Application of voc translationtools a case study

International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)
6510(Online), Volume 4, Issue 1, January- February (2013)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 1, January- February (2013), pp. 24-37
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APPLICATION OF VOC TRANSLATIONTOOLS-A CASE STUDY

Thomas Joseph1, Dr. Kesavan Chandrasekaran2
1
Doctoral scholar-Birla Institute of Technology and Science, Pilani, INDIA
2
Dean- Faculty, RMK College of Engineering, Kavaraipettai, Tamilnadu, INDIA

ABSTRACT

In today's highly competitive environment, where sources of product and process-
based competitive advantage are quickly imitated by competitors, it is becoming increasingly
difficult to differentiate on technical features and quality alone. Companies may overcome
this problem by incorporating the ‘voice of the customer’ into the design of new products and
focusing on customer value, thereby offering total solutions to customer needs. Therefore, it
is critical for all technology- based companies to gain an accurate understanding of the
potential value of their offerings, and to learn how this value can be further enhanced. There
are many tools that help translate the Voice of the customer, to the Voice of the designer, but
it is important to choose the right tool and in the correct sequence, for a successful product
development.An important tool to elicit customer value at an early stage of the product
development is the conjoint analysis. Conjoint analysis is a research technique for measuring
customers' preferences, and it is a method for simulating how customers might react to
changes in current products or to new products introduced into an existing competitive
market. The paper discusses, how the ‘hardware’ features and the ‘software’ features, from a
customer’s point of view, need to be translated into product features and also, the sequencing
of the tools, to achieve a perfect drill-down into conjoint analysis, which is an ultimate tool to
help translate the Voice of the customer.

Index Terms: Conjoint analysis, Kano analysis, Pugh Matrix, New Product Development,
QualityFunction Deployment, Voice of Customer.

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I. INTRODUCTION

Companies must develop new products to grow and stay competitive, but innovation
is risky andcostly. A great majority of new products never makes it to the market and those
new products that enter the market place, face very high failure rates. Exact figures are hard
to find and vary depending on the type of market (industrial versus consumer) and product
(high tech versus fast moving consumer goods). Moreover, different criteria for the definition
of success and failure make it complicated to compare. However, failure rates have remained
high, averaging 40% (Griffin, 1997).According to (Crawford, 1987), the average failure rate
is about 35%. Later, (Cooper, 1994), a leading researcher in the field of new product
development (NPD), estimates a failure rate in the order of 25-45%.Since the 1990s it
became apparent that the high failure rates of new products justified research to examine the
reasons for success and failure. Later on it became clear that many other factors are also very
relevant. The first studies on NPD performance showed that the market place played a
major role in stimulating the need for new and improved products. Since the
pioneering studies of (Booz, Allen and Hamilton, 1968), the success and failure of
new products has been studied intensively. Much has been written about the most
appropriate NPD practices, which can lead to product marketplace success. Success depends,
among other factors, on the degree to which the new product successfully addresses
identified consumer needs and at the same time excels competitive products. Unfortunately,
although past research on NPD performance has shown that even the slightest improvements
in an organisation’s NPD process could yield significant savings (Montoya-Weiss and
O’Driscoll,2000), bringing successful new products to the market is still a major
problemfor many companies. Despite increasing attention to NPD, the new product success
rate has improvedminimally (WindandMahajan, 1997). (Cooper, 1999) states: ‘Recent
studies reveal that the art of product development has not improved all that much- that the
voice of the customer is still missing, that solid up-front homework is not done, that many
products enter the development phase lacking clear definition, and so on.’
The key learning emerging from NPD performance analysis is that success is primarily
determined by a unique and superior product and that the achievement of that is primarily
driven by the effective marketing-R&D interfacing at the very early stages of the NPD
process (opportunity identification). Hence, the paradox here is that while failure reasons (at
strategy, process and product level) are quite well understood and documented, still a high
proportion of new products fails. One reason for this may be that factors of success and
failure have not been translated into meaningful guides for action. Consequently,companies
still have problems with effectively and efficiently implementing the factors of success into
NPD practice. Consumer research at the earliest stages of NPD that helps bridge
marketing and R&D functions is crucial in this process. (Miller and Swaddling, 2002) argue
that the shortcomings in the current state of NPD practice can be directly or indirectly tied
with consumer research done in conjunction with NPD. As this appears a major bottleneck,
this paper looks atthe probablereasons for not embracing the ‘mantra’ of consumer research
for the New product development anddiscusses the selection and application of the VOC
tools to translate the customer’s voice to product attribute and features for a successful
product development.

