Journal of Intelligent Manufacturing

, Volume 30, Issue 5, pp 2231–2243 | Cite as

User selection for collaboration in product development based on QFD and DEA approach

  • Xuefeng ZhangEmail author


User collaboration has been recognized as a critical factor in successful product development. To select the proper users who have a higher satisfaction to meet product development requirements, this study proposes an integrated approach based on quality function deployment (QFD) and data envelopment analysis (DEA). The proposed approach considers product development requirements, the inner dependencies of user evaluation criteria, and the relationships between requirements and criteria simultaneously and presents them in house of quality (HoQ). For the fuzzy and imprecise information in HoQ, fuzzy weighted average method is employed to determine the weight of each user evaluation criterion. Furthermore, to determine the priorities of users with a large number efficiently, this study implements linguistic variables to assess the weight of each user under each criterion, the DEA method to determine the optimal values of linguistic variables, and the simple additive weighting approach to aggregate the weight of each criteria and the local score of each user with respect to each criterion. An illustrative case is presented to demonstrate the applications of the proposed approach based on QFD and DEA in this paper.


User selection Collaborative product development Quality function deployment Multiple-criteria decision making Data envelopment analysis 



This work was supported by The Key Project of Academic Humanities and Social Science of Anhui Education Department (SK2017A0120), and The Scientific Research Starting Foundation of Anhui Polytechnic University for Talent Introduction (2016YQQ008). The author would like to thank the anonymous reviewers and the editor for their constructive comments and suggestions.


