concurrent design method for developing a new product

International Journal of Industrial Ergonomics 29 (2002) 41–55

Concurrent design method for developing a new product

Shih-Wen Hsiao*

Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan 70101, ROC

Received 1 July 1997; received in revised form 31 May 2001; accepted 31 May 2001

Abstract

A concurrent customer-oriented design method for developing a new product is addressed in this article. The design

of a secure music-toy for children aged under seven is taken as a case study to specify the implementation procedures ofthis method. The qualitative design parameters and design criteria are first deployed with the quality functiondeployment and failure mode and effect analysis technologies and then quantified with the analytic hierarchy process

technology to get the best design targets with which the detail design is completed. The design for assembly technologyis also used to analyze the assembly performance and the costs of design alternatives. In this manner, a product thatmore nearly fits the consumer needs and is of higher competitiveness can be designed and the total quality is managed

after the design process has been completed.

Relevance to industry

Developing a high quality and low cost product is an important policy of an enterprise in today’s highly competitivemarketplace. To reach this objective, a systematically transparent method by integrating several techniques is proposedin this study. With this systematic methodology, a high quality and low cost product that more fits the consumer needs

is to be designed and consequently the competitiveness of the product is improved. r 2002 Elsevier Science B.V. Allrights reserved.

Keywords: Concurrent engineering; Product design; Quality function deployment; Analytic hierarchy process; Failure mode and effect

analysis; Design for assembly

1. Introduction

The development of new product is rewardingand necessary to maintain a healthy organization.For example, in a survey of 700 firms (60%industrial, 20% consumer durable, and 20%consumer nondurables) Booz et al. (1982) foundthat over a five-year period new products ac-

counted for 28% of these companies’ growth. In asimilar survey, primarily of industrial firms, theConference Board (Duerr, 1986) found that 35%of the current revenue was derived from productsthat were not on the market 10 years previously. Ina 1990 study sponsored by the Marketing ScienceInstitute researchers found that 25% of currentsales were derived from new products introducedin the last three years (Wind et al., 1990).Although there are rewards to successful in-

novation, new product failure rates are substantialand the cost of failure is large. Booz et al. (1982)

*Tel.: +886-6-275-7575; fax: +886-6-274-6088.

E-mail address: [email protected]

(S.-W. Hsiao).

0169-8141/02/$ - see front matter r 2002 Elsevier Science B.V. All rights reserved.

PII: S 0 1 6 9 - 8 1 4 1 ( 0 1 ) 0 0 0 4 8 - 8

found that the failure rate of new productsactually introduced in the market remained inthe 33–35% range between 1963 and 1981.Crawford (1979) concluded that about 20–25%of industrial and 30–35% of consumer productsfail. The Association of National Advertisers(1984) found that 46% of the new products thatwere introduced to new categories failed. Onthe other hand, based on the universal successcurve, Stevens and Burley (1997) found that about3000 raw ideas are required to produce onesubstantially new commercially successful indus-trial product.New product development is also costly. For

example, Booz et al. (1982) found that only oneof seven new-product ideas are carried to thecommercialization phase. This means that thesuccessful product must not only return its uniquedevelopment cost, but cover the costs of the othersix products that received attention but were notintroduced.The high failure rates and the high costs make

new product development risky. But new productdevelopment can be managed so that the risks areminimized and the profit maximized. The failurerates can be reduced if high-quality products areproduced. Quality Function Deployment (QFD) isa tool for this purpose (Erikkson and McFadden,1993 and Graessel and Zeidler, 1993). Morerecently, Lester (1998) argues that the success ofa new product development effort hinges on 16critical factors in five areas: (1) senior managementcommitment, (2) organizational structure andprocesses, (3) developing attractive new productconcepts, (4) forming a venture team, and (5)project management.Products have characteristics that describe their

performance relative to customer requirements orexpectations. The quality of a product is measuredin terms of these characteristics. A basic principleof Total Quality Management (TQM) is thatquality must be built into the development process.If the process is not controlled the quality of theproducts is random and has to be tested post facto.If the process is controlled it is possible to predictthe quality of the products. Simultaneous Engi-neering is importance in present industry (Gordonand Isenhour, 1990). The theory behind Simulta-

neous Engineering is to create the ‘perfect’ design.In this instance ‘perfect’ stands for the best designpossible in terms of its aesthetics, efficiency,practicality, easy assembling and manufacturingqualities as well as lowest overall cost. In thisstudy, the techniques of QFD, failure mode andeffect analysis (FMEA), design for assembly(DFA), and analytic hierarchy process (AHP) areintegrated to develop a new product such thatthe total quality of the product can be managed.Though a small product is employed as anexample, this methodology can also be applied todevelop other more complicated products.