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II. LITERATURE SURVEY

New product development encompasses a wide variety of aspects from concept to
reality. According to (Rosenau, 1996), a new product development (NPD) process defines
and describes the means by which a company or organization can convert new ideas and
innovative concepts into marketable product or services. The NPD process can broadly be
divided into four phases, namely, (1) concept exploration; (2) design and development; (3)
manufacturing and assembly; and (4) product launch and support.(Calantoneand Benedetto,
1988) proposed an integrative model of the new product development process, which is
based on technical and market factors for parallel implementation of the new product
development process.
(Song and Montoya-Weiss, 1998), through research and literature review, identified
the following six sets of general NPD activities: (1) Strategic planning for integration of
product resource and market opportunities; (2) Idea generation and elaboration, and
evaluation of the potential solution; (3) Business analysis for converting new product idea
into design attributes that fulfill customer needs and desires; (4)
Manufacturingdevelopmentforbuilding the desired physical product; (5) Testing the
product itself which includes all individual andintegrated components; (6) Coordination,
implementation, and monitoring of the new product launch.
Similarly, (Jones and Stevens, 1999) proposed the NPD process which forms market
strategy points of view and mainly concerns the use of marketing techniques for
generating the new product idea.
To design a product well, a design team needs to know what it is they are designing, and
what the end-users will expect from it. Quality Function Deployment is a systematic
approach to design based on a close awareness of customer desires, coupled with the
integration of corporate functional groups. It consists in translating customer desires into
design characteristics for each stage of the product development (Rosenthal,1992).Ultimately
the goal of QFD is to translate often subjective quality criteria into objective ones that can be
quantified and measured and which can then be used to design and manufacture the product.
It is a complimentary method for determining how and where priorities are to be assigned in
product development. The intent is to employobjective procedures in increasing detail
throughout the development of the product (Reilly, 1999).Quality Function Deployment was
developed by YojiAkao in Japan in 1966. By 1972 the power of the approach had been well
demonstrated at the Mitsubishi Heavy Industries Kobe Shipyard (Sullivan, 1986) and in 1978
the first book on the subject was published in Japanese and then later translated into English
in 1994 (Mizuno and Akao,1994). Customer focused product development brought focus on
the interpretation of the voice of customers and subsequently derivation of explicit
requirements that can be understood by marketing and engineering (Jiao and Chen, 2006). In
general, it involves three major issues, namely (1) understandingof customer
preferences(2)requirement prioritization and (3) requirement classiﬁcation. Among many
approaches that address customer need analysis, the Kano model has been widely practiced in
industries as apreferred tool of understanding customer preferences owing to its convenience
in classifying customer needs based on survey data (Kano et al., 1984).
The Pugh Matrix is a type of Matrix Diagram (Burge, S.E 2006) that allows for the
comparison of a number of design candidates leading ultimately to which best meets a set of
criteria. It also permits a degree of qualitative optimisation of the alternative concepts through
the generation of hybrid candidates. Fundamentally a Pugh Matrix can be used when there is

