A Simulating functional and dysfunctional answer from given Kano evaluation for Product Development, Proceedings of 1st International Conference on Mechanical, Industrial and Energy Engineering 2010, 23-24, December, 2010, Khulna, Bangladesh, pa

ABSTRACT Voice of Customers (VoCs) is important for product development. Kano model links VoCs with product attribute (KE). VoCs regarding KE is considered functional form (FF) and dysfunctional form (DF) of Kano model. Both FF and DF (Like, Must-be,
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   International Conference on Mechanical, Industrial and Energy Engineering 2010 23-24 December, 2010, Khulna, BA  #  GLADESH   *Corresponding author. Tel.: +88-02-8117405~07 Ext.114E-mail address:; MIE10-040  Asimulating functional and dysfunctional answer of customer from given Kanoevaluation for Product Development  MdMamunur Rashid  1,*   1 Faculty, Bangladesh Institute of Management, 4, Sobhanbag, Dhaka-1207(, BANGLADESH andPresently working at Graduate School of Kitami Institute of Technology, Kitami, Hokkaido 090-8507, JAPAN. ABSTRACT Voice of Customers (VoCs) is important for product development. Kano model links VoCs with product attribute (KE).VoCs regarding KE is considered functional form (FF) and dysfunctional form (DF) of Kano model. Both FF and DF (Like,Must-be, Neutral, Live-with and Dislike) are important to select the Kano evaluations (KE). These FF and DF of customerhave been applied for selection KE. The choices KE have been outlined by Attractive, One-dimensional, Must-be,Indifferent, Reverse, and Questionable. In the presented computer system can be simulated the FF and DF for a given KE byMonte-Carlo simulation method and adapted with Kano model for product development. This system cans compliance KEto VoCs (FF and DF). As a result it is reduced the uncertainty of producer regarding customer needs and product attributes. Keywords: Voice of Customers, Computer Simulation System, Kano Evaluation, Product Attribute. 1. Introduction Voice of Customers (VoCs) is now challenging issue forproduct development [1] .The first step to develop aproduct is to identify a set of customer needs. Besides of these, in quality function deployment (QFD) of firsthouse, customer requirements (CRs) are consideredarbitrary basis. In this regard, Kano model is a enhancedmethod for selecting CRs. Using Kano model, a productdeveloper can identify whether or not a product attributeis attractive, must-be, or alike. To do so, it is importantto obtain customers' opinions using a prescribedquestionnaire. All most cases, questionnaire is a vehicleto know the VoCs regarding product development.Kano model links VoCs with Product attribute (KE).Many Researchers have been worked on Kano modelfor Product development [2,3] .Among traditional,analytical, quantitative methods [4-17] were used forcustomer needs analysis for innovative and new productdevelopment. VoCs for specific product attribute andmarket niche is normally survey regarding. Yet,researcher could not be developed generic unknowncustomer evaluation by computer system or traditionalsystem regarding Kano model. In this regards, Ullah andTamaki, 2010 [18] presented a proposition that therespondents of unknown answers might have selectedthe states randomly from the functional/dysfunctionalanswer of Kano model. Although a numerical methodfor customer needs analysis is presented [19] to simulatefunctional and dysfunctional answer independently andthen subsequently simulate Kano Evaluation fordifferent aspects. For this purpose, Kano model [7] isadapted with computer for customerfunctional/dysfunctional