Logical Aggregation of Customer Needs Assessment
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Logical Aggregation of Customer Needs Assessment
Md. Mamunur Rashid*1, A. M. M. Sharif Ullah*1, M. A. Rashid Sarkar*2, Jun’ichi Tamaki*1 and Aki
hiko Kubo*1
*1Kitami Institute of Technology, Japan
*2Bangladesh University of Engineering and Technology, Bangladesh
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Logical Aggregation of Customer Needs Assessment
23/04/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 2
Contents:1.Introduction2.Customer Needs Data Acquisition3.Problem Identification4.Logical Aggregation Process5.Results6.Concluding Remarks7.References
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Logical Aggregation of Customer Needs Assessment
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Introduction
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Logical Aggregation of Customer Needs Assessment
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Product Developers
Customers
Strategies
Landfill RecycleDisposal ManufacturingUse
Ideas Virtual Models Selected Solutions
Physical Models
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Product Developers
Customers
Strategies
IdeasVirtual Models
Conceptual Stage (1)
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Questionnaires
Product Developers
LikeDislikeLive with….
Somewhat should be
Must be
Could be
Potential Customers(Respondents)
Satisfied !?
Somewhat should be
Must be
Could be
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Customer Needs Data Acquisition
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Your car is SedanYour car is not Sedan
•Like•Must-be•Neutral•Live-with•Dislike
•Like•Must-be•Neutral•Live-with•Dislike
Somewhat should be
Must be
Could be
Somewhat should be
Must be
Could be
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Like Must-be Neutral Live-with Dislike
Function or
Feature is Present
Like Q A A A OMust-be R I I I MNeutral R I I I MLive-with R I I I MDislike R R R R Q
Attractive (A), Indifferent (I), Must-be (M), One-dimensional(O), Questionable (Q), and Reverse (R)
Somewhat should be
Must be
Could be
Somewhat should be
Must be
Could be
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One dimensional (O)
Must-
be (M
)
Attractive(A)
Reverse (R)
Indifferent (I
)
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From the Daily Star, May 4, 2012
DhakaBangladesh
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(Profession) (Income)
Gender No of Respondent
Male 82
Female 18
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A- self-drivingB- hired-driverC- long tripsD- commutingE- essentialF- luxuryG- green-awareness
Purpose and attitude toward vehicle usage
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Problem Identification
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Somewhat should be
Must be
Could be
Attractive (A) One-dimensional(O)Must-be (M) Indifferent (I)Reverse (R) andQuestionable(Q)
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Somewhat should be
Must be
Could be
Somewhat should be
Must be
Could be
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Somewhat should be
Must be
Could be
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Logical Aggregation Process
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Steps:1. Establishing relationship between
feature status and Kano Evaluation.2. Determining the Degree of Belief
(DoB) of each status of a feature.3. Determining a 2-D Entropy of a
feature.4. Evaluating the features considered.
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Status Proposition (p(Fi,Sj)) Kano Evaluation
Sedan must be included in car population
Sedan is either O or M
Sedan should be included in car population
Sedan is A
Sedan could be included in the car population
Sedan is not I or not R
Sedan is a unreliable feature Sedan is Q
Sj S = {must be, should be, could be, unreliable}; Fi = Sedan
Step 1
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Step 2
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Kano Evaluation
relative frequency
(fr)
Linguistic truth value
(LT)
Expected value E(LT) Pr(.)
