Logical Aggregation of Customer Needs Assessment

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


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



Introduction



Product Developers

Customers

Strategies

Landfill RecycleDisposal ManufacturingUse

Ideas Virtual Models Selected Solutions

Physical Models



Product Developers

Customers

Strategies

IdeasVirtual Models

Conceptual Stage (1)



Questionnaires

Product Developers

LikeDislikeLive with….

Somewhat should be

Must be

Could be

Potential Customers(Respondents)

Satisfied !?

Somewhat should be

Must be

Could be



Customer Needs Data Acquisition



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



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



One dimensional (O)

Must-

be (M

)

Attractive(A)

Reverse (R)

Indifferent (I

)



From the Daily Star, May 4, 2012

DhakaBangladesh



(Profession) (Income)

Gender No of Respondent

Male 82

Female 18



A- self-drivingB- hired-driverC- long tripsD- commutingE- essentialF- luxuryG- green-awareness

Purpose and attitude toward vehicle usage



Problem Identification



Somewhat should be

Must be

Could be

Attractive (A) One-dimensional(O)Must-be (M) Indifferent (I)Reverse (R) andQuestionable(Q)



Somewhat should be

Must be

Could be

Somewhat should be

Must be

Could be



Somewhat should be

Must be

Could be



Logical Aggregation Process



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.



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

Step 2


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




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



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



best

worst

intermediate

Step 3



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



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



Results





Somewhat should be

Must be

Could be



Somewhat should be

Must be

Could be

must be included

unreliable



Somewhat should be

Must be

Could be



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



Concluding Remarks



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.



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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Logical Aggregation of Customer Needs Assessment