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a need to decide amongst various alternatives objectively.
Conjoint analysis is an experimental approach for measuring customers’ preferences about
the attributes of a product or service. Originally developed by psychologist (Luce and
statistician Tukey, 1964) in the field of mathematical psychology,Conjoint analysis is known
for being a research technique by which one can investigate combinations of features to
identify the predicted consumer preferences. (Green and Rao, 1971) drew upon the conjoint
measurement theory, adapted it to the solution of marketing and product-development
problems, considered carefully the practical measurement issues, and initiated a flood
ofresearch opportunities and applications (Wittink and Cattin, 1989). Furtherdeveloped and
customized by Paul Green and his colleagues at Wharton School of the University of
Pennsylvania (Green and Srinivasan, 1981, 1990), (Wind, Green, Shifflet and Scarborough,
1989) (Green and Krieger, 1991, 1996), conjoint analysis has evolved into a mainstay of the
research profession.(Green and Srinivasan, 1990), (Green and Krieger, 1995) argue that
conjoint analysis has multiple advantages in quantifying consumer preferences. It assumes
that a product or service can be described as an aggregate of its conceptual components:
attributes (also called variables, silos or categories) and elements (levels) (Krieger et al.,
2004; Moskowitz et al., 2005). By presenting a series of concepts, which are
combinations of elements from different attributes, to a number of respondents and finding
out which are most preferred, conjoint analysis allows the determination of utilities of each of
the elements called the individual utility scores (part-worth or impact scores) of elements
(Kessels et al., 2008).Understanding precisely how people make decisions, producers can
work out the optimum level of features and services that balance value to the customer
against cost to the company.Conjoint analysis, sometimes called ‘trade-off analysis’, reveals
how peoplemake complex judgments. The technique is based on the assumption that complex
decisions are made not based on a single factor, but on several factors CONsideredJOINTly,
hence the term Conjoint.

III. CONSUMER RESEARCH: PROBABLE REASONS FOR ITS DISUSE

1. Consumer research lacks credibility (Nijssen and Lieshout 1995) (Song, Neeley and
Zhao, 1996)(Gupta, Raj and Wilemon, 1985; Moenaert and Souder, 1990)

2. Consumer research does not help to come up with innovative new product
ideas(Ortt and Schoormans, 1993; Ottum and Moore, 1997)(Ulwick, 2002)(Wind
and Lilien, 1993). (Ozer, 1999)(Burton and Patterson 1999) (Smith 2003)(Day,
1994)

3. Consumer research delays product development process (Miller and
Swaddling,2002).

4. Consumer research lacks comprehensibility(Moenaert and Souder, 1990).(Moenaert
and Souder, 1996)(Dougherty 1990)

5. Consumer research lacks actionability for R&D(Moenaert and Souder, 1996;
Madhavan and Grover, 1998)(Shocker and Srinivasan, 1979)(Gupta, Raj,
Wilemon, 1985)(Bailetti and Litva, 1995)

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IV. ATTRIBUTES OF EFFECTIVE CONSUMER RESEARCH

First, effective consumer research for opportunity identification must be
comprehensive in that it provides a detailed insight into the relation between product
characteristics and consumers’ need fulfillment and behaviour. Consumer research for NPD
is often thought of as existing of historical purchase information or product evaluations.
However, understanding consumer behaviour encompasses much more than just getting
insight into how consumers evaluate and purchase products and services (Jacoby, 1979).
(Sheth, Mittal and Newman 1999) define consumer behaviour as all mental and physical
activities undertaken by consumers that result in decisions and actions to pay for, buy, and
use products and services. For consumers to decide to buy a product they must be convinced
that the product will satisfy some benefit, goal, or value that is important to them (Gutman,
1982; Walker and Olson,1991). To develop a superior new product, consumer research needs
to identify consumers’product attribute perceptions, including the personal benefits and
values that provide theunderlying basis for interpreting and choosing products . As such,
it makes a number of key considerations explicit. This provides a common basis for the
different functional disciplines involved in the NPD process. In addition, it makes clear
which crucial factors affect consumer perceptions, preferences and choices, and what trade-
offs need to be applied in designing a product. This needs VOC (VOICE OF CUSTOMER)
translation tools.

V. A FEW PROVEN VOCTOOLS

a. QFD
b. Pugh Matrix
c. Kano
d. Conjoint Analysis
The above are few proven tools that have proved their worth in product development. Here
is a brief about each of them.
a) QFD:

In Akao’s words, QFD "is a method for developing a design quality aimed at satisfying the
consumer and then translating the consumer's demand into design targets and major quality
assurance points to be used throughout the production phase. ... [QFD] is a way to assure the
design quality while the product is still in the design stage." As a very important side benefit
he points out that, when appropriately applied, QFD has demonstrated the reduction of
development time by one-half to one-third. (Akao, 1990)
The 3 main goals in implementing QFD are:
1. Prioritize spoken and unspoken customer wants and needs.
2. Translate these needs into technical characteristics and specifications.
3. Build and deliver a quality product or service by focusing everybody toward customer
satisfaction.

