBUTIONS TO THE FIELD OF ENGINEERING DESIGN .............. ......... 308

FIGURE 8-8 THE RESEARCH AVENUES STEMMING FROM THE HPPRM FRAMEWORK......... 317

LIST OF TABLES


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8. CLOSURE

NumTax TechDiff c-DSP HPPRM Closure, demonstrating

• research validity

• research originality

• research significance

XXX Xx

x x

x x

x x

x x

x x

x

In this dissertation a framework has been developed, presented, and tested to facilitate the

realization of large, complex and expensive MTO systems that are traditionally produced in small

numbers using much of the same technology. Our objective in this chapter is then to bring the

development and presentation of this framework to a closure. By closure we mean to demonstrate that we

have achieved the principal research objective and answered the questions we posed in Chapter I, in a

satisfactory manner.

We do this by first demonstrating (by means of the validation square) that we have added newknowledge to the field of engineering design. Then, we evaluate this new knowledge in terms of its

originality and significance, in order to fully demonstrate that this research comply with the requirements

posed for the Doctor of Philosophy degree in Mechanical Engineering.

Finally, we discuss the limitations of the HPPRM framework in terms of its constructs, its

robustness, its validity, and its industrial implementation. Based on this, we give recommendations on

“This must n ot be taken as the end;

i t may poss ib ly be the beginn ing of the end;

but i t is cer ta in ly the end of the beginn ing” – Winston Churchill

In speech at the Mansion House in London City in late 1942


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future research building on this framework, both in academia and in industry, before we close this

dissertation.


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8.1 DEVELOPING THE HPPRM –

ADDING NEW KNOWLEDGE?

In this section we demonstrate by means of the validation square (see Section 1.6.2) that the

HPPRM gives reliable and useful output for valid input, hence, that it adds new knowledge to the field of

engineering design. In Section 8.1.1 we revisit the research questions and hypotheses that were posed in

Section 1.5. For reference, in Figure 8-1 we show where the research questions and hypotheses are

embedded in the HPPRM structure. In Section 8.1.2 we give an overview of the procedure for

substantiating that new scientific knowledge has been added to the field of Engineering Design, and in

Sections 8.1.3 through 8.1.6 we demonstrate that that new scientific knowledge has been added to the

field of Engineering Design. Adding originality and significance in Section 8.2, we can assert compliance

with the Ph.D. requirements as given by the GWW School of Mechanical Engineering.

8.1.1 Revisiting the Research Questions and Hypotheses

Validation in context of Ph.D. research rests on (1) having answered the posed questions, (2)

the answers being in accordance with the hypotheses, and (3) the answers being acceptable from a Ph.D.

requirement perspective. Hence, we start the validation procedure presentation by revisiting the questions

and the hypothesis posed for this research.

Fundamental Research Question: How can large and complex made-to-order systems produced in smallnumbers using much of the same technology be realized more effectively and efficiently for different

applications?

Fundamental Hypothesis: Large and complex made-to-order systems as characterized above can be

realized more effectively and efficiently for different applications by developing Hierarchical Product

Platforms (see Section 1.3.3) from an evolutionary perspective (see Chapter II).

As stated in Section 1.5, the fundamental question has been partitioned into three questions,

where each question deals with some fundamental aspects regarding how to develop a Hierarchical

Product Platform from an Evolutionary Perspective. These questions and their corresponding hypotheses

are formulated as follows.

Research Question 1: Based on a firm’s existing designs; how can HPPs be defined in terms of what

physical entities to standardize for which products?

Hypothesis 1: Numerical Taxonomy (Sneath and Sokal 1973) provides a good framework for defining

HPPs in terms of what physical entities to standardize for which products (answering Research

Question 1 acknowledging Path-Dependence).


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… By designing Hierarchical Product Platforms

from an Evolutionary Perspective, characterized

by Path-dependence, Population-thinking, and

Probabilistics.

… By designing Hierarchical Product Platforms

from an Evolutionary Perspective, characterized

by Path-dependence, Population-thinking, and

Probabilistics.

How can large and complex made-to-order systems produced in

small numbers using much of the same technology be realized

more effectively and efficiently for different applications?

How can large and complex made-to-order systems produced in

small numbers using much of the same technology be realized

more effectively and efficiently for different applications?

The HPPRM

Gather Data

Cluster Data

For each Assembly Level (AL)

P h a s e

I : D E F I N E

( R e s e a r c

h Q u e s t i o n 1 )

Establish

Design Requirements

Partition Realization

Processes

P h a s e I I : M O D E L

( R e s e a r c h Q u e s t i o n 2 )

Model Relationships

and formulate c-DSPs

Establish ScenariosSolve for each

Scenario

P h a s e I I I : S O L V E

( R e s e a r c h Q u e s t i o n 3 )

Decide on HPP

For each Assembly Level (AL)

For ALL Assembly Levels (AL)

Hypothesis 1

Hypothesis 2

Hypothesis 3

Numerical Taxonomy

(Sneath & Sokal 1973)

Technology Diffusion

(Silverberg, Dosi et al. 1988)

Compromise DSP

(Mistree, Hughes et al. 1993)

The Research Hypotheses

THE FUNDAMENTAL QUESTION THE FUNDAMENTAL HYPOTHESIS

Figure 8-1

How the Research Questions and Hypotheses Connect to the HPPRM


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Research Question 2: In order to better adapt products to their requirements we want to consider

realizing them with alternative processes; how can we evaluate the effects such a transition may have

on a product’s operational performance, its cost and its schedule, accounting for the learning that has

to take place in a transition phase?

Hypothesis 2: Technology Diffusion (Silverberg, Dosi et al. 1988) is a viable concept to include learning

when evaluating the effects of transferring to new processes (answering Research Question 2 from a

Population-Thinking perspective).

Research Question 3: How can products and processes be synthesized in a mathematical model,

acknowledging that it is based on limited information and ‘volatile’ assumptions?