answer evaluation fromconsider given Kano evaluations randomly. Therefore,this presented system is simulating functional anddysfunctional answer of customer from given Kanoevaluation. In effects, this computer system was appliedto prove the assumption regarding product attribute(KE) is acceptable or not with field survey. As well,raises a fundamental question that is how manymaximum customers should be asked to make a reliableconclusion for any product attribute regarding Kanomodel. For this purpose, we have study must-beattribute [20] of bicycle for minimum numbers of respondents. In this paper, we will be going to show thegeneric results related to the abovementioned issueregarding Kano model based all product attributes anddiscuss the system into a real-life product developmentprocess for maximum number of respondents.Present paper deal with the computational issuesrelevant to above scenario. The remainder of this paperis organized as follows: section 2 shows methods forgeneral settings of the simulation process; section 3discusses a method to simulate thefunctional/dysfunctional answers from given Kanoevaluation the proposed simulation process and section4for results and discussions. 2. Methods For n -event simulation process is defined by Eq. (1). In(1), E =(  E  1 ,…,  E  n )is the event vector, P =(Pr(  E  1 ),…,Pr(  E  n )) is the probability vector, and S =( S 1 ,…, S  #  )is the simulated event vector. ( )( ) ( )( )( ) ( ) ( ) ForEndyprobabilitcumulative // PrPrCPr ,,1For :Calculateiterationsof  //numberity vector //probabilPr,,Pr ctor //event ve,, :Input 111 iinn  E  E  E ni  #   E  E  E  E  ++==== LLLL PE (1)  MIE10-040-2 [ ] ( ) [ )( ) ( ) [ )( ) ( ) [ ] ( ) orevent vected //simulat :OutputForEndThenCPr,CPrIf  ForEndThenCPr,CPrIf  1,,2For ElseThenCPr0,If  0,1intervalinthenumberrandomais // generate ,,1For :Simulate 11111  #  k nk nnk  ik iik  k k k k   ,S , ,S ,S  E S E  E r   E S E  E r  ni E S E r  r r   #  k  LLLL ==  =  ==  =  S The probability (strictly speaking the relative frequency)of events  E  1 ,…,  E  N in S denoted by Pr  (.) can bedetermined using the formulation defined by Eq. (2). ( )( )( ) ( )( ) vectoryprobabilit d//simulaterP,,rP :Output ForEnd inof ity//probabilrP ForEnd1ThenIf ,,1For 0,,1For :Calculatevectorevent d//simulate :Input  11 niiiiiik  iN k  E E E N count E count count E S  N k count ni ,S  , ,S  ,S   =  =  +====== LLLLL PSS Therefore, simulation  Error  (summation of absolutedifference between given and simulated probabilities of each event) can be defined by the expression in Eq. (3). ( ) ( )  =  = niii E E Error  1 rPPr (3) 3. Relationship among Kano Evaluation, Functionaland Dysfunctional Answers In this section is illustrated the relationships amongKano evaluation (KE) or product attribute, Functional(FA) and Dysfunctional (DFA) Answers. For thispurpose sub-section 3.1 describes the Kano model; sub-section 3.2 shows a relationship of KE from FA andDFA and their events, probability and cumulativeprobability; sub-section 3.3 shows logical structure andrules of the system and sub-section 3.4 shows at aglance computations method for the relationship amongKE, FA and DFA. 3.1 Kano model Kano model   [7] defines the relationship betweenproduct attributes and customer satisfaction. Five typesof product attributes of this model are: Must-be (M),One-dimensional (O), Attractive (A), Indifferent (I) andReverse (R) as schematically illustrated in Fig.1. Kano Model Classification of Customer Needs Low satisfaction (Disgusted) Performance fully absent(Dysfunctional)Performance fully present(Functional)High satisfaction (Delighted) Must