Web Based Market Research Methodology For Unmet Customer Needs:
Estimating Cost Functions For Design Pallet
by
Shyn-Ren Chen
MS Communication Engineering, National Chiao Tung University (1995)
BS Electrical Engineering, National Taiwan University (1993)
Submitted to the System Design and Management Program in Partial Fulfillment of the Requirements for
the Degree of
Master of Science in Engineering and Business Management
at the
Massachusetts Institute of Technology
May 2001
2001 Shyn-Ren Chen. All rights reserved.
The author hereby grants MIT permission to reproduce and distribute publicly paper and electronic copies
of this thesis document in whole or in part.
Signature of Author
Shyn-Ren Chen
System Design and Management Program
May, 2001
Certified by_-
_.._.
Glen L. Urban
Professor of Management
Thesis Supervisor
Accepted by
Paul A. Lagace
LFM/SDM Co-Director
Professor of Aeronautics & Astronautics and Engineering Systems
Accepted by___
A
Stephen C. Graves
MASSACHUSETTS INSTITUTE
OF TECHNOLOGY
G 0Abraham
LIBRAKIt
LFM/SDM Co-Director
BAUGARK
BARKER
Siegel Professor of Management
Web Based Lead User Market Research Methodology
For Unmet Customer Needs: Estimating Cost Functions For
Design Pallet
by
Shyn-Ren Chen
Submitted to the System Design and Management Program on May 4, 2001
in partial fulfillment of the requirements for the degree of
Master of Science in Engineering and Business Management
Abstract
With the rapid adoption of Internet technology, consumers are actively doing product
research online to get more information before making a buying decision. The end
result of this process is the product selection but many product improvement and
development opportunities are exposed in this researching process. Previous MIT
works focused on generating trusted Virtual Advisor to help consumers make product
selection decisions. Virtual advisor could also identify consumers' unmet needs that
became excellent new product opportunities for corporations.
This thesis extends past MIT research and focuses on how to conduct online market
research to help corporations design and develop a better product that fits into the
Based on the concept of Quality Function
identified unmet customer needs.
Deployment (QFD) and lead users innovation, a qualitative methodology - Virtual
Engineer and a quantitative methodology - Design Pallet are both proposed to achieve
this objective.
The major contribution of this thesis is to estimate the cost functions, which provide
Design Pallet with interactive features to complete design iterations with users. These
functions are estimated based on statistical analysis of existing pickup trucks data and
significant result is achieved. For example, a R2 = 0.929 was found in predicting price
as a function of the truck size, 4WD, the transmission, and the engine type. Other
models were built to predict fuel economy, payload, and towing capacity with average R2
= 0.80.
Thesis Supervisor: Glen L. Urban
Professor of Management, MIT Sloan School of Management
2
Acknowledgements
I would like to take this opportunity to thank Professor Glen Urban for the opportunity to
work on this research project.
His insights into consumer market research, product
development and internet marketing have enhanced and extended my MIT education
experience.
To my diligent and intelligent team members in this research project,
Thomas Cheng, Jim Ryan, Stanley Chueng, Brian Chan, , and Ray Ro.
I thank you for
your cooperation and assistance to make this research experience rewardful and
enjoyable.
I would also like to thank the director, Denny Mahoney, and the staff members of MIT
System Design and Management program for providing a challenging, innovative and
comfortable education environment.
To my fellow classmates in the System Design and
Management program, I thank you for your friendship and wish you all success and
happiness.
Finally, I would like to express my deepest gratitute to my family members and dear
friends.
To my parents, Mr. King-Chen Chen and Mrs. Yu-Mei Cheng Chen, I thank
your for your constant support and encouragement of my academic pursuits.
To my
amazing wife, Hui-Ying, I thank you for your endless love that keeps you wait for me in
the dark night when I went home late.
To my dear friends, I thank you for giving
hapiness to me with your kindly sharing.
3
Table of Contents
I
2
3
1.1
1.2
1.3
Introduction ...........................................................................................................
Background .......................................................................................................
Previous W orks on Virtual Advisor ...................................................................
Detecting Unm et N eeds..................................................................................
8
8
11
18
1.4
Objectives ...........................................................................................................
21
Literature Review .............................................................................................
2.1 Quality Function Deploym ent ........................................................................
24
24
2.2
Lead User and User Innovation......................................................................
29
2.3
Internet Im pacts ...............................................................................................
34
Study of Unm et Needs ......................................................................................
3.1 Qualitative Study of Unm et Needs - Virtual Engineer..................................
3.2 Quantitative Study of Unm et Needs - Design Pallet ....................................
37
39
46
4.1
4.2
4.3
4.4
Formulation System in Design Pallet ...............................................................
System Overview ...............................................................................................
Statistical Regression Result ...........................................................................
Logical Considerations for Regression M odels .............................................
Final M odels ....................................................................................................
53
53
58
68
75
5.1
5.2
Conclusion............................................................................................................78
Contributions and Benefits.............................................................................
Future Research Areas....................................................................................
78
80
4
5
Bibliography ...................................................................................................................
84
Appendix A Virtual Engineer Dialogues ..........................................................................
86
Appendix B Residual Analysis of Design Pallet Formulation System...........................
4
100
List of Tables
Table 1: Strong conflict pairs that trigger Virtual Engineer............................43
Table 2: Available user input features and options in Design Pallet ......................
50
Table 3: Initial Regression Model for Price.............................................63
Table 4: Initial Regression Model for City Fuel Economy................................64
Table 5: Initial Regression Model for Highway Fuel Economy.......................65
Table 6: Initial Regression Model for Payload............................................66
Table 7: Initial Regression Model for Towing Capacity................................67
Table 8: Modified Regression Model for Price............................................70
Table 9: Modified Regression Model for City Fuel Economy............................71
Table 10: Modified Regression Model for Highway Fuel Economy..................73
Table 11: Modified Regression Model for Payload.....................................74
Table 12: Modified Regression Model for Towing Capacity.........................76
Table A: Virtual Engineer dialogue list .................................................................
86
Table B: Towing capacity outliers summary for Ford F150 truck models ............... 103
5
List of Figures
Figure 1: Top ten product types people research online and purchase offline.......... 9
Figure 2: Flow chart of trusted Virtual Advisor.....................................................
13
Figure 3: Constant-Sum question from Virtual Advisor..........................................
14
Figure 4: Example Virtual Advisor's attribute question for usage and feature..... 16
Figure 5: Continuously rising utility profile from Virtual Advisor dialogue........... 19
Figure 6: Drops in maximum overall utility identify unmet needs .........................
20
Figure 7: The House of Quality from Quality Function Deployment.....................
26
Figure 8: System architect's view of QFD .............................................................
28
Figure 9: Matrix of market research methods ........................................................
35
Figure 10: Flow chart for Virtual Advisor, Virtual Engineer and Design Pallet ........ 38
Figure 11: Partial display of correlation matrix used to identify conflict pairs .......... 40
Figure 12: Virtual Advisor introduces Virtual Engineer..........................................
41
Figure 13: M eet with Virtual Engineer ..................................................................
42
Figure 14: Virtual Engineer dialogue for compact truck .......................................
44
Figure 15: Virtual Engineer dialogue for high horse power ..................................
45
Figure 16: Virtual Engineer open-end dialogue .......................................................
46
6
Figure 17: Introduce D esign Pallet.........................................................................
47
Figure 18: Design Pallet launch screen ..................................................................
48
Figure 19: Start D esign Pallet .................................................................................
49
Figure 20: D esign Pallet layout ...............................................................................
49
Figure 21: Final comparison for Design Pallet .......................................................
52
Figure 22: Correlation between horsepower and dim ..............................................
60
Figure 23: Relationship between horsepower and engine cylinders ........................
61
Figure B 1: Histogram of residuals for price regression model ................
100
Figure B2: GMCSierraC3 and Ford_F150_SVT ..................................................
101
Figure B3: Histogram of residuals for towing capacity regression model........ 102
7
Chapter 1
Introduction
1.1
Background
Inspite of the high tech stock market crash in 2001, Internet technology already
dramatically changed people's buying behavior.
surpassed radio and TV.
The adoption rate of internet has
According to International Data Corp., world wide internet
users reached 318.6 million in 2000, which is up from 39.4 million in 1995.
With
the rapid adoption of internet, consumers are actively doing product research online to
get more information before making a buying decision.
process is the product selection.
The end result of this
However, many opportunities exposed in this
product research process provide corporations with great product improvement and
development opportunities.
In the fourth quarter of 2000, the most extensive shopping season in United States,
retail e-commerce reached $8.6 Billion, an increase of 67% from the fourth quarter of
1999 and 36% from the priori quarter of 2000 [1].
under-estimate the potential of e-commerce.
However, this number may
According to Forrest Research report
[2], 29% of the online population researched purchases online but bought them
offline.
It shows part of online shopping population only conducts target products
8
information research online but somehow does not execute purchases over the
Internet.
Figure 1 shows the top 10 product types people did most research online but purchase
offline.
These 10 product types fall into two major categories.
The first one is
expensive, highly considered products like automobiles, computer hardware, and
consumer electronics.
This type of product usually requires a long research time,
even an advisor, before making purchase decision.
items, like books, CDs, and appliances.
from local stores.
The other one is convenience
This type of product is rather easy to obtain
For people who want to save shipping cost or own it right away
they tend to buy it offline.
'What type of product did you most recendy research onfine and purchase offline?"
Computer ha. dwart
Trave
Consumer electronmcs
4%
SrAo tng goodi jM
aCtH r g
%
Major applar ces
3%
Researched puchases
Cvernence items
Healthcare
Base- Onfine consumers who have researched a product online
and purchased it offfne in the past three months
Figure 1 - Top 10 product types people research online but purchase offline
The extensive product research process before making a purchase decision provides
corporations, like automobile manufacturer and consumer electronics designer, an
excellent opportunity to understand
market needs.
9
By observing customers'
preferences and finding out what are the product features that customers want but not
offered by
existing products,
corporate
product designers
can
obtain useful
information for new product development and also reduce the uncertainties for new
products significantly.
In response to the heavy utilization of the Internet to do product or service researches,
current Internet websites offer many different kinds of shopping agents.
The first
generation of shopping agents is bargain finder, like www.dealtime.com.
It purely
However, this kind of
performs price comparison and looks for best deal.
single-dimensional comparison agents cannot fulfill customers who are looking for
other purchase information, like quality or after sale service.
The second generation
shopping agents performs multi-dimensional comparison, like www.mysimon.com.
It provides customers an "intelligent agents" service that can collect online retail
shops information and adapt to each individual customer's shopping preference to
make purchasing recommendations.
But what will be the next generation shopping agents?
Professor Glen Urban of MIT
Sloan School of Management proposed trust will be the key element for future online
shopping agents [3].
Among thousands of retail web sites, consumers want to visit
the ones that can offer unbiased product or service information and provide
guarantees for privacy, delivery, and after sales service.
There are many different
ways to build trust on web sites; one of them is "Virtual Advisor".
A Virtual Advisor
developed through a software program imitates the real time human advisor's
behavior with the ability to serve many customers at the same time and lower cost to
be implemented.
In June 1997, Professor Urban conducted a study of trust-based
Internet marketing and performed the Truck Town prototype to assess the viability of
10
a Virtual Advisor.
1.2
This research project forms the foundation of this thesis.
Previous Works
This basis work of this thesis was completed over several years by an extremely
dedicated group of researchers
and students at the Massachusetts Institute of
Technology's Center for Innovative Product Development.
This original works
focused on the facilitation and improvement of online consumer research and
purchase process through the development and application of a trusted online advisor
for use in the purchase of trucks.
Several research papers and theses were published
over the course of this research effort. [4] [5] [6] [7] [8] [9] [2]
Figure 2 shows an overview of the virtual trusted advisor's recommendation and
dialog process that was developed from this research project.
The process begins
with the virtual trusted advisor introducing himself or herself to the consumer.
The
goal is to establish an initial basis of trust through the revelation of the Virtual
Advisor's purpose and affiliations.
Once the Virtual Advisor greets and engages the
consumer, the dialogue between them is enabled through a set of questions that focus
upon the identification of the customer's needs and their intended usage of the
product.
In general, the Virtual Advisor presents the dialogue and questions to the
customer and iterates through a set of uses and needs questions.
As the customer answers the questions, the Virtual Advisor dynamically calculates the
11
utility profile of the customer and locates a set of products with the highest utility
based on the customer's response.