Hypothesis 3: The c-DSP (Mistree, Hughes et al. 1993) provides a structure and an anchoring that is well

suited for solving models that are acknowledged to be based on incomplete information and ‘volatile’

assumptions (answering Research Question 3 acknowledging the Probabilistic nature of the problem)

As stated, the validity of the research in context of Ph.D. requirements rests on having

answered the questions according to the hypotheses in an acceptable way. In this research, the answers

correspond to the hypotheses, and hypotheses tested valid according to the process given in Section 1.6,

are asserted to constitute new scientific knowledge. In addition, hypotheses found to be significant and

original in addition to being valid are considered to be contributions to the field of Engineering Design,

see Section 8.2.

8.1.2 Validating New Scientific Knowledge – an Overview

In general a research question is answered when the corresponding hypothesis is validated.

However, in this dissertation there is a hierarchical organization of questions and hypotheses, where a

fundamental question and hypothesis is partitioned into three supporting questions and hypotheses. The

hierarchical organization implies that the answer to the fundamental question lies in validating the

fundamental hypothesis; validating the fundamental hypothesis lies in answering the supporting

questions; answering the supporting questions lies in validating the supporting hypotheses. Hence, in

order to answer the fundamental question, we validate each of the supporting hypotheses. This is done

according to the process given in Section 1.6, where each of the quadrants in the

Validation Square to the right are tested separately for each supporting

hypothesis. Then, by adding additional information, the validity of the

fundamental hypothesis – i.e., the validity of the HPPRM – is asserted through

induction. In the following, we present the procedure for testing each hypothesis

according to the process outlined in Section 1.6, and in Figure 8-2 we present a

THEORETICAL

STRUCTURAL

VALIDITY

EMPIRICAL

STRUCTURAL

VALIDITY

EMPIRICAL

PERFORMANCE

VALIDITY

THEORETICAL

PERFORMANCE

VALIDITY


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guide to where the various testing takes place in the dissertation (where the answers to the questions are

provided).


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Ch. I

NumTax TechDiff c-DSP HPPRM Demonstrating• Originality of the HPP: Sc. 1.3.5

Ch. V

NumTax TechDiff c-DSP HPPRM

x

Ch. IV


xXx x

Xx x

Xx x x

Demonstrating that constructs are

• accepted in general: Sc. 4.1.5, 4.2.4, 4.3.3

• accepted in HPPRM: Sc. 4.1.6, 4.2.5, 4.3.4

• useful in HPPRM: Sc. 4.1.7, 4.2.6, 4.3.5

X X X x

Ch. VI


X X x x

Case Study, demonstrating that

• HPPRM fits problem: Sc. 6.1• usefulness of Num Tax: Sc. 6.6

Ch. VIII

NumTax TechDiff c-DSP HPPRM Closure, demonstrating

• research validity: Sc. 8.1

• research originality: Sc. 8.2.2

• research significance: Sc. 8.2.2

XXX Xx

x x

x x

x x

x x

x x

x

Ch. III

NumTax TechDiff c-DSP HPPRM Demonstrating internal consistency

of the HPPRM by Flow Charts

- Phase I Define: Sc. 3.2

- Phase II Model: Sc. 3.3- Phase III Solve: Sc. 3.4

x

Ch. VII


X X x

Case Study, demonstrating

• usefulness of Tech Diff: Sc. 7.4.5

• usefulness of c-DSP: Sc. 7.5

• usefulness of HPPRM: Sc. 7.10


Ch. II

Demonstrating applicability of

• Evolutionary approach: Sc. 2.1-3

• Numerical Taxonomy: Sc. 2.4

• Technology Diffusion: Sc. 2.5

• c-DSP: Sc. 2.6X X X x

xxx xxx

x

Demonstrating internal consistency

of the HPPRM by Example Problem

- Phase I Define: Sc. 5.2

- Phase II Model: Sc. 5.3

- Phase III Solve: Sc. 5.4x x xx x x

x

x

Figure 8-2

Hypotheses Testing Guide: Where the Answers are Provided


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8.1.3 Testing the Theoretical Structural Validity (TSV)

The hypothesis-testing starts with the Theoretical Structural

Validity (TSV) – the main pillar in the bridge over the ‘chasm of new

knowledge’ – and the process of demonstrating the TSV of the HPPRM is

illustrated in Figure 8-3.

In context of a design method, this validity rests on (1) the

‘correctness’ of the individual constructs constituting the complete method and (2) the internal

consistency in the way the constructs are organized within the method. Together this validates that the

results are obtained in a correct and consistent manner, implying that for valid input the output can be

trusted. The specific approach to demonstrate ‘correctness’ of the individual constructs is by literature

referrals, and the specific approach to demonstrate internal consistency is by flow-chart representation of the method.

The design method to be tested is the HPPRM. Embedded in this method are the three

hypothesized constructs, namely, Numerical Taxonomy, Technology Diffusion, and the compromise DSP.

Hence, testing the Theoretical Structural Validity of the HPPRM lies in testing the Theoretical Structural

Validity of each of these constructs in addition to testing the internal consistency of the HPPRM. The

Theoretical Structural Validity of the key constructs is demonstrated as follows.

§ The Theoretical Structural Validity of Numerical Taxonomy is demonstrated in Section 4.1 in

general and in Section 4.1.5 in particular. This is done by demonstrating that Numerical Taxonomy not only is generally accepted for revealing the inherent structures / pattern in data-sets, but it is used

to benchmark new methods as well.

§ The Theoretical Structural Validity of Technology Diffusion is demonstrated in Section 4.2 in

general and in Section 4.2.2 in particular. This is done by demonstrating that Technology Di ff usion

is accepted for simulating the impact on market shares when firms transfer to new technology within

the same industry.

§ The Theoretical Structural Validity of the compromise-DSP is demonstrated in Section 4.3 in general

and in Section 4.3.2 in particular. This is done by demonstrating that the compromise-DSP is

generally accepted as a construct for multi-objective optimization used in searching for ‘satisficing’

rather than optimal solutions.

The internal consistency of the HPPRM is demonstrated in Sections 3.2 through 3.4, where

each phase is elaborated and visualized in a separate flow-chart. In doing so, the information flow is

emphasized and the consistency is demonstrated in terms of (1) there is no redundant information being

generated and (2) the underlying assumptions are identified, evaluated and found valid.