be (M)One-dimensional (O)Attractive (A)Indifferent (I)Reverse (R) Fig.1 Kano model for customer satisfactionAKano questionnaire is described in Table 1:Table1 Kano questionnaire   Functional SideDysfunctional side If the processor speed of computer is very high , howdo you feel?If the processor speed of computer is not very high ,how do you feel?An ideal Answer: Ilike it that way Ilike it that wayIt must be that wayIt must be that wayIam neutralI am neutralIcan live with that way Ican live with that way Idislike it that wayI dislike it that way From Table 1, a customer (respondent) can select one of the answer out of Like, Must-be, Neutral, Live-with,and Dislike from the functional side stating his/her levelof satisfaction. The customer also can to select one of the answer (out of the same choices) from dysfunctionalside stating his/her level of satisfaction. A customerselects “I like it that way” from the functional side (theprocessor speed of computer is very high) and “I canlive with that way” from dysfunctional sides (theprocessor speed of computer is not very high). Thiscombination makes the underlying attribute anAttractive attribute (KE) according to Kano evaluationTable 2 Kano evaluation table adapted from Berger et al.(1993) [8]   Like (L)Must-be (M)Neutral (N) Live-with (Lw) Dislike (D) Like (L) QAA A OMust-be (M)RIIIMNeutral (N)RIIIMLive-with (Lw)RIIIMDislike (D)RRRRQAttractive (A),Indifferent (I),Must-be (M),One-dimensional (O),Questionable (Q),Reverse ( R ) Functional Answer (FA)Dysfunctional Answer (DFA) Table 2. However, at the same time as responding toKano questionnaires the respondents are allowed tochoose any combination of the answer from FA andDFA. In the similar way any respondents can chooseany combination from FA and DFA. Any combinationmakes an attribute among, KE  {A, O, M, I, R, Q} .(2)(1)  MIE10-040-3Thus the combination of FA and DFA is used to identifythe status of the attributes (KE) in term of: M, O, A, I, Rand Q.Customer perceptions are depended on attribute presentorabsent. Table 3 shows the perceptions of customers,when attribute is present, while when attribute is absent.Table 3 Five categories of product attributes based onKano et al. (1984) [7]  KE When attributeis present?When attributeis absent?One-dimensionalSatisfied DissatisfiedMust-be No feeling DissatisfiedAttractive Satisfied No feelingIndifferent No feeling No feelingReverse Dissatisfied SatisfiedQuestionable Nothing NothingBeside of five types of product attribute, Questionable(Q) attribute is occurred, when one selects “Like” or“Dislike” from both FA and DFA; as a result thisanswer does not make any sense. It is also existed in theKano model. 3.2 Relationship among FA, DFA and KE Table 4 is a straightforward outline of Kano model.This is a real picture of relationship among FA, DFAand KE. It is also shown frequency 25 for each FA,DFA and KE regarding events, which are defined inTables 5-6. This rule was applied for selection of thesimulated KE  {A, O, M, I, R, Q} from simulated FAand DFA, which is described in subsection 3.3.Table 4: Relationship among FA,DFA and KE Sl KEFADFA 1 Questionable (Q) Like Like2 Attractive (A) Like Must-be3 Attractive (A) Like Neutral4 Attractive (A) Like Live-with5 One-dimensional (O) Like Dislike6 Reverse ( R) Must-be Like7 Indifferent (I) Must-be Must-be8 Indifferent (I) Must-be Neutral9 Indifferent (I) Must-be Live-with10 Must-be (M) Must-be Dislike11 Reverse ( R) Neutral Like12 Indifferent (I) Neutral Must-be13 Indifferent (I) Neutral Neutral14 Indifferent (I) Neutral Live-with15 Must-be (M) Neutral Dislike16 Reverse ( R) Live-with