A BWM-based approach for customer-oriented product development with insufficient information and its application to 5 G smartphone design
Pages 12443 - 12458
Abstract
Quality function deployment (QFD) is a customer-oriented tool for developing products. Based on the idea of the best-worst method (BWM), a novel model is developed to determine the relative importance ratings (RIRs) of customer requirements (CRs) with interval grey linguistic (IGL) information, which plays a significant role in QFD. CRs are rated with IGL variables, and the degree of greyness degree function that can be used to handle the IGL variables is defined based on the power utility function. Then, considering customer heterogeneity, a model is constructed to derive the RIRs of CRs by following the logic of the BWM. Finally, a case study of 5 G smartphone development is provided to verify the validity and the feasibility of the proposed method.
References
[1]
Ma H., Chu X., Xue D. and Chen D., Identification of to-be-improved components for redesign of complex products and systems based on fuzzy QFD and FMEA, Journal of Intelligent Manufacturing 30(2) (2019), 623–639.
[2]
Wang F., Li H. and Liu A., A novel method for determining the key customer requirements and innovation goals in customer collaborative product innovation, Journal of Intelligent Manufacturing 29(1) (2018), 211–225.
[3]
Zheng P., Xu X. and Xie S.Q., A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning, Journal of Intelligent Manufacturing 30(1) (2019), 3–16.
[4]
Li Q., Diao Y., Gong Z. and Hu A., Grey language hesitant fuzzy group decision making method based on kernel and grey scale, International Journal of Environmental Research and Public Health 15(3) (2018), 436.
[5]
Gonçalves-Coelho A.M., Mourao A.J., and Pereira Z.L., Improving the use of QFD with Axiomatic Design, Concurrent Engineering 13(3) (2005), 233–239.
[6]
Chan L.K., Kao H.P. and Wu M.L., Rating the importance of customer needs in quality function deployment by fuzzy and entropy methods, International Journal of Production Research 37(11) (1999), 2499–2518.
[7]
Ramanathan R. and Yunfeng J., Incorporating cost and environmental factors in quality function deployment using data envelopment analysis, Omega 37(3) (2009), 711–723.
[8]
Nahm Y.E., Ishikawa H. and Inoue M., New rating methods to prioritize customer requirements in QFD with incomplete customer preferences, The International Journal of Advanced Manufacturing Technology 65(9-12) (2013), 1587–1604.
[9]
Li Y.L., Chin K.S. and Luo X.G., Determining the final priority ratings of customer requirements in product planning by MDBM and BSC, Expert Systems with Applications 39(1) (2012), 1243–1255.
[10]
Gou X., Xu Z. and Liao H., Hesitant fuzzy linguistic possibility degree-based linear assignment method for multiple criteria decision-making, International Journal of Information Technology & Decision Making 18(01) (2019), 35–63.
[11]
Li Y., Tang J., Luo X. and Xu J., An integrated method of rough set, Kano’s model and AHP for rating customer requirements’ final importance, Expert Systems with Applications 36(3) (2009), 7045–7053.
[12]
Wang Y.M. and Chin K.S., A linear goal programming approach to determining the relative importance weights of customer requirements in quality function deployment, Information Sciences 181(24) (2011), 5523–5533.
[13]
Song W., Ming X., Han Y. and Wu Z., A rough set approach for evaluating vague customer requirement of industrial product-service system, International Journal of Production Research 51(22) (2013), 6681–6701.
[14]
Jamalnia A., Mahdiraji H.A., Sadeghi M.R., Hajiagh S.H.R. and Feili A., An integrated fuzzy QFD and fuzzy goal programming approach for global facility location-allocation problem, International Journal of Information Technology & Decision Making 13(02) (2014), 263–290.
[15]
Jin F., Liu P. and Zhang X., The multi-attribute group decision making method based on the interval grey linguistic variables weighted harmonic aggregation operators, Technological and Economic Development of Economy 19(3) (2013), 409–430.
[16]
Wu Q., Zhou L., Chen Y. and Chen H., An integrated approach to green supplier selection based on the interval type-2 fuzzy best-worst and extended VIKOR methods, Information Sciences 502 (2019), 394–417.
[17]
Liu P., The multi-attribute group decision making method based on the interval grey linguistic variables weighted aggregation operator, Journal of Intelligent & Fuzzy Systems 24(2) (2013), 405–414.
[18]
Zhang N., Method for aggregating correlated interval grey linguistic variables and its application to decision making, Technological and Economic Development of Economy 19(2) (2013), 189–202.
[19]
Jin F., Liu P. and Zhang X., The multi-attribute group decision making method based on the interval grey linguistic variables weighted harmonic aggregation operators, Technological and Economic Development of Economy 19(3) (2013), 409–430.
[20]
Rezaei J., Best-worst multi-criteria decision-making method, Omega 53 (2015), 49–57.