At last, the Virtual Advisor leads customer to
showroom for recommending the customer's the highest utility vehicles.
The
prototype of this virtual trusted advisor and Truck Town is available online at
http://mishkee.mit.edu/trucktown/.
12
Virtual Advisor
Greeting, Purpose
Virtual Advisor
Constant-Sum
preference question
Calculate initial
utility value
(EQ 1)
Virtnal Advisor
Ask highest
information value
attribute question
Calculate Bayesian
attribute effect
(EQ 2)
Calculate attribute
information value
(EQ 3)
Populate
recommendation with
highest utility
products
4
NO
All attribute
questions asked?
YES
Send customer to
showroom for
recommendations
Figure 2 - Flow chart of trusted Virtual Advisor
13
The first question Virtual Advisor asks to build up initial utility value of the customer
is a constant-sum preference question shown in Figure 3.
The Virtual Advisor asks
the customer to allocate a constant 100 points among price, performance, fuel
economy, reliability and safety.
Based on the customer's response, Virtual Advisor
is able to understand this customer's personal trade-offs and preferences for making a
purchase decision for a truck.
Figure 3 - Constant-Sum question from Virtual Advisor
Then Virtual Advisor calculates the customer's initial utility value based on EQ 1.
This is done through the usage of the exponential logit function against the customer's
stated preference along a set of perceptual dimensions of the products.
of information are required for EQ 1.
Two pieces
The first is the customer's stated preference
14
along a sub-set of the product's perceptual dimensions and the second is a database of
popular ratings of each perceptual dimension for each product within the Virtual
Advisor's knowledge database.
These two pieces of information are then used
within an exponential logit function for the initial utility calculation and apriori
purchase probability as seen below in EQ 1.
X
ed -Dda
d
e
P(A)=
-Dd, a
(EQ 1)
Yed
a
Aa = Product Alternative a
P(Aa) = Probability of purchase of product alternative a
a
d =
Dda
Constant-Sum importance of dimension d given by customer
= Standardized database value of dimensions d for product alternative a
The standardized database value
like consumer reports.
Dd,a
is obtained from impartial secondary sources
By collecting data from independent sources, the Virtual
Advisor is able to state that its recommendations are truly without bias.
The values
from the independent sources are normalized over the range of product ratings as a
linear distribution.
Once the initial utility of the customer is calculated for each product alternative, the
Virtual Advisor continues the product recommendation dialogue with the customer by
asking usage and feature questions.
After each attribute question is asked, the
Virtual Advisor is able to update product recommendations based on customer's
15
response using EQ 2.
(EQ 2)
= P(Aa)P(Rq IAa)
P(Rrq
)
P'(At) = P(A I Rq
P'(Aa) = Updated probability of buying product alternative a
P(AalRr,q) = Probability of buying product alternative a given a customer response
r to Virtual Advisor's attribute question g
P(Rr,qIAa) = Conditional probability of answering Virtual Advisor's attribute question
_q with response r given preference for product alternative a
P(Rr,q) = Marginal probability of response r on attribute question g, i.e.,
P(Rrq)=
P(A,)
P(Rrq I A)
An example of Virtual Advisor's attribute question is shown in Figure 4.
Figure 4 - Example Virtual Advisor's attribute question for usage and feature
16
As the consumer answers these usage and feature dialogue questions, the Virtual
Advisor translates the responses into model level attribute responses.
These features
responses are then used within a Bayesian updating formula to account for attribute
level effects on consumer utility.
This utility profile updating procedure will repeat
until all attribute questions are asked.
These attribute questions are asked in the
order of greatest Bayesian information value which is defined in EQ 3.
)
YP( A, IR,,) - P(,,
(EQ 3)
I(q) =
n2
a
Pb(Aa) = P(Aal all the answered attribute questions)
= baseline probability for each vehicle
IP(AaIRr,q)-Pb(Aa)I = Bayesian attribute effect influence of each possible response
n = total number of possible responses
For Virtual Advisor, asking usage and feature questions in the order of highest
Bayesian information value will provide the most useful information about the
customer's preference in order to build up a complete customer utility profile.
all
attribute
questions
recommendations
are
asked,
Virtual
Advisor
can
then make
and take customer to showroom for demonstration
recommended products.
After
product
of the
For more information about the Virtual Advisor, please
refer to the related papers for this research project as mentioned above.
17
1.3
Detecting Unmet Needs
In the latest work of this research project, a "Listening In" approach is proposed to
detect
customer's
unmet
needs.
Mann
[10]
defined
customer's
utility
as
Max[P'n(Aa)] which, by the definition of EQ 2, means the maximum probability to
buy product alternative a after attribute question n.
Figure 5 shows a customer's
response example case to the Virtual Advisor's attribute questions result in a
continuously rising utility curve.
On the vertical axis, the Virtual Advisor's attribute
questions are shown in the order from top to bottom and the customer's responses to
each question are labeled by question title, the customer's response in brackets [], and
the name of the highest utility vehicle after the question response.
This kind of continuously rising utility curve occurs when a product is currently
available within the Virtual Advisor's product database whose product dimensions
combined with its model level features meet all of the customer's needs.
this will not always be the case.
Obviously
Another example case shown in Figure 6
demonstrated customer's utility drop occurred while asking questions about styling
and commercial usage Off road/Towing/Hauling.
The mathematical representation of how to detect the customer's utility drop is shown
in EQ 4.
(EQ 4)
Max[P'n(Aa)] < Ma[P'n-I(Aa)]
Where
Max[P'n(Aa)] = Maximum utility value after attribute question n
Max[P'nI(Aa)] = Maximum utility value after previous attribute question n-1
18
Listening In! - Utility Profile
EOriginal Utility
0
0.0
1) Constant Sum Pref - ($)(FE)(R)(HP)(S) [70,30,0,0,0]MazdaB2300_(2WD)
33
0 0 35
6) Price Range [14K - 16K] MazdaB2300(2WD)
2) Conpact/Full [Compact] MazdaB2300_(2WD)
10.0861
3) # Passengers (1-5) [1] MazdaB2300_(2WD)
1
10) Driver Height [Under 6'] MazdaB2300_(2WD)
U)
4)
a1
0
0.1200
15) 2w d / 4w d [2w d] MazdaB2300_(2WD)
12) Engine Size [6 Cyl] FordRanger_(2WD)
10.1243
8) # in Front Seat [1] FordRanger_(2WD)
14) Styling (1-5)
CI
26
[2] FordRanger_(2WD)
0 1376
9) Biggest Avail (1-5) [1] FordRanger_(2WD)
01440
4) Off road/Towing/Hauling [N,N,N] FordRanger(2WD)
145,9
0.1440
13) Quiet & Comfort (1-5) [2] FordRanger_(2WD)
.
11) Bed Length [Short] FordRanger-(2WD
5) Construction/Plow
ing [N,N
0.1467
01467
Ford-Ranger_(2WD)
0
0.02
0.04
0.06
0.08
Utility
0.1
0.12
0.14
0.16
II
Listening In!
-
Unmet Needs
e Original Utility
005
1) Constant Sum Pref - ($)(FE)(R)(HP)(S) [70,30,0,0,0] Nbzda_B2300_(2WD)
6) Price Range [14K - 16K] WzdaB2300_(2WD)
7
3
y
,7
2) Corrpact/Full (Corrpact] bzdaB2300_(2WD)
3) # Passengers (1-5) (1]
0 0 B1
WzdaB2300_(2WD)
0.11 i
0.
10) Driver Height [Under 6] WzdaB2300_(2WD)
0.1 200
15) 2w d / 4w d [2w d] t1zda_B2300_(2WD)
U)
*a
01243
12) Engine Size (6 Cyl] FordRanger_(2WD)
8) # in FrontSeat [1]FordRanger_(2WD)
0
14) Styling (1-5)
0.1328
[4] FordRanger_(2WD) "DROP"
0,195-
SDrops
13) Quiet & Cornort(1-5) [2] FordRanger_(2WD)
4)
"K
"'
Offroadfrow ing/Hauing [NY,Y]FordRanger(WD)
Utility
0DROP
1
Q 1177
9) BiggestAvail (1-5) [1] FordRanger_(2WD)
11) Bed Length [Short] FordRanger12WD
1192
5) Construction/Plowing [NN] Ford-Ranger_(2WD)
41,
0
0.02
0.04
0.06
0.08
utility
0.1
inC
0.1297
''Maximum
1192
0.12
0.14
The Listening-In methodology defines any drop in the maximum utility value from
attribute question to attribute question as the identification of an unmet customer need.
Realizing there is an unmet customer needs provides at least three benefits to
corporations.
First, by
clustering
customers' unmet
needs
into
categories,
corporations can identify new product introduction opportunities and estimate the
potential market share for the new product assuming the product mix is the same
within the market when the new product is introduced.
This analysis process can
dramatically reduce the uncertainty risk in marketing the new product.
Furthermore,
through adequate design of analysis tools, corporations can understand profoundly the
unmet customer needs and come out with a better solution to fulfill the unmet needs.
At the same time, if adequate analysis tools are applied to innovative customers, like
lead users that we will discuss more in the next chapter, corporations can even get
new product innovation idea from customers.
1.4
Objectives
After Virtual Advisor identified customers' unmet needs, further studies about these
unmet needs should be conducted to translate them into new product development
opportunities.
The objective of this thesis is to design a web based market research
Two analysis tools, Virtual Engineer and
methodology for unmet customer needs.
Design Pallet, are proposed to help corporations understand the specific unmet
customer needs and assess innovative users' new product introduction ideas.
21
New product development brings key competitive advantage to corporations.
Ulrich
and Eppinger [11] argue that the economic success of manufacturing firms depends on
their ability to identify the needs of customers and to quickly create products that
meet these needs and can be produced at low cost.
McGrath [12] argues that
listening to customer needs is an opportunity driven innovation strategy for high-tech
companies that want to achieve growth through product innovation.
It takes the
solution to a specific problem and generalizes it to provide a broader set of problems.
In this way, the solution addresses a market opportunity rather than a customer
specific fix.
Customers are an obvious source of innovation ideas.
Professor Hippel [18]
proposed the concept of lead users and user innovation to help corporations design a
better product.
Customers, or lead users, are the ones living with the problems that
an innovation can solve and they form the eventual market for the innovation.
In
some cases, a customer may challenge a company to solve a particular problem and
force the company to come up with an innovative solution.
Subsequently, there may
be opportunities to generalize this solution to create an innovative product.
A good
example of identifying product innovation opportunities from customer needs is the
microprocessor.
In 1969, a Japanese calculator manufacturer asked Intel to design a
set of chips for a family of programmable calculators.
at least 12 custom chips.
single-chip
The original design called for
But Intel engineers came up with a solution to develop a
general-purpose
logic
device
thus
they
opened
the
history
of
custom-designed integrated circuit.
Identifying innovation opportunities from listening to customers provides insights to
their unmet needs, especially for those customers who are at the forefront and solving
22
emerging problems.
listening to them.
The key is to identify these customers and invest time in
However, it could be a vast investment.
Traditional market
research methodologies to listen to customers include questionnaire survey, individual
interview and focus group.
But they either lack interaction (questionnaire) or require
huge cost (individual or group interview).
This thesis proposes a complementary
method that is web based to accomplish this task.
By taking the advantage of
Internet technology, it is low cost but provides a certain level of interaction.
The structure of this thesis consists of five chapters.
Chapter 1 served as an
introduction and chapter 2 will review several literatures that stimulate the idea of this
thesis.
Chapter 3 will introduce the solution to this web based market research tools,
Virtual Engineer (qualitatively) and Design Pallet (quantitatively).
Chapter 4
provides insight to the key component behind the Design Pallet, the cost model that
enables Design Pallet to interact with customer and understand the tradeoffs for the
customer.
At last, chapter 5 concludes this thesis and discusses some future
development directions for online market research methodologies.
23
Chapter 2
Literature Review
Following the previous research work done on Virtual Advisor, an appropriately
designed market research toolkit should be able to help corporations introduce new
In this chapter, recent academic research in product development area is
products.
examined.
This research inspired me to consider a web-based system to help
corporations design and develop better products to fulfill customers' unmet needs.
2.1
Quality Function Deployment
To be successful, all product development projects need a heavy dose of external
reality
[13].
"Voice
of customers"
is usually the guideline
and customer
requirements represent the beginning of new product design and development [11].