Like17 Indifferent (I) Live-with Must-be18 Indifferent (I) Live-with Neutral19 Indifferent (I) Live-with Live-with20 Must-be (M) Live-with Dislike21 Reverse ( R) Dislike Like22 Reverse ( R) Dislike Must-be23 Reverse ( R) Dislike Neutral24 Reverse ( R) Dislike Live-with25 Questionable (Q) Dislike Dislike Probability provides the real knowledge when outcomeof events is uncertain. In the present study, eventsprobabilities are equivalent to relative frequency of those events. Generally, an event is a set of outcomes towhich a probability is assigned. Events of FA, DFA andKE are considered from above Table. These aredescribed in Tables 5-6. Following table shows the bothFAand DFA events, probability vector Pr (.) andcumulative probability CPr (.):Table 5. Events, Probability and Cumulative Probabilityof FA and DFA Events (E)Frequency, fProbability, Pr (.)Cumulative Probability, CPr (.)Like (L)50.20.2Must-be (M)50.20.4Neutral (N)50.20.6Live-with (Lw)50.20.8Dislike (D)50.21 Following table shows the KE events, probability vectorPr (.) and cumulative probability CPr (.).Table 6 Events, Probability and Cumulative Probabilityof KE Events (E)Frequency, fProbability, Pr (.)Cumulative Probability, CPr (.)Attractive (A)30.120.12Indifferent (I)90.360.48Must-be (M)30.120.6One-dimensional (O)10.040.64Questionable (Q)20.080.72Reverse ( R) 7 0.28 1 3.3 Rules of the system The following table representation of FA, DFA and KEevents and probability of Kano model are derived fromtables 7. Accordingly second column of table 7represents the customer Kano evaluation and then nextcolumn shows the frequency of Kano evaluation. 4 th -6 th column shown the Functional answer (FA) and 7 th -9 th column shown the dysfunctional answer withprobability and cumulative probability of respectiveKano evaluation.Table 7 A Kano rule in tabular form with eventsprobability Sl. 4 o.Customer Kano Evaluation(KE)Frequency,fFunctionalAnswer (FA)ProbabilityCumulative ProbabilityDysfunctional Answer (DFA)ProbabilityCumulative Probability 1 Attractive 1 Like 0.333 Live-with 0.333 0.3332 Attractive 1 Like 0.333 Must-be 0.333 0.6663 Attractive 1 Like 0.333 Neutral 0.333 1 Frequencyfor Attractive = 3 4 One-dimensional 1 Like 1 1 Dislike 1 1 Frequencyfor One-dimensional = 1 5 Must-be 1 Live-with 0.333 0.333 Dislike 0.3336 Must-be 1 Must-be 0.333 0.666 Dislike 0.3337 Must-be 1 Neutral 0.333 1 Dislike 0.333 Frequencyfor Must-be= 3 8 Indifferent 1 Live-with 0.111111111 Live-with 0.111111119 Indifferent 1 Live-with 0.111111111 Must-be 0.1111111110 Indifferent 1 Live-with 0.111111111 Neutral 0.1111111111 Indifferent 1 Must-be 0.111111111 Live-with 0.1111111112 Indifferent 1 Must-be 0.111111111 Must-be 0.1111111113 Indifferent 1 Must-be 0.111111111 Neutral 0.1111111114 Indifferent 1 Neutral 0.111111111 Live-with 0.11111111 0.33315 Indifferent 1 Neutral 0.111111111 Must-be 0.11111111 0.66616 Indifferent 1 Neutral 0.111111111 Neutral 0.11111111 1 Frequencyfor Indifferent= 9 17 Reverse 1 Dislike 0.142857143 Live-with 0.14285714 0.14285714318 Reverse 1 Dislike 0.142857143 Must-be 0.14285714 0.28571428619 Reverse 1 Dislike 0.142857143 Neutral 0.14285714 0.42857142920 Reverse 1 Dislike 0.142857143 Like 0.1428571421 Reverse 1 Live-with 0.142857143 0.714285714 Like 0.1428571422 Reverse 1 Must-be 0.142857143 0.857142857 Like 0.1428571423 Reverse 1 Neutral 0.142857143 1 Like 0.14285714 Frequencyfor Reverse =7 24 Questionable 1 Dislike 0.5 0.5 Dislike 0.5 0.525 Questionable 1 Like 0.5 1 Like 0.5 1 Frequencyfor Questionable = 2 TotalKano Evaluation=25 1 10.33330.66610.5714285711  MIE10-040-4According above table is directed for making followinglogical