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b) Pugh Matrix:

Pugh concept selection:There are many conventional screening methods, such as
Technology Readiness Assessment (TRA), GO/NO-GO Screening (Ullman, 2003), etc.,
available for simple concept evaluation. However, for complex cases, Pugh Concept
Selection method is generally used. This method is very effective for comparing concepts that
are not well refined for direct comparison with the engineering requirements. Basically, it
is an iterative evaluation that tests the completeness and understanding of
requirements, followed by quick identification of the strongest concept. It is particularly
effective if each member of the design team performs it independently. The results of the
comparison will usually lead to repetition of the method, with iteration continued until the
team reaches a consensus.

c) Kano:

Analysis of customer need information is an important task.The advantages of classifying
customer requirements by means of the Kano method are very clearpriorities for product
development. It is, for example, not very useful to invest in improving must-be requirements
which are already at a satisfactory level but better to improve one-dimensional or attractive
requirements as they have a greater influence on perceived product quality and consequently
on the customer’s level of satisfaction.Product requirements are better understood: if the
product criteria which have the greatest influence on the customer’s satisfaction can be
identified. Classifying product requirements into must-be, one-dimensional and attractive
dimensions can be used to focus onKano’s model of customer satisfaction can be
optimally combined with quality function deployment and Pugh-matrix. A pre-requisite is
identifying customer needs, their hierarchy and priorities (Griffin/Hauser, 1993). Kano’s
model is used to establish the importance of individual product features for the customer’s
satisfaction and thus it creates the optimal prerequisite for process-oriented product
development activities.Kano’s method provides valuable help in trade-off situations in the
product development stage. If two product requirements cannot be met simultaneously due to
technical or financial reasons, the criterion can be identified which has the greatest influence
on customer satisfaction.Must-be, one-dimensional and attractive requirements differ, as a
rule, in the utility expectations of different customer segments. From this starting point,
customer-tailored solutions for special problems can be elaborated which guarantee an
optimal level of satisfaction in the different customer segments.Discovering and fulfilling
attractive requirements creates a wide range of possibilities for differentiation. A product
which merely satisfies the must-be and one-dimensional requirements is perceived as average
and therefore interchangeable (Hinterhuber, AichnerandLobenwein, 1994).

d) CONJOINT ANALYSIS

Designing and Executing a Conjoint Study: In designing and executing a conjoint study,
the researcher is faced with three steps that are unique to conjoint research:

• Selecting the appropriate type of conjoint
• Selecting the attributes and levels
• Developing and interpreting utilities

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Selecting the Appropriate Type of Conjoint: In practice, trade-offs matrices are rarely used,
narrowing the choice to either ratings-based or choice-based methods. While researchers are
divided on this topic, we typically recommend methods that allow respondents to make
comparative judgments, such as paired-comparisons and choice-based conjoint.
We believe the choice between these two approaches depends on the point in the product
development cycle. The earlier in the cycle, the more is the ‘paired-comparison method’ as
the focus is on product-specific features. Later in the cycle, choice-based methods are more
appropriate because many of the development priorities have been solved and the focus is
more on the final product configuration, price and brand and competitive reaction.

Selecting Attributes and Levels:The single most important component of executing a
conjoint study is selecting conjoint attributes and levels. In general, attributes describe
product features. Conjoint analysis also frequently includes the attributes of price and brand.
Many other attributes are possible though, including distribution channel, service or warranty
options, product promotions, or positioning statements. The actual attributes used should also
follow these guidelines:

1. The attributes must all influence real decisions. That is, the attributes must be
determinant.
2. The attributes must be independent.
3. The attributes should measure only one dimension.

Levels must be chosen so that each product can be defined by only one of the levels. The
levels should include a wide enough range to allow the current and future markets to be
simulated, as well as a nearly equal number of levels for each attribute. In general,
extrapolation of utilities to levels not included should be avoided. If, after including a
complete range of levels, the researcher finds many unrealistic combinations of levels, the
category definition needs to be revised or respondents need to be given customized
conjoint studies.Conducting Preference SimulationsConjoint utilities are most frequently
used in market simulators that are used to answer “what if” scenarios. After conducting a
conjoint study and modeling the current market, a researcher might be interested in the effect
of a possible product design change.These scenarios can be investigated in a market
simulator. Simulations produce shares of preference that resemble—but are not the same
as—market share. The researcher must make several decisions in conducting preference
simulations. The first decision is which choice model to use.
There are basically two choice models in common use today: the first choice model and
the probabilistic model. Each will be discussed in detail below. The discussion of these
models is centered on individual level data.