Although this principle seems obvious, many engineering organizations with product
development responsibility get so excited about their advanced technology that they
often neglect to confirm with their intended customers what features and attributes are
actually viewed as desirable and worth paying for.
A poor design of interface
between marketing and technology always leads to this category of new product
failure [14].
24
Well-established research in management of technology suggests that cooperation and
communication
among
marketing,
technology
development,
engineering,
and
manufacturing leads to greater new product success and more profitable products [15].
Quality Function Deployment (QFD) is known as a very useful tool to increase the
cooperation inside the corporations.
It uses perceptions of customer needs as lenses
through which to understand how product characteristics and service policies affect
customer preference, satisfaction, and sales.
It offers a high-level overview of a
range of issues in customer driven product development, including identification of
key product features, relation of these features to perceived customer requirements,
identification of product technologies for their delivery, and assessment of competing
products.
The other advantage of QFD is that it improves communication among marketing,
R&D, engineering, and manufacturing by linking the voice of customers to corporate
decision.
QFD uses a visual data presentation format that both engineers and
marketers find easy to use.
This format provides a natural link among functions in
the product development organization.
QFD uses four houses to present data.
As shown in Figure 7, the first house, House
of Quality, links customer needs to design attributes.
customer needs, are listed in the left column.
format.
The voices of customers, or
It is usually presented in a qualitative
For example, a computer customer might say that he or she needs something
makes it "easy to read what I am working on the computer."
One solution to this
need is to provide computer customers with monitors for viewing their work in
progress.
25
Conflict
Between
esign Attribute
Design Attributes
Customer
Needs
0
Relationship
between
Customer Needs
and
Customer
Perceptions
Design Attributes
Engineering
Measures
Figure 7 - The House of Quality from Quality Function Deployment
Design attributes are descriptive specifications of product performance.
usually presented in a criteria format.
They are
In the computer monitor example, design
attributes for the monitor might be the physical measurement like the illumination of
alphanumeric characters, the focus of the characters, the judged readability at certain
distances, etc.
Engineering measures are the specifications of design attributes.
presented in a quantitative format.
They are usually
For example, in order to achieve the judged
readability at certain distances for the design attribute of a computer monitor, the
engineering measures might be the number of pixels, the size of the screen, the
26
intensity of the pixels, or the screen refresh rate.
The product development team in corporations may work according to the following
process.
First, marketers contact customers to define "quality," or requirements, in
the customer's own terms through techniques such as perceptual semantics and
attribute trade-offs.
These customer requirements are then transformed into design
attributes under the cooperation of marketing and R&D organization.
After the
design attributes are collected, engineering and manufacturing people will work with
marketing and R&D people to finalize engineering specifications for those design
attributes.
Through an integrated design effort that evaluates alternative product
designs and production processes simultaneously, the product is designed to meet both
functional specifications and manufacturing constraints and efficiencies.
The best part of QFD is that it makes the product development team realize the design
trade-offs.
If trade-offs are made, they reflect all three aspects of customer
preference: engineering requirements, product reliability and manufacturing cost.
The product is then manufactured with strict quality control and active worker
participation.
A strong customer service function is charged with ensuring that the
customer ultimately receives the benefits originally identified as the purpose of the
new product.
This product development process is intended to force integration of
customer needs, responsive product design, manufacturing quality control, and
service.
The most challenging work for the product development team is how to establish the
correct relationship between customers needs and design attributes, i.e., the center of
the House of Quality.
It is also the "integrated design" part of the House of Quality.
27
To do a better job on this part, the marketing and R&D people must understand
clearly the customers' preferences and trade-offs, especially how to quantify them.
This thesis provides two toolkits; Virtual Engineer and Design Pallet, to help
marketing and R&D people achieve this goal.
Another point of view for QFD is provided by Crawley [16].
In his lecture notes,
Crawley proposed the following framework for a system architect:
Form
Concept
Figure 8 - System architect's view of QFD
Crawley defined function to be "what the system eventually does, the behavior which
emerges" and form to be "how the system is eventually implemented and operated."
The most important is the concept, which "maps function to form and embodies
working principles."
A system architect, or product architect, generates the concept
to perform functions in certain forms.
The idea is similar to QFD if the following
mapping is done:
28
Function = Customer Needs
Form = Design Attributes
Concept = Integrated Design
In other words, marketing and R&D people are playing the role of system architects
to create amazing concepts that link the function, customer needs, to the form, design
attributes.
In a more specific description of the two toolkits proposed in this thesis: Virtual
Engineer collects the function, or the customer needs, in a solution-neutral format
from unsatisfied customers; Design Pallet detects the form, or the design attributes,
from customers' minds in a solution-specified format.
Through study of how
unsatisfied customers link the functional requirements in Virtual Engineer and the
design attributes in Design Pallet, corporations can learn more innovative product
concepts from customers.
2.2
Lead User and User Innovation
However, not every customer can provide advanced product concepts for corporate
product design teams.
The concerns that "customers do not know what they want
until they see it" and that "the technology is changing so fast that marketing research
will soon be out of date" [14] are valid.
concerns by reaching "lead users".
But corporations can overcome these
Lead users are the customers who not only have
29
unmet needs but may also have developed solutions to the problems resulting from
their needs.
They are the users far ahead of the majority of the market in
understanding technology and struggling today with needs that the remainder of the
market will only face well into the future.
Under this definition, lead users can
provide an opportunity to understand future customer needs and buying behavior so
the product development team can forecast much better.
Involving customers in the product innovation process is a good solution to reduce the
high risk related to newly developed products or services.
The summary of
empirical studies including 10,963 innovation projects in Luthje's recent paper [17]
suggests that not only are customers involved in the product innovation process but
they also contribute at three different levels:
*
Customer as initiator - Users initiate innovations by introducing their needs and
requests into the organization of corporations.
"
Customer as idea generator - Users provide concrete ideas or concepts for
product innovations.
*
Customer as inventor - Users who are highly active develop prototypes without
participation of corporations.
The evidence of the existence of lead users is also presented in Luthje's empirical
study about outdoor sport related consumer products [17].
Within Luthje's usable
153 questionnaires, more than one third of the customers (37%) generated at least one
idea for improved or new products.
This share of innovating users seems very high;
however, only 3% out of the 37% actually cooperate with corporations in the new
product development process.
The other 34% either did not contact with the
30
corporations or did not cooperate with the corporations.
The reasons that these
innovative users did not work with corporations can be concluded as:
>
Disappointing prior experiences with corporations
>
No expectation for appropriate rewards
>
Fear of a time-consuming cooperation with corporations
>
Fear of being cheated by corporations
>
No general interest of realization of idea by corporations
Loss of the chance to work with lead users is a big opportunity cost for corporations.
The product development team in corporations should put more focus on how to get
these frustration factors out of the communication channel with lead users in order to
avoid the gap between lead users and corporations.
As the empirical studies has proven the existence of lead users and user innovation
did play an important role in the product development process of corporations, the
next problem would be how to provide an appropriate tool for lead users to
communicate with corporations about their innovative ideas.
Professor Hippel [18]
highlighted the following five objectives to design an effective toolkit for user
innovation.
These objectives can help corporations eliminate
some of those
frustration factors for lead users.
Trial-and-error to complete desian cycles
It is crucial that toolkits for user innovation enable users to go through complete
trial-and-error cycles as they create their designs.
A properly designed toolkit would
allow users to have a temporarily final product prototype so that they could try it out
by a simulation test and see what happened.
For example, an electronic circuit
simulator should be able to show the output waveforms for the user-input waveforms.
31
It provides feedback to users so they can start their designs over if there is anything
they are not fully satisfied with.
Without this element, users have no way to test the
functional effects of their choices before first field use and if users regret the outcome,
it is too late for corporations.
An appropriate solution space
Some certain design rules should apply to the user design toolkit.
A reasonable
design solution space encompasses the designs users want to create.
Large solution
spaces allow users to manipulate and combine relatively basic and general-purpose
building blocks and operations of products.
It is helpful when corporations are
looking for the next generation of products or long-term solutions.
In contrast, small
When corporations are
solution spaces allow users to make combination of options.
looking for relatively short-term solutions, small solution spaces usually work better.
The reason to enforce design constraints on the toolkit is that custom products can
only be produced at reasonable prices when user designs can be implemented by
simply making low-cost adjustments to the production process.
An appropriate
design solution space maintains the customer's trust to the corporation and reduces
the fear that customers have of being cheated by corporations.
User-friendly design interface
Toolkits for user innovation are most effective and successful when they are made
user friendly by enabling users to use the skills they already have and work in their
own customary and well-practiced design language.
have
to
learn
the
typically
corporation-based designers.
different
design
This means that users don't
skills
and
language
used
by
It will require much less training to use the toolkit
effectively and thus reduce customers' fear of getting into a time consuming process
32
to work with corporations.
A friendly or even interesting user interface design can
also help the toolkit increase general interest of customers to see the realization of
their ideas by corporations and provide certain rewards to customers, like gifts to the
winner of a design competition.
Module libraries
Toolkits should contain libraries of commonly used modules that the users can
incorporate into their custom designs immediately.
This will allow users to focus
their design efforts on the truly unique elements of that design.
For example,
designers of custom integrated circuits find it very useful to incorporate pre-designed
elements in their custom designs ranging from operational amplifiers to complete
microprocessors that they draw from a library in their design toolkit.
It is also a
good method to facilitate the design process for users who just want to make a little
improvement on currently existing products instead of making a completely new
design since they can build their own designs from a starting point.
Translatini user desikns for production
A properly designed toolkit will ensure that customer products and services designed
by users will be producible on suppliers' production equipment without a great
modification of the current production system.
The language of a toolkit for user
innovation must be convertible without error into the language of the intended
production system at the conclusion of the user design work.
If this is not so, then
the entire purpose of the toolkit is lost because corporations receiving a user design
essentially have to do the design all over again.
Translation between user design
language and production language could be a painstaking effort and probably take a
long time to execute.
Without this feature in the user design toolkit, the cycle time
33
to feed back a real product to customers will increase and the market dynamic could
wash the outstanding product out and thus corporations might lose great profit
opportunity.
User innovation could give corporations competitive advantages.
The question is
how to locate lead users, how to attract lead users to work with corporations, and how
to provide an appropriate toolkit for lead users.
Virtual Engineer and Design Pallet,
proposed later in this thesis, offer an example of design toolkits for the automobile
industry and hopefully they can motivate more discussions on this topic and extend
the application to a broader area.
2.3
Internet Impacts
As mentioned in Chapter 1, after Virtual Advisor has identified unmet customer needs,
the next step is to explore what corporations can do with these unmet needs.
With
the introduction of QFD and lead user innovation toolkit concepts, two Internet-based
tools, Virtual Engineer and Design Pallet, will be proposed in the next chapter.
But can the Internet bring benefits to this process?
Traditional methods to interact
with customers in front-end product development processes are questionnaires,
individual or group interviews, and observations of the product in use.
If we
examine these methods in the dimensions of cost and interactive degree, we can find
that Internet based tools offer a medium solution beyond the traditional market
34
research methods.
Interactive degree
High
Low
Low
Internet based tools
Questionnaire
Cost
Individual interview
Focus group
Observation of use
High
Figure 9 - Matrix of market research methods
Figure 9 shows how Internet-based tools provide a low cost solution with a higher
level of interactivity to communicate with customers.
With JAVA applet running on
the Internet, the program can feed back in real time based on customer choices.
Traditional questionnaire cannot achieve this level of interactivity.
However, Internet tools are not able to substitute for questionnaires and interviews.
Face to face interviews and observations definitely have irreplaceable value since the
product development team has the chance to talk to customers, watch their responses,
and interacts with customers in real time.
35
Questionnaires also have irreplaceable
value because of their popularity and ease of use.
But leveraged by the Internet
technology, Virtual Advisor, combine with Virtual Engineer and Design Pallet,
supplies corporations a total solution toolkit on the Internet to get involved and
interact with customers.
In the next chapter, Virtual Engineer and Design Pallet will be proposed and examined.
Some further discussions on Design Pallet will continue in Chapter 4.
Then the
conclusion of these two Internet toolkits and the future development of Internet-based
marketing research tools will be presented in chapter 5 as the conclusion of this thesis.
36
Chapter 3
Study of Unmet Needs
After the detection of unmet needs as described in chapter 1, this chapter will
introduce Virtual Engineer dialogue based on conflicts identified through trusted
Virtual Advisor product research process and Design Pallet that enables customers to
There are two differences between Virtual
make their own design over the Internet.
