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Logistics Decision Analysis Methods Quality Function Deployment – Part IV Presented by Tsan-hwan Lin E-mail: [email protected]

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Logistics Decision Analysis Methods. Quality Function Deployment – Part IV Presented by Tsan-hwan Lin E-mail: [email protected]. Construction of the HOQ. The first section of the HOQ to be constructed will almost always be the Customer Needs/Benefits section. - PowerPoint PPT Presentation

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Page 1: Logistics Decision Analysis Methods

Logistics Decision Analysis Methods

Quality Function Deployment – Part IV

Presented by Tsan-hwan Lin

E-mail: [email protected]

Page 2: Logistics Decision Analysis Methods

Construction of the HOQ The first section of the HOQ to be constructed will almost

always be the Customer Needs/Benefits section. Sections are also referred to as “rooms.”

The Planning Matrix (also, Preplanning Matrix) is often the second section to e constructed.

The third section of the HOQ to complete is the Technical Response (also, Corporate Expectations) section.

The fourth step is to complete the “Relationship” section of the HOQ.

The fifth and sixth steps in completing the HOQ are Competitive Benchmarking and Target Setting.

The seventh and usually final step in completing the HOQ is to fill in the Technical Correlations Matrix.

This part is also referred to as “roof.”

Page 3: Logistics Decision Analysis Methods

Q & A

Page 4: Logistics Decision Analysis Methods

Technical Correlation - Introduction QFD is a key to concurrent engineering because it

facilitates team members communicating with each other.

The Technical Correlations section will show us for which technical areas close communication and collaboration are important, and for which it is not.

It will also show us where design bottlenecks may occur, and therefore where design breakthroughs are necessary.

The section is probably the most underexploited part of the House of Quality. Few QFD applications use it, yet its potential benefits are great.

Page 5: Logistics Decision Analysis Methods

Technical Correlation – Meaning (1) The Technical Correlations section maps interrelationships and

interdependencies between Substitute Quality Characteristics. The section consists of that half of a matrix that lies above the

matrix’s diagonal. Very often, especially after a technical concept has been decided upon

and is somewhat understood, the developers will be able to see that as SQCx is moved in the direction of goodness, SQCy will be influenced, either in its direction of goodness or in the opposite direction.

The degree and direction of influence can have a serious impact on the development effort.

Example: For an automobile, increased BTU rating of an automobile air conditioner (SQCx, more is better) may have a negative impact on automobile weight (SQCy, less is better).

Notice how the SQCs are somewhat solution dependent (higher BTU rating => heavier equipment).

Had this incompatibility not been discovered during the product planning phase, dollars will be wasted in preliminary development work.

Page 6: Logistics Decision Analysis Methods

Technical Correlation – Meaning (2) In QFD we usually identify five degrees of technical

impact. These symbols carry no directional connotation. It is more constructive to indicate a direction of impact, since a

developer can often make a strong argument for impact of SQCx upon

SQCy, but not impact of SQCy upon SQCx.

Strong positive impact

Moderate positive impact

<blank> No impact

X Moderate negative impact

XX Strong negative impact

Strong positive impact, left to right

Moderate positive impact, right to left

<blank> No impact

X Moderate negative impact, right to left

XX Strong negative impact, left to right

Page 7: Logistics Decision Analysis Methods

Roof of the House of Quality

Direction of goodness: More is better Less is better Target is best

SQ

C 1

SQ

C 2

SQ

C 3

SQ

C 4

SQ

C 5

XX

X

Example: Moving SQC 1 in the direction of goodness has a moderate negative impact on SQC 5’s direction of goodness.

Page 8: Logistics Decision Analysis Methods

Responsibility and Communication One of the most important benefits of the Technical Correlations is

to indicate which teams or individuals must communicate with each other during the development process.

One method for making this information more explicit is to construct a Responsibility Matrix (in addition to using “roof” directly) .

This matrix would display the SQCs along the left, and the possible responsible teams along the top. A cell in the matrix would indicate the relationship of the team to the SQC.

A good management practice is to assign responsibility for an objective to a single individual or a single organization. Therefore, the responsibility matrix would have a single in each row.

