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    Application of voc translationtools a case study Application of voc translationtools a case study Document Transcript

    • 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 IJM© IAEME: www.iaeme.com/ijm.html ©IAEMEJournal Impact Factor (2012): 3.5420 (Calculated by GISI)www.jifactor.com 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 todays 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. 24
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)I. INTRODUCTION Companies must develop new products to grow and stay competitive, but innovationis 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 hardto 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 definitionof success and failure make it complicated to compare. However, failure rates have remainedhigh, averaging 40% (Griffin, 1997).According to (Crawford, 1987), the average failure rateis about 35%. Later, (Cooper, 1994), a leading researcher in the field of new productdevelopment (NPD), estimates a failure rate in the order of 25-45%.Since the 1990s itbecame apparent that the high failure rates of new products justified research to examine thereasons for success and failure. Later on it became clear that many other factors are also veryrelevant. The first studies on NPD performance showed that the market place played amajor role in stimulating the need for new and improved products. Since thepioneering studies of (Booz, Allen and Hamilton, 1968), the success and failure ofnew products has been studied intensively. Much has been written about the mostappropriate NPD practices, which can lead to product marketplace success. Success depends,among other factors, on the degree to which the new product successfully addressesidentified consumer needs and at the same time excels competitive products. Unfortunately,although past research on NPD performance has shown that even the slightest improvementsin an organisation’s NPD process could yield significant savings (Montoya-Weiss andO’Driscoll,2000), bringing successful new products to the market is still a majorproblemfor many companies. Despite increasing attention to NPD, the new product successrate has improvedminimally (WindandMahajan, 1997). (Cooper, 1999) states: ‘Recentstudies reveal that the art of product development has not improved all that much- that thevoice of the customer is still missing, that solid up-front homework is not done, that manyproducts enter the development phase lacking clear definition, and so on.’ The key learning emerging from NPD performance analysis is that success is primarilydetermined by a unique and superior product and that the achievement of that is primarilydriven by the effective marketing-R&D interfacing at the very early stages of the NPDprocess (opportunity identification). Hence, the paradox here is that while failure reasons (atstrategy, process and product level) are quite well understood and documented, still a highproportion of new products fails. One reason for this may be that factors of success andfailure have not been translated into meaningful guides for action. Consequently,companiesstill have problems with effectively and efficiently implementing the factors of success intoNPD practice. Consumer research at the earliest stages of NPD that helps bridgemarketing and R&D functions is crucial in this process. (Miller and Swaddling, 2002) arguethat the shortcomings in the current state of NPD practice can be directly or indirectly tiedwith 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 researchfor the New product development anddiscusses the selection and application of the VOCtools to translate the customer’s voice to product attribute and features for a successfulproduct development. 25
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)II. LITERATURE SURVEY New product development encompasses a wide variety of aspects from concept toreality. According to (Rosenau, 1996), a new product development (NPD) process definesand describes the means by which a company or organization can convert new ideas andinnovative concepts into marketable product or services. The NPD process can broadly bedivided 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 isbased on technical and market factors for parallel implementation of the new productdevelopment process. (Song and Montoya-Weiss, 1998), through research and literature review, identifiedthe following six sets of general NPD activities: (1) Strategic planning for integration ofproduct resource and market opportunities; (2) Idea generation and elaboration, andevaluation of the potential solution; (3) Business analysis for converting new product ideainto design attributes that fulfill customer needs and desires; (4)Manufacturingdevelopmentforbuilding the desired physical product; (5) Testing theproduct 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 marketstrategy points of view and mainly concerns the use of marketing techniques forgenerating the new product idea. To design a product well, a design team needs to know what it is they are designing, andwhat the end-users