Engineer and Design Pallet.
First, Virtual Engineer focuses on collecting functional
requirements or reasons why customers have specific unmet needs while Design
Pallet focuses on exploring user innovated solutions to specific unmet customer needs.
Second, Virtual Engineer uses simple questionnaire format that only allows one-way
communication while Design Pallet offers interactive features that users can get real
time feedback for their design activities.
Figure 10 shows the process flow chart for the overall system including Virtual
Advisor, Virtual Engineer and Design Pallet.
updated progress in this research project.
It is based on Mann's thesis [10] with
Based on customers' preference, they can
choose if they want to meet Virtual Engineer and use Design Pallet.
Advisor will lead customers directly to showroom for recommendations.
37
If not, Virtual
Virtual Advisor
Greeting, Purpose
,
Virtual Advisor
Constant-Sum
preference question
Virtual Engineer
Detect drop in
highest utility
Virtual Engineer
Greeting, Purpose
Drop in highest
utility?
Virtual Engineer
Generate dialogues
based on conflict pairs
recorded before
NO
YE
Virtual Engineer
Identify and Record
conflict pairs
Calculate initial
utility value
(EQ 1)
Virtual Advisor
Ask highest
information value
attribute question
Virtual Engineer
Comment and
Open-end question
Ask for permission to
introduce Design
Pallet System
Calculate Bayesian
attribute effect
(EQ 2)
Agree to introduce
Design Pallet System?
Calculate attribute
information value
(EQ 3)
YES
Populate
recommendation with
highest utility
products
NOAll attribute
questions asked?
Design Pallet System
Launch design pallet
system
~
YES
Ask for permission
to introduce
Virtual Engineer
Design Pallet System
Allocate preferences
between
recommended and
self-desi ned truck
<
gree to
Vita
-NO
introduceYE
nier
Send Customer to
showroom for
recommendations and
record data for
analysis
Figure 10 - Flow chart for Virtual Advisor, Virtual Engineer and Design Pallet
38
NO
Qualitative Study of Unmet Needs
-
3.1
Virtual Engineer
Virtual Advisor and the process to detect drop in highest utility in Figure 10 already
covered in Chapter 1.
The next process will be for Virtual Engineer to identify and
record conflict pairs for the customer.
This process is not included in Virtual
Advisor but parallel to it while the customer has dialogue with Virtual Advisor.
At
this time, Virtual Engineer will not show up on the screen but instead, Virtual
Engineer will keep track of the customer's highest utility drop and record unmet
needs, i.e., conflict pairs.
The conflict pairs are identified through the correlation matrix that Mann proposed
[10].
In the correlation matrix, the numbers represent the relationship between the
initial perceptual
dimensions of the constant-sum preference question and the
conditional probabilities.
These variables and relationships are developed through
the calculation of the correlation coefficients of variables both within and across the
vehicle information database,
Again,
Dd,a
Dd,a,
and the conditional probability database,
P(Rr,qIAa).
values are the normalized ratings of each product across the initial
dimensions of the constant-sum preference question.
P(Rr,,qAa) values, if derived
from actual consumer surveys, show there are true preferences for vehicle alternative
a given a response r to question _q.
These correlations are aggregated into a single
correlation database that is used by the Virtual Engineer for the identification of the
unmet need, i.e., conflict pairs.
Of note, this correlation database does not include
any engineering variables for the products.
This is due to the current focus upon
identification of the unmet needs and not of the actual design constraint behind the
39
unmet need.
Engineering variables will be used later to clarify the design level
attributes that have been traded-off to create the unmet needs.
Figure 11 shows a partial display of the correlation matrix that is employed in this
research project.
The numbers in the correlation matrix can be calculated based on
following procedures:
Let C = a combined array of [P(RrqIAa), Dd,a]
For every conflict pair (p,q):
pcp, Cq
Cov(Cp, Cq)
=
0-Cp *
-Cq
where
Cov(Cp,Cq) = Covariance between the variable dataset
aCp = Standard deviation of the values of Cp
full
compact
full
standard trans-yes
automatic-trans-yes
2WD-yes
4WD-yes
Price_10-12K
Price 12_14K
Price_14_16K
Price 16.18K
Price 18_20K
Price 20 22K
Price 22_24K
Price_24_26K
Price 26_28K
Price_28 30K
Price_30_32K
Price Over 32K
Offroad-no
Off road yes
Towing.no
Towing yes
Hauling-no
Hauling._yes
Construction no
Construction yes
PlowingSnow no
PlowingSnow.yes
0.005410503
-0.005410503
0.048051704
-0.048051704
-0.529521584
-0.583792879
-0.607009827
-0.63212606
-0.62617736
-0.580651496
-0.533990298
-0.481917364
-0.458538711
-0.454721244
-0.486512772
-0.511823935
0.048051704
-0.048051704
0.658706625
-0.658706625
0.544807837
-0.544807837
0.709915857 95
0 7 1 0 46 3 3 4
.
ME
5
i_
654
automatic trans yes
standard trans yes
4WDyes
2WD yes
1
-0.005410503
1
0.005410503-
-1
0.136960734
-0.048051704
0.048051704
0.529521584
0.583792879
0.607009827
0.63212606
0.62617736
0.580651496
-0.136960734
-0.44453167
-0.437685613
-0.433023613
-0.422416365
-0.409068678
-0.399617359
-0.424919018
-0.445451404
-0.456431315
-0.438524454
-0.410555551
-0.428609593
0.136960734
-0.136960734
0.007356298
-0.007356298
-0.137472634
0.137472634
0.13753186
-0.13753186
0.533990298
0.481917364
0.458538711
0.454721244
0.486512772
0.511823935
-0.048051704
0.048051704
-0.658706625
0.658706625
-0.544807837
0.544807837
ARM"15
0.709915957
.AN1, , ~10
0.715046334
0.099543638
-0.099543638
1
-0.136960734
0.136960734
0.44453167
0.437685613
0.433023613
0.422416365
0.409068678
0.399617359
0.424919018
0.445451404
0.456431315
0.438524454
0.410555551
1
1
1
-0.362888M38
-0.415216264
-0.430180713
-0.409952188
-0.370894928
-0.342501871
-0.325221981
-0.29115608
-0.286854363
-0.272214269
-0.282587142
-0.299523983
0.428609593
-0.136960734
1
0.136960734014ii
-0.007356298
0.092220117
0.007356298
-0.092220117
0.137472634
-0.050251303
-0.137472634
0.050251303
-0.13753186
0.070327295
0.13753186
-0.070327295
0.732628719
-0.099543638
0.099543638kl.4i
627t
0.362888638
0.415216264
0.430180713
0.409952188
0.370894928
0.342501871
0.325221981
0.29115608
0.286854363
0.272214269
0.282587142
0299523983
1
0.092220117
0.092220117
0.050251303
-0.050251303
-0.070327295
0070327295
,
'
Correlation Matrix
compact
o
0.732628719
Figure 11 - Partial display of correlation matrix used to identify conflict pairs
In Figure 11, every negative correlation coefficient, which is marked, stands for a
40
conflict pair.
The smaller the negative number, the stronger the conflict.
Each
conflict pair represents a possible unmet customer need based on current products
available on market.
For example, in Figure 11, there are conflict pairs for the
customer who wants a compact truck that can be used for commercial application like
construction and plowing.
The current available trucks on the market do not
combine these features so there is a smaller than -0.7 correlation coefficient, which is
a strong conflict.
Of note, for those conflict pairs with correlation coefficients equal
to -1, it means an intra-attribute conflict and is not reasonable.
a -1
For example, there is
in Figure 11 when customer wants a truck with both compact and full size.
Under rational circumstance, this kind of conflict should not happen.
After Virtual Advisor finished all attribute questions, the recorded conflict pairs
triggered Virtual Engineer dialogue.
In order to make a smooth transfer from Virtual
Advisor to Virtual Engineer, Virtual Advisor finished the dialogue with an option for
the customer to meet with Virtual Engineer or go directly to the showroom:
Figure 12 - Virtual Advisor introduces Virtual Engineer
41
If the permission is granted, Virtual Engineer will greet the customer with a statement
of dialogue purpose:
Figure 13 - Meet with Virtual Engineer
Now Virtual Engineer is ready to generate dialogue with the customer based on his
unmet needs that are identified through the Virtual Advisor dialogue.
Since this
dialogue focuses on collecting functional requirements for specific unmet needs, the
dialogue must be pre-defined.
Due to the limitation of program execution time and
storage, 71 strong conflicts, which have correlation coefficients smaller than -0.7 are
assigned to trigger Virtual Engineer dialogue in this research project.
list of the 71 strong conflicts is shown in Table 1:
42
The detailed
Table 1 - Strong conflict pairs that trigger Virtual Engineer
First conflict attribute
Second conflict attribute
High Fuel Economy
High Horse Power
High Safety
Full Size
Hauling
Construction
Plowing
Biggest available
Long Bed
8 cylinder engine
10 cylinder engine
Big & comfortable
High Horse power
Compact
Smallest available
Short Bed
4 Cylinder engine
Low Budget
High Safety
Compact
Smallest available
Short Bed
4 cylinder engine
Compact
Hauling
Construction
Plowing
Biggest available
Long Bed
8 cylinder engine
10 cylinder engine
Big & comfortable
Tall passenger
High Budget
Full
Smallest available
Short bed
4 cylinder engine
Low Budget
Off Road driving
2 W D
Towing
2 WD
Hauling
Smallest available
Short bed
4 cylinder engine
Construction
Smallest available
Short bed
Big & comfortable
4 cylinder engine
Plowing
Smallest available
Short bed
Big & comfortable
4 cylinder engine
Smallest available
Long bed
8 cylinder
10 cylinder
Big & comfortable
High Budget
Biggest available
Short bed
4 cylinder
Low Budget
Short bed
8 cylinder engine
10 cylinder engine
Big & comfortable
43
Take the (High horse power - Compact) conflict pair as an example.
wants a compact truck with a high horse power engine.
The customer
Under current truck design it
is a strong conflict because usually a compact truck comes with a low horsepower
engine but the customer specifies that high horse power is required for the ideal truck.
In order to understand the reason why customer wants this combination of truck
features, Virtual Engineer asks questions about why customer needs a compact truck
and a high horse power engine separately as in Figure 14 and Figure 15:
Figure 14 - Virtual Engineer dialogue for compact truck
44
Figure 15 - Virtual Engineer dialogue for high horse power
There are two advantages for Virtual Engineer to ask questions separately.
First,
according to the axiom design principle, it is better to collect functional requirements
independently so engineering design team can face fewer trade-offs.
Second, it is
helpful to reduce system load because if Virtual Engineer dialogue is generated by
conflict pairs, it will need at least 71 different pre-defined dialogues; if Virtual
Engineer dialogue is generated by attributes independently, it will only need 20-30
dialogues.
After Virtual Engineer goes through all conflict pairs based on the identified unmet
needs, there will be an open-end comment space for the customer to input any other
functional requirements that were not covered in pre-defined dialogue.
It gives
customers opportunities to make all different kinds of requests for their ideal truck.
The open-end comment space of Virtual Engineer is shown in Figure 16.
45
Figure 16 - Virtual Engineer open-end dialogue
With all the responses collected from customers, the new product development team
can understand the problem that users face everyday and design a better product
accordingly.
It is also helpful if the new product development team can make
statistical clustering to estimate market potential for a new product feature.
The
detailed Virtual Engineer dialogue is attached as Appendix A.
Quantitative Study of Unmet Needs
-
3.2
Design Pallet
Design Pallet is a design toolkit for user innovation.
After Virtual Engineer collects
functional requirements from customers about their unmet needs, the new product
46
development team in corporations should clarify the problem customers had.
Furthermore, customers may help the new product development team by providing
solutions to their own unmet needs based on the concept of lead users.
It can help
marketing managers and R&D engineers get new ideas from a larger candidate pool
instead of a limited number of team members.
As usual, the customer should have the privilege to skip Design Pallet and go to the
showroom for recommended product directly.
Virtual Engineer will end the
dialogue with the transfer to Design Pallet:
Figure 17 - Introduce Design Pallet
If the customer grants permission, Design Pallet will launch with an option to go
through a tutorial about how to use Design Pallet.
The tutorial is designed to help
customer understand the user interface of Design Pallet and realize the design features
and options available for each design feature.
Also the interpretation of the design
outcome will be explained so that the user can make design decision and completes
47
the design iteration.