Primary responsibility

Supporting role

Should be informed

Page 9: Logistics Decision Analysis Methods

Correlations Network An alternative but equivalent representation of the

correlations in the roof is the Relationship Network Diagram (or, Relationship Digraph).

In this diagram, the SQCs are represented by the circles, and the SQC affected is shown by the arrows connecting the circles.

The degree and direction of influence is shown by the and indications written alongside the arrows.

SQC with arrows emanating from it only is called the driver on the sense that it influences other SQCs but is not in turn influenced by any SQCs.

SQC with incoming arrows only is called indicator. It is usually not worthwhile to invest resources in it.

Page 10: Logistics Decision Analysis Methods

Technical Benchmarks: Introduction - 1 No organization would invest in the development of a

product or service without knowing enough about the competition to be sure that their design is competitive.

Only the most important SQCs (i.e., with highest priorities) will be benchmarked and target-set.

A critical question is: how should the targets be set? How aggressive do they need to be?

To a great extent, development teams can be guided by the competition’s performance as well as their own performance (on the most important SQCs) (to make crucial strategic decisions: to match, exceed, or concede).

Page 11: Logistics Decision Analysis Methods

Introduction - 2 In general, competitive benchmarking is the process of

examining the competition’s product or service according to specified standards, and comparing it to one’s own product or service, with the objective of deciding how to improve one’s own product or service.

The QFD process provides the basis for strategic competitive benchmarking (i.e., examining only those highest-ranking SQCs).

In the process, the language of Substitute Quality Characteristics and the definition of direction of goodness become important determiners of the work.

Only two types of SQCs, performance measures and product functions, will be examined in the benchmarking process here.

Page 12: Logistics Decision Analysis Methods

Target Setting - 1 Setting targets is of course a matter of greatest interest

to product and service developers. Obviously, setting SQC targets will drive all subsequent

development activity. Development teams set targets for themselves whether or

not they use QFD to plan their project.

It (i.e., setting targets) takes up the QFD process after the development team has determined the most important SQCs and has benchmarked the competition.

Page 13: Logistics Decision Analysis Methods

Target Setting - 2 Some of the linkages in the Relationships section might not be

linear, because the associated SQCs may be Dissatisfiers or Delighters.

The target-setting stage is a good time to deal with these Kano classifications. For those SQCs classified as potential Delighters, the team must decide how

aggressively it can afford to be in target setting. There is relatively little downside risk in setting a conservative goal: customers

will not notice the absence of a Delighter. However, the potential gaining of setting a goal that beats the competition is high.

For those SQCs classified as Dissatisfiers, the team cannot afford not to be aggressive.

For those SQCs classified as Satisfiers, the team can expect that the better they perform on the SQCs, the greater the customer satisfaction performance will be for the linked customer needs. (Focus of the following discussion)

Page 14: Logistics Decision Analysis Methods

Target Setting - 3 We will look (1) at setting numeric targets for SQCs

that have been expressed as performance measures, and (2) at setting function or feature (nonnumeric) targets for SQCs that have been expressed as features.

Numeric Targets Comparison with Competitions Mathematical Modeling

Nonnumeric Targets

Page 15: Logistics Decision Analysis Methods

Q & A

Page 16: Logistics Decision Analysis Methods

Matrix above the Diagonal

The SQCs are arrayed along the top and side. The matrix is then rotated 45 degrees, and since the SQCs are already

available along the top (of the HOQ), they double as the labels for both the rows and the columns (of the roof), making the row and column labels unnecessary.

SQ

C 1

SQC 1

SQC 2

SQC 3

SQC 4

SQC 5

SQ

C 2

SQ

C 4

SQ

C 5

SQ

C 3

SQC 1

SQC 2

SQC 3

SQC 4

SQC 5

SQC

1

SQC 1

SQC 2

SQC 3

SQC 4

SQC 5

SQC

2SQ

C 4

SQC

5

SQC

3

SQ

C 1

SQ

C 2

SQ

C 3

SQ

C 4

SQ

C 5

Page 17: Logistics Decision Analysis Methods

Responsibility Matrix

Because changes in SQC 1 strongly affect SQC 4, the organizations responsible for SQC 4 (Organization E) must be informed of progress on SQC 1 (by Organization A).