T S V

EPVTPV

E S V


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Taking the Theoretical Structural Validity of the hypothesized constructs together with the

internal consistency of the HPPRM, it is asserted that the HPPRM is Theoretical Structural Valid.


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Numerical

Taxonomy:

Sneath & Sokal, 1973

Keller, 1992

…ü

Technology

Diffusion:

Hall, 1994

Silverber, Dosi et al. 0988

…ü

c-DSP; Mistree et al. 1990

Lewis et al. 1995

Chen et al. 1996

…ü

HPPRM

Theoretical

Structural

Validü

CRITERIA

Theoretical Structural Validity

Tools / Methods Used in

HPPRM Considered Valid

within their Specified Ranges, i.e., for Certain Applications

The Internal Structure of

HPPRM Considered

Consistent

ü

ü

I

I

I

TI

I

I T

I

TI

I T

?

T

I

I

I

I

T

TI

I

T

I

T

I

I I

I T

I

I

I

T

T

I

I

T

I

T

I

I

I

Figure 8-3

The Theoretical Structural Validation Process –

A Pictorial Representation


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8.1.4 Testing the Empirical Structural Validity (ESV)

The hypothesis-testing continues with the Empirical Structural

Validity (ESV) – the other pillar in the bridge over the ‘chasm of new

knowledge’ – and the process of demonstrating the ESV of the HPPRM is

illustrated in Figure 8-4.

In context of a design method, this validity refers to (1) applying

the method within acceptable ranges of the method-constructs, (2) the proposed case studies being

appropriate, and (3) adequate quality of the data associated with the case studies. Together this validates

that the problem fits the method, that the case studies fit the problem, and that the testing fits the case

studies, i.e., that the input intended for the method is valid. The specific approach to demonstrate that the

problem fits the method is by comparing the intended problem to the problems for which the constructsare generally accepted. The specific approach to demonstrate that the case studies fits the problem is by

comparing the characteristics of the chosen case studies to the characteristics of the general problem as

given in Section 1.5.1. The specific approach to demonstrate that the available data for the case studies

have sufficient quality to support a conclusion is by induction.

First of all, does the problem fit the method? In other words, is the problem suited for the

constructs constituting the method? This is elaborated in Chapter IV, where the applicability of each of

the key constructs in the HPPRM is critically evaluated. The applicability of Numerical Taxonomy is

elaborated in Section 4.1.6; the applicability of Technology Di ff usion is being elaborated in Section 4.2.5;

and the applicability of the c-DSP is being elaborated in Section 4.3.4.

Secondly and thirdly, do the case studies fit the problem, and does the testing fit the case

studies? In other words, are the case studies appropriate for demonstrating the usefulness of the method?

For the purpose of testing the method performance, one Example Problem and one Case Study is used,

namely, a Family of Gravitational Separators (Chapter V) and a Family of Marginal Field Vessels

(Chapter VI and VII). The appropriateness of the Example Problem is elaborated in Section 5.1.3,

demonstrating that the Gravitational Separators are representative of the general problem and that the

data, being ‘tailored’ to test the clustering algorithm, is suited to demonstrate the methodology. Note that

even if the data is ‘tailored’ to test the clustering algorithm, it also demonstrates the usefulness of the

method for the hypothetical design portfolio. The appropriateness of the Case Study is elaborated in

Section 6.1.2, demonstrating that the Marginal Field Vessels are representative of the problem and that

the data, being real life data, is suited to demonstrate the usefulness of the method.

EPVTPV

E S V

T S V


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Given that the Gravitational Separator example problem and the Marginal Field Vessel case

study are appropriate for testing the Empirical Performance Validity of the HPPRM, we assert that the

HPPRM is valid for its intended input, hence, the HPPRM is Empirical Structural Valid.

ü

CRITERIA

Empiri cal Structural Validity

Intended Application ofTools / Methods in HPPRM

within their Specified Ranges

Examples / Case studies

Representative for Problem

Able to Support Conclusions

ü

ü

Numerical

Taxonomy: Sneath & Sokal, 1973

Keller, 1992

…

Valid Applications

Classification

Pattern recognition

… ü

ü

Technology

Diffusion: Hall, 1994

Silverber, Dosi et al. 0988

…

Valid Applications

Discounting Performance

Learning

… ü

ü

c-DSP; Mistree et al. 1990

Lewis et al. 1995

Chen et al. 1996

…

Valid Applications

Multi-Objective optimization

Distance to Target

… üü

ü HPPRM TheoreticalStructural

Valid

Empirical

Structural

Validü

ü

ü


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Figure 8-4

The Empirical Structural Validation Process–



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8.1.5 Testing the Empirical Performance Validity (EPV)

Having demonstrated that the HPPRM is likely to provide

trustworthy output for its intended and validated input, the next step is to

validate the usefulness of this output; i.e., to test the Empirical Performance

Validity (EPV). This is viewed as the first stone paving the bridge over the

‘chasm of new knowledge’, and the process of demonstrating the EPV of

the HPPRM is illustrated in Figure 8-5.

In context of testing the HPPRM, EPV refers to whether the HPPRM is able to produce useful

results for the chosen Example Problem and the chosen Case Study, and whether the key HPPRM

constructs (Numerical Taxonomy, Technology Diffusion and the compromise-DSP) contribute positively

to this usefulness. Usefulness in this context refers to whether the resulting HPP contributes to reducingcost and time while maintaining acceptable operational performance (increase competitiveness).

First of all, the usefulness of the Hierarchical Product Platform (HPP) synthesized from the

HPPRM is demonstrated in Section 5.4.4 for the Example Problem and in Section 7.10 for the Case Study.

Having answered the major performance validity question, the remaining issue is whether the key

constructs are contributing to this usefulness.

Numerical Taxonomy is anticipated to contribute to the HPPRM usefulness by identifying the

potential for standardization in an existing design portfolio, hence, reducing the combinatorial problem to

a manageable size. This is demonstrated in Section 4.1.7.

Technology Diffusion is anticipated to contribute to the HPPRM usefulness by facilitating

evaluation of alternative processes for various steps in the fabrication process, hence, advocating a

continuous improvement approach. This is demonstrated in Section 4.2.6.