A 0.1 quite false (qf) 0.133 0.138
I 0.5neither true nor
false (tf) 0.5 0.518
M 0.2 quite false (qf) 0.133 0.138
O 0.05mostly false
(mf) 0.033 0.034
Q 0.05mostly false
(mf) 0.033 0.034
R 0.1quite false
(qf)0.133 0.138
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0
0.5
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Do
B(L
T)
c
mf qf sf tf st qt mt
c is a crisp valueLT = Linguistic truth-value or likelihood
Step 2
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Kano Evaluation Pr(.) DoB(.) DoB(.) Status (Sj)
A 0.138 0.266 0.266 must beI 0.518 1 0.266 should be
M 0.138 0.266 0.734 could beO 0.034 0.066 0.066 unreliableQ 0.034 0.066 - -R 0.138 0.266 - -
Probability Possibility
Step 2
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best
worst
intermediate
Step 3
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p(Fi,Sj) DoB(p(Fi,Sj))Information Content (Ic)
Certainty Compliance
(CC)Sedan must be …
0.1 0.2
CC =0.325
(entropy of opinions)
Sedan should be …
0.4 0.8
Sedan could be …
0.9 0.2
Sedan is a unreliable …
0.05 0.1
Step 3
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p(Fi,Sj) DoB(p(Fi,Sj))a =
max(DoB)b =
min(DoB)Requirement
(RE)DoB(RE)
Sedan must be … 0.1 0.9 0.05
Sedan is a should
be feature0.4
Sedan should be …
0.4 Requirement Compliance (RC)= (0.9-0.4)/(0.9-0.05)
= 0.588 (Sedan partially fulfills the
requirement)
Sedan could be …
0.9
Sedan is a unreliable …
0.05 Step 3
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Results
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Somewhat should be
Must be
Could be
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Somewhat should be
Must be
Could be
must be included
unreliable
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Somewhat should be
Must be
Could be
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Somewhat should be
Must be
Could be
Must be Should be Could be
Somewhat should be
Must be
Could be
Somewhat should be
Must be
Could be included in car population in Bangladesh
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Concluding Remarks
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1. To deal with intrinsic complexity in the customer needs analysis, logical aggregation of customer opinions is a better choice compared to that of frequency based analysis. This faculty of thought is demonstrated to be true by logically aggregating the field data of customer needs collected from Bangladesh on small passenger vehicles.
2. The multi-valued logic plays an important role in the logical computation. For the sake of a better understanding, Kano-model-based customer answers are considered.
3. Further study can be carried out extending the presented logical computation to other customer needs models.
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References:Kahn, K.B. (Ed.) (2004). The PDMA Handbook of New Product Development (2nd Edition), Wiley: New York.Kano, N., Seraku, N., Takahashi, F. and Tsuji, S. (1984). Attractive quality and must-be quality, Hinshitsu, vol. 14(2), pp. 39-48. (In Japanese).Xu, Q., Jiao, R.J., Yang, X., Helander, M., Khalid, H.M. and Opperud, A. (2009). An analytical Kano model for customer need analysis, Design Studies, vol. 30(1), pp. 87-110.Sharif Ullah, A.M.M. and Tamaki, J. (2011). Analysis of Kano-Model-Based Customer Needs for Product Development, Systems Engineering, vol. 14(2), pp. 154-172.Rashid, M.M., Tamaki, J., Sharif Ullah, A.M.M. and Kubo, A. (2011). A Numerical Kano Model for Compliance Customer Needs with Product Development, Industrial Engineering & Management Systems: An International Journal, vol. 10(2), pp. 140-153.Zadeh, L.A. (1978). Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, vol. 1, pp. 3-28.Dubois D. and Prade H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Press: New York.Klir, G.J. (1999). On fuzzy-set interpretation of possibility theory, Fuzzy Sets and Systems, vol. 108, pp. 263-273.Yamada, K. (2001). A Study on Probability-Possibility Transformation Methods Based on Evidence Theory, Journal of Japan Society for Fuzzy Theory and Systems, vol. 13(3), pp. 302-312. (In Japanese).Dubois, D., Foulloy, L., Mauris, G. and Prade, H. (2004). Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities, Reliable Computing, vol. 10, pp. 273-294.Masson,M.H. and Denoeux, T. (2006). Inferring a possibility distribution from empirical data, Fuzzy Sets and Systems, vol. 157(3), pp.319-340.Mouchaweh, M.S., Bouguelid, M.S., Billaudel, P. and Riera, B. (2006). Variable Probability-Possibility Transformation, Proceedings of the European Annual Conference on Human Decision-Making and Manual Control (EAM’06), Valenciennes, France, 27-29 September, 2006.Mauris, G. (2011). Possibility distributions: A unified representation of usual direct-probability-based parameter estimation methods, International Journal of Approximate Reasoning, vol. 52(9), pp. 1232-1242.Sharif Ullah, A.M.M. (2005). A Fuzzy Decision Model for Conceptual Design, Systems Engineering, vol. 8(4), pp. 296-308.Sharif Ullah, A.M.M., Rashid, M.M. and Tamaki, J. (2012). On Some Unique Features of C-K Theory of Design, CIRP Journal of Manufacturing Science and Technology, vol. 5(1), pp. 55-66.Sharif Ullah, A.M.M., Harib, K.H. and Al-Awar, A. (2007). Minimizing Information Content of a Design using Compliance Analysis, SAE Technical Paper 2007-01-1209, 2007.Sharif Ullah, A.M.M. and Harib, K.H. (2008). An Intelligent Method for Selecting Optimal Materials and its Application, Advanced Engineering Informatics, vol. 22(4), pp. 473-483.
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Thanks for your attentions !