will expect from it. Quality Function Deployment is a systematicapproach to design based on a close awareness of customer desires, coupled with theintegration of corporate functional groups. It consists in translating customer desires intodesign characteristics for each stage of the product development (Rosenthal,1992).Ultimatelythe 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 inproduct development. The intent is to employobjective procedures in increasing detailthroughout the development of the product (Reilly, 1999).Quality Function Deployment wasdeveloped by YojiAkao in Japan in 1966. By 1972 the power of the approach had been welldemonstrated at the Mitsubishi Heavy Industries Kobe Shipyard (Sullivan, 1986) and in 1978the first book on the subject was published in Japanese and then later translated into Englishin 1994 (Mizuno and Akao,1994). Customer focused product development brought focus onthe interpretation of the voice of customers and subsequently derivation of explicitrequirements that can be understood by marketing and engineering (Jiao and Chen, 2006). Ingeneral, it involves three major issues, namely (1) understandingof customerpreferences(2)requirement prioritization and (3) requirement classification. Among manyapproaches that address customer need analysis, the Kano model has been widely practiced inindustries as apreferred tool of understanding customer preferences owing to its conveniencein 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 thecomparison of a number of design candidates leading ultimately to which best meets a set ofcriteria. It also permits a degree of qualitative optimisation of the alternative concepts throughthe generation of hybrid candidates. Fundamentally a Pugh Matrix can be used when there is 26
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)a need to decide amongst various alternatives objectively. Conjoint analysis is an experimental approach for measuring customers’ preferences aboutthe attributes of a product or service. Originally developed by psychologist (Luce andstatistician Tukey, 1964) in the field of mathematical psychology,Conjoint analysis is knownfor being a research technique by which one can investigate combinations of features toidentify the predicted consumer preferences. (Green and Rao, 1971) drew upon the conjointmeasurement theory, adapted it to the solution of marketing and product-developmentproblems, considered carefully the practical measurement issues, and initiated a floodofresearch opportunities and applications (Wittink and Cattin, 1989). Furtherdeveloped andcustomized by Paul Green and his colleagues at Wharton School of the University ofPennsylvania (Green and Srinivasan, 1981, 1990), (Wind, Green, Shifflet and Scarborough,1989) (Green and Krieger, 1991, 1996), conjoint analysis has evolved into a mainstay of theresearch profession.(Green and Srinivasan, 1990), (Green and Krieger, 1995) argue thatconjoint analysis has multiple advantages in quantifying consumer preferences. It assumesthat 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 arecombinations of elements from different attributes, to a number of respondents and findingout which are most preferred, conjoint analysis allows the determination of utilities of each ofthe elements called the individual utility scores (part-worth or impact scores) of elements(Kessels et al., 2008).Understanding precisely how people make decisions, producers canwork out the optimum level of features and services that balance value to the customeragainst cost to the company.Conjoint analysis, sometimes called ‘trade-off analysis’, revealshow peoplemake complex judgments. The technique is based on the assumption that complexdecisions 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) 27
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)IV. ATTRIBUTES OF EFFECTIVE CONSUMER RESEARCH First, effective consumer research for opportunity identification must becomprehensive in that it provides a detailed insight into the relation between productcharacteristics and consumers’ need fulfillment and behaviour. Consumer research for NPDis often thought of as existing of historical purchase information or product evaluations.However, understanding consumer behaviour encompasses much more than just gettinginsight into how consumers evaluate and purchase products and services (Jacoby, 1979).(Sheth, Mittal and Newman 1999) define consumer behaviour as all mental and physicalactivities undertaken by consumers that result in decisions and actions to pay for, buy, anduse products and services. For consumers to decide to buy a product they must be convincedthat 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 needsto identify consumers’product attribute perceptions, including the personal benefits andvalues 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 clearwhich 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. Hereis a brief about each of them. a) QFD: In Akao’s words, QFD "is a method for developing a design quality aimed at satisfying theconsumer and then translating the consumers demand into design targets and major qualityassurance points to be used throughout the production phase. ... [QFD] is a way to assure thedesign quality while the product is still in the design stage." As a very important side benefithe points out that, when appropriately applied, QFD has demonstrated the reduction ofdevelopment 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. 28