2. Theoretical background

2.1. Quality function deployment (QFD)

QFD was developed in Japan in 1972 andintroduced in the United States in the late 1983(Akao, 1990). Using this method, Toyota was ableto reduce the costs of bringing a new car model tomarket by over 60% and to decrease the timerequired for its development by one-third (Ullman,1992). QFD consists of several activities supportedby various tables and matrices. The basic idea is totranslate customers’ requirements into the appro-priate technical requirements for each stage ofproduct development and production. The proce-dures are divided into the following six steps(Fig. 1).

Step 1: Identifying the customers.Step 2: Determining customer requirements.Step 3: Determining relative importance of therequirements.Step 4: Competition benchmarking.Step 5: Translating customer requirements intomeasurable engineering requirements.Step 6: Setting engineering targets for thedesign.

The benefits of using QFD are:

1. The lead time of developing a new product isshortened.

2. The number of design changes is reduced.3. The uncertainty of the design problem is reduced.

S.-W. Hsiao / International Journal of Industrial Ergonomics 29 (2002) 41–5542

4. The designed product more fits the customerneeds.

2.2. Analytic hierarchy process (AHP)

Since Saaty’s development of the AHP in the1970s, numerous books and papers have beenpublished concerning its theory and applications(Saaty, 1980 and Zahedi, 1986). The basic problemof decision making is to choose the best one from aset of competing alternatives that are evaluatedunder conflicting criteria. Since the product designis a multi-solution problem affected by variousvisible, invisible, qualitative and quantitativefactors such as the functions, aesthetics, safety,cost, operation, reliability and life cycle etc., usinga systematic method to evaluate the prioritiesamong the related factors is necessary. The AHPprovides us with a comprehensive framework forsolving such problems. The procedure is todecompose a complex system into a hierarchy tocapture the basic elements of the problem and thencalls for simple pairwise comparison judgments todevelop priorities in each hierarchy. The hierarchystructure contributes to understanding the designalternatives and to get quantified results to make

decisions and to reduce the risk of making a wrongdecision.

2.2.1. Implementation procedure of AHPUsing AHP in solving a decision problem

involves three phases (Saaty, 1980).

(1) Phase 1: Structure a hierarchy of the criteriathat influence the behavior of the problem. It hasbeen shown that 7 is an optimum number ofelements which can be compared with any reason-able (psychological) assurance of consistency(Saaty, 1980). Thus, we must have at most sevenelements in each cluster in each level of thehierarchy.(2) Phase 2: Calculate the vectors of priorities

between levels. In this phase, three steps arecontained.(i) Construct a pairwise comparison matrix.

Assume that n activities are being considered by agroup of interested people. We assume that thegroups’ goals are:(a) to provide judgments on the relative

importance of these activities,(b) to ensure that the judgments are quantified

to an extent which also permits a quantitativeinterpretation of the judgments among all activ-ities.

Fig. 1. QFD matrix analysis model.

S.-W. Hsiao / International Journal of Industrial Ergonomics 29 (2002) 41–55 43

The pairwise comparison matrix is a method ofderiving, from the group’s quantified judgments(i.e., from the relative values associated with pairsof activities), a set of weights to be associated withindividual activities.Let C1;C2; :::; Cn be the set of activities. The

quantified judgments on pairs of activities Ci; Cjare represented by an n� n matrix

A ¼ ðaijÞ; i; j ¼ 1; 2; 3; :::; n: ð1Þ

The entries aij are defined by the following entryrules based on the scale of relative importance inTable 1.Rule 1: If aij ¼ a; then aji ¼ 1=a; aa0:Rule 2: If Ci is judged to be of equal relative

importance as Cj ; then aij ¼ 1; aji ¼ 1; in parti-cular, aii ¼ 1 for all i:Thus the matrix A has the form