Figure 18 - Design Pallet launch screen
If the customer chooses to start Design Pallet right away and skip the tutorial, Design
Pallet will start with the truck model that recommended as a result of Virtual Advisor
Design Pallet will also start with guidance for customers to encourage
dialogue.
them to solve their identified unmet needs.
Figure 19 shows the initial screen for
Design Pallet.
For example, suppose the customer wants a compact truck with a high horsepower V8
engine.
The word "compact" is fuzzy to truck designers because it depends on the
customer's perception to decide exactly what size a truck can be categorized as a
compact truck.
Design Pallet presents this question that a new product development
team will ask and asks customers to use Design Pallet to clarify the meaning of
"compact".
48
Figure 19 - Start Design Pallet
Design Pallet screen contains three areas as depicted in Figure 20.
the user input area.
The first area is
Customers can pick different options for product features in this
area and the resulting design outcome will be displayed in the graphic output area
and/or the parameter output area.
The available user input features and options are
listed in Table 2.
Graphic Output Area
User Input Area
Parameter Output Area
Figure 20 - Design Pallet layout
49
Table 2 - Available user input features and options in Design Pallet
Design dimension
Power
Design option
Engine
2WD/4WD
Transmission
Haul capacity
Tow capacity
Steering
Bed length
Size
Cab size (by doors)
Cab size (by dimensions)
Color
Body style
Interior
Exterior
Style
Feature
50
The output of Design Pallet includes graphic output and parameter output.
The
graphic output will change according to the customer's design decision on truck size,
color, and body style.
The graphic output needs extensive advance effort to prepare
hundreds of truck pictures for different sizes and colors and styles.
It is not possible
to have high resolution on both user input and graphic output so the trade-off must be
made between technical limitation and the user's design degree of freedom.
The parameter output includes price, fuel economy, payload capacity, towing capacity,
and dimension.
These parameters are widely considered to be the major decision
criteria in order for customers to choose the right truck.
One or more parameters
will change based on the selected design options in the user input area.
The details
of the formulation system between parameter output and user input will be further
explained in Chapter 4 and Appendix B.
The principle in designing the graphic and parameter output of Design Pallet is that at
least one item should change in response to any design decision made in the user
input area.
This important principle enables Design Pallet to interact with customers
and help customers complete design iterations.
After asking customers to provide more specific information regarding their identified
unmet needs, Design Pallet will ask customers to create their own design freely.
This helps corporations capture other market needs from users that are not
accommodated by Virtual Advisor.
This kind of market need has very high potential
value for next generation products.
When customers finish their creative design and want to go to the showroom, a
51
side-by-side comparison of the recommended product and the user-designed product
will be made at the end of Design Pallet and customers will be asked to allocate
preferences between them.
Figure 21 illustrates this comparison.
This comparison
concludes design iterations with a perceptual satisfaction measurement so that
corporations can understand customers' evaluations of new products and estimate the
market potential for them.
original
new
1frn done with mylevaluatiol,
original
new
Est. Price:
$19588
$19588
Towing Cap.: 4700 lbs
6795 lbs
Bed Size:
4 x5x2
4 x 5 x 2 (L-AfH in feet)
Payload Cap.: 103 lbs
103 lbs
Cab size:
12 x 5 x 5
12 x 5 x 5 (LW'xH in feet)
Wheel Drive:
4WD
Fuel Econ (city):
15.921 mpg
15.921 mpg
Transmission: Manual
Fuel Econ (hiwy):
20.798 mpg
19.574 mpg
Engine:
original
4WD
6 Cylinders
new
Manual
6 Cylinders
Figure 21 - Final comparison for Design Pallet
This concludes the system overview of Virtual Advisor, Virtual Engineer, and Design
Pallet.
In the next chapter, the formulation of cost model in Design Pallet is
presented and discussed.
52
Chapter 4
Formulation System in Design Pallet
In this chapter, a formulation system in Design Pallet is presented to enable real-time
interaction with users.
The system converts users' design inputs for trucks to output
parameters including price, fuel economy, payload, and towing capacity so that users
can start another design iteration based on the output of their design.
This is a very
important feature that distinguishes Design Pallet from traditional market research
methodology since it involves real time feedback to customers at very low cost.
4.1
System Overview
The formulation system is a subsystem of Design Pallet.
It captures design activities
in the user input area of Design Pallet and then displays necessary changes in the
parameter output area.
To aid understanding, the formulation system can be
represented graphically as a system block diagram or, mathematically, as a matrix.
As a system block diagram, it can be represented thus:
53
User Input
1
Formulation
System
0 Parameter Output
In matrix terms, it can be represented as:
Y1
X1
Fii
*
*
*
F
*
0
0
*Xi
Xi
Y1,e
F,
-Fi
Fin
Inu paa
=j
e
000
o e ficF
e
eters
e
Xi,... Xi= Output parameters
iS
Y,..Y
F11 , ..
I
=
Input parameters
Fj = Formulation system coefficients
F X Y
=Xi for I=1..i
z:'
Cost functions for output parameters
k =1
This chapter will discuss how to determine the formulation system coefficients,
F1 1.. .Fij.
Before entering into the discussion of how to determine formulation
coefficients, the input and output parameters of the formulation system must be
clarified.
Input parametersof the formulation system
Input parameters are captured from the user input features and options listed in Table
2.
It is essential that they be able to be represented in numerical form so that the
formulation system can process them mathematically.
54
Based on this principle, these
design options in Table 2 can be further consolidated into three categories:
"
Powertrain: Engine, horsepower, transmission, and wheel drive are the major
factors in designing the powertrain system for a truck.
They determine the
performance of the truck.
*
Appearance: Length (including bed length), width, height, color, style, and cab
type are the major factors in designing the frame of a truck.
external look of a truck.
They determine the
However, color is not suitable for treatment as an input
parameter in Design Pallet because it has no mathematical meaning.
Instead,
color is a very important factor for the graphic output, which is not covered in this
thesis.
Someone
consideration.
might argue that style should also not be taken
into
Nevertheless, based on the analysis of observations in the truck
database that will be disclosed in the next section, truck style is an important
factor for many output parameters.
Therefore four different body styles are
coded into formulation system.
"
Optional parts: Entertainment equipment, interior decoration, wheels/tires, and
extra packages are optional parts that are not included in the base model of the
truck.
They can be added at the customer's request.
These optional parts
usually do not affect powertrain and appearance, but only price.
In Design Pallet,
optional parts are dealt with independently because of their less interaction with
powertrain and appearance.
Thus, based on the information from the truck database on the market and the analysis
of user input choices of Design Pallet in Table 2, the input parameters of the
55
formulation system are:
"
Engine (4, 6, 8, 10 cylinders and diesel engines)
"
Horsepower (in HP)
"
Transmission (automatic or manual)
*
Wheel drive (2WD or 4WD)
*
Length (in inches)
*
Width (in inches)
"
Height (in inches)
"
Style (four body styles: traditional, rugged, sporty, very sporty)
"
Cab type (three cab types: regular, extended, crew)
"
Optional parts
Output Parameters of the formulation system
Output parameters of formulation system should correlate to customers' needs
segmentation so users can determine if the design outcome can fulfill their needs.
In
Mann's thesis [10], he defines primary need dimensions for trucks as the following 10
attributes:
*
Price
*
Power
"
Fuel economy
"
Hauling capacity
"
Overall size
*
Style
"
Passenger compartment
56
* Reliability
"
Safety
*
Luxury
In Design Pallet, the above 10 attributes are consolidated.
Since the output
parameters are presented in numerical format, reliability, safety and luxury are not
considered to be good candidates because they are highly perceptual attributes.
Moreover,
style and passenger compartment are considered better presented in
graphical format; thus they are reflected in the graphic output area.
The other need
dimensions are discussed in the following.
*
Price: A very important attribute and often the major requirement for customers
purchasing a truck.
"
It is one of the output parameters.
Fuel economy: A similar attribute to price.
Customers often think about the
future investment on the truck, so fuel economy is another important decision
factor.
In fact, both city fuel efficiency and highway fuel efficiency are usually
listed.
*
Power: Towing capacity is considered to be a good measurement of truck power.
Although truck frame strength also has influence, towing capacity will be used to
stand for power need segmentation in Design Pallet.
"
Hauling capacity: The major purpose for purchasing a truck is to take advantage
of the specifically designed truck bed, so hauling capacity is also a major criteria
to select a truck.
Payload is the standard representation of hauling capacity of a
57
truck today.
*
Overall size:
This attribute is important because the driver needs this number to
decide if the truck is small enough to fit into a garage or big enough to provide
comfortable space.
market.
It is presented in (Length x Width x Height) format on the
In Design Pallet, Dimension is used to represent overall size.
Based on the analysis of need segmentations and today's truck data format on the
market, the following parameters are selected as the output parameters of the
formulation system:
*
Price (in dollars)
"
City Fuel Efficiency (in Miles Per Gallons, MPG)
"
Highway Fuel Efficiency (in Miles Per Gallons, MPG)
*
Payload (in lbs)
*
Towing Capacity (in lbs)
*
Dimension - Length x Width x Height (in inches or square inches)
4.2
Statistical Regression Result
There are many different ways to determine the formulation system coefficients.
The most straightforward method is to ask partner truck manufacturers in this research
project to provide the internal cost function model.
this method.
There are two disadvantages to
First, the whole system from Virtual Advisor to Virtual Engineer to
Design Pallet is based on trusted marketing, which means this system is not biased
58
toward any specific corporation.
trust will be lost.
If the internal cost function model is used, then the
Second, as shown in the truck database collected for this research
project, the cost function is very different across different manufacturers even
different truck models.
It is not robust if the formulation system is built based on
limited information.
A better and unbiased method to build the formulation system is to use the statistical
regression technique.
Since the required input and output for the formulation system
are determined, applying regression analysis over current on-market truck data will
In order to do this, a truck data survey for
deliver better and unbiased cost functions.
112 Year 2001 models is done.
It covers 8 different manufacturers with different
models and related input and output parameters are all collected for regression
analysis.
SPSS is the statistical analysis tool used for this thesis and the coefficient
of determination R2 is used to represent if the resulting model is a good fit to real data.
Before getting into regression
analysis,
there are three problems.
representative measurement for overall truck size is needed.
First,
a
Length, width and
height individually are not good enough to represent overall truck size.
A better
measurement for overall truck size should cover the material cost for making the truck
frame.
Thus surface area is a good measurement.
In this thesis, a derivative input
parameter dim is used to represent overall truck size:
Dim = 2* (Length*Width + Width*Height + Height*Length)
unit: inch2
Of note, the length in this formula includes bed length of the truck.
dim stands for
dimension of the truck but is truncated to avoid confusion with the Dimension in
output parameters.
It is a measurement of surface area for a cubit that is able to
59
Someone may argue that there is a big empty space
accommodate the whole truck.
over the truck bed that should be taken out.
However, the empty area is not
consistent across different manufacturers and models.
It also depends on the body
style and the cab type and these two factors are already taken into consideration.
So
the empty area over the truck bed is not treated as a key factor in the model.
The second problem is multi-colinearity, which occurs when multiple regressions are
performed over highly
correlated independent
variables.
If two independent
variables have an effect on each other, i.e., X, changes when X 2 changes, it is hard to
interpret the final regression model.
A good method to get rid of this problem is to
add one derivative input variable called the interaction term.
With this interaction
term, the second order effect of the linear regression model is taken into account.
For the truck database used, a high correlation between horsepower and dim is
observed:
400
s um
300
a
aa
1
a
200
130M
0i a3
2P
on a
I
100
50000
as0
a
x
aw
asa
ri1 a0 w
W
a
a
a
r
ana
a
60000
70000
80000
90000
100000
110000
DIM
Figure 22 - Correlation between horsepower and dim
And the correlation coefficient is 0.824 between these two attributes.
60
Thus an
interaction term hpdim is used.
It is simply the product of horsepower and dim:
hpdim = hp * dim
This interaction term corrects the model if both horsepower and dim are used to
predict a dependent variable.
As we will see in later discussion, hpdim usually has
different sign from horsepower and dimension in the regression model.
Finally, nonlinear regression is also considered to improve the regression result.
However, nonlinear regression is not very helpful in this application since many
independent variables are bivariate variables.
In this application, an obvious
Instead of using the log-linear function of
non-linearity is found on the horsepower.
horsepower to represent its non-linearity, an observation of horsepower and engine
cylinders proves that engine cylinders can represent the non-linearity of horsepower:
400
-F---
300'
CB8
-
200,
1
100.
