APPLICATION OF VOC TRANSLATION TOOLS-A CASE STUDY

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International Journal of Management (IJM), ISSN 0976 6502(Print), ISSN 0976

6510(Online), Volume 4, Issue 1, January- February (2013)

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APPLICATION OF VOC TRANSLATION TOOLS-A CASE STUDY

Thomas Joseph1, Dr. Kesavan Chandrasekaran

21Doctoral scholar-Birla Institute of Technology and Science, Pilani, INDIA

2Dean- 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

customers point of view, need to be translated into product features and also, the sequencingof 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.

INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)

ISSN 0976 6367(Print)

ISSN 0976 6375(Online)

Volume 4, Issue 1, January- February (2013), pp. 24-37

IAEME:www.iaeme.com/ijm.htmlJournal Impact Factor (2012): 3.5420 (Calculated by GISI)

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I A E M E


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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 thosenew 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 ofnew 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 organisations NPD process could yield significant savings (Montoya-Weiss and

ODriscoll,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 customers voice to product attribute and features for a successful

product development.


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II. LITERATURESURVEY

New product development encompasses a wide variety of aspects from concept to

reality. According to (Rosenau, 1996), a new product development (NPD) process definesand 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 bequantified 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 knownfor 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. CONSUMERRESEARCH:PROBABLEREASONSFORITSDISUSE

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

2. Consumer research does not help to come up with innovative new productideas(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 andSwaddling,2002).

4. Consumer research lacks comprehensibility(Moenaert and Souder, 1990).(Moenaertand 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. ATTRIBUTESOFEFFECTIVECONSUMERRESEARCH

First, effective consumer research for opportunity identification must be

comprehensive in that it provides a detailed insight into the relation between productcharacteristics 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 consumersproduct 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 thedifferent 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. AFEWPROVENVOCTOOLS

a. QFDb. Pugh Matrixc. Kanod. 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 Akaos 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 customers level of satisfaction.Product requirements are better understood: if the

product criteria which have the greatest influence on the customers satisfaction can be

identified. Classifying product requirements into must-be, one-dimensional and attractive

dimensions can be used to focus onKanos 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). Kanos

model is used to establish the importance of individual product features for the customers

satisfaction and thus it creates the optimal prerequisite for process-oriented productdevelopment activities.Kanos 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 ANALYSISDesigning 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 customizedconjoint 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 resemblebut are not the same

asmarket 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. Thesereal 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-worths 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-worths of the attributes.

The following case study illustrates the use of a few of the above tools and the sequence ofapplication, as the product development success, depends on the right attribute, filtered

appropriately into the drawing board.

VI. CASESTUDY

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 dealers voice.

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.

TierI

Supplier

OEM DEALERConsumer


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Figure: 2- Picture showing

Brief Description:

The hydraulic system consists

(Power transfer output)-A unit w

hydraulic pump, Pumps, Valves,

a Hydraulic tank.

The hydraulic pump is couple

is pumped into the cylinder, the

material that was being carried i

Company A is a market leader

vehicle segment. Company B is

(Hydraulic actuation systems),

enjoys, 80% market share in the

balance to many fragmented cowith Company As product offe

withdrawn from the market. A v

company Bs technological pr

Company B, decides to do a

significant market share, in thi

customer) analysis to elicit the

hydraulic system to the vehicle a

agement (IJM), ISSN 0976 6502(Print),

1, January- February (2013)

32

the Hydraulic schematic of a Truck with Tippi

of Operating lever, Hydraulic hoses & contr

hich couples the engine to the pump, to enable

Hydraulic cylinders- Multi stage, Hydraulic h

, with the engine, the tipper valve is actuated.

piston rods actuate, thereby lifting the tipper,

the truck.

in supplying a Hydraulic tipping system to th

world leader, in supplying the similar technol

to all the different segments of the market.

25Tonvehicle segment, leaving 15% to the co

petition. Company B, launches a new producring. This product has very early failures and

ry expensive recall and repair campaign is initi

wess and market credibility, this launch w

zero based, redesign and re-launch, to try a

s segment. Company B carries out a VOC (

direct response from the various dealers, who

nd the end consumers, who buy and use the pro

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driving of the

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assemble the

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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 drivers cabin rattle during lifting or cabin jerk duringretraction 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 andMarketing. This step is recommended for highly technical products, where attributes and

level selections 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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Figure

Using the QFD and Pugh Matrix

capture the emotional voice, the tconjoint analysis gives the approprcreate value for the customers. T

remarkable accuracy. It is also usefcreate value for company. Further

of the market to changes in existiThus, with the above mentionedcomplete and accurate translatio

development.Conjoint analysis is

decisions.

agement (IJM), ISSN 0976 6502(Print),

1, January- February (2013)

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: 3 VOC Translation tools-A framework

to capture the engineering voice, coupled with

tal VOICE OF THE CUSTOMER, is captured. Thiate consumers voice in terms of different producthus it enables to estimate the value created to c

ul for market segmentation decisions and other impore, models based on conjoint data allow predictin

g product concepts or price before the actual decVOC tools and the recommended sequence (refn of the VOC can be achieved, for a succ

lso very useful method in making optimal pricin

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ano model to

e results of theattributes that

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VIII. REFERENCES

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Management.Vienna, 1994.


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