Logical Aggregation of Customer Needs Assessment
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Logical Aggregation of Customer Needs Assessment
- 1. 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 Akihiko Kubo*1 *1 Kitami Institute of Technology, Japan *2 Bangladesh University of Engineering and Technology, Bangladesh
- 2. Logical Aggregation of Customer Needs Assessment Contents: 1.Introduction 2.Customer Needs Data Acquisition 3.Problem Identification 4.Logical Aggregation Process 5.Results 6.Concluding Remarks 7.References 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 2
- 3. Logical Aggregation of Customer Needs Assessment Introduction 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 3
- 4. Logical Aggregation of Customer Needs Assessment Landfill Recycle Disposal Use Manufacturing Product Developers Physical Models Strategies Customer Ideas Virtual Models Selected Solutions s 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 4
- 5. Logical Aggregation of Customer Needs Assessment Conceptual Stage (1) Product Developers Strategie s Customers Ideas Virtual Models 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 5
- 6. Logical Aggregation of Customer Needs Assessment Potential Customers Product Developers (Respondents) Like Dislike Live with Satisfied …. !? Questionnaires 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 6
- 7. Logical Aggregation of Customer Needs Assessment Customer Needs Data Acquisition 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 7
- 8. Logical Aggregation of Customer Needs Assessment Your car is not Sedan Your car is Sedan •Like •Like •Must-be •Must-be •Neutral •Neutral •Live-with •Live-with •Dislike •Dislike 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 8
- 9. Logical Aggregation of Customer Needs Assessment Like Must-be Neutral Live-with Dislike Function Like Q A A A O or Must-be R I I I M Feature is Present Neutral R I I I M Live-with R I I I M Dislike R R R R Q Attractive (A), Indifferent (I), Must-be (M), One-dimensional(O), Questionable (Q), and Reverse (R) 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 9
- 10. Logical Aggregation of Customer Needs Assessment Ind Attractive(A) One dimensional (O) iff ere t (I n ) ) Re ve rs e (R ) (M t- be us M 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 10
- 11. Logical Aggregation of Customer Needs Assessment From the Daily Star, May 4, 2012 Dhaka Bangladesh 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 11
- 12. Logical Aggregation of Customer Needs Assessment (Profession) (Income) Gender No of Respondent Male 82 Female 18 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 12
- 13. Logical Aggregation of Customer Needs Assessment A- self-driving B- hired-driver C- long trips D- commuting E- essential F- luxury G- green- awareness Purpose and attitude toward vehicle usage 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 13
- 14. Logical Aggregation of Customer Needs Assessment Problem Identification 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 14
- 15. Logical Aggregation of Customer Needs Assessment Attractive (A) One-dimensional(O) Must-be (M) Indifferent (I) Reverse (R) and Questionable(Q) 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 15
- 16. Logical Aggregation of Customer Needs Assessment 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 16
- 17. Logical Aggregation of Customer Needs Assessment 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 17
- 18. Logical Aggregation of Customer Needs Assessment Logical Aggregation Process 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 18
- 19. Logical Aggregation of Customer Needs Assessment 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. 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 19
- 20. Logical Aggregation of Customer Needs Assessment Step 1 Status Proposition (p(Fi,Sj)) Kano Evaluation Sedan must be included in car Sedan is either O or M population Sedan should be included in car Sedan is A population Sedan could be included in the Sedan is not I or not R car population Sedan is a unreliable feature Sedan is Q Sj ∈ S = {must be, should be, could be, unreliable}; Fi = Sedan 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 20
- 21. Logical Aggregation of Customer Needs Assessment Step 2 relative Linguistic Expected Kano frequency truth value value Evaluation (fr) (LT) E(LT) Pr(.) A 0.1 quite false (qf) 0.133 0.138 neither true nor I 0.5 false (tf) 0.5 0.518 M 0.2 quite false (qf) 0.133 0.138 mostly false O 0.05 (mf) 0.033 0.034 mostly false Q 0.05 (mf) 0.033 0.034 quite false R 0.1 0.133 0.138 (qf) 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 21
- 22. Logical Aggregation of Customer Needs Assessment Step 2 mf qf sf tf st qt mt 1 ) (L T 0.5 D B o 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 c c is a crisp value LT = Linguistic truth-value or likelihood 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 22
- 23. Logical Aggregation of Customer Needs Assessment Step 2 Kano Evaluation Pr(.) DoB(.) DoB(.) Status (Sj) A 0.138 0.266 0.266 must be I 0.518 1 0.266 should be M 0.138 0.266 0.734 could be O 0.034 0.066 0.066 unreliable Q 0.034 0.066 - - R 0.138 0.266 - - Probability ≤ Possibility 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 23
- 24. Logical Aggregation of Customer Needs Assessment Step 3 worst intermediate best 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 24
- 25. Logical Aggregation of Customer Needs Assessment Step 3 Certainty p(Fi,Sj) DoB(p(Fi,Sj)) Information Compliance Content (Ic) (CC) Sedan must be 0.1 0.2 … Sedan should CC = 0.4 0.8 be … 0.325 Sedan could (entropy of 0.9 0.2 opinions) be … Sedan is a 0.05 0.1 unreliable … 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 25
- 26. Logical Aggregation of Customer Needs Assessment a= b= Requirement p(Fi,Sj) DoB(p(Fi,Sj)) DoB(RE) max(DoB) min(DoB) (RE) Sedan is Sedan must be … 0.1 0.9 0.05 a should 0.4 be feature Sedan should be 0.4 Requirement Compliance (RC) … = (0.9-0.4)/(0.9-0.05) Sedan = 0.588 could be 0.9 … (Sedan partially fulfills the Sedan is a requirement) unreliable 0.05 Step 3 … 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 26