65
0J
N=
16
4.00
59
71
6.00
8.00
StandNumCyl
Figure 23 - Relationship between horsepower and engine cylinders
61
Thus in the regression analysis of this thesis, the engine cylinders are often used to
represent the engine performance whenever the dependent variables have nonlinear
relationship with the engine.
However, using both horsepower and engine cylinders
in one cost function may distort the effect of the engine performance and increase the
correlation between independent variables.
This is not appropriate.
If only one out
of horsepower and engine cylinders should be used in one cost function, which one
should be used will base on a trial-and-error basis.
The SPSS output for regression models of the five output parameters are shown below.
Of note, "Dimension" does not need regression because it can be directly obtained
from users' input.
Many input parameters are coded as bivariate variables here.
Following is the list of bivariate variables:
*
wd4
"
trans
=
=
1 if the truck has 4 wheel drive and wd4 = 0 if the truck has 2 wheel drive
1 if the truck has automatic transmission and trans = 0 if the truck as
manual transmission
*
cabO = 1 if the truck has a regular cab otherwise cabO = 0; cabl = 1 if the truck
has an extended cab otherwise cabi = 0; cab2 = 1 if the truck has a crew cab
otherwise cab2 = 0.
For a truck, only one of cabO, cabl, cab2 can equal 1 and
the others must equal 0.
"
body1 = 1 if the truck has a traditional body style otherwise = 0; body2 = 1 if the
truck has a rugged body style otherwise = 0; body3 = 1 if the truck has a sporty
body style otherwise = 0; body4 = 1 if the truck has a very sporty body style
62
otherwise = 0.
For a truck, only one of body1, body2, body3, body4 can equal 1
and the others must equal to 0.
*
V4 = 1 if the truck has a 4 cylinder engine otherwise = 0; V6 = 1 if the truck has a
6 cylinder engine otherwise = 0; V8 = 1 if the truck has an 8 cylinder engine
otherwise = 0.
For a truck, only one of V4, V6, V8 can equal 1 and the others
must equal 0.
63
Table 3: Initial regression model for price
Model Summary
Model
R
R Square
Adjusted R
Square
Std. Error of
the Estimate
a. Predictors: (Constant), HPDIM, BODY4, CABI, WD4,
TRANS, BODY1, CAB2, V4, BODY3, V8, DIM, HP
Coefficients a
Standardi
zed
Coefficie
Unstandardized
nts
Coeff icients
Model
B
Std. Error
Beta
1
(Constant)
-3278.649
6096.168
WD4
2747.136
295.029
.281
TRANS
2089.795
354.847
.185
CAB1
1955.281
336.794
.196
CAB2
2903.414
410.658
.244
HP
41.374
23.357
.501
V4
-129.875
559.509
-.009
V8
203.859
627.319
.020
BODY1
791.152
649.854
.055
BODY3
617.651
488.543
.061
BODY4
3302.549
631.531
.193
DIM
.185
.086
.424
- 1AP - A7
-1 V7ra-b Ann
HPndim
a. Dependent Variable: MSRP
t
-.538
9.311
5.889
5.806
7.070
1.771
-.232
.325
1.217
1.264
5.229
2.154
Price =
-
3278.649
+ 2747.136*(wd4)
+ 2089.795*(trans)
+ 1955.218*(cabl) + 2903.414*(cab2)
+ 791.152*(body1) + 617.651*(body3) + 3302.549*(body4)
- 129.875*(V4) + 203.859*(V8)
+ 41.374*(hp)
+ 0.185*(dim)
- 0.000107*(hpdim)
+ Optional parts
64
Sig.
.592
.000
.000
.000
.000
.080
.817
.746
.226
.209
.000
.034
Table 4: Initial Regression Model for City Fuel Economy
Model Summary
Model
R
R Square
Std. Error of
the Estimate
Adjusted R
Square
a. Predictors: (Constant), HPDIM, BODY4, CAB1, WD4,
TRANS, BODYI, CAB2, V4, BODY3, V8, DIM, HP
Model
1
(Constant)
WD4
TRANS
CAB1
CAB2
HP
V4
V8
Coefficients a
Standardi
zed
Coefficie
Unstandardized
Coefficients
nts
B
Std. Error
Beta
30.567
4.181
-.610
.202
-.114
6.735E-02
.243
.011
.420
.231
.077
.318
.282
.049
-4.46E-02
.016
-.989
3.987
.384
.523
-1.756
.430
-.314
BODY1
BODY3
BODY4
DIM
-. 161
-.487
-. 856
-1.95E-04
HPndimn
1t-07 Vra
t
7.310
-3.014
.277
1.817
1.128
-2.782
10.389
-4.082
Sig.
.000
.003
.783
.072
.262
.006
.000
.000
.446
.335
.433
-.021
-.088
-.091
-. 360
-1.454
-1.975
.719
.149
.051
.000
-.823
-3.321
.001
1A6l e
7CF EC7
a. Dependent Variable: CFECON
City Fuel Economy =
+ 30.567
- 0.610*(wd4)
+ 0.06735*(trans)
+ 0.42*(cabl) + 0.318*(cab2)
- 0.161*(body l) - 0.487*(body3) - 0.856*(body4)
+ 3.987*(V4) - 1.756*(V8)
- 0.0446*(hp)
- 0.000195*(dim)
+ 0.0000005918*(hpdim)
65
Table 5: Initial Regression Model for Hi2hway Fuel Economy
Model Summary
Adjusted R
Std. Error of
R Square
Square
the Estimate
R n*1 279
ina
R9A__
1
WD4,
CAB1,
BODY4,
HPDIM,
a. Predictors: (Constant),
TRANS, BODY1, CAB2, V4, BODY3, V8, DIM, HP
Model
R
Coefficients a
Standardi
zed
Coefficie
Unstandardized
nts
Coefficients
Std. Error
Beta
Model
B
1
(Constant)
32.413
5.415
WD4
-1.435
.262
-.251
TRANS
-9.67E-02
.315
-.015
CAB1
.119
.299
.020
CAB2
-3.97E-02
.365
-.006
HP
-4.38E-02
.021
-.908
V4
3.261
.497
.399
V8
-1.060
.557
-.177
BODY1
.582
.577
.069
BODY3
-.380
.434
-.064
BODY4
-.618
.561
-.062
DIM
-1.29E-04
.000
-.507
7ep-e7
e A: IFE
A1 Vra1
HPa.M nno
a. Dependent Variable: H-FECON
Highway Fuel Economy
=
+ 32.413
- 1.435*(wd4)
- 0.0967*(trans)
+ 0.119*(cab1) - 0.0397*(cab2)
+ 0.582*(bodyl) - 0.38*(body3) - 0.618*(body4)
+ 3.261*(V4) - 1.06*(V8)
- 0.0438*(hp)
- 0.000129*(dim)
+ 0.000000447*(hpdim)
66
t
5.986
-5.478
-.307
.397
-.109
-2.112
6.562
-1.902
1.009
-.875
-1.102
-1.693
Sig.
.000
.000
.760
.692
.913
.037
.000
.060
.315
.383
.273
.094
7An
oa _A1 777 -r2gq
Table 6: Initial Regression Model for Payload
Model Summary
IModel
R
R Square
Std. Error of
the Estimate
Adjusted R
Square
,
a. Predictors: (Constant), HPDIM, BODY4, CAB1, WD4,
TRANS, BODYl, CAB2, V4, BODY3, V8, DIM, HP
Coefficients
Unstandardized
Coefficients
Std. Error
B
Model
1
(Constant)
7604.566
2248.401
WD4
-84.803
108.813
TRANS
-394.738
130.875
CAB1
-285.973
124.217
CAB2
-364.157
151.460
HP
-43.689
8.615
V4
-148.096
206.359
V8
-169.555
231.369
BODY1
-313.568
239.681
BODY3
-203.444
180.185
BODY4
-453.245
232.922
DIM
-8.20E-02
.032
77P AYL O
A AQQFe-AVa r
HPim
a. Dependent Variable: PAYLOAD
a
Standardi
zed
Coefficie
nts
Beta
-.038
-.153
-.126
-.134
-2.316
-.046
-.072
-.096
-.088
-.116
-.825
Payload =
+ 7604.566
- 84.803*(wd4)
- 394.738*(trans)
- 285.973*(cabl) - 364.157*(cab2)
- 313.568*(bodyl) - 203.444*(body3) - 453.245*(body4)
- 148.096*(V4) - 169.555*(V8)
- 43.689*(hp)
- 0.082*(dim)
+ 0.0006399*(hpdim)
67
t
3.382
-.779
-3.016
-2.302
-2.404
-5.072
-.718
-.733
-1.308
-1.129
-1.946
-2.592
Sig.
.001
.438
.003
.023
.018
.000
.475
.465
.194
.262
.055
.011
Table 7: Initial Regression Model for Towing Capacity
Model Summary
I Model
R
RAa
R Squa
Std. Error of
the Estimate
Adjusted R
Square
Rqare
qae
29071
WD4,
CAB1,
BODY4,
HPDIM,
a. Predictors: (Constant),
1
TRANS, BODY1, CAB2, V4, BODY3, V8, DIM, HP
Coefficients
Model
1
L7R11R
Unstandardized
Coefficients
B
Std. Error
15558.764
9819.808
-252.640
475.238
-341.720
571.594
-275.448
542.514
404.360
661.495
-37.815
37.624
-2.876
901.266
1961.355
1010.496
-1831.437
1046.796
-2200.940
786.953
-2083.256
1017.280
-.174
.138
(Constant)
WD4
TRANS
CAB1
CAB2
HP
V4
V8
BODY1
BODY3
BODY4
DIM
HPDIM
7 QAAF-.OA
a. Dependent Variable: TOWING
a
Standardi
zed
Coefficie
nts
Beta
-.034
-.040
-.037
.045
-.606
.000
.253
-.169
-.288
-.161
-.527
o1
I A1A
t
1.584
-.532
-.598
-.508
.611
-1.005
-.003
1.941
-1.750
-2.797
-2.048
-1.256
1
Towing capacity =
+ 15558.764
- 252.64*(wd4)
- 341.72*(trans)
- 275.448*(cabl) + 404.36*(cab2)
- 1831.437*(body1) - 2200.94*(body3) - 2083.256*(body4)
- 2.876*(V4) + 1961.355*(V8)
- 37.815*(hp)
- 0.174*(dim)
+ 0.0007944*(hpdim)
68
Sig.
.116
.596
.551
.613
.542
.317
.997
.055
.083
.006
.043
.212
4.3
Logical Considerations for Regression Models
2
Although R2, the measurement of how much variation in the dependent variable can
be explained by this model, for the above models are mostly close to 0.8, there are
some logical considerations for using these regression models.
several outliers found while making the model.
First, there are
Not only can taking out these
outliers help improve the model but studying outliers can also find the new product
trend of trucks.
Second, some independent variables are not significant and some
coefficients of the independent variables are not reasonable in predicting the
dependent variable.
constant.
For example, the regression model for price has a negative
Finally, for towing capacity, the R2 is only close to 0.6, which means only
60% of the variation is captured by the model.
This model should be improved.
This section discusses the logical considerations for fixing the models in Section 4.2
based on the analysis of outliers and dependent variables.
In order to get models fit
most usual trucks, some outliers are taken out before getting into the analysis of
dependent variables.
For outliers and residual analysis about regression models,
please refer to Appendix B.
In this section, the rationale and improvement of
regression models for different independent variables, i.e., output parameters, are
reviewed separately since they vary from variable to variable.
Price
The most unreasonable thing about the price model is the constant.
constant means the linear model has negative interception.
69
A negative
It is fine mathematically
but for truck cost function, it makes no sense.
The second problem is that the
significance for V4 and V8 is low, which could be due to the nonlinear relationship
between the engine cylinders and the horsepower.
In order to get a positive constant
and improve the significance of engine performance without decreasing R2 , the
trial-and-error method is used to decide either the horsepower or the engine cylinders
should be used to represent the engine performance.