First Choice Model
The first choice model is the more straightforward of the two models discussed. In the first
choice model, the researcher sums the utility of each product configuration being simulated
and, for each respondent, assumes that the respondent will buy the product with the highest
utility. The share of preference estimates, then, become the proportion of respondents for
which each product had the maximum utility.While this initially seems reasonable, it
might be too simplistic. Very minor differences in summed utilities can have a huge impact
on predicted shares of preference.

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Probabilistic Model
Researchers at first develop a set of alternative products (real or hypothetical) in terms of
bundles of quantitative and qualitative attributes through fractional factorial designs. These
real or hypothetical products, referred to as profiles, are then presented to the customers
during the survey. The customers are asked to rank order or rate these alternatives, or choose
the best one. Because the products are represented in terms of bundles of attributes at mixed
good and bad levels, the customers have to evaluate the total utility from all of the attribute
levels simultaneously to make their judgments. Based on these judgments, the researchers can
estimate the part-worth utilities for the attribute levels by assuming certain composition rules.
The rulesexplain the structure of customer's individual preferences. The manner that
respondents combine the part-worth’s in total utility of product can be explained by these
rules. Thesimplest and most commonly used model is the linear additive model. This model
assumes that theoverall utility derived from any combination of attributes of a given good or
service is obtained from the sum of the separate part-worth’s of the attributes.
The following case study illustrates the use of a few of the above tools and the sequence of
application, as the product development success, depends on the right attribute, filtered
appropriately into the drawing board.

VI. CASE STUDY

The case is about an engineering product (Hydraulic system) that is designed,
developed and supplied by a Tier 1 company to an OEM (Original equipment manufacturer),
where the assembly is done and the vehicle is then sold through dealers to the end consumers.
There are essentially two consumers for the supplier (A) OEM customer, who buys the
hydraulic system and (B) the end consumer, who buys the truck with the tipping unit. Of-
course, the dealer is a catalyst, in between. He is also a ‘customer’ in a true sense of the word,
as he is responsible for the warranty period service of the vehicle. So, serviceability,
durability, warranty are some of his concerns. This paper, has consciously excluded the
translation of the ‘dealer’s voice’.

Tier
I OEM DEALER
Consumer
Supplier

Figure: 1- Schematic showing the relationship between Supplier, Intermediate customer
and end Consumer.

The truck tipping units are used for transporting building materials like sand, stones,
cement or bulk materials like lime, coal or ore. The vehicles are of different configurations,
like 10T, 25T, 40T, 65T and 100T. The tipping unit is actuated using a hydraulic cylinder,
which is operated by a hydraulic system. Refer Figure2.

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6510(Online), Volume 4, Issue 1, January February (2013)
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Figure: 2- Picture showing the Hydraulic schematic of a Truck with Tipping system.

Brief Description:

The hydraulic system consists of Operating lever, Hydraulic hoses & control wires PTO
he lever wires,
(Power transfer output)-A unit which couples the engine to the pump, to enable driving of the
A enable
hydraulic pump, Pumps, Valves, Hydraulic cylinders- Multi stage, Hydraulic hose Filter and
s, ose,
a Hydraulic tank.
The hydraulic pump is coupled, with the engine, the tipper valve is actuated. Hydraulic oil
actuated.
is pumped into the cylinder, the piston rods actuate, thereby lifting the tipper, to unload the
material that was being carried in the truck.

Company A is a market leader in supplying a ‘Hydraulic tipping system’ to the commercial
‘
vehicle segment. Company B is a world leader, in supplying the similar technological product
world
(Hydraulic actuation systems), to all the different segments of the market. Company A
, a
enjoys, 80% market share in the 25Tonvehicle segment, leaving 15% to the company B and
%
balance to many fragmented competition. Company B, launches a new product, to compete
ted competition.
with Company A’s product offering. This product has very early failures and the product is
ompany
withdrawn from the market. A very expensive recall and repair campaign is initiated. Despite,
company B’s technological prowess and market credibility, this launch was a disaster.
Company B, decides to do a zero based, redesign and re-launch, to try and capture, a
re launch,
significant market share, in this segment. Company B carries out a VOC (Voice of the
customer) analysis to elicit the direct response from the various dealers, who assemble the
) dealers,
hydraulic system to the vehicle and the end consumers who buy and use the product
consumers, product.