  1. Aghdam, M. M., Jelodar, M. J., & Mahdavi, I. (2015). Engineering design requirements ranking in QFD by fuzzy data envelopment analysis. Arth Prabandh: A Journal of Economics and Management, 4(3), 134–150.Google Scholar
  2. Azadi, M., & Saen, R. F. (2013). A combination of QFD and imprecise DEA with enhanced Russell graph measure: A case study in healthcare. Socio-Economic Planning Sciences, 47(4), 281–291.Google Scholar
  3. Bilgram, V., Brem, A., & Voigt, K.-I. (2008). User-centric innovations in new product development—Systematic identification of lead users harnessing interactive and collaborative online-tools. International Journal of Innovation Management, 12(03), 419–458.Google Scholar
  4. Bouchereau, V., & Rowlands, H. (2000). Methods and techniques to help quality function deployment (QFD). Benchmarking: An International Journal, 7(1), 8–20.Google Scholar
  5. Cariaga, I., El-Diraby, T., & Osman, H. (2007). Integrating value analysis and quality function deployment for evaluating design alternatives. Journal of Construction Engineering and Management, 133(10), 761–770.Google Scholar
  6. Chen, L. H., & Ko, W. C. (2009). Fuzzy linear programming models for new product design using QFD with FMEA. Applied Mathematical Modelling, 33(2), 633–647.Google Scholar
  7. Chen, L. H., & Weng, M. C. (2006). An evaluation approach to engineering design in QFD processes using fuzzy goal programming models. European Journal of Operational Research, 172(1), 230–248.Google Scholar
  8. Djelassi, S., & Decoopman, I. (2013). Customers’ participation in product development through crowdsourcing: Issues and implications. Industrial Marketing Management, 42(5), 683–692.Google Scholar
  9. Dursun, M., & Karsak, E. E. (2013). A QFD-based fuzzy MCDM approach for supplier selection. Applied Mathematical Modelling, 37(8), 5864–5875.Google Scholar
  10. Eilat, H., Golany, B., & Shtub, A. (2008). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega, 36(5), 895–912.Google Scholar
  11. Faems, D., Van Looy, B., & Debackere, K. (2005). Interorganizational collaboration and innovation: Toward a portfolio approach. Journal of Product Innovation Management, 22(3), 238–250.Google Scholar
  12. Franke, N., & Shah, S. (2003). How communities support innovative activities: An exploration of assistance and sharing among end-users. Research Policy, 32(1), 157–178.Google Scholar
  13. Franke, N., Von Hippel, E., & Schreier, M. (2006). Finding commercially attractive user innovations: A test of lead-user theory. Journal of Product Innovation Management, 23(4), 301–315.Google Scholar
  14. Gok, A. (2015). A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA. Measurement, 70, 100–109.Google Scholar
  15. Greer, C. R., & Lei, D. (2012). Collaborative innovation with customers: A review of the literature and suggestions for future research. International Journal of Management Reviews, 14(1), 63–84.Google Scholar
  16. Hadi-Vencheh, A., & Mohamadghasemi, A. (2011). A fuzzy AHP–DEA approach for multiple criteria ABC inventory classification. Expert Systems with Applications, 38(4), 3346–3352.Google Scholar
  17. Jeppesen, L. B., & Frederiksen, L. (2006). Why do users contribute to firm-hosted user communities? The case of computer-controlled music instruments. Organization Science, 17(1), 45–63.Google Scholar
  18. Jeppesen, L. B., & Molin, M. J. (2003). Consumers as co-developers: Learning and innovation outside the firm. Technology Analysis & Strategic Management, 15(3), 363–383.Google Scholar
  19. Karsak, E. E. (2008). Using data envelopment analysis for evaluating flexible manufacturing systems in the presence of imprecise data. The International Journal of Advanced Manufacturing Technology, 35(9–10), 867–874.Google Scholar
  20. Karsak, E. E., & Dursun, M. (2014). An integrated supplier selection methodology incorporating QFD and DEA with imprecise data. Expert Systems with Applications, 41(16), 6995–7004.Google Scholar
  21. Karsak, E. E., & Dursun, M. (2015). An integrated fuzzy MCDM approach for supplier evaluation and selection. Computers & Industrial Engineering, 82, 82–93.Google Scholar
  22. Kauffmann, P., Unal, R., Fernandez, A., & Keating, C. (2000). A model for allocating resources to research programs by evaluating technical importance and research productivity. Engineering Management Journal, 12(1), 5–8.Google Scholar
  23. Kausch, C. (2007). A risk-benefit perspective on early customer integration. Contributions to management science (pp. 1–218). Springer.Google Scholar
  24. Kilincci, O., & Onal, S. A. (2011). Fuzzy AHP approach for supplier selection in a washing machine company. Expert Systems with Applications, 38(8), 9656–9664.Google Scholar
  25. Lüthje, C. (2004). Characteristics of innovating users in a consumer goods field: An empirical study of sport-related product consumers. Technovation, 24(9), 683–695.Google Scholar
  26. Lüthje, C., & Herstatt, C. (2004). The Lead User method: An outline of empirical findings and issues for future research. Social Science Electronic Publishing, 34(5), 553–568.Google Scholar