structure in graphical form of Kano Evaluation(KE). These can be Attractive (A), One-dimensional(O), Must-be (M), Indifferent (I), Reverse (R) andQuestionable (Q). Generic Logical structure of KanoModel is shown in Fig.2Fig. 2 Generic Logical Structure of Kano ModelGeneric Rule for logic tree is following: If FA= (Like  Must-be  Neutral  Indifferent  Reverse  Dislike) andDFA= (Like  Must-be  Neutral  Indifferent  Reverse  Dislike) then KE= (Attractive  One-dimensional  Must-be  Indifferent  Questionable).Row numbers 1-3 of table 7 are shown quantitativelogical form of attractive attribute of Kano Evaluation(KE) between linkage with functional form (FA) anddysfunctional form (DFA) of voice of customers(VoCs). In table 7 is also shown in row numbers in 1-3functional answer is absolutely like, while dysfunctionalanswers are live-with, must-be and neutral.Computation of Logical Structure for attractive attributeis shown in Fig.3. Simulate Kano Evaluation (KE) E =(A, M, I, O, R, Q)Must-be (M)Must-beDislikeKano RuleSimulate Functional Answer (FA)DislikeSimulate Dysfunctional Answer (DFA)Must-beNeutralLive-withFA/KE=1/3All=DFA/FA=1/3Must-be/Neutral/Live-with Fig. 3 Computation of Logical Structure for attractiveattributeSimilarly, we can show logical structure for one-dimensional, must-be, indifferent, reverse attributes . 3.4 Simulation process for selection FA and DFAfrom KE In this simulation process, event vectors, probability   vector, cumulative probability has been applied. Theirapplications are shown in Fig. 4 according to steps 1-8.This figure also shows a customer need analysis modelfor the proposed simulation process and representationof the relationship among KE, FA and DFA of Kanomodel. The proposed simulation process is constructedthe selection of the simulated KE for simulated FA andsimulated DFA as described below: Input Steps: Step 1: Choices of events and probability vector of KEof Kano model, KE  {A, O, M, I, R, Q}   according to table 6.Step 2: Determine the number of iterations. Calculate: Step 3: Generate a set of random inputs in the interval[0, 1].Step 4: Applied the concept of cumulative probability of the events according to table 6.Step 5: Simulated events vector according to Eq.1. Outputs: 1-3 Step 6. Simulated events of KE of customer accordingto Eq.(1) then calculated simulated probabilityvector according to Eq.(2).Step 7. Simulated events of FA from Output 1 andaccording to Table7 then calculated simulatedprobability vector according to Eq.(2).Step 8. Simulated events of DFA from Output 1 andaccording to Table7 then calculated simulatedprobability vector according to Eq.2.     P   r   o     b   a     b     i     l     i    t   y S= (L,M,N,Lw,D)S= (L,M,N,Lw,D) Attractive (A)Indifferent(I)Must-be(M)One-dimensional(O)Questionable (Q)Reverse (R )   a  a  y Events Attractive (A)Indifferent (I)Must-be (M)One-dimensional(O)Questionable (Q)Reverse (R) Events Simulate Kano Evaluation (KE) E = (A, M, I, O, R, Q)Kano RulesS1SimulateFunctionalAnswer (FA)SimulateDysfunctionalAnswer(DFA) (M)Neutral (N)Live-with (Lw)Dislike (D)     P   r   o     b   a     b     i     l     i    t   y (L)Must-be (M)Neutral(N)Live-with (Lw)Dislike (D)     P   r   o     b   a     b     i     l     i    t   y Output-1     P   r   o     b   a     b     i     l     i    t   y Output-2Output-3Input  Fig.4 Customer need analysis numerical simulationmodel 4. Results and Discussion Following scenario 1 shows customer needs analysis onKano evaluation based. What happens of scenario 1 isthe system input equal probability vector (0.16667) of choices incorporated of succeeding steps. The outputs