The final model for price is:
Table 8: Modified Regression Model for Price
Model Summary
Model
R
R Square
g7a
Q-A
Std. Error of
the Estimate
Adjusted R
Square
qqQ
11
a. Predictors: (Constant), BODY4, CAB2, WD4, BODY1,
TRANS, V6, CABO, BODY3, V4, DIM
Coefficients
Model
I
Unstandardized
Coefficients
B
Std. Error
6166.922
2087.112
.196
.024
-1914.605
284.104
1411.466
324.524
3050.159
250.302
2034.661
296.222
-2718.822
640.376
-1328.307
450.805
-405.970
528.159
-418.094
413.033
(Constant)
DIM
CABO
CAB2
WD4
TRANS
V4
V6
BODY1
BODY3
ROQY4
1?r%7 7Q1
a. Dependent Variable: MSRP
a
Standardi
zed
Coefficie
nts
Beta
.490
-.206
.129
.337
.191
-.212
-.147
-.031
-.045
srARRR
n72
t
2.955
8.263
-6.739
4.349
12.186
6.869
-4.246
-2.947
-.769
-1.012
2 1R2
Sig.
.004
.000
.000
.000
.000
.000
.000
.004
.444
.314
Q3I
Notice that all independent variables are very significant and the R2 is even higher
than that in the initial model.
Moreover, the coefficients make more sense.
For
example, the 4 wheel-drive feature costs about $3,000 based on the feedback from the
project sponsor and is verified by this regression model that estimates $3,050 for the 4
70
wheel-drive feature.
City Fuel Economy
The initial cost function for city fuel economy has high R 2 ~ 0.865.
But there are
First, cab type and body style are not very significant.
still two problems to discuss.
If taking out cab type and body style from the independent variables does not
dramatically decrease R 2, it is better to do so.
The other problem is that only one of
horsepower and engine cylinders should be kept in the cost function.
iterations show that engine cylinders provide better prediction.
Regression
It can also conclude
from the significant data since engine cylinders are indeed more significant than
horsepower.
Thus the resulting SPSS output for city fuel economy is:
Table 9: Modified Regression Model for City Fuel Economy
Model Summary
d
eI
Alia
1
Std. Error of
rSquarethe Estimate
I djusted R
Rua
R
AAn
con9
a. Predictors: (Constant), V8, TRANS, WD4, V4, DIM
Model
1
(Constant)
DIM
WD4
TRANS
V4
V8
Coefficients a
Standardi
zed
Unstandardized
Coefficie
Coefficients
nts
B
Std. Error
Beta
19.206
1.092
-4.03E-05
.000
-.173
-.625
.201
-.119
.142
.234
.023
4.619
.313
.619
-1 71A
n
-nq
t
17.583
-2.536
-3.107
.605
14.764
-A 77r
Sig.
.000
.013
.002
.546
.000
nnnI
a. Dependent Variable: CFECON
Someone may ask why transmission type is necessary to keep in this cost function
since it is not very significant.
Based on logical considerations, transmission type
71
should affect fuel economy in city driving.
Users expect to see different city fuel
economy data between automatic and manual transmissions because people switch
gear a lot for city driving.
If users change transmission type but do not observe any
change in city fuel economy, users may stop trusting Design Pallet.
Thus
transmission type is kept in the city fuel economy cost function based on this
psychological reason.
Hikhway Fuel Economy
In the highway fuel economy cost function, engine cylinders still show better
regression result than horsepower so engine cylinders are kept.
Cab type is not
significant and transmission type is no longer a psychological concern because users
will not expect different highway fuel economy data for different transmission type.
People usually do not need to switch gear frequently on highways.
other psychological factor is body style.
However, the
Users expect to see different highway fuel
economy data for different body styles since they have different wind resistance.
the resulting cost function regression model is:
72
So
Table 10: Modified Regression Model for Highway Fuel Economy
IModel]
Model Summary
Adjusted R
R Square
Square
Std. Error of
the Estimate
a. Predictors: (Constant), BODY4, WD4, BODY1, V6,
BODY3, V4, DIM
Coefficients a
Unstandardized
Coefficients
B
Std. Error
1.942
21.177
(Constant)
DIM
-2.66E-05
.000
WD4
-1.523
.257
V4
5.591
.651
V6
1.536
.460
BODY1
1.271
.525
BODY3
8.719E-02
.400
Ra DY4 1e Vai bl: H7A 7FE
Model
a.
Standardi
zed
Coefficie
nts
Beta
-.107
-.272
.704
.274
.156
.015
t
10.905
-1.187
-5.922
8.592
3.338
2.420
.218
Sig.
.000
.238
.000
.000
.001
.017
.828
Dependent Variable: HFECON
Of note, the predictor now becomes V8 for this new highway fuel economy cost
function.
Payload
The conflict between engine cylinders and horsepower can be easily solved in payload
because engine cylinders are very non-significant in the payload regression model, so
horsepower is used.
The other non-significant independent variables, including
wheel-drive type and body style, are also taken out.
The psychological factors in the
payload cost function are the cab type and the bed length.
Users expect to carry less
cargo if the truck has a extended or crew cab, or a short bed.
Bed length is
particularly used as an independent variable out of truck's total length in payload
because of its significance.
Thus the final cost function for payload is:
73
Table 11: Modified Regression Model for Payload
Model Summary
Model
del
R Square
R
Adjusted R
Std. Error of
th
Asquaree
the
Estimate
a. Predictors: (Constant), BEDLENG, CAB1, TRANS,
CAB2, HP, DIM, HPDIM
Coefficients a
Standardi
zed
Coefficie
Unstandardized
Coefficients
Model
1
B
(Constant)
CABI
CAB2
TRANS
HP
DIM
4207.399
-200.166
-44.881
-334.049
-32.058
-7.27E-02
HPDIM
5.067E-04
nts
Std. Error
RFnl FNQ
9A nQA
a. Dependent Variable: PAYLOAD
Beta
1594.935
115.572
196.319
120.507
6.517
.023
-.087
-.017
-. 127
-1.607
-.733
.000
2.916
Q
19R
The R2 is higher than the initial regression model.
t
Sig.
2.638
-1.732
-.229
-2.772
-4.919
-3.162
.010
.086
.820
.007
.000
.002
5.633
99REr
.000
O-r;
One question that may be asked
here is why there is a negative coefficient for horsepower and dimension.
Dimension is necessary for payload because users expect to carry more cargo in a
larger truck.
Horsepower is also required to represent the effect of the engine since
it is more significant than engine cylinders.
The interaction term provides valuable
correction in this cost function because it neutralizes the effect of adding horsepower
and dimension to the regression model.
The positive coefficient of the interaction
term hpdim cancels out the negative coefficients of horsepower and dimension.
74
Towing Capacity
2
Of the cost functions, towing capacity has the lowest R.
No independent variables
are very significant; the residual analysis finds highly spread-out error distribution
and more outliers.
This is probably because the towing capacity provided on the
market has no official standard.
The towing capacity on the market is based on
estimation and some trucks may have optional towing packages to increase towing
capacity.
In order to provide a better regression model for most trucks, more outliers are taken
out of the regression database (refer to Appendix B).
Because the R2 is very low, as
many as possible independent variables are kept regardless of their significances.
However, the cab type is taken out because of inconsistency; i.e., the extended cab has
lower towing capacity than the regular cab while the crew cab has higher towing
capacity than the regular cab.
The conflict between horsepower and engine
cylinders can be resolved by their significances.
The 8 cylinders engine is a very
significant factor in the regression model; therefore, engine cylinders are used to
represent engine effect in this cost function.
75
The final regression result is:
Table 12: Modified Regression Model for Towing Capacity
Model Summaryb
I
Mdl
R
R
Model
Asna
1
Square
79-A
Std. Error of
the Estimate
1;'A RAM
Adjusted R
Square
7nq
a. Predictors: (Constant), V8, TRANS, BODY4, WD4,
BODY1, V4, BODY2
b. Dependent Variable: TOWING
Coefficients a
Standardi
zed
Coefficie
Unstandardized
Coefficients
nts
B
Std. Error
Beta
Model
1
(Constant)
4415.606
415.071
WD4
-36.267
394.346
-.005
TRANS
-371.878
435.494
-.048
BODY1
319.940
569.647
.033
BODY2
2415.443
584.181
.347
BODY4
985.574
737.743
.078
V4
-309.182
587.249
-.033
A22ai nA
. r.AA547 57R 7A22 nn
Vs
a. Dependent Variable: TOWING
t
10.638
-.092
-.854
.562
4.135
1.336
-.526
Sig.
.000
.927
.395
.576
.000
.185
.600
Notice that rugged body style (body2) plays a very important factor in this cost
The R2 is improved to 0.7, compared to 0.61 in the initial model.
function.
A
further improvement in predicting towing capacity can be made if the official standard
for testing towing capacity can be set up.
4.4
Final Models
The final regression models, which are used in Design Pallet as the cost functions, are
listed in this section.
considerations.
These final regression models already have added the logical
Instead of showing SPSS output result, models are translated to text
format for easy reading and understanding.
Price =
+ 6166.922
76
+ 0.196(dim)
- 1914.605(cab0) + 1411.466(cab2)
+ 3050.159(wd4)
+ 2034.661(trans)
- 2718.822(V4) - 1328.307(V6) + 428.668(V1O)
- 405.97(bodyI) - 418.094(body3) + 1257.791(body4)
+ Optional parts
City Fuel Economy
+ 19.206
- 0.625(wd4)
+ 0.142(trans)
+ 4.619(V4) - 1.716(V8) - 2.292(V1O)
- 0.0000403(dim)
Highway Fuel Economy
+ 21.177
- 1.523(wd4)
+ 1.271(bodyI) + 0.087(body3) + 0.163(body4)
+ 5.591(V4) + 1.536(V6) - 1.076(V1O)
- 0.0000266(dim)
Payload =
+ 4207.399
- 0.0727(dim)
- 32.058(hp)
+ 0.0005067(hpdim)
- 200.166(cabl) - 44.881(cab2)
- 334.049(trans)
+ 26.096(bedleng)
Towing capacity
+ 4415.606
- 36.267(wd4)
- 371.878(trans)
+ 319.940(bodyl) + 2415.443(body2) + 985.574(body4)
- 309.182(V4) + 4223.086(V8) + 4673.086(V1O)
77
Chapter 5
Conclusion
This research project including Virtual Advisor, Virtual Engineer, and Design Pallet
for 2001 truck models will be field-tested during summer 2001.
gathered and analyzed after the field test.
More data will be
Before lead users' feedback for Virtual
Engineer and Design Pallet can be summarized, however, some final thoughts about
the contribution of this thesis and future research steps are suggested in this chapter.
5.1
Contributions and Benefits
The major contribution of this thesis is to provide a formulation system for Design
Pallet to accomplish design iterations with lead users.
The formulation system
interacts with users by reading users' design input and translating to parameters output
so that users can complete design iterations.
regression
analysis
The formulation system is based on the
of existing product data.
Design
Pallet has very low
implementation cost and provides a complementary market research method beyond
traditional market research methodologies.
This thesis also presents the development process of Virtual Engineer and Design
Pallet.
Virtual Engineer has the ability to study identified unmet customer needs
78
qualitatively and Design Pallet has the ability to study identified unmet customer
needs quantitatively.
Virtual Engineer dialogue is generated from conflict pairs in
the correlation matrix based on a systematic process.
Design Pallet helps lead users
create their own design and provides solutions for their own unmet needs.
The
corporate product development team can then design a new product and estimate the
potential market for the new product using information from Virtual Engineer and
Design Pallet.
The application of Virtual Engineer and Design Pallet in this thesis is pickup trucks.
However, the same idea and development process can be well applied to any other
consumer products on the market.
As mentioned in Chapter 1, the consumer
products that require long research time before users make the purchase decision are
probably the best candidates to implement this system.
The major benefit for corporations that implement Virtual Advisor, Virtual Engineer
and Design Pallet is to provide a low cost market research tool with tremendous
feedback for the corporate product development teams.
Not only market needs are
gathered and analyzed; product innovation ideas from out of corporations can also be
collected.
corporations
At the same time, customers
for new product
are more likely to cooperate with
development because
this system provides
an
appropriate user design toolkit that is easy to use and time saving.
This system may also help both customers and corporations to find hidden new
product development opportunities.
Some new product development opportunities
are not revealed unless discussions about existing products have happened among
different interested parties.
The overall system of Virtual Advisor, Virtual Engineer
79
and Design Pallet offers a good circumstance for this kind of discussion.
The
automatic detection of the highest utility drops in Virtual Advisor systematically
analyzed customers' needs.
Through the assistance of Virtual Engineer and Design
Pallet, these potential new product development opportunities that are stimulated from
unmet customers needs are turned into a realistic prototype.