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) b) Pugh Matrix: Pugh concept selection:There are many conventional screening methods, such asTechnology Readiness Assessment (TRA), GO/NO-GO Screening (Ullman, 2003), etc.,available for simple concept evaluation. However, for complex cases, Pugh ConceptSelection method is generally used. This method is very effective for comparing concepts thatare not well refined for direct comparison with the engineering requirements. Basically, itis an iterative evaluation that tests the completeness and understanding ofrequirements, followed by quick identification of the strongest concept. It is particularlyeffective if each member of the design team performs it independently. The results of thecomparison will usually lead to repetition of the method, with iteration continued until theteam reaches a consensus. c) Kano: Analysis of customer need information is an important task.The advantages of classifyingcustomer requirements by means of the Kano method are very clearpriorities for productdevelopment. It is, for example, not very useful to invest in improving must-be requirementswhich are already at a satisfactory level but better to improve one-dimensional or attractiverequirements as they have a greater influence on perceived product quality and consequentlyon the customer’s level of satisfaction.Product requirements are better understood: if theproduct criteria which have the greatest influence on the customer’s satisfaction can beidentified. Classifying product requirements into must-be, one-dimensional and attractivedimensions can be used to focus onKano’s model of customer satisfaction can beoptimally combined with quality function deployment and Pugh-matrix. A pre-requisite isidentifying customer needs, their hierarchy and priorities (Griffin/Hauser, 1993). Kano’smodel is used to establish the importance of individual product features for the customer’ssatisfaction and thus it creates the optimal prerequisite for process-oriented productdevelopment activities.Kano’s method provides valuable help in trade-off situations in theproduct development stage. If two product requirements cannot be met simultaneously due totechnical or financial reasons, the criterion can be identified which has the greatest influenceon customer satisfaction.Must-be, one-dimensional and attractive requirements differ, as arule, in the utility expectations of different customer segments. From this starting point,customer-tailored solutions for special problems can be elaborated which guarantee anoptimal level of satisfaction in the different customer segments.Discovering and fulfillingattractive requirements creates a wide range of possibilities for differentiation. A productwhich merely satisfies the must-be and one-dimensional requirements is perceived as averageand 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 29
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) 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 aredivided on this topic, we typically recommend methods that allow respondents to makecomparative judgments, such as paired-comparisons and choice-based conjoint. We believe the choice between these two approaches depends on the point in the productdevelopment cycle. The earlier in the cycle, the more is the ‘paired-comparison method’ asthe focus is on product-specific features. Later in the cycle, choice-based methods are moreappropriate because many of the development priorities have been solved and the focus ismore on the final product configuration, price and brand and competitive reaction. Selecting Attributes and Levels:The single most important component of executing aconjoint study is selecting conjoint attributes and levels. In general, attributes describeproduct features. Conjoint analysis also frequently includes the attributes of price and brand.Many other attributes are possible though, including distribution channel, service or warrantyoptions, product promotions, or positioning statements. The actual attributes used should alsofollow these guidelines: 1. The attributes must all influence real decisions. That is, the attributes must bedeterminant. 