  27. Lüthje, C., Herstatt, C., & Von Hippel, E. (2005). User-innovators and "local" information: The case of mountain biking. Research Policy, 34(6), 951–965.Google Scholar
  28. Li, Y. L., Tang, J. F., & Luo, X. G. (2010). An ECI-based methodology for determining the final importance ratings of customer requirements in MP product improvement. Expert Systems with Applications, 37(9), 6240–6250.Google Scholar
  29. Lin, M.-J. J., & Huang, C.-H. (2012). The impact of customer participation on NPD performance: The mediating role of inter-organisation relationship. Journal of Business & Industrial Marketing, 28(1), 3–15.Google Scholar
  30. Liu, F.-H. F., & Hai, H. L. (2005). The voting analytic hierarchy process method for selecting supplier. International Journal of Production Economics, 97(3), 308–317.Google Scholar
  31. Liu, S.-T. (2005). Rating design requirements in fuzzy quality function deployment via a mathematical programming approach. International Journal of Production Research, 43(3), 497–513.Google Scholar
  32. Mehrjerdi, Y. Z., Owlia, M., & Dorodzani, A. T. (2012). Evaluation and ranking the relative importance of design requirements by combining QFD and DEA techniques (case study: Tile industry of Iran). International Journal of Industrial Engineering, 23(2), 175–186.Google Scholar
  33. Miotti, L., & Sachwald, F. (2003). Co-operative R&D: Why and with whom?: An integrated framework of analysis. Research Policy, 32(8), 1481–1499.Google Scholar
  34. Nieto, M. J., & Santamaría, L. (2007). The importance of diverse collaborative networks for the novelty of product innovation. Technovation, 27(6), 367–377.Google Scholar
  35. Noguchi, H., Ogawa, M., & Ishii, H. (2002). The appropriate total ranking method using DEA for multiple categorized purposes. Journal of Computational and Applied Mathematics, 146(1), 155–166.Google Scholar
  36. Olson, E. L., & Bakke, G. (2001). Implementing the lead user method in a high technology firm: A longitudinal study of intentions versus actions. Journal of Product Innovation Management, 18(6), 388–395.Google Scholar
  37. Ramanathan, R., & Yunfeng, J. (2009). Incorporating cost and environmental factors in quality function deployment using data envelopment analysis. Omega, 37(3), 711–723.Google Scholar
  38. Schaarschmidt, M., & Kilian, T. (2014). Impediments to customer integration into the innovation process: A case study in the telecommunications industry. European Management Journal, 32(2), 350–361.Google Scholar
  39. Schreier, M., Oberhauser, S., & Prügl, R. (2007). Lead users and the adoption and diffusion of new products: Insights from two extreme sports communities. Marketing Letters, 18(1–2), 15–30.Google Scholar
  40. Schreier, M., & Prügl, R. (2008). Extending lead-user theory: Antecedents and consequences of consumers’ lead userness. Journal of Product Innovation Management, 25(4), 331–346.Google Scholar
  41. Shanmugam, R., & Johnson, C. (2007). At a crossroad of data envelopment and principal component analyses. Omega, 35(4), 351–364.Google Scholar
  42. Song, L. J., Yang, Y., Zhang, X. D., & Yang, J. (2007). Preference-based algorithm for collaborative design partners evaluation & selection. Computer Integrated Manufacturing Systems, 13(10), 2053–2059.Google Scholar
  43. Von Hippel, E. (1986). Lead users: A source of novel product concepts. Management Science, 32(7), 791–805.Google Scholar
  44. Wang, C. H., & Wu, H. S. (2016). A novel framework to evaluate programmable logic controllers: A Fuzzy MCDM perspective. Journal of Intelligent Manufacturing, 27(2), 315–324.Google Scholar
  45. Wang, W.-L., Yang, Y., & Wang, M.-K. (2007). RS and SVM- based partner selection research for customer collaborative innovation. Jisuanji Gongcheng yu Yingyong(Computer Engineering and Applications), 42(29), 245–248.Google Scholar
  46. Wang, Y.-M., & Chin, K.-S. (2011). Technical importance ratings in fuzzy QFD by integrating fuzzy normalization and fuzzy weighted average. Computers & Mathematics with Applications, 62(11), 4207–4221.Google Scholar
  47. Wang, Y., Chin, K., & Yang, J. (2007). Three new models for preference voting and aggregation. Journal of the Operational Research Society, 58(10), 1389–1393.Google Scholar
  48. Wang, Y. M., Liu, J., & Elhag, T. M. S. (2008). An integrated AHP–DEA methodology for bridge risk assessment. Computers & Industrial Engineering, 54(3), 513–525.Google Scholar
  49. Yang, J., Yang, Y., Wang, W., Zhao, X., & Song, L. (2008). Evaluation of collaborative innovative customer based on PWNN model and its application. Computer Integrated Manufacturing Systems, 14(5), 882–890.Google Scholar
  50. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 8(3), 199–249.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Industrial Engineering, School of Management EngineeringAnHui Polytechnic UniversityWuhuChina

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