The compromise DSP is anticipated to contribute to the HPPRM usefulness by integrating the

product and process model, hence, facilitating solving for multiple objectives and advocating robust and

adaptive solutions. This is demonstrated in Section 4.3.5

Having demonstrated that Hierarchical Product Platforms are useful in terms of saving time

and cost for large and complex made-to-order systems, and that the key constructs in the HRRPM

contributes to this usefulness, we assert that the HPPRM is useful at least for the chosen Example Problem

and Case Study. Hence, we assert that the HPPRM is Empirical Performance Valid,

EPV

T S V

TPV

E S V


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c-DSP:

“Probabilistic”

…

Purpose:

Gain appreciation .

about problem to

support decisionüü

ü

Technology

Diffusion:

Hall, 1994 “Population-

… Thinking”

Purpose:

Discount process

performance as

learning takes

place

ü

ü

ü

Numerical

Taxonomy:

Sneath, 1973

Keller, 1992 “Path-… Dependence”

Valid App… Purpose:

Classification Find potential for

Pattern rec… standardization

… within existing

portfolio ü

ü

CRITERIA

Empir ical Perf ormance Vali dity

Numerical Taxonomy to find

potential for Standardization

Technology Diffusion useful to

discount process performance

c-DSP useful to gain apprecia-

tion about problem

HPP useful to reduce cost and

time while maintaining oper. perf.

ü

ü

ü

ü ü

ü

HPPRM

Theoretical

Structural

Valid

Empirical Empirical

Structural Performance

Valid Validü

ü

ü

...

ü

Figure 8-5

The Empirical Performance Validation Process –



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8.1.6 Testing the Theoretical Performance Validity (TPV)

Having demonstrated validity for a selected set of case studies,

what is left is to build confidence in the validity beyond this set of case

studies, i.e., to build confidence in generality. This validity is termed

Theoretical Performance Validity (TPV), and is viewed as the last stone

paving the bridge over the ‘chasm of new knowledge’. This process of

demonstrating the TPV of the HPPRM is illustrated in Figure 8-6.

In context of testing the HPPRM it refers to the HPPRM being capable of producing useful

results beyond the Gravitational Separator Family example problem and the Marginal Field Vessel Family

case study. In order to substantiate a generality claim for the HPPRM, we apply a piecewise strategy: The

Theoretical Performance Validity of the HPPRM rests on three pillars, namely;

1. that the key method-constructs are applicable for problems beyond the Example Problem and

the Case Study. Their generality and applicability is demonstrated in Sections 4.1.5, 4.2.4 and

4.3.3, and in Sections 8.1.3 and 8.1.4 as part of the Theoretical and Empirical Structural

Validation.

2. that the Example Problem and Case Study are representative for the general problem. This is

part of the Empirical Structural Validation, see Sections 8.1.4.

3. that the HPPRM is useful for at least some selected case studies. This is part of the Empirical

Performance Validation, see Sections 8.1.5.

However, the final acceptance of the HPPRM being Theoretical Performance Valid requires a ‘ leap of

faith’ , where the three other bridge elements (Theoretical Structural Validity, Empirical Structural

Validity, and Empirical Performance Validity) are viewed as ‘leap facilitators’. This is illustrated in

Figure 8-6, and it concludes the validation of the Hypotheses and hence, the answering of the questions in

compliance with the requirements of the GWW School of Mechanical Engineering for Ph.D. research.

If we have been successful in facilitating a ‘leap of fai th ’, this research is accepted as having

added new scientific knowledge to the field of Engineering Design. Further, we assert that we have

achieved the principal objective of this dissertation, namely, that we have

developed a conceptual framework for realizing Hierarchical Product Platforms from which a family

of products using much of the same technology can be designed and realized for different

applications.

What is left to demonstrate is that the HPPRM is significant and original. This demonstration

starts with identifying the specific contributions to the field of Engineering Design from developing the

EPVTPV

‘Leap of Faith’

E S V

T S V


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HPPRM, i.e., from achieving the principal objective. Then, we evaluate these contributions in terms of

their significance and originality. This is done in the next section.

HPPRM

Theoretical

Structural

Valid

Empirical Empirical


Valid Valid for

case study

ü

ü

ü

CRITERIA

Empir ical PerformanceValidity

HPPRM valid beyond

example problem and

case study

ü

HPPRM

Theoretical Theoretical


Valid Valid

Empirical Empirical


Valid Validü

ü

ü

ü

HPPRM

Theoretical

Structural

Valid

Empirical Empirical


Valid Valid for

example problem

ü

ü

ü

Through the process we

hope to have built

confidence in generality

“a Leap of Faith ”

Figure 8-6

The Theoretical Performance Validation Process –



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8.2 DEVELOPING THE HPPRM –

ACHIEVEMENTS AND CONTRIBUTIONS

Assuming that developing the HPPRM adds new scientific knowledge to the field of

Engineering Design, what is left to demonstrate (according to the Ph.D. research requirements) is that this

added scientific knowledge is significant and original. This demonstration is based on (1) identifying

potential contributions to the field of engineering design, and evaluating these potential contributions in

terms of their (2) significance and (3) originality. If the potential contributions are found to be both

significant and original, we assert that developing the HPPRM is a contribution to the field of Engineering

Design, and hence, this research complies with the Ph.D. requirements as given by the GWW School of

Mechanical Engineering.

8.2.1 Contributions to the Field of Engineering Design

First of all, in Section 8.1 we demonstrate that we have achieved our principal goal, namely, to

develop a framework for realizing Hierarchical Product Platforms from an Evolutionary Perspective.

From this achievement we derive the following contributions to the field of engineering design, see Figure

8-7:

1) the Hierarchical Product Platform Realization Method (HPPRM);

2) an Evolutionary Approach to engineering systems development;

3) the Validation Square anchored in the relativistic school of epistemology;

8.2.2 Original and Significant: Completing the Criteria for a Ph.D.

The three contributions are apparently interconnected, however, we will evaluate them

separately for their significance and originality. This is based on the assumption that even if one or more

of the three potential contributions are found not to be significant and / or original, the others still may be.