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. Thelevels should include a wide enough range to allow the current and future markets to besimulated, 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 acomplete range of levels, the researcher finds many unrealistic combinations of levels, thecategory definition needs to be revised or respondents need to be given customizedconjoint studies.Conducting Preference SimulationsConjoint utilities are most frequentlyused in market simulators that are used to answer “what if” scenarios. After conducting aconjoint study and modeling the current market, a researcher might be interested in the effectof a possible product design change.These scenarios can be investigated in a marketsimulator. Simulations produce shares of preference that resemble—but are not the sameas—market share. The researcher must make several decisions in conducting preferencesimulations. The first decision is which choice model to use. There are basically two choice models in common use today: the first choice model andthe probabilistic model. Each will be discussed in detail below. The discussion of thesemodels is centered on individual level data. First Choice Model The first choice model is the more straightforward of the two models discussed. In the firstchoice model, the researcher sums the utility of each product configuration being simulatedand, for each respondent, assumes that the respondent will buy the product with the highestutility. The share of preference estimates, then, become the proportion of respondents forwhich each product had the maximum utility.While this initially seems reasonable, itmight be too simplistic. Very minor differences in summed utilities can have a huge impacton predicted shares of preference. 30
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) Probabilistic Model Researchers at first develop a set of alternative products (real or hypothetical) in terms ofbundles of quantitative and qualitative attributes through fractional factorial designs. Thesereal or hypothetical products, referred to as profiles, are then presented to the customersduring the survey. The customers are asked to rank order or rate these alternatives, or choosethe best one. Because the products are represented in terms of bundles of attributes at mixedgood and bad levels, the customers have to evaluate the total utility from all of the attributelevels simultaneously to make their judgments. Based on these judgments, the researchers canestimate the part-worth utilities for the attribute levels by assuming certain composition rules.The rulesexplain the structure of customers individual preferences. The manner thatrespondents combine the part-worth’s in total utility of product can be explained by theserules. Thesimplest and most commonly used model is the linear additive model. This modelassumes that theoverall utility derived from any combination of attributes of a given good orservice 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 ofapplication, as the product development success, depends on the right attribute, filteredappropriately 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 thehydraulic 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 thetranslation of the ‘dealer’s voice’. Tier I OEM DEALER Consumer Supplier Figure: 1- Schematic showing the relationship between Supplier, Intermediate customerand 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. 31
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – ational6510(Online), Volume 4, Issue 1, January February (2013) January- 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 enablehydraulic 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 thematerial 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 , aenjoys, 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 ompanywithdrawn 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 thecustomer) 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
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013) The ‘hardware’ inputs of the VOC, like Lifting load, Lifting speed, are then processedthrough a Pugh analysis matrix and QFD (Quality function deployment) HoQ (House ofQuality), 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 duringretraction of the tipping unit, aesthetics of the Hydraulic systems, corrosion resistance of thesystem, are processed using ‘Kano Model Analysis’. The ‘hardware’ inputs, namely the engineering criteria, that affect the ‘fit and function’ arebest 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 ModelAnalysis. Thus, using the QFD and Pugh Matrix and combining the Kano analysis, the entireVOC of the customer, is translated as inputs for the Conjoint experiment. The 26 different product and design characteristics are then discussed, using 4 focusgroups, consisting of the OEM (original equipment manufacturer) CFT (Cross functionalgroup), having members from Product development, Procurement, Manufacturing andMarketing. This step is recommended for highly technical products, where attributes andlevel 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 2levels, were finalised. With 5 attributes, each at 2 levels, 2^5 combinations of products, ispossible. A Choice based Conjoint exercise was launched, using a questionnaire, addressed toabout, 150 dealers, in India. A total of 104 valid responses were obtained. Each, respondentranked the 32 products. A Conjoint analysis was performed, on the ranked products, to findthe part worth utilities, which helped in guiding the team, to re-design the productsuccessfully and re-launch it in the market, successfully.VII. CONCLUSION Given the increasing intensity of business competition and the strong trend