Organization A

Organization B

Organization C

Organization D

Organization E

Organization F

Organization G

SQC 1

SQC 2

SQC 3 SQC 4

SQC 5

SQ

C 1

SQ

C 2

SQ

C 3

SQ

C 4

SQ

C 5

XX

X

Page 18: Logistics Decision Analysis Methods

Relationship Network Diagram

SQC 1

SQC 3

SQC 2

SQC 4

SQC 5

X

XX

Page 19: Logistics Decision Analysis Methods

Benchmarking Performance Measures If the SQCs were defined as performance measures,

the benchmarking process becomes one of measuring the competition’s performance and one’s own performance in terms of these measures.

To the extent that the performance measures were defined independently of the design of the product or service, the benchmarking process provides ideal “apple-to-apple” comparative data between the two.

The results of measuring the two products or services can be laid down side by side (one above the other in the HOQ) and evaluated at a glance.

Page 20: Logistics Decision Analysis Methods

Benchmarking Functionality If the SQCs were defined in a more solution-specific manner,

with product or service functions explicitly defined, the comparisons must be much more subjective.

One way to deal with differences in functionality (designed in each product/service) is to decompose the high-ranking SQCs into “sub-SQCs” (i.e., lower levels of the Function Tree or the Affinity Diagram) and compare these “sub-SQCs” to the competitions.

The number or percentage of subfunctions that correspond (to the competition’s subfunctions) provides valuable numerical information (such as, where the competition provides more functionality, or how the competition’s design solves the same problem differently from the development team’s design).

Page 21: Logistics Decision Analysis Methods

Comparison with Competitive Benchmarks

Impo

rtan

ce to

cus

tom

er

Relationships

Technical Response

Priorities

Technical

Correlations

Cus

tom

er N

eeds

Competitive Benchmarks

Own PerformanceTechnical

Matrix

Cus

tom

er s

atis

fact

ion

perf

orm

ance

Com

petit

ive

satis

fact

ion

perf

orm

ance

Goa

l

Impr

ovem

ent r

atio

Raw

wei

ght

Nor

mal

ized

raw

wei

ght

Sale

s Po

int

Cum

ulat

ive

Nor

mal

ized

Raw

wei

ght

Planning Matrix

Targets

Page 22: Logistics Decision Analysis Methods

QFD and Target Setting With QFD, the targets have a context (前後關聯) :

They (i.e., targets) are related to customer needs, to the competition’s performance, and to the organization’s current performance. (section A and B)

The rank ordering of the targets is based on the systematic analysis done in the Relationships section (and all the prior QFD analysis). (section C and D)

The rank ordering process is traceable, because all the decisions affecting the

rank ordering are recorded in the QFD matrix.

The QFD process itself provides no cookbook approach for setting targets for SQCs.

The most vital information not explicit visible in the HOQ is the business know-how and technical expertise of the development team. However, the HOQ provides much of the strategic information needed, laid out in a compact form.

Page 23: Logistics Decision Analysis Methods

Comparison with Competition - 1 One approach to setting targets is similar to the process of

setting customer satisfaction performance goals in the Planning Matrix.

The primary inputs to target value setting of SQCs are Rank order of substitute Quality Characteristics (Priorities) Competition’s technical performance (Competitive Benchmarks) The development team’s product’s technical performance (Own

Performance)

The primary inputs to goal setting for customer satisfaction performance in the Planning Matrix are

Importance of customer attribute to customer Our current satisfaction performance rating Competition’s satisfaction performance rating.

Page 24: Logistics Decision Analysis Methods

Comparison with Competition - 2 The line of reasoning for setting targets is also similar (to that

used in setting goals in the Planning Matrix). Starting with the highest ranking SQC, determine the strength of the

development team’s position relative to that of the competition. Based on the team’s knowledge of the difficulty of performing well

on the SQC, the team can decide whether to aim to do better than the competition, to match the competition, or to concede technical leadership to the competition.

As a general rule, the goal should be set for technical performance that exceeds the best in the world for those SQCs that matter the most to overall customer satisfaction.