The significance and originality are demonstrated in the following.

The HPPRM – Original and Significant?

The originality of the HPPRM lies in the Hierarchical Product Platform concept and the

method-constructs, namely, Numerical Taxonomy and Technology Diffusion. In Section 1.3.4 we

demonstrate the originality of the HPP by pointing to how the HPP is an advancement to current product


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platform and product family design. In Section 2.4.3 we demonstrate the originality of Numerical

Taxonomy in the context of engineering design, by pointing to how it integrates Group Technology,

clustering and Product Platform thinking. In Section 2.5.2 we demonstrate the originality of Technology

Diffusion by pointing to its origin being in the economics, and that it have not been applied in engineering

design before.

The significance of the HPPRM lies in the usefulness of the HPP from the perspective of

reducing cost and schedule while maintaining operational performance at an acceptable level. In Table 7-

22 we show the CAPEX savings to be around 6 mill USD and the time saving to be around 8 months for

the CAPEX driven HPP. In Table 7-23 we show the CAPEX savings to be around 3 mill USD and the

time saving to be around 1.5 years for the Marginal Field driven HPP. In context of developing marginal

fields, we consider these figures to be significant, and hence, we assert that the HPPRM is both an original

and significant contribution to the field of engineering design.

The Evolutionary Approach – Original and Significant?

The originality of the evolutionary approach lies in the fact that it is adopted from an entirely

different domain, namely, from biology. Even if taken from a different domain, it does not seem to

contradict conventional knowledge in the field of engineering design, and in Section 2.3.2 we explicitly

demonstrate that the evolutionary approach actually compliments it.

Since the HPPRM is based on the evolutionary approach, its significance is directly linked to

the significance of the HPPRM. The contribution from the evolutionary approach comes from introducing

a new perspective where adapting to change becomes the imperative objective, and where variation is seen

as the key to become increasingly adaptive. Based on this, we assert the evolutionary approach to be both

an original and significant contribution to the field of engineering design.

TheValidation Square – Original and Significant?

The originality of the Validation Square lies in the application rather than the origin. The

Validation Square comes from the realm of system dynamics testing (Richardson and Pugh 1988), then it

was used in (Bailey 1997), before we expand it in this dissertation to comprise design method testing.

The significance of the Validation Square lies in its comprehensiveness where all aspects of

verification / validation is covered. Based on this we assert that the Validation Square constitutes an

original and significant contribution to the field of engineering design.

We have now demonstrated that the HPPRM represents new knowledge to the field of

Engineering Design, and that the contributions stemming from developing this framework are original


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and significant. Hence, we assert that this research complies with the Ph.D. requirements as given by the

GWW School of Mechanical Engineering.


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FOUNDATION FOR RESEARCHFOUNDATION FOR RESEARCH

THE “EVOLUTIONARY APPROACH”THE “EVOLUTIONARY APPROACH”

THE “HPPRM”THE “HPPRM”

BIOLOGY

Evolution

ECONOMY

Technologydiffusion

ENGINEERING

Optimization

NumericalTaxonomy

(Hypothesis 1)

EvolutionaryTechnology Diffusion

(Hypothesis 2)

Compromise DecisionSupport Problems

(Hypothesis 3)

AssemblyTaxonomy

Performancediscounting

C-DSP formulationfor HPPs

HPP definition(RQ1)

Process Modeling

incl. learning (RQ2)

Product

Model

HPP c-DSP;an Integrated Product

and Process Model

Process

Model

Synthesis of Product

and Process (RQ3)

Standardization

Taxonomy

StandardizationTaxonomy

Theoretical TheoreticalStructural Performance

Validity Validity

Empirical Empirical

Structural PerformanceValidity Validity

Theoretical TheoreticalStructural PerformanceValidity Validity

Empirical EmpiricalStructural PerformanceValidity Validity

THE “VALIDATION SQUARE”

PHILOSOPHY

Epistemology

Social / holistic /relativistic School

of Epistemology

Process of

building confidencein usefulness

Figure 8-7

The Contributions to the Field of Engineering Design


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8.3 CRITICAL ANALYSIS –

RESEARCH LIMITATIONS

Our objective in this section is to identify the limitations to the presented research. We do this

by first evaluating (in Section 8.3.1) the limitations of the method-constructs themselves and what

limitations this imposes on the total framework. Then, in Section 8.3.2 we evaluate the robustness of the

framework by asking ‘what-if’ questions regarding the objective (the what) and the approach (the how).

Then, in Section 8.3.3 we take a critical look at the HPPRM validity to identify its most significant

weaknesses. Finally, in Section 8.3.4 we evaluate what it will take to implement such a structure in, for

example, Kvaerner. The information from this exercise is then used to identify avenues for future work,

which is dealt with next in Section 8.4.

8.3.1 The HPPRM – Limitations to its Constructs and Framework

The HPPRM is a framework intended for identifying a direction into the future based on

leveraging the past – a process that rests on the following core assumptions.

§ The HPPRM is developed for the Offshore Oil and Gas industry, based on the existence of a

portfolio of similarly but not identically configured products;

§ Standardization improves cost and schedule while compromising operational performance, and

we are seeking the standardization level that gives the best compromise;

§ When seeking the standardization level that gives the best compromise, we prefer robust

solutions to optimized solutions, i.e., we seek solutions that are significantly better than what

we started out with and not likely to change for relatively small changes in the environment;

§ In addition to seeking a standardization level, we seek a corresponding process that enhances

the benefits of standardization, hence, we assume the existence of information to support the

estimates of learning rates and initial skill levels.

Based on these assumptions, we analyze the product portfolio, at various levels of assembly, to

identify where there is a potential for standardization. In other words, we identify parts / assemblies that

are so similar that it seems justifiable to reduce the number of options to one, i.e., to standardize for this

particular part / assembly. We recognize that the mentioned similarities can appear at different levels of

assembly and can involve different end products at the various assembly levels. Hence, at different levels

of assembly there can be items standardized for different end products. This is the premise for the

Hierarchical Product Platform, and the following discussion will be in context of the above. Note,

however, that this approach is very much linked to how much is invested in the past, hence, the HPPRM


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is not intended for industries where product changes can be made without substantial consequences to the

project execution.