of simulated event probabilities are shown in lower portion KE=Attractive v One-dimensional v Must-be v Indifferent v Reverse v QuestionableFA=Like v Must-be v Neutralv Live-with v DislikeDFA=Like v Must-be v Neutral v Live-with v Dislike  MIE10-040-5of Table 7. All simulated evaluation outputs probabilityrange 0.15815~ 0.1723 are consistent of the systeminput value 0.1667 The scenario shows that probabilityof simulated functional answer in Table 7, Likeattribute range is 41.335%~42.205%; Must-be, Neutraland Live-with are likely equal around 13.41%, whereas Dislike attributes range is 17.435~18.085%.Table 7 Result of Successive Simulations forScenario 1 12345678910Like0.41860.422050.41410.413350.41470.41380.421050.417550.420250.421Must-be0.13570.13440.133550.137250.135350.13610.13210.13520.130.132Neutral0.1350.13290.13420.13750.133850.13580.133750.13460.136650.134Live-with0.135750.133550.13970.13730.13640.133450.131150.13580.138750.134Dislike0.174950.17710.178450.17460.17970.180850.181950.176850.174350.178Like0.180250.185750.17440.17740.17930.17770.178950.18070.177650.181Must-be0.132750.13240.13360.134950.134050.13430.132850.132750.13610.135Neutral0.13630.13310.13410.13420.13560.134050.133350.132050.133050.138Live-with0.131850.13260.13220.135950.1330.137450.136750.132950.13890.135Dislike0.418850.416150.42570.41750.418050.41650.41810.421550.41430.411Attractive0.165750.16280.165950.16340.167250.166750.170650.16640.168650.17Indifferent0.163550.1670.16430.17230.16330.16820.158150.16220.168450.167Must-be0.16870.160950.173250.166150.170050.16420.166950.17060.16590.163One-dimensional0.16960.17030.16660.16860.163150.16650.167450.16680.16910.166Questionable0.16380.173850.16740.16410.169150.166350.166650.16850.16180.168Reverse0.16860.16510.16250.165450.16710.1680.170150.16550.16610.166Simulated Dysfunctional Probabilities (DFA)Simulation of Kano Evaluation (KE)SimulationSimulated Functional Probabilities (FA) The scenario also shows that simulated dysfunctionalanswer in Table 7, Like attributes range is17.44%~18.575%, Must-be, Neutral and Live-with arelikely equal around 13.335%, where as Dislikeattributes range is 41.10%~42.57%.. It is shown that thepresented computer system consistent regarding Monte-Carlo simulation method with Kano model. Abovescenario facilities us to consider system input as a KEprobability, which is described in table 6 for thescenario 2 has been constructed in Fig.2. A method isdeveloped for uncertain customer need analysis [17] .Intheir study out of 25 individuals, only 14 of themsubmitted a Kano questionnaire with their answers ontime. 11 individuals, i.e .44% of the answers wereunknown or technically uncertain. Their study wasconstrained to finding this unknown customerevaluation. Their next study [18] made a proposition forgeneric unknown customer evaluation. In this case allstates (Like, Must-be, Neutral, Live-with, and Dislike)are equally likely occur in the simulated answer.According to table 5 is also shown equal probability of FA and DFA. This FA and DFA are considered thegeneric unknown customer evaluation. The result of successive simulations for scenario 2 is presented table8. Table 8 shows that all states (Like, Must-be, Neutral,Live-with, and Dislike) simulated probability range0.1927~0.206 are consistent with 0.20 of table 5 andproposition of Ullah and Tamaki [18] .Adeterministic system is a system in which norandomness is involved of the system. A deterministicmodel thus produces the same output for a givenstarting condition In the presented study, random inputsgave deterministic result, because of the last row of Table 8 shows that simulated