towardsglobalization, the attitude towards the customer is very important, their role has changed fromthat of a mere consumer to the role of co-designer, co-operator, co- producer, co-creator ofvalue and co-developer of knowledge and competencies. Furthermore, thecomplexcompetitive environment in which companies operate has led to the increase incustomer demand for superior value. While there are many tools, to capture the voice of thecustomer, 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 Analysisis rooted deeply in statistics, there is no ‘personal judgment’ clouding the decision makingprocess. It is pure mathematics. The input data, in the form of, correct attributes and theirlevels, is a pre-requisite toperforming a correct Conjoint Analysis.These inputs, needs to bescientifically filtered and presented into the Conjoint function. The use of QFD, Pugh Matrixand Kano complements well with Conjoint Analysis, ensuring synthesis of strategicallyimportant customer value dimension and customer focused product design. 33
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 – ational6510(Online), Volume 4, Issue 1, January February (2013) January- Figure: 3 VOC Translation tools-A framework Using the QFD and Pugh Matrix to capture the ‘engineering voice’, coupled with ‘Kano model’ tocapture the ‘emotional voice’, the total VOICE OF THE CUSTOMER, is captured. The results of theconjoint analysis gives the appropriate consumer’s voice in terms of different product attributes that s differentcreate value for the customers. Thus it enables to estimate the value created to customers withremarkable accuracy. It is also useful for market segmentation decisions and other improvements thatcreate value for company. Furthermore, models based on conjoint data allow predicting the response ermore, conjointof 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) acomplete and accurate translation of the VOC can be achieved, for a successful productdevelopment.Conjoint analysis is also very useful method in making optimal pricing and productdecisions. 34
    • International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –6510(Online), Volume 4, Issue 1, January- February (2013)VIII. REFERENCES[1] Akao, Y., ed. (1990). Quality Function Deployment, Productivity Press, Cambridge MA.Becker Associates Inc, http://www.becker-associates.com/qfdwhatis.html[2] Bailetti, A.J. and Litva, P.F. (1995).Integrating customer requirements into productdesign.Journal of Product Innovation Management, 12(1), 3-15.[3] Booz, Allen and Hamilton (1968).Management of new products. Chicago: Booz, AllenandHamilton Inc.[4] Burton, A. and Patterson, S. (1999). Integration of consumer and management inNPD.Journal of the Market Research Society, 41(1), 61-74.[5] Burge S E (2006) “Matrix Diagram” from the Systems ThinkingTool Boxwww.Burgehugheswalsh.co.uk[6] Calantone, R.J. and Di Benedetto, C.A. (1988). An Integrative Modelof New ProductDevelopment Process: An Empirical Validation.Journal of Product Innovation Management5(3):201–215.[7] Robert G. Cooper, (1994) "New Products: The Factors that Drive Success", InternationalMarketing Review, Vol. 11 Iss: 1, pp.60 – 76.[8] Crawford, C.Merle. New product failure rates: Research Technology Management30(4):20-24 (July-August 1987)[9] Day, G.S. (1994). Continuous learning about markets. California Management Review,summer, 9-31.[10] Dougherty, D. (1990). Understanding new markets for new products.StrategicManagementJournal, 11, 59-78.[11] Paul, E.Green, Vithala, R. Rao (1971) Conjoint Measurement For QuantifyingJudgemental Data. Journal of Marketing Research. Vol. 8 No.3 (Aug, 1971) pp 355-363.[12] Green, P.E. and Rao, V.R. (1972).Applied multidimensional scaling. Hinsdale, IL:DrydenPress.[13] Green and Srinivasan, 1981, 1990a, 1990b, Wind, Green, Shifflet and Scarbrough, 1989;Green and Krieger, 1991, 1996)[14] Green, P.E and V. Srinivasan (1978), “Conjoint Analysis in Consumer Research: Issuesand Outlook,” Journal of Consumer Research, 5 (September), 103-23.[15] Green, P.E and V. Srinivasan (1990), “A Bibliography on Conjoint Analysis and RelatedMethodology in Marketing Research, “ working paper, Wharton School, University ofPennsylvania (March).[16] Green, P.E. and Abba, M. Krieger (1995), “Attribute Importance Weights Modificationin Assessing a Brand’s Competitive Potential”, Marketing Science, 42 (June), 850-67.[17] Griffin, A. (1997) PDMA Research on New Product Development Practices: UpdatingTrends and Benchmarking Best Practices. Journal of Product Innovation Management, vol.14, pp. 429 – 458.[18] Griffin, A , Hauser, J. R(1993). The Voice of the Customer.Marketing Science (winter1993) Vol 12: pp.1-27[19] Gupta, A.K., Raj, S.P. and Wilemon, D. (1985). The R&D-Marketing interface inhigh- technology firms. Journal of Product Innovation Management, 2(1), 12-24.[20] Gutman, J. (1982). A means-end chain model based on consumer categorizationprocesses.Journal of Marketing, 46(Spring), 60-72H.[21] H. H. Hinterhuber, H. Aichner, W. Lobenwein. Unternehmenswert und LeanManagement.Vienna, 1994. 35
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