Numerical Taxonomy – Limitations

We use Numerical Taxonomy to identify the potential for standardization by quantifying the

similarities between the parts we compare. Numerical Taxonomy is a stand-alone method based on one

initial phase for determining what is to be compared (the OTUs), one intermediate phase for clustering,

and a final phase for construction of taxa. The first and last phase involves the use of heuristics, which

have not been formalized in this research, hence, affecting the repeatability. This constitutes a problem

from the scientific method perspective, where repeatability is normally seen as part of verification /

validation. However, this is only a major limitation in the cases where we look for one particular solution,

e.g., an optimum. In the intermediate phase involving clustering, we do not involve heuristics, but we

have only considered the UPGMA clustering algorithm and the similarity coefficient of Gower, see

Section 4.1. Nevertheless, the consequence is the same as for applying heuristics; we cannot claim

optimality for our proposed solution(s).

Technology Diffusion – Limitations

We use Technology Diffusion to discount (i.e., reduce) the anticipated benefits of

implementing new technology / processes / etc., to reflect the true benefits during a transition period

where an organization learns how to utilize the changes to their full potential. The Technology Diffusion

operator (see Section 4.2.1) takes learning rate and initial skill levels as input. The learning rate is linked

to the speed with which the organization is able to acquire skills, while the initial skill level refers to the

amount of skills that are directly transferable to the new situation.

The learning rate and initial skill levels are not trivial to estimate, and we have used heuristics

in this research based on comparing the situation at hand with a known benchmark situation in terms of

the time it takes to reach a certain level of skills. Further, we have only considered Technology Diffusion

as given in (Silverberg, Dosi et al. 1988; Silverberg 1991), and hence, there may be other and better ways

of modeling learning for the purpose of evaluating the true benefits of changing to new processes. Thus,

optimality cannot be claimed.

c-DSP – Limitations

We use the compromise DSP as a construct to organize our multi-objective model. Solving a

multi-objective model implies finding some sort of compromise between the various objectives. To help


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find a compromise that comes closest to satisfying all the goals, we use scenarios that are based on non-

formalized heuristics. Hence, a solution found by one designer may not be the same as the solution found

by another, everything else being equal, i.e., the input to the method is person dependent, which affects

repeatability.

Further, we have not considered alternative ways to organize and solve our model. Hence, we

cannot claim optimality in terms of the solving correctness nor the solving speed. The example problem

and the case study represent discrete ‘optimization’ problems and are small enough to allow enumeration.

Hence, we have not solved these problems by means of an optimization routine, which a bigger problem

may require. In such a situation, we do not know how the compromise DSP would work, especially if we

have to use DSIDES, the computer infrastructure in which the compromise DSP normally is solved.

The HPPRM Framework– Limitations

Being an integrator of the previously elaborated constructs, the major limitation to the HPPRM

in its present state is repeatability, stemming from the use of non-formalized heuristics. However, we try

to alleviate this lack of repeatability by giving detailed examples of our calculations in Appendix C.

Further, the lack of comparison of alternative constructs implies that optimality cannot be claimed, which

makes the HPPRM a non-rigorous framework for realizing Hierarchical Product Platforms. However, we

believe that rather having a solution that is optimal for a short time, if ever, we hope our solutions are

robust and applicable as the world is changing, and when no longer applicable, that they can by adapted to

the new situations in an affordable way. To say it with Robin Cooper (the father of Activity Based

Costing); we prefer the solution to be “approximately right rather than exactly wrong.”

When it comes to comparison of alternatives, we believe there is a right time for everything.

The scope in this research has been firstly to identify what kind of products we should be seeking to

facilitate reuse in a marginal field context (i.e., product platforms and product families). Secondly, to

identify how to realize the identified products (i.e., the evolutionary approach). Thirdly, to develop a

framework to facilitate the right realization of the right products (i.e., the HPPRM). Hence, the issue of

comparing the initially chosen constructs to other alternatives, is viewed as part of the next phase –

continuous improvement by changing the process.

8.3.2 The HPPRM – Its Robustness

The HPPRM is based on some principles, namely, clustering of existing data, modeling of

alternative processes, and solving based on the compromise DSP construct. What if we for some reasons


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cannot use one or more of these principles; will the HPPRM still hold as a framework? We answer this

question by first looking at the clustering.

Clustering is a means of identifying a potential for standardization. As the pool of items to

compare increases, and as the characters for which they are to be compared increases, the need for

clustering also increases. However, if there are other ways of identifying / quantifying similarities in order

to identify a potential for standardization, these may be interchanged with clustering at any time.

Evaluating alternative processes is part of increasing the ‘adaptiveness’ of an organization.

This is a preferred but not a crucial task. What is crucial, however, is the ability to link proposed

standardization schemes to cost and schedule impacts, so as to evaluate their goodness’. If this

information is not readily available, or if this information cannot be estimated, the usefulness of this

approach becomes very limited. The reason why is that the HPPRM rests on the very assumption that

standardization (i.e., reduce the number of options) will improve cost and schedule, while compromising

operational performance. Hence, we would like to find the right compromise, and without any means of

comparing benefits with drawbacks, there is little point in doing this exercise.

The compromise DSP construct is used to organize the problem in order to prepare it for

solution, i.e., organization and preparation for solving is the main issue. Hence, any construct that can

facilitate this can be used in the HPPRM framework with the right amount of adjustments. In this context,

the solution we seek is a Hierarchical Product Platform, i.e., the variables we want to FIND are the level

of standardization at each assembly level and the processes that gives he best combination of cost,

schedule and operational performance. If we for some reason want another solution, this is easily adjusted

in the FIND and he MINIMIZE part of the compromise DSP. Based on this we assert that the HPPRM

framework is quite robust when it comes to accommodating changes and / or alternative constructs.