probability rangecombined of Indifferent and Reverse is 0.6361~0.6463,which is consistent with 0.64.Table 8 Result of Successive Simulations forScenario 2 1 2 3 4 5 6 7 8 9 10Like 0.2023 0.20125 0.20035 0.2042 0.1996 0.1972 0.20035 0.202 0.20165 0.2036Must-be 0.1991 0.19665 0.19555 0.19385 0.204 0.2014 0.19955 0.1965 0.1973 0.1979Neutral 0.20245 0.2002 0.19895 0.20185 0.1994 0.19795 0.2019 0.2037 0.1982 0.2037Live-with 0.2039 0.20075 0.2022 0.1999 0.19925 0.2039 0.20245 0.19825 0.2015 0.19625Dislike 0.19225 0.20115 0.20295 0.2002 0.19775 0.19955 0.19575 0.19955 0.20135 0.19855Summation 1 1 1 1 1 1 1 1 1 1Like 0.19655 0.2003 0.19435 0.2075 0.20325 0.1966 0.2016 0.19945 0.20165 0.1985Must-be 0.1978 0.20085 0.2037 0.19935 0.1967 0.2001 0.19835 0.20255 0.1987 0.20055Neutral 0.2034 0.20345 0.19775 0.19665 0.1989 0.2044 0.20085 0.20165 0.2012 0.2036Live-with 0.2002 0.19485 0.20565 0.1971 0.2016 0.2021 0.20295 0.2036 0.19675 0.2024Dislike 0.20205 0.20055 0.19855 0.1994 0.19955 0.1968 0.19625 0.19275 0.2017 0.19495Summation 1 1 1 1 1 1 1 1 1 1Attractive 0.12135 0.1222 0.11965 0.12125 0.11885 0.1205 0.1208 0.1241 0.1184 0.12265Indifferent 0.366 0.3578 0.3659 0.3517 0.3599 0.36455 0.3629 0.3616 0.3588 0.3618Must-be 0.1211 0.12085 0.11735 0.12015 0.12035 0.1191 0.11965 0.1174 0.11735 0.11805One-dimensional 0.04255 0.03985 0.0414 0.04065 0.0396 0.03825 0.0382 0.03795 0.04155 0.03985Questionable 0.0768 0.07905 0.0791 0.0809 0.08075 0.0779 0.07975 0.07735 0.0845 0.07815Reverse 0.2722 0.28025 0.2766 0.28535 0.28055 0.2797 0.2787 0.2816 0.2794 0.2795Summation 1 1 1 1 1 1 1 1 1 1Indifferent and Reverse 0.6382 0.63805 0.6425 0.63705 0.64045 0.64425 0.6416 0.6432 0.6382 0.6413Successive SimulationSimulation Results of Functional Answer (FA)Simulation Results of Dysfunctional Answer (DFA)Simulation Results of Kano Evaluation (KE) This result ensures that the simulation provides theconsistent deterministic result not uniquelydeterministic. This proposition of Ullah and Tamaki [18] is suitable. They also conclude generic unknowncustomer evaluation “Indifferent or Reverse”. Thisstudy shows that always the probability of indifferentattribute range 0.3517~0.366 is greater than Reverseattribute range 0.2722~0.28535. It shows that thisproposition of Ullah and Tamaki, 2010 [18] regardingKano model based generic customer evaluations is notcompletely appropriate. While, Indifferent attribute ispredominated for generic unknown customer evaluation.The following figure shows the evaluations of virtualcustomers for real life situations. 10 100 1000 AttractiveOne-dimensionalMust-beIndifferentReverseQuestionable Numberof Iterations , NProbability Fig. 5 Virtual Customer Evaluations This system facilitates to consider average probabilityfrom 10 successive simulations. This system runs forthe iteration numbers (virtual customers) of 10,25,50,100,200,500,1000 and 2000. The system showsvulnerability up to iteration numbers 200. After 200iterations, the system consistency is appeared forgeneric case. This number can be considered forminimum needed respondents for generic any productevaluations for real life product development processdecision making. . If we can study specific case, thisnumber can also vary [19] .Moreover, suppose a producer is considered 0.80probabilities for one dimensional and others 0.2 for aproduct attribute, what happens for customer functionalanswer (satisfaction) with customer dysfunctionalanswer (dissatisfaction) for this product. This systemcan to evaluate functional answer (FA) anddysfunctional answer (DFA) regarding above product
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