- 27. Logical Aggregation of Customer Needs Assessment Results 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 27
- 28. Logical Aggregation of Customer Needs Assessment 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 28
- 29. Logical Aggregation of Customer Needs Assessment 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 29
- 30. Logical Aggregation of Customer Needs Assessment unreliable must be included 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 30
- 31. Logical Aggregation of Customer Needs Assessment 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 31
- 32. Logical Aggregation of Customer Needs Assessment Must be Should be Could be included in car population in Bangladesh 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 32
- 33. Logical Aggregation of Customer Needs Assessment Concluding Remarks 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 33
- 34. Logical Aggregation of Customer Needs Assessment 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. 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 34
- 35. Logical Aggregation of Customer Needs Assessment 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. 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 35
- 36. Logical Aggregation of Customer Needs Assessment Thanks for your attentions ! 09/29/12 ISCIIA2012, 20-26 August 2012, Sapporo, Japan 36
Editor's Notes
- Hi, Every body, My name is Rashid. I would like to present a paper called “ Logical Aggregation of Customer Needs Assessment”. We are five Authors here: myself, Sharif Ullah, Rashid Sarker, Junichi Tamaki and Akihiko Kubo . Prof. Rashid Sarkar came from Bangladesh University of Engineering and Technology, Bangladesh. Other Authors are Kitami Institute of Technology. This work is part of my doctoral thesis. Prof Sharif Ullah and Prof Jun’ichi Tamaki are my PhD Advisor. I am very glad to be here today.
- This slide shows the Contents of our presentation.
- Let use first introduce a scenario of product development. We can divide it into three stages called (i) conceptual stage, (ii) product realization stage and (iii) use and recovery stage.
- On of the key issues of conceptual stage is to identifying product ideas through customer needs evaluation.
- In customer needs evaluation, the potential customers answer a set of questions, providing the levels of satisfaction for some product features.
- Let us introduce you the customer needs data acquisition.
- We prepared questionnaires according to Kano model. First we ask a question when a feature/function is present. The options are like, must-be, neutral, live-with and dislike. The respondent chooses one. Then we ask another question when the feature/function is not present. The options are: same. Again the respondent chooses one.
- Based on the answers, a feature is classified into Attractive (A), Indifferent (I), Must-be (M), One-dimensional(O), Questionable (Q), or Reverse (R).
- The meaning of Attractive (A), Indifferent (I), Must-be (M), One-dimensional(O), Questionable (Q), and Reverse (R) is shown here.
- We went to Dhaka, the capital of Bangladesh to collect customer opinion on some features of passenger vehicles. 100 respondents answer the questions on 38 features.
- We show here the demographic details of the respondents.
- We show here the physiographic details of the respondents.
- Let us introduce the problem identification of our study.
- When we just consider the frequency based data analysis we see that the Kano evaluation does not match the reality. For example, here we see that Sedan (a feature) is an Indifferent feature. In Bangladesh we see most of the cars are Sedan-type and customers are more or less happy with this type of cars.
- One of the reasons of such misleading evaluation is that the respondents answer Neutral a lot. Neutral is actually a less informative answer. We can avoid this kind of answers.
- When we ignored the answers containing Neutral, we find that Sedan becomes a Reverse attribute. This means that customers in Bangladesh hate Sedan. This is not true. Therefore, we should not analyze the data in terms of relative frequency.
- To overcome this, we propose a logical aggregation process.
- The process consists of four steps Establish Relationship between Feature Status and Kano Evaluation Determine Degree of Belief (DoB) of each Status of a feature Determine 2-D Entropy of a feature Evaluation of the feature
- We consider four status of a feature. They are related with Kano evaluation. The summary is shown in this table.
- We can determine the probability of Kano evaluation from their relative frequencies. To do this we use the linguistic truth values defined by fuzzy numbers in the next slide. (explain one)
- These are the fuzzy numbers of the linguistic truth value.
- Degree of probability is converted into degree of belief based on probability-possibility consistency. From the degree of belief of Kano evaluation the degree of belief of the status is calculated.
- Once the degree of belief is known we can calculate the 2 dimensional information content. One of the dimension is certainty compliance (CC) or certainty entropy. The other dimension is requirement compliance (RC) requirement entropy. CC determines the diversity of the opinion. RC whether or not a requirement is fulfilled by the feature. Requirement means here one of the chosen status.
- Here we show the calculation process of CC.
- Here we show a calculation process of RC.
- Let us introduce you to the results of this study.
- In this slide shows a Probabilistic Possibilistic interface software to determine the entropy of each feature.
- Entropy can be minimized if sedan is considered either must be or should be.
- Entropy can be minimized if SUV is considered a must be feature.
- Entropy can be minimized if VAN is considered a could be feature.
- This slide shows a preferential chart of customer choices regarding cars selection.
- Let us introduce you to the concluding remarks of this study.