A ¼

1 a12 ? a1n

1=a12 1 ? a2n

^ ^ ? ^

1=a1n 1=a2n ? 1

26664

37775: ð2Þ

(ii) Evaluate the vectors of priorities and overallpriority vector. The method of calculating theeigenvalue is usually used by AHP to evaluate thevectors of priorities of parameters. The vector ofpriorities of the parameters in the lower level in thehierarchy is first calculated and then it progressesto get the overall priority vector. In addition to theeigenvalue method for exact solution, four othersimple methods for approximate the solution are

usually used to estimate the vectors of priorities orweighting functions.(a) NRA method. NRA (normalization of row

average) is to sum the elements in each row andnormalize by dividing each sum by the total of allthe sums. In mathematical form, we have

wi ¼Xnj¼1

aij

�Xni¼1

Xnj¼1

aij ; i; j ¼ 1; 2; y; n: ð3Þ

(b) NRC method. NRC (normalization of thereciprocal sum of columns) is to take the sum ofthe elements in each column and form thereciprocals of these sums. Then normalize bydividing each reciprocal by the sum of thereciprocals. In mathematical form, we have

wi ¼ 1

�Xni¼1

aij

!�Xnj¼1

1

�Xni¼1

aij

!;

i; j ¼ 1; 2; y; n: ð4Þ

(c) ANC method. ANC (average of normalizedcolumns) is to divide the elements of each columnby the sum of that column (i.e., normalize thecolumn) and then add the elements in eachresulting row and divide this sum by the numberof elements in the row (n). This is a process ofaveraging over the normalized columns. In math-ematical form, the vector of priorities can becalculated as

wi ¼1

n

Xnj¼1

aijPni¼1 aij

; i; j ¼ 1; 2; y; n: ð5Þ

Table 1

Scale of relative importance

Intensity of relative importance Definition Explanation

1 Equal importance Two activities contribute equally to the objective

3 Weak importance of one over another Experience and judgment slightly favor one activity

over another

5 Essential or strong importance Experience and judgment strongly favor one activity

over another

7 Demonstrated importance An activity is strongly favored and its dominance is

demonstrated in practice

9 Extreme importance The evidence favoring one activity over another is of

the highest possible order of affirmation

2, 4, 6, 8 Intermediate values between the two

adjacent judgments

When compromise is needed


(d) NGM method. NGM (normalization of thegeometric mean of the rows) is to multiply the nelements in each row and take the nth root. Thennormalize the resulting numbers as follows:

wi ¼Ynj¼1

aij

!1=n�Xni¼1

Ynj¼1

aij

!1=n;

i; j ¼ 1; 2; y; n: ð6Þ

It is important to note that these methods givedifferent results for the general case where a matrixis not consistent. By comparing these fourmethods we note that the accuracy improves from(a) to (b) to (c), although they increase incomplexity of computation. If the matrix isconsistent all these four vectors would be thesame. The consistency will be described later.Method (d) gives a very good approximation if theeigenvalue method is not considered.(iii) Evaluate the consistency. The consistency

ratio (CR) is used to estimate the consistency ofthe judgments of the participants. The CR isdefined as (Saaty, 1980)

CR ¼ CI=RI; ð7Þ

where CI is called the consistency index which isdefined as

CI ¼lmax � n

ðn� 1Þ; ð8Þ

where lmax represents the maximum or principaleigenvalue of the pairwise comparison matrix andn represents the number of activities in the matrix.The closer lmax is to n the more consistent is theresult. The notation RI is the average randomindex, which is a statistical number obtained byOak Ridge National Laboratory (Saaty, 1980).The average random indices for different orders ofmatrices are given in Table 2. In this Table, thefirst row gives the order of the matrix and thesecond row gives the corresponding determined

average random index. A CR of 0.10 or less isconsidered acceptable.(3) Phase 3: After the consistency of the

judgments is assured, the best design alternativecan be selected according to the evaluated overallpriority vector obtained in step (ii) of phase 2.