Page 25: Logistics Decision Analysis Methods

Mathematical Modeling - 1 While QFD is certainly not a precise mathematical model of

the relationship between technical performance and customer satisfaction performance, a little bit of simple mathematics could serve as a guide to the development team in setting targets.

In the case of a SQC for which Less Is Better: Equation

A similar but slightly more complex relationship can be modeled in the case of Target Best (TB):

Equation

Page 26: Logistics Decision Analysis Methods

Mathematical Modeling - 2 Mathematical model provides some insight for setting the target for a SQC,

but care must be exercised in its use. There is no guarantee that the relationship between any SQC and the

corresponding customer attribute satisfaction performance is precisely linear or precisely quadratic.

Customer satisfaction performance for any attribute is usually not the function of a single SQC, but of several SQCs.

Since there are no reliable multivariate models for setting target values in QFD, a reasonable approach could be the following:

Treat each SQC as if it were the only SQC contributing to customer satisfaction performance of an attribute.

Use the simple models to create a first estimate of an appropriate target value. Repeat this analysis for all the customer satisfaction attributes that this SQC is

linked to. This creates multiple target values for the same SQC. Choose the most aggressive of these target values.

Page 27: Logistics Decision Analysis Methods

Satisfaction Vs. Technical Performances - LTB

denotes customer satisfaction performance on a customer need as a function of a SQC p of the type Less The Better

denotes customer satisfaction performance with the best product in the market (by market research)

denotes customer satisfaction performance with the development team’s current product (by market research)

denotes technical performance of a SQC

denotes technical performance of a SQC with the best product in the market (by competitive benchmarking)

denotes technical performance of a SQC with the development team’s current product (based on laboratory measurement)

sLTB(p)

sworld-class

s0

p

pworld-class

p0

20 40 60 80 100 120 1400

1

2

3

4

5

Team’s product

World-class product

Target

Technical performanceSati

sfact

ion

perf

orm

an

cesLTB(p)

p

Page 28: Logistics Decision Analysis Methods

Satisfaction Vs. Technical Performances - TB

Team’s product

World-class product

Target value

sLTB(p)

p

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

0

1

2

3

4

5

Page 29: Logistics Decision Analysis Methods

Relationship Section

SQ

C U

SQ

C V

SQ

C W

SQ

C X

SQ

C Y

SQ

C Z

Attribute A

Attribute B

Attribute C Attribute D

Attribute E

Page 30: Logistics Decision Analysis Methods

Nonnumerical Targets - 1 Setting targets for SQCs defined as features or

processes is obviously more difficult than dealing with numbers.

A number is one-dimensional, but features and processes are multidimensional and multifaceted.

There are two helpful ways of thinking about targets for nonnumerical SQCs: the continuum model and the subfeature model.

Page 31: Logistics Decision Analysis Methods

Continuum Model In the continuum model, we may imagine the SQCs to be on a

continuum. This continuum could have as its endpoints “stripped down” and

“deluxe.” The development team could judge where on the continuum their

current offering lies, and where the best in the world lies.

To clarify these judgments, they would do well to make their subjective judgments as objective as possible by documenting:

The differences between “Best in world” and “Deluxe” The differences between “Development team’s” and “Best in world” The differences between “Development team’s” and “Target”

Page 32: Logistics Decision Analysis Methods

Continuum Model - Figure

1 2 3 4 5 6 7 8 9 10

Str

ipp

ed d

ow

n

Delu

xe

Develo

pm

ent

team

’s

Best

in

worl

d

Targ

et

Page 33: Logistics Decision Analysis Methods

Subfeature Model By using the subfeature model, the development team can explode

each feature to be targeted into its component subfeatures. Each subfeature could be evaluated according to the continuum model, or

could be exploded into lower-level subfeatures. Targets could be set by continuum, where “Best in world” and “Development

team’s” subfeatures line up, and by identifying subfeatures to be added, where the features don’t line up.

Development team’s Best in world Target

Subfeature a 7 7 8

Subfeature b 5 4

Subfeature c 6 8 8

Subfeature d 3 3

Subfeature f 8

Subfeature g 6 6

Outperform

Sustain

Concede