32


The ‘hardware’ inputs of the VOC, like Lifting load, Lifting speed, are then processed
through a Pugh analysis matrix and QFD (Quality function deployment) HoQ (House of
Quality), to convert, the customer expectations, into product and design characteristics. The
‘software’ inputs of the VOC, like driver’s cabin rattle during lifting or cabin jerk during
retraction of the tipping unit, aesthetics of the Hydraulic systems, corrosion resistance of the
system, are processed using ‘Kano Model Analysis’.

The ‘hardware’ inputs, namely the engineering criteria, that affect the ‘fit and function’ are
best captured using Pugh Matrix and QFD and the ‘software’ inputs, namely the aesthetics,
the ‘look and feel’ features, that affect the ‘form’ are best captured using the Kano Model
Analysis. Thus, using the QFD and Pugh Matrix and combining the Kano analysis, the entire
VOC of the customer, is translated as inputs for the Conjoint experiment.

The 26 different product and design characteristics are then discussed, using 4 focus
groups, consisting of the OEM (original equipment manufacturer) CFT (Cross functional
group), having members from Product development, Procurement, Manufacturing and
Marketing. This step is recommended for highly technical products, where attributes and
level selection’s technical and manufacturability needs to be assessed and decided upon.

The data from the focus group is statistically analysed and the vital 5 attributes, each at 2
levels, were finalised. With 5 attributes, each at 2 levels, 2^5 combinations of products, is
possible. A Choice based Conjoint exercise was launched, using a questionnaire, addressed to
about, 150 dealers, in India. A total of 104 valid responses were obtained. Each, respondent
ranked the 32 products. A Conjoint analysis was performed, on the ranked products, to find
the part worth utilities, which helped in guiding the team, to re-design the product
successfully and re-launch it in the market, successfully.

VII. CONCLUSION

Given the increasing intensity of business competition and the strong trend towards
globalization, the attitude towards the customer is very important, their role has changed from
that of a mere consumer to the role of co-designer, co-operator, co- producer, co-creator of
value and co-developer of knowledge and competencies. Furthermore, the
complexcompetitive environment in which companies operate has led to the increase in
customer demand for superior value. While there are many tools, to capture the voice of the
customer, there is none, having the caliber of CONJOINT ANALYSIS. This is due to the
‘statistical’ nature of the tool, which is objective and repeatable. In fact, as Conjoint Analysis
is rooted deeply in statistics, there is no ‘personal judgment’ clouding the decision making
process. It is pure mathematics. The input data, in the form of, correct attributes and their
levels, is a pre-requisite toperforming a correct Conjoint Analysis.These inputs, needs to be
scientifically filtered and presented into the Conjoint function. The use of QFD, Pugh Matrix
and Kano complements well with Conjoint Analysis, ensuring synthesis of strategically
important customer value dimension and customer focused product design.

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6510(Online), Volume 4, Issue 1, January February (2013)
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Figure: 3 VOC Translation tools-A framework
Using the QFD and Pugh Matrix to capture the ‘engineering voice’, coupled with ‘Kano model’ to
capture the ‘emotional voice’, the total VOICE OF THE CUSTOMER, is captured. The results of the
conjoint analysis gives the appropriate consumer’s voice in terms of different product attributes that
s different
create value for the customers. Thus it enables to estimate the value created to customers with
remarkable accuracy. It is also useful for market segmentation decisions and other improvements that
create value for company. Furthermore, models based on conjoint data allow predicting the response
ermore, conjoint
of the market to changes in existing product concepts or price before the actual decision is made.
Thus, with the above mentioned VOC tools and the recommended sequence (refer Figure 3) a
complete and accurate translation of the VOC can be achieved, for a successful product
development.Conjoint analysis is also very useful method in making optimal pricing and product
decisions.

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