2.3. Design for assembly (DFA)

DFA is a systematic methodology that reducesmanufacturing costs by reducing the total numberof individual parts in a product and redesigningthe remaining parts in the product for ease ofhandling and insertion (Boothroyd and Dewhurst,1991a). The DFA is a two-step process:

Step 1: Evaluate the assemblability of the indivi-dual partsFthat is to evaluate the in-dividual parts as to whether they are easyto be assembled or not.

Step 2: Evaluate the theoretical minimum numberof parts that should be in the product.

In step 1 the designer uses some established ratingsystem, such as the DFA Toolkit (Boothroyd andDewhurst, 1991b and Zorowski, 1988), to evaluateeach individual part with respect to its:

1. GraspabilityFto check that the part is easyto be grasped or not during the period ofassembly.

2. OrientabilityFto check if the part is easy to beoriented or not when it is being assembled.

3. TransferabilityFto check whether the part iseasy to be transferred to the work position ornot.

4. InsertabilityFto check if the part is easy to beinserted into the correct position or not when itis being assembled.

5. SecurabilityFto check whether the part or theproduct is secure or not after the part has beenassembled.

Table 2

Random index of analytic hierarchy process (AHP)

Order 1 2 3 4 5 6 7 8 9 10 11 12 13

RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48 1.56


The theoretical minimum number of parts isevaluated by the part redundancy criteria. Thedesigner is asked the following three questionsabout each part: (1) does it move relative toadjacent parts, (2) do adjacent parts need to bemade of a different material, and (3) does the partneed to be separate to permit assembly ordisassembly? A ‘‘no’’ answer to all three questionsrecognizes that there is a high probability that thepart can be eliminated through redesign. Elimina-tion of extraneous parts always improves assembl-ability. If the assembly contains sub-assembliestreat them as ‘‘parts’’ and assign an identificationnumber to each item, then analyze the sub-assemblies later with the above method.

2.4. Failure mode and effect analysis (FMEA)

FMEA is an important design and manufactur-ing engineering tool intended to help preventfailures and defects from occurring and reachingthe customer (Gordon and Isenhour, 1990). Itprovides the design team with a methodical way offinding the causes and effects of failures before thedesign is finalized. In performing an FMEA, theproduct and/or production system is examined forall the ways in which failure can occur. Typicalfailure modes would be:

1. Failures due to incorrect design or improperdesign.

2. Failures due to improper manufacturing meth-od and incorrect assembly.

3. Failures due to bad quality management.4. Failures due to incorrect operation.5. Failures due to ill-considered aspects in safetydesign.

The implementation procedure for FMEA isshown below:

1. Identify the functions of parts.2. Investigate the reasons of unsmooth operation.3. Analyze the degree of influence and select keyfactors.

4. Propose the improvement strategy for theselected key factors.

For convenience to analyze the failure modes,four grades are divided as shown in Table 3.They will be used in the case study to analyze the

failure modes of the parts deployed in the qualityhouse.

3. Case studyFdesign a music toy for children

3.1. Marketplace investigation and product analysis

To establish the state-of-the-art of the musictoys in the marketplace for children aged underseven, eight different products are analyzedand located on the conceptual map with thevertical axis represented by price and the horizon-tal axis by safety as shown in Fig. 2. In this figurewe see that only two products have the character-istics of low price, high safety and can becommunicated in two directions. This shows thatthere is the potential to develop a highly compe-titive product.

3.2. Set the design criteria

After analyzing the marketplace and consumerneeds, the design criteria are established based onthe required characteristics in functions, appear-ance, safety and assemblability as follows:

* High quality and low cost.* Easy to manufacture, assembly, orientation,and good security.

* Smooth surface with no acute angles and noburrs.

* Easy to place on the palm and easy to becarried.

* Compact and light.

Table 3

The grades of failure modes

Grade Degree of failure Explanation

1 Extreme serious Cause a huge lost in life

and safety

2 Very strong Have a large lost

3 Moderate Nearly no lost

4 Light Can be neglected


3.3. Quality function deployment and FMEAanalysis

To create a high quality product, the quality andfailure modes for parts of the product are deployedwith QFD and FMEA technologies based oncustomer needs. The results are shown in Table 4in which three major characteristics: manufactur-ability, security (safety) and formation (aesthetics)are deployed into several sub-level parameters andthe relationships between these demanded qualityand design functions and the possible failuremodes are established. The reason why theseparameters are taken will be described later inSection 3.5.1. The FMEA method was then usedto analyze the possible failure modes of parts.Based on the method described in Section 2.4 andthe grades of failure mode defined in Table 3, thepossible failure modes for the parts are given andpresented in the last column of Table 4. In thisTable, the failure modes are divided into 4 gradesaccording to the criteria shown in Table 3. Basedon the grades of the failure modes, the designerwill pay more attention to seek the appropriatestrategies to design the parts that more easily causefailure i.e. those with low grades of failure mode,to prevent the failure of the designed product andconsequently improve the product’s quality.