Furthermore, because
the overall system is based on trust marketing, customers will feel comfortable and
safe during the product discussion process.
5.2
Future Research Areas
For the Virtual Advisor, future research about enhancing the dialogue process and
clustering unmet customer needs would be useful.
The current dialogue process is
static.
The questions about product attributes are fixed, although asked in Bayesian
order.
A dynamic dialogue process that has different attribute questions based on
early identification of the customer's market segment can be done to improve
dialogue process efficiency.
Moreover, a robust and systematic clustering of unmet
needs provides the corporate product development team with a better estimation of
potential market for those unmet needs.
This improvement can dramatically
decrease market risks in developing new products.
For Virtual Engineer, future research can focus on extending conflict pairs that
triggered Virtual Engineer and improving the link among study questions for unmet
needs.
So far only the 71 conflict pairs with the correlation coefficient smaller
80
than -0.7 will trigger Virtual Engineer.
If the system loading allows, more conflict
pairs can be considered in the future.
This improvement helps corporate product
development teams cover more unmet needs, thus discovering more new product
opportunities.
When more conflict pairs are used to trigger the Virtual Engineer, the link among
Virtual Engineer dialogue must be enhanced.
Although independent design of
Virtual Engineer dialogue offers advantages, as mentioned in Chapter 3, there are
some weaknesses as well.
Customers will feel the dialogue with Virtual Engineer is
unnatural if they have many unmet needs and Virtual Engineer just goes through
questions without any cross analysis done first.
The cross analysis of unmet needs
for the single customer may decrease or increase questions in the Virtual Engineer
dialogue.
However, the cross analysis prevents redundancy and irrational questions
in Virtual Engineer dialogue.
This improvement requires advanced computer
programming skills to perform better Artificial Intelligence.
For Design Pallet, the most important research for future is to extend the design space.
Chapter 2 mentioned an appropriate solution space is a key objective for a user design
toolkit.
Currently Design Pallet is developed on an appropriate solution space that is
achievable under present technologies.
Corporate product development teams may
also be interested in getting some design ideas that are not achievable under present
technologies from lead users.
These design ideas may indicate the next generation
product trend, thus corporations can make basic research decisions for next generation
technologies based on these information.
However, it will be a big challenge for the formulation system.
81
Currently the
formulation system uses existing product information to produce statistical regression
models as the cost functions link the user design inputs and parameters outputs.
The
out of space design means there is no existing product information available so the
cost functions will be very difficult to produce.
One solution to this problem is to do
time series data analysis for the previous technologies.
By observing the maturity
process of the previous technologies, forecasting cost functions for out of space
design solutions can be achievable.
Another future research area can focus on building further trust with customers by
better the integration of Virtual Advisor, Virtual Engineer and Design Pallet.
In real
world, customers give more trust to professional sale persons who have profound
product knowledge and provide not only current product information but also future
product development insights.
This is the same on the Internet.
So far, Virtual
Advisor for product research, Virtual Engineer for qualitative unmet needs study and
Design Pallet for quantitative unmet needs study are individually designed.
In the
future, if a better-integrated system can be designed to perform all functions in one
single user interface, the overall system can play similar roles as professional sales
persons in real world.
Finally, as the Internet technology advanced, many improvements from a technology
point of view can be done on the overall system.
High speed Internet can provide
more dynamic and interactive features to the overall system.
For example, Virtual
Advisor and Virtual Engineer can be animated lively thus customers feel more
comfortable to interact with them.
Design Pallet can offer a real pallet so customers
can freely draw the profile of their ideal trucks without any limited design space.
A
broader communication channel can then be built between customers and corporate
82
product development teams and the information exchanged can be very close to
face-to-face meeting.
83
Bibliography
1.
Census Bureau, U., U.S. Departmentof Commerce News. 2001.
2.
Christopher, M.K., "Why People Research Online To But Offline?" 2000. Forrest
Research Inc.
3.
Urban, G.L., E Sultan, and W.J. Qualls, Placing Trust at the Center of Your
.
Internet Strategy. MIT Sloan Management Review, 2000.42(1), p. 3 9
4.
Urban, G.L., F. Sultan, and W. Qualls, Trust based marketing on the Internet.
1998. Cambridge, MA. Sloan School of Management Massachusetts Institute of
Technology.
5.
Urban, G.L., F. Sultan, and W. Qualls, Design and Evaluation of a Trust Based
Advisor on the Internet.1999. Massachusetts Institute of Technology.
6.
Benabadji, A., Building a trust-based electronic commerce interface. 1997. p. 9 4
leaves.
7.
Lynch, K., AI Needs-Based ConfiguratorImplemented in JAVA. 1998. MIT.
8.
Tian, R.F., Nonlinear navigation and context-sensitive help in the Sloan
Electronic Commerce Project.1998.p.57 leaves.
9.
Wang, X. and Massachusetts Institute of Technology. Dept.
of Electrical
Engineering and Computer Science., Extending the Sloan E-commerce Project
with intelligent user interactions.1999.p.44leaves.
10.
Mann, C.C. and System Design and Management Program, Listening In
developing a virtual engineer for the online identification of unmet customer
needs.2000.p.92.
11.
Ulrich, K.T. and S.D. Eppinger, Product design and development.2nd ed.2000.
Boston. Irwin/McGraw-Hill.xxvi, 358.
84
12. McGrath, M.E., Product Strategy for High-Technology Companies. Second
ed.2000. McGraw-Hill.
13.
Fine, C.H., Clockspeed : winning industry control in the age of temporary
advantage. 1998. Reading, Mass. Perseus Books.xv, 272.
14.
Urban, G.L. and S.H. Star, Advanced marketing strategy: phenomena, analysis,
and decisions.1991. Englewood Cliffs, N.J. Prentice Hall.xx, 563.
15.
Griffin, A., J.R. Hauser, and Marketing Science Institute, The voice of the
customer. Report (Marketing Science Institute); no. 92-106.1992. Cambridge,
Mass. Marketing Science Institute.53.
16.
Crawley, E., System Architecture Lecture Note.2000.
17. Luthje, C., Characteristics of Innovating Users - Am empirical study of
sport-relatedproduct consumers.2000.
18.
Hippel, E.v. and Sloan School of Management, Toolkits for user innovation: the
design side of mass customization.1999. Cambridge, MA. Sloan School of
Management Massachusetts Institute of Technology. 18.
85
Appendix A
Virtual Engineer Dialogues
In Chapter 3, the Virtual Engineer is introduced and the detail dialogues for Virtual
Engineer are listed in this Appendix.
First, Table A summarized all truck attributes
with the corresponding Virtual Engineer questions.
Then the following pages show
the detail Virtual Engineer questions in the order of right column of Table A.
Table A - Virtual Engineer dialogue list
Introduction
0
Towing
1
Compact
2
Smallest available
2
Budget too low
3
Big & comfortable
4
Passenger capacity
4
Hauling
5
Offroad
6
2 WD
7
Budget too high
8
Full
9
Biggest available
9
Short bed
10,11
Comments
12
Conclusion
13
Construction
14
Plowing
15
Long bed
16,17
4 cylinder
18
8 cylinder
19
10 cylinder
19
High Horse Power
20
Manuever
21
Easy rear entry
22
Tall passenger
23
High safety
24
86
87
88
89
90
91
92
93
94
-.-
95
I
96
97
98
7.
99
Appendix B
Residual Analysis of Design Pallet Formulation System
This Appendix discusses the outliers mentioned in Chapter 4.
In order to get a
regression model fitting better to the most trucks data on the market, some outliers
that are discovered during statistical regression process must be removed to prevent
misjudgment of the precision of regression models.
are 112 truck cases collected from market.
In the original database, there
To analyze the residual of regression
models, un-standardized residuals are saved for each case.
For example, the
un-standardized residual in the regression model for price is the difference between
real truck MSRP and forecasted price based on the regression model.
It shows
which trucks are the most difficult to forecast.
Figure B1 shows the histogram of residuals for the price regression model.
There
are two trucks have about $10,000 price difference based on the regression model.
40
30
20
10
Std. Dev = 1844.05
Mean = 0.0
N = 112.00
0
SNO' 0
o do 6 o
'o9
o
0
9
Unstandardized Residual
Figure BI - Histogram of residuals for price regression model
100
The two outliers are GMCSierraC3 and FordF-150_SVT.
They are both all-new
2001 models and equipped with an 8 cylinders engine under very sporty body style.
Figure B2 - GMCSierraC3 and FordF-150_SVT
There two trucks are deleted from the original database for the regression analysis
about price, city fuel economy, highway fuel economy, and payload because they do
not fit into the general design rules about trucks.
new design direction for trucks is happening.
However, they do represent that a
Under traditional design rules, sporty
trucks come with lower performance engines and rugged trucks come with higher
performance engines.
GMCSierraC3 and FordF-150_SVT come with high
performance engines (8 cylinders) but the body styles are very sporty.
The other outliers are found during the regression process for towing capacity.
As
mentioned in chapter 4, towing capacity does not have an official standard to measure
so the market report could be very diversified among manufacturers and models.
Moreover, some market report data may include optional packages to increase towing
capacity.
Figure B3 shows the histogram of un-standardized residuals for the 110
trucks after removing GMCSierraC3 and FordF-150_SVT from the original
database.
101
30
20-
10Std. Dev = 2289.01
Mean = 0.0
N = 110.00
0
'97q,%
0fO ?
0 0 000
'
0f0,, 7
0On00, 00 000
Unstandardized Residual
Figure B3 - Histogram of residuals for towing capacity regression model
The deviation is higher than the price regression model.
for towing capacity, total seven outliers are taken out.
the right hand side of Figure B3.
their
towing
capacity.
The first three cases are on
It means the regressions model underestimates
They
ChevySilverado_2500HD_(2WD),
In the regression process
are
ChevySilverado_2500HD_(4WD),
ChevySilverado_2500HDExtended_(2WD).
They all come with 8 cylinders, 300 HP engines and have optional towing modes.
It
is interesting that for the extended cab model, only 2WD is an outlier but 4WD is not.
Based on Autosite, ChevySilverado_2500HDExtended_(2WD)
capacity
15,800
reports towing
lbs while ChevySilverado_2500HDExtended_(4WD)
towing capacity 12,000 lbs.
reports
It is an error since the final regression model for towing
capacity shows in average, 4WD trucks report 36 lbs less towing capacity than 2WD
trucks of the same model.
102
On the left hand side of Figure B3, there are four outliers taken out.
regression
model
overestimates
capacity.
towing
their
It means the
They
are
FordF150SupercabFlareside_4X4,
FordF150_SuperCabStyleside_4X4,
Ford_F150_SuperCab_Styleside_4X2,
Ford_F150_ RegularCabStyleside_4X2.
Table B summarized some towing capacity related features for FordF150_Flareside
and FordF150_Styleside models and the outliers are highlighted.
Table B - Towing capacity outliers summary for Ford F150 truck models
Towing
wd4
HP
14
V6
V8
FordF150_Flareside4X2(RegularCab)
2000
0.00
202
0.00
1.00
0.00
Ford F150 Flareside_4X2(SuperCab)
2000
0.00
202
0.00
1.00
0.00
FordF150_Flareside 4X4j(RegulaCab)
1900
1.00
202
0.00
1.00
0.00
1.00
240
0.00
0.00
1.00
ei
FordH
F50 Flareside.4XMi6uperCab)
.1600
.
.
_(120n)
2000
0.00
202
0.00
1.00
0.0
Ford F150 RegularCabStyleside_4X4_(120in)
3200
1.00
202
0.00
1.00
0.00
FordF150 SuperCab Styleside_4X2_(139in)
1600
0.00
202
0.00
1.00
0.00
Ford_F150.SuperCab..Styesde_4X4.(139in)
2000
1.00
240
0.00
0.00
1.00
FordP150_Regu1ar~abSty1eside
It is obvious that the highlighted trucks report lower towing capacity compared to the
similar models.
For example, FlaresideSupercab_4X4 reports 400 lbs less towing
capacity than FlaresideRegularcab_4X4 even it has a higher performance engine.
Styleside_4X2 also reports 1,200 lbs less towing capacity than Styleside_4X4 while
there is only 36 lbs towing capacity difference between 2WD and 4WD in average for
all other truck models.
Finally, for StylesideSupercab, both 2WD and 4WD models
report less towing capacity than StylesideRegularcab but the cab type is not a
significant independent variable for the other trucks.
models seems to be manipulated under different criteria.
103
The towing capacity for these