8.3.3 The HPPRM – Limitations to the Validation Process

In Section 8.2 we have validated the HPPRM framework by means of the validation square

process. In this process, the assumption that has the weakest base is the Empirical Performance Validity

(the usefulness). The reason why, is that it is based on our subjective estimates of benefits and operational

scenarios. In order to alleviate this situation we have used the available information to establish upper and

lower limits for what is possible, and have tried to operate conservatively between these limits to make

sure we are not painting a too optimistic picture. In that respect, we see that standardization improves

schedule and increase initial CAPEX, as would be expected. However, we see that downstream

conversions are where the true benefits take place, as would be expected too. (This is confirmed by the


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Lockheed strategy for the C-130 Hercules). Hence, being the weakest link in the validation process, it is

still strong enough to support the overall validity.

8.3.4 The HPPRM – Limitations to Implementation

The HPPRM is developed for one particular industry, namely, the offshore oil and gas industry,

and we now estimate what it will take to implement this framework in this very industry. Firstly, the

information to be clustered has to be available in some, way, shape or form. The level of readiness will

vary, and this will impact the usefulness of clustering in terms of saving time and money to reduce the

combinatorial problem. In this research, the information has been taken off of drawings and manually put

into a database. We believe that as design increases the usage of centralized databases, it should not be too

difficult to get hold of the raw data from which aggregated characteristics can be derived.

If clustering is chosen to identify potentials for standardization, we recommend using a

commercial software package. From such software one gets all kinds of information about the goodness of

the clustering itself, and normally, different similarity coefficients are available to choose from. Hence, as

long as the data is available, we don’t see a big problem in getting it clustered.

Evaluating the clusters require experienced people, that can identify which parts are not

compatible for standardization, e.g., ship side and double bottom as discussed in Section 6.6.7. This

assumption is valid in the case of Kvaerner, but may not be valid in other cases. However, analyzing a

portfolio of existing designs indicates that there is. Over time, the expertise held by the experienced

persons can be transformed into formalized heuristics and computerized, increasing the automation level.

Regarding the latter, estimating learning rates and initial skill levels is a challenge, and will

constitute a problem that has to be addressed. The important thing, however, is that we advocate to

continuously look for better ways of executing a project. If alternatives are proposed, the effect of

implementing them should get attention. Furthermore, by demonstrating that the learning rate has an

impact, we hope to emphasize that design is truly human-centered, and that continuous learning at the

individual level may be the single most important thing that can improve an organization’s performance.

In addition, by demonstrating that the initial skill levels also has a substantial impact, we hope to

encourage small and frequent steps rather than few and large ones, i.e., we advocate evolution over

revolution.

Regarding modeling and solving of problems in industry, this does not constitute a dramatic

departure from existing practices. The type of problem may be different, and the input information may

have to be generated, hence, consuming resources. Further, the solving scheme may not have an adequate

infrastructure (discrete optimization problem), and the interpretation of the results may have to be learnt.


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What is important, however, is that if a company finds is worth while to pursue this strategy, they may do

so without having to dramatically change their ways – it pretty much boils down to believing in the

benefits.

Finally, the HPPRM is not to be constantly applied, but at certain points in time where the

product portfolio proliferates due to introduction of new family members and / or sub-systems addressing

completely new applications, etc. In these instances, Path-Dependence is still applicable and the task

becomes to look at the new items and compare them to the standardized ones.

Based on this, we are ready to recommend some avenues for future work that in our opinion

will improve this framework.


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8.4 RECOMMENDATIONS –

AVENUES FOR FUTURE WORK

Having critically evaluated the research presented in this dissertation, our objective in this

section is to identify some fruitful ways of expanding this work. Since this research is conducted in an

academic setting based on an industrial problem, we see the road forking when it comes to future

research. One avenue goes into industry dealing with implementation and further validation of our

research, and one goes into academia dealing with further refinement of domain independent issues risen

by our research, see Figure 8-8. Hence, in Section 8.4.1 and 8.4.2 we examine the industrial and academic

avenues respectively.

8.4.1 Industrializing the HPPRM – the Industrial Avenue

Further validation of the HPPRM framework is linked to a successful industrial

implementation; first in one industry, then in other industries. However, we only make recommendations

regarding how to implement the HPPRM in the offshore oil and gas industry. We still assume that the

objective is to find the level of standardization and the corresponding process(es) that gives the best

combination of cost, schedule and operational performance. In this context

§ we recommend to revisit what to compare for similarities; there might be something more

promising than plates, stiffeners, girders, etc. For example, maybe topside is more interesting

from a standardization perspective than the hull.

§ Having established what to compare, we recommend to look at how to make the required input

readily available. This is directly affecting how to design and how to document the design.

§ Having clustered the input, we recommend to look at meaningful interpretations of the

clustered data from an industrial perspective. This should be linked to clustering research in

academia, especially to the formalization of how to construct taxa, see Section 8.4.2.

§ Regarding process partitioning, we recommend to identify which parts of the realization

process(es) are more interesting from an improvement perspective. This should be linked to

research regarding how to represent process partitioning in academia, see Section 8.4.2.

§ Regarding the standardization itself, we recommend to formalize a set of heuristics in order to

comply with ISO 9000 requirements regarding tractability of design decisions.

§ Regarding modeling, we recommend evaluating the availability of required information. If not

readily available we propose to change either the modeling or the performance variables. This

should be linked to research regarding model representations in academia, see Section 8.4.2.


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§ Regarding solving, we recommend to research what scenarios represent our customers best;

what objective function(s) addresses the customer requirements best; and what solutions

(robust / optimized) gives the best overall economy. This should be linked to research in

academia regarding solving algorithms, see Section 8.4.2.