3.4. Idea development

Four design ideas were developed based on thedesign criteria and the quality house deployed inTable 4 by using the design for function (DFF)

method (Hsiao, 1999) and the morphological chartmethod (Hsiao and Chen, 1997) and are shown inFigs. 3–6. In these design ideas not only theconfigurations are different but the connectiontype (or assemblability) are also varied. Thus, anoverall perspective evaluation for these ideas isneeded to select the best design alternative.

3.5. Idea evaluation

3.5.1. Construct the hierarchy structureUnder the assumption that seven, plus or minus

two, is the limit number of elements which can becompared with any reasonable (psychological)assurance of consistency (Miller, 1956; Saaty,1980), the evaluation parameters were decom-posed into clusters of this size (up to seven in thisstudy), which was called the magical number, towhich the AHP method may still be applied. Thus,a four-level hierarchy of the evaluation parameterswas constructed in Fig. 7. These parameters werethen used to develop a quality house, Table 4, forthe product. At the top of the hierarchy lies themost macro decision objective, such as theobjective of making the best decision (or selectingthe best alternative). The lower level of thehierarchy contains attributes (objectives) whichcontribute to the quality of the decision. Sincethese attributes (objectives) are numerous, wedivided them into two levels (levels 2 and 3) withthree clusters such that the pairwise comparisonparameters can be controlled under the magicalnumber (seven). The last level contains decisionalternatives or selection choices. In this Figure we

Fig. 2. Perceptual map of eight commercialized music toys.


Table 4

A quality house for the product features

Demanded quality

Phase No 1 Level 1 No 2 Level 2 Weighting Weight Volume Visibility Mecha-nismdesign

Moulddesign

Anti-breaktest

Toxicitytest

Humanfactor

Surfacetreatment

Anti-gulptest

ABSplastic

Aes-thetics

Failuremode

Manufacture/assembly

1 Easy toassemble

1.1 Easy to beoriented

2 ’ ’ ’ 1

1.2 Assemblyvisibility

2 ’ ’ ’ ’ 1

1.3 Connectiontype

2 ’ ’ 3

1.4 No screw orbolt

2 ’ 4

1.5 Low assemblycost

2 ’ 4

1.6 High assemblyefficiency

2 ’ 4

1.7 Mass productionmaterial

2 ’ 4

Securitydesign

2 Protect thechildren

2.1 No acute angle 2 ’ ’ ’ 2

2.2 No bur 2 ’ ’ ’ 22.3 Uneasy to be

broken2 ’ 4

2.4 Uneasy to gulpdown

2 ’ 4

2.5 No toxicity 3 ’ 42.6 Smooth surface 3 ’ ’ 32.7 With rounded

corner3 ’ ’ ’ 2

Formationdesign

3 Compact andgoodappearance

3.1 Easy to becarried

3 ’ ’ 3

3.2 Can be put on thepalm

3 ’ ’ ’ 2

3.3 Good appearance 2 ’ 43.4 Changable music

scale2 ’ 4

3.5 Optional timbre 2 ’ 4

S.-W

.Hsiao/Intern

ationalJournalofIndustria

lErgonomics

29(2002)41–55

48

see that the quality of the product is to beevaluated by its manufacturability/assemblability,security, aesthetics and their sub-factors.

3.5.2. DFA analysisSeveral methods for analyzing the assembly

merit have been developed by authors (Boothroydand Dewhurst, 1991a; Zorowski, 1988). Forconvenience, the assembly merit (assemblability)of the design ideas in this study were analyzed byusing the DFA analysis package developed byBoothroyd and Dewhurst (1991b). The resultsobtained are summarized in Table 5 (the principlesof the DFA analysis were described in Section 2.3).These results were further transformed intoquantified data with the analytic hierarchy process

(AHP) method described in Section 2.2 to performa whole evaluation. This portion will be trans-formed quantitatively into Table 9 through the useof Table 8a.

