Selecting Product Configuration Using a Combination of Fuzzy-ANP and ELECTRE-TOPSIS Approaches

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, University of Qom, Iran Head of Department of Industrial Engineering, Faculty of Technology and Engineering, University of Qom, Head of ICT Cemter, University of Qom

2 Department of Industrial Engineering, University of Qom, Iran

3 Department of Industrial Engineering, Islamic Azad University, Nowshahr Branch, Iran

Abstract

Product selection is done according to its specifications. In modern competitive markets, product survival refers back to its appropriate price, quality, and innovations in accordance with customers’ needs. In order to increase customers’ satisfaction, the quality of products and services should be improved. In this study, we evaluated different configurations of laptops using Multi-criteria Decision Making (MCDM) approaches. First, we employed a structured questionnaire to collect important features about laptop selection from customers’ viewpoints, and the customers scored the features based on their own opinions. Then, in solving the problem, it was used fuzzy Analytical Hierarchy Process (AHP) to weigh criteria such as product weight, price and time spending for full battery charge. Afterwards, TOPSIS-ELECTRE approach was used to rank laptop alternatives to propose the best one. Based on the results, good price and having main features at a desirable level were identified as main factors to improve configuration and customer satisfaction.

Keywords


  1. Ho, T.H. and Tang, C.S. (1998). “Product Variety Management: Research Advance”, Kluwer Academic, Boston, MA.
  2. Shao, X.Y., Wang, Z.H., Li, P.G. and Feng, X.J. (2006). “Integrating data mining and rough set for customer group-based discovery of product configuration rules”, International Journal of Production Research, Vol. 44, No. 14, PP. 2789–2811.
  3. Matzler, K. and Hinterhumber, H.H. (1998). “How to make product development projects more successful by integrating Kano's model of customer satisfaction into quality function deployment”, Technovation, Vol. 18, No. 1, PP. 25–38.
  4. Gangurde, S.R. and Akarte, M.M. (2013). “Customer preference oriented product design using AHP-modified TOPSIS approach”, BIJ, Emerald Group Publisher, Vol. 20, No. 4, PP. 549–564.
  5. Park, J. and Han, S.H. (2004). “A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design”, International Journal of Industrial Ergonomics, Vol. 34, No. 1, PP. 31–47.
  6. Kwong, C.K., Wong, T.C. and Chan, K.Y. (2009). “A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach”, Expert Systems with Application, Vol. 36, No. 8, PP. 11262–11270.
  7. Lin, Y.C., Lai, H.H. and Yeh, C.H. (2007). “Consumer-oriented product form design based on fuzzy logic: A case study of mobile phones”, International Journal of Industrial Ergonomics, Vol. 37, PP. 531–543.
  8. Wang Chih-Hsuan, Hsueh One-Zen, (2013). “A novel approach to incorporate customer preference and perception into product configuration: A case study on smart pads”, Computer Standards & Interfaces, Vol. 35, PP. 549–556.
  9. Kumar, A., Shankar, R. and Debnath, R.M. (2015). “Analyzing customer preference and measuring relative efficiency in telecom sector: A hybrid fuzzy AHP/DEA study”, Telematics and Informatics, Vol. 32, No. 3, PP. 447–462.
  10. Bayraktar, E., Tatoglu, E., Turkyilmaz, A., Delen, D. and Zaim, S. (2012). “Measuring the efficiency of customer satisfaction and loyalty for mobile phone brands 500 with DEA”, Expert System, Vol. 39, No. 1, PP. 99–106.
  11. Chen, Y., Tang, J., Fung, R.Y.K. and Ren Z. (2004). “Fuzzy regression-based mathematical programming model for quality function deployment”, International Journal of Product Research, Vol. 42, PP. 1009–1027.
  12. Chen Y. and Chen L. (2005). “Anon-linear possibilistic regression approach to model functional relationships in product planning”, Int. J. Adv. Manuf. Technol, Vol. 28, PP. 1175–1181.
  13. Fung, R.Y.K., Chen, Y., Tang, J. and Tu Y. (2006). “Estimating functional relationships for product planning under uncertainties”, Fuzzy Sets Syst, Vol. 157, No. 8, PP. 98–120.
  14. Goode, M.H., Davies, F., Moutinho, L. and Jamal, A. (2005). “Determining customer satisfaction from mobile phones: a neural network approach”, Journal of Marketing Management, Vol. 21, No. 7/8, PP. 755–778.
  15. Haverila Matti, (2011). “Mobile phone feature preferences, customer satisfaction and repurchase intent among male users”, Australasian Marketing Journal, Vol. 19, PP. 238–246.
  16. Barajas, M. and Agard, B. (2011). “Selection of products based on customer preferences applying fuzzy logic”, Int J Interact Des Manuf, Vol. 5, PP. 235–242.
  17. Wang, H.J., Sun, B.Y., Zhang, J.M., Wang, J.J. and Wei, X.P. (2005). “Modular product configuration design for customer requirement-driven engineering”, Chin. J. Mechanical Eng., Vol. 41 No. 4, PP. 85–91.
  18. Ostrosi, E. and Bi, S.T. (2010). “Generalized design for optimal product configuration”, International Journal of Advanced Manufacturing Technology, Vol. 49, No. 4, PP. 13–25.
  19. Sabin, D. and Weigel, R. (1998). “Product configuration frameworks – a survey”, IEEE Intelligent Systems and their Applications, Vol. 13, No. 4, PP. 42–49.
  20. Siddique, Z. and Rosen, DW. (2001). “On discrete design spaces for the configuration design of product families”, AIEDAM, Vol. 15, No. 2, PP. 91–108.
  21. Siddique, Z. and Boddu, K.R. (2005). “A mass customization information framework for integration of customer in the configuration/design of a customized product”, AIEDAM, Vol. 18, No. 1, PP. 71–85.
  22. Han, S.H. Kim, K.J. Yun, M.H. Hong, S.W. and Kim, J. (2004). “Identifying mobile phone design features critical to user satisfaction”, Human Factors and Ergonomics in Manufacturing, Vol. 14, No. 1, PP. 15–29.
  23. Chuang, M.C. Chang, C.C. and Hsu, S.H. (2001). “Perceptual factors underlying user preferences toward product form of mobile phones”, International Journal of Industrial Ergonomics, Vol. 27, No. 4, PP. 247–258.
  24. Tucker, C.S. and Kim, H.M. (2008). “Optimal product portfolio formulation by merging predictive data mining with multilevel optimization”, Journal of Mechanical Design, Vol. 130, No. 4, PP. 1–15.
  25. Zandi, E. Roghanian, E. (2013). “Extension of Fuzzy ELECTRE based on VIKOR method”, Computers & Industrial Engineering, Vol. 66, No. 4, PP. 258–263.
  26. Patil, S.K. and Kant, R. (2014). “A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers”, Expert System with Applications, Vol. 41, No. 2, PP. 679–693.
  27. Sadegh-Amalnik, M., Ansarinejad, A., Ansarinejad, S. and Miri-Nargesi, S. (2010). “Finding casual relationship and ranking of CSFs in information system implementations project by using the combination of fuzzy ANP and fuzzy DEMATEL”, Journal of Industrial Engineering, Vol. 44, No. 2, PP. 195–212.
  28. Baradaran, V., Baradaran-Kazemzadeh, R., Amiri, A.M. and Mogouei, M. (2011). “Developing Principal Component Analysis Approach for Multi Attribute Decision Making Problems with Correlated Criteria”, Journal of Industrial Engineering, Vol. 46, No. 2, PP. 133–145.