THE HPPRM FRAMEWORK --

BASIS FOR SUGGESTING

FUTURE RESEARCH

Establish

Scenarios

Establish

Scenarios

THE ACADEMIC

RESEARCH AVENUE

(domain independent)

THE INDUSTRIAL

RESEARCH AVENUE

(domain dependent)

What to standardize

Information availability

What to partition

Info availabilityMetrics for evaluation

What to model

Info availabilityMetrics for evaluation

Mapping of customers

- weighting schemes

- operational scenarios

Decision criteria

- robust / optimized

How to cluster

- similarity coefficients

- clustering algorithms

- cluster representation

- construction of taxa

How to partition

- division criteria- metrics

How to model:

- cost/time/performance- learning

How to solve- discrete problems

- integer problems

Research validation

Metrics for evaluation

What to solve for - objective functions

Formalize heuristics

Gather DataGather Data

Standardize

Designs

Standardize

Designs

Decide on HPPDecide on HPP

Solve for eachScenario

Solve for each

Scenario

PartitionProcesses

Partition

Processes

Model

Relationships

Model

Relationships

Cluster DataCluster Data


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Figure 8-8

The Research Avenues Stemming from the HPPRM Framework


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8.4.2 Improving the HPPRM Constructs – the Academic Avenue

Having presented some recommendations regarding how to implement the HPPRM in the

offshore oil and gas industry, we now present the corresponding domain independent research aspects. All

of these aspects should be researched in parallel with research in industry in order harmonize the

WHAT’s and the HOW’s regarding the HPPRM framework. Based on this we elaborate on some of the

generalized concepts stemming from the HPPRM framework, see Figure 8-8 and Section 8.4.1.

Clustering – How to Improve the Identification of Standardization Potentials

Clustering is used to identify potentials for standardization by quantifying similarities. In our

research we have only considered the UPGMA clustering algorithm, the similarity coefficient of Gower

and the dendrogram representation, see Section 4.1. We recommend to evaluate whether other alternatives

are better suited for the purpose of identifying potentials for standardization, i.e., to evaluate which

alternatives are better for which situations.

Having a clustering, the next thing is construction of taxa. In our research we did this by trial

and error, applying our experience to evaluate when a good partitioning appeared. We recommend to look

into how to formalize a set of heuristics in order to computerize this process.

Process Partitioning – How to Represent a Process

Process partitioning is introduced to identify where the process can be improved in general,

and where standardization creates new opportunities for process improvements in particular. In our

research we partition the process along a timeline, i.e., design, fabrication, installation, operation and

maintenance, etc. From the perspective of improving the processes, there might be other partition schemes

and we recommend looking into this. On the same token, metrics for evaluating how well a partitioning

suits a given purpose also needs attention, and we recommend this as a future research area as well.

Modeling – Revealing the Performance of the Proposed Standardization Schemes

The purpose of modeling is to investigate the impact of standardization on cost, schedule and

operational performance. In this research, we estimate deviations from a base-case for the various

standardization schemes. However, this can only be done where detailed accounts for a base case exists,

an assumption that rarely holds in academia. Hence, we recommend to look for other indicators that can

be used to quantify how standardized systems compare to non-standardized systems in terms of cost, time

and operational performance.


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In particular, we recommend to look at learning within an organization, not only from a

process change perspective, but from a more general adapt-to-change perspective. In this respect, we

recommend to look at ways of estimating learning rates and initial skill levels; what organizational

characteristics are good indicators of learning rate, and what are good indicators of how much can be

leveraged from current processes to new processes.

Solving – Arriving at Acceptable Solutions

The purpose of solving is to find the set of free variables that give the best performance for the

system as described by an objective function. In this research, we have used the compromise DSP

construct to facilitate solution, and we have solved by enumeration for both the example problem and the

case study. However, we foresee situations where a search approach may be more appropriate, and we

recommend further research regarding implementation of search algorithms into the HPPRM. In this

respect, gradient based search does not seem too promising considering the nature of the discrete problem.

Hence, we have indicated Genetic Algorithms or the Foraging ALP as given in (Lewis 1996) as

promising starting points.

Validation – Supporting the Decisions

The purpose of validation is to build confidence that we make the right decisions, whether it is

final decision on a standardization scheme or on implementing alternative processes. In this research, we

have introduced the validation square, but the final step requires a ‘leap of faith’. We recommend to look

into how this leap can be made even shorter, i.e., how can we improve documentation of usefulness with

respect to a purpose. We have suggested documentation by means of example problems and case studies,

however, in academia example problems and / or case studies often become hypothetical and / or trivial.

These potential avenues for further research are just a sample of the realm of topics waiting to

be addressed. It is our hope that at least some of these topics are indeed taken on by researcher’s to come,

so that this work may live on. Based on this we proceed to give a concluding remark in Section 8.5 to sum

up the whole ‘shebang’.


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8.5 CONCLUDING REMARKS

Before we conclude we look to Bob Mandel’s words:

“Every Problem Was Once A Solut ion To A Previous Problem ”

- Bob Mandel

We certainly hope that the HPPRM is not an end in itself, but a stepping stone for future

research work in many fields of engineering – that we have created future problems for future researchers.

We belive that the evolutionary approach shows prospects of providing an overall perspective on design

that can help designers from different disiciplienes come together to ‘co-evolve’ products rather than sub-

optimizing them.

With this we conclude this dissertation to the words of Krishnamurti:

“The wor ld wi l l change only w hen we have changed ourse lves and

– mo st signi f icant ly – abandon ed our resis tance to change” Krishnamurti


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REFERENCES

Bailey, R. (1997). The Design of Industrial Ecosystems. GWW School of Mechanical Engineering.

Atlanta, Georgia Institute of Technology.

Lewis, K. (1996). An Algorithm for Integrated Subsystem Embodiment and System Synthesis.

Mechanical Engineering. Atlanta, Georgia Institute of Technology: 329.

Mistree, F., O. F. Hughes and B. A. Bras (1993). The Compromise Decision Support Problem and the

Linear Adaptive Programming Algorithm. Structural Optimization: Status and Promise, Washington D.C.

Richardson, G. P. and A. L. Pugh (1988). Introduction to System Dynamics Modeling with DYNAMO.

Cambridge, MA, the MIT Press.

Silverberg, G. (1991). “Adoption and Diffusion of Technology as a Collective Evolutionary Process.”

Technological Forecasting and Social Change 39: 67-80.

Silverberg, G., G. Dosi and L. Orsenigo (1988). “Innovation, diversity, and diffusion: a self-organisation

The Economic Journal 98: 1032-1354.

Sneath, P. H. A. and R. R. Sokal (1973). Numerical Taxonomy. San Francisco, W.H. Freeman and

Company.

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