3.5.3. Construct the pairwise comparison matrix forthe AHP analysisAfter the hierarchy structure is constructed, the

questionnaires shown in Table 6 are given to theparticipants to get the entries of the pairwisecomparison matrix of evaluation parameters. Inthe left column (column I, Table 6) we list all thealternatives to be compared for dominance withother alternatives in the right column (column II,Table 6). The alternatives listed in columns I andII may be the evaluation parameters, the designideas or others, which is decided depending onwhich one is to be analyzed. Here the term ‘idea’ isused in this Table. The number of the alternativesshould also be changed depending on the numberof the items to be compared. For example, if wewant to list the priority of the importance among

Fig. 3. Design idea #1.





the sub-factors in level 3 of the attribute‘assemblability’ in the hierarchy structure(Fig. 7), the number of the items in columns Iand II in Table 6 should be increased to 6 and 7,respectively, instead of 3 and 4. To get the‘judgment matrix’, we give the design ideas andthe questionnaire in Table 6 to the participantsand ask them to give the relative values bycomparing the relative importance, which indi-cates the dominance of the element in the leftcolumn (column I in Table 6) over the correspond-

ing one in its row in the right column (column II inTable 6), based on the scale given in Table 1. Theresults for the judgments on the relative impor-tance of the evaluation parameters on second-levelare shown in Table 7. Applying the method ofnormalization of the geometric mean of the rows,NGM (Eq. (6)), yields the column vector ofpriorities (last column of Table 7), which aretaken as the weighting functions of the evaluationcriteria and expressed as follows:

W½ � ¼

0:285

0:571

0:143

264

375weighting of assemblability

weighting of security

weighting of aesthetics

: ð9Þ

The lmax for the matrix in Table 7 is 3. This gives(3�3)/2=0 for the CI, that was defined in Eq. (8).To determine how good this result is we divide itby the corresponding value of the random indexfor a third order matrix (RI=0.58, Table 2). TheCR defined in Eq. (7) is 0/0.58=0 which is lessthan the acceptable value (0.1).In the same manner, the weighting functions for

the sub-factors (third level) were obtained and areshown in Tables 8 (column 2) and 9 (column 3).

3.5.4. Measurement of the degrees of satisfactionfor design ideasTo get the degrees of satisfaction that people

project on the design alternatives, another ques-tionnaire, shown in Table 8, was designed. Theblank questionnaires and the mock-ups ofthe design alternatives in Figs. 3–6, were givento the participants. The subjects were then askedto express their mental images for the given designalternatives by setting one point on the corre-sponding blanks inside the frame for each designalternatives. Then the previously obtained weight-ing functions for all evaluation parameters arefilled into this Table and the total score of eachevaluation parameter is calculated, by the tester,by multiplying the weighting function and themean value of the scores given by the subjects.Table 8 shows an example for evaluating thevalues of design idea #1. In the same manner, thevalues for the other design alternatives wereobtained.

Fig. 7. A hierarchy structure of the evaluation parameters for

music toy design.

Table 5

Summary of the DFA analysis

Idea Assembly cost

(US$)

Assembly time

(s)

Assembly efficiency

(%)

1 0.53 43 37

2 0.45 46 35

3 0.54 41 41

4 0.51 37 44


The evaluated values of the design parametersfor all the four different design ideas are shown inTable 9.

3.5.5. Selection of the best design alternativeAfter the evaluation values for all design

alternatives are obtained, we can further calculatethe overall priority vector to select the best designalternative. The overall priority vector can beobtained by multiplying the evaluation points forthe design alternatives by the vector of priority (i.e.weighting function) of the evaluation parametersin level-2 as follows:

0:495 0:740 0:730

0:405 0:320 0:620

0:435 0:750 0:730

0:565 0:730 0:760

26664

37775

0:285

0:571

0:143

264

375

¼

0:668

0:386

0:656

0:680

26664

37775idea #1

idea #2

idea #3

idea #4

: ð10Þ

The elements in the first matrix in Eq. (10)represent the total scores for four different designideas (column) with respect to top three evaluationparameters: assembly, security and aesthetics (row),which were obtained in Table 9, and those in thesecond matrix are the weighting functions of thesethree parameters. The result shows that the columnvector of priorities is (0.668, 0.386, 0.656, 0.680)suggesting that the priority of the design alternativesis idea #4>idea #1>idea #3>idea #2. Thus, design#4 is selected and the final design is shown in Figs. 8and 9. Fig. 8 shows the explosive diagram whileFig. 9 shows the assembly drawing.

Table 6

Questionnaire for pairwise comparison

Extreme

importance

Demonstrated

importance

Essential

importance

Weak

importance

Equal

importance

Weak

unimportance

Essential

unimportance

Demonstrated

unimportance

Extreme

unimportance

Column I 9 : 1 7 : 1 5 : 1 3 : 1 1 : 1 1 : 3 1 : 5 1 : 7 1 : 9 Column II

8 : 1 6 : 1 4 : 1 2 : 1 1 : 2 1 : 4 1 : 6 1 : 8

idea 1 F F F F F F F F F idea 2

F F F F F F F Fidea 1 F F F F F F F F F idea 3





F F F F F F F F

Table 7

Judgment matrix for design criteriaa

Assemblability Security Aesthetics Eigenvec-

tor

Assemblability 1 1/2 2 0.285

Security 2 1 4 0.572

Aesthetics 1/2 1/4 1 0.143

almax =3, CI=0, RI=0.


4. Discussion

This case originated one year ago as a graduatestudent’s project in my course ‘Advanced ProductDesign’. On presenting the results of this designwork to a toy design and manufacture company, itwas evaluated as a good design and now we areperforming the post process for commercializa-tion.In the design process, the designer or a design

team usually need to consider the problem ofcoupled consumer’s needs which interfere witheach other. For example, there is difficulty inmeeting the size and weight needs of a component.This problem is solved in this paper by evaluatingthe relative importance between needs and giving aweighting function to each need. The effect of the

weighting function affecting on choosing thedesign alternative is considered by usingthe weighted evaluation method. Furthermore,the concept of giving a weighting function to eachrequirement can also be used for design for anumber of niche markets that overlap in theirrequirements i.e. to solve the problem of so-called‘Mass Customisation’. On the other hand, theconcept of ‘modular product’ is a good solutionfor this problem. For a modular product, whichmight require more than one component to coverthe full set of requirements, the relationshipsconcerned in the individual component or asubassembly and the adjacent parts can also beevaluated by using the DFA analysis criteria.Therefore, not only the criteria of DFA analysispresented in Section 2.3 can easily be used to

Table 8

Questionnaires for the evaluation of the design ideasFan example for design idea #1

Assemblability Weighting 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Score

(a) Evaluation for assemblability

Easy to be oriented 0.25 d 0.125

Assembly visibility 0.25 d 0.150

Connection type 0.15 d 0.105

No screw or bolt 0.1 d 0

Low assembly cost 0.1 d 0.030

High assembly efficiency 0.1 d 0.040

Mass production material 0.05 d 0.045

Total 1.0 0.495

(b) Evaluation for security

Security Weighting 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Score

No acute angle 0.25 d 0.175

No burr 0.25 d 0.200

Uneasy to be broken 0.05 d 0.030

Uneasy to be gulped 0.05 d 0.030

No toxicity 0.05 d 0.055

With smooth surface 0.15 d 0.090

With rounded corner 0.20 dTotal 1.0 0.160

0.740(c) Evaluation for aesthetics

Aesthetics Weighting 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Score

Easy to be carried 0.20 d 0.120

Can be put on the palm 0.30 d 0.240

Good appearance 0.20 d 0.100

Changeable music scale 0.15 d 0.135

Optional timbre 0.15 d 0.135

Total 1.0 0.730


improve the assemblability for a simple product,but it can also be used to improve a modularproduct.A number of companies have adopted the

philosophy, ‘‘You will not design a new part.You will work with what we already have’’.However, to improve the quality or reduce thecost of a product, some new parts will obviously berequired. The DFA and FMEA analyses in thismethodology can address this requirement.Though a small product is taken as an example,this methodology can also be applied to developother more complicated products with high

quality. Of course, even if a very complex productsuch as an automobile or an oil platform etc., a bigteam of designers or other methods may also beneeded to improve the design efficiency, thismethod can still be followed to improve theproduct’s quality and reduce the developmentcost. Since the impact of consumer satisfactionon the design ideas were evaluated with the mock-ups and design drawings by using the AHPmethod to select the best design after the designideas had been developed, this method guaranteesthat the designed product will match the market-place.

Table 9

Evaluated scores for the design alternatives

Phase Demanded

quality or

(Design criteria)

Weighting

(A)

Evaluation score for individual idea

(B)

Total score for each design idea

(C ¼ A� B)

idea 1 idea 2 idea 3 idea 4 idea 1 idea 2 idea 3 idea 4

Manufacture/

assembly

1 Easy to be

oriented

0.25 0.5 0.4 0.4 0.5 0.125 0.100 0.100 0.125

2 Assembly visibility 0.25 0.6 0.5 0.5 0.5 0.150 0.125 0.125 0.125

3 Connection type 0.15 0.7 0.3 0.5 0.8 0.150 0.045 0.075 0.120

4 No screw or blot 0.10 0.0 0.0 0.2 0.6 0 0 0.020 0.060

5 Low assembly cost 0.10 0.3 0.6 0.4 0.4 0.03 0.060 0.040 0.040

6 High assembly

efficiency

0.10 0.4 0.3 0.3 0.5 0.04 0.030 0.030 0.050

7 Mass production

material

0.05 0.9 0.9 0.9 0.9 0.045 0.045 0.045 0.045

Total 1 0.495 0.405 0.435 0.565

Security

design

1 No acute angle 0.25 0.7 0.2 0.8 0.8 0.175 0.050 0.200 0.200

2 No burr 0.25 0.8 0.3 0.8 0.7 0.200 0.075 0.200 0.175

3 Uneasy to be

broken

0.05 0.6 0.6 0.6 0.4 0.030 0.030 0.030 0.020

4 Uneasy be gulped 0.05 0.6 0.7 0.5 0.5 0.030 0.035 0.025 0.025

5 No toxicity 0.05 0.5 0.5 0.5 0.6 0.055 0.025 0.025 0.030

6 Smooth surface 0.15 0.6 0.3 0.6 0.9 0.090 0.045 0.090 0.120

7 With rounded

corner

0.20 0.8 0.3 0.9 0.9 0.160 0.060 0.180 0.160

Total 1 0.74 0.32 0.75 0.73

Formation

design

1 Easy to be carried 0.20 0.6 0.6 0.7 0.6 0.120 0.120 0.140 0.120

2 Can be put on the

palm

0.30 0.8 0.5 0.6 0.7 0.240 0.150 0.180 0.210

3 Good appearance 0.20 0.5 0.4 0.7 0.8 0.100 0.080 0.140 0.160

4 Changeable music

scale

0.15 0.9 0.9 0.9 0.9 0.135 0.135 0.135 0.135

5 Optional timbre 0.15 0.9 0.9 0.9 0.9 0.135 0.135 0.135 0.135

Total 1 0.73 0.62 0.73 0.76


5. Concluding remarks

To fit the change in social environment andcompetitiveness of the marketplace, developing ahigh quality product with low cost and closer fit tothe needs of consumers is the key policy of anenterprise. In this study a systematic methodintegrating QFD, FMEA, AHP and DFA tech-nologies for developing a new product is addressedto meet this purpose and some conclusions aredrawn.

1. The quality and cost of the product can beevaluated during the design process.

2. The quality of the product is deployed based onconsumers’ needs, so that the customer willsatisfy the designed product.

3. The AHP method can be used to quantify thedesign criteria and further to evaluate thepriority vector for the design alternatives.

4. With this method, the design uncertainties canbe reduced so that the product can be designedin a more transparent process. In addition, thedesign parameters can be quantified such thatthe best design alternative can be selected basedon the quantified results.

Acknowledgements

The author is grateful to the NationalScience Council of the Republic of China for

Fig. 8. Explosive diagram of designed product.

Fig. 9. Assembly drawing of designed product.


supporting this research under grant NSC87-2213-E006-016.

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