[21]
Rezaei J., Nispeling T., Sarkis J. and Tavasszy L., A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method, Journal of Cleaner Production 135 (2016), 577–588.
[22]
Dwivedi R., Prasad K., Mandal N., Singh S., Vardhan M. and Pamucar D., Performance evaluation of an insurance company using an integrated Balanced Scorecard (BSC) and Best-Worst Method (BWM), Decision Making: Applications in Management and Engineering 4(1) (2021), 33–50.
[23]
Mi X., Tang M., Liao H., Shen W. and Lev B., The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega 87 (2019), 205–225.
[24]
Wu Q., Zhou L., Chen Y. and Chen H., An integrated approach to green supplier selection based on the interval type-2 fuzzy best-worst and extended VIKOR methods, Information Sciences 502 (2019), 394–417.
[25]
Pamucar D., Chatterjee K. and Zavadskas E.K., Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers, Computers & Industrial Engineering 127 (2019), 383–407.
[26]
Li Y.L., Du Y.F. and Chin K.S., Determining the importance ratings of customer requirements in quality function deployment based on interval linguistic information, International Journal of Production Research 56(14) (2018), 4692–4708.
[27]
Du Y. and Liu D., A novel approach to relative importance ratings of customer requirements in QFD based on probabilistic linguistic preferences, Fuzzy Optimization and Decision Making (2020), 1–31.
[28]
Nie R., Tian Z., Sang C.K. and Wang J.Q., Implementing healthcare service quality enhancement using a cloud-support QFD model integrated with TODIM method and linguistic distribution assessments, Journal of the Operational Research Society (2020), 1–23.
[29]
Yang Q., Chan C.Y., Chin K.S. and Li Y.L., A three-phase QFD-based framework for identifying key passenger needs to improve satisfaction with the seat of high-speed rail in China, Transportation (2020), 1–36.
[30]
Jafarzadeh H., Akbari P. and Abedin B., A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency–combination of fuzzy QFD and DEA, Expert Systems with Applications 110 (2018), 237–249.
[31]
Palominos P., Quezada L.E. and Gonzalez M.A., Incorporating the voice of the client in establishing the flexibility requirement in a production system, International Journal of Production Economics 211 (2019), 34–43.
[32]
Huang J., You X.Y., Liu H.C. and Si S.L., New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory, International Journal of Production Research 57(5) (2019), 1283–1299.
[33]
Dincer H., Yüksel S. and Martinez L., Balanced scorecard-based Analysis about European Energy Investment Policies: A hybrid hesitant fuzzy decision-making approach with Quality Function Deployment, Expert Systems with Applications 115 (2019), 152–171.
[34]
Peng J.G., Xia G., Sun B.Q. and Wang S.J., Systematical decision-making approach for quality function deployment based on uncertain linguistic term sets, International Journal of Production Research 56(18) (2018), 6183–6200.
[35]
Haktanır E. and Kahraman C., A novel interval-valued Pythagorean fuzzy QFD method and its application to solar photovoltaic technology development, Computers & Industrial Engineering 132 (2019), 361–372.
[36]
Ko W.C., Construction of house of quality for new product planning: A 2-tuple fuzzy linguistic approach, Computers in Industry 73 (2015), 117–127.
[37]
Zadeh L.A., Fuzzy sets, Information and Control 8(3) (1965), 338–353.
[38]
Deng J.L., Control problems of grey systems, Systems & Control Letters 1(5) (1982), 288–294.
[39]
Haeri S.A.S. and Rezaei J., A grey-based green supplier selection model for uncertain environments, Journal of Cleaner Production 221 (2019), 768–784.
[40]
Wu L. and Zhang Z., Grey multivariable convolution model with new information priority accumulation, Applied Mathematical Modelling 62 (2018), 595–604.
[41]
Zeng B. and Li C., Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application, Computers & Industrial Engineering 118 (2018), 278–290.
[42]
Liu Y., Du J.L. and Wang Y.H., An improved grey group decision-making approach, Applied Soft Computing 76 (2019), 78–88.
[43]
Dai Y., Zhang N. and Ma Z.J., MAGDM method based on interval grey linguistic correlated ordered geometric operators, Journal of Intelligent & Fuzzy Systems 35(3) (2018), 3115–3123.
[44]
Wang F., Du D.Y. and Zhao J., A Novel SIR·Choquet Method for Multiple Attributes Group Decision-Making with Interval Grey Linguistic, International Journal of Fuzzy Systems 21(6) (2019), 1771–1785.
[45]
Li C. and Yuan J., A new multi-attribute decision-making method with three-parameter interval grey linguistic variable, International Journal of Fuzzy Systems 19(2) (2017), 292–300.
[46]
Liu S. and Forrest J.Y.L., Grey systems: theory and applications (Springer Science & Business Media, 2010).
[47]
Yang Y. and John R., Grey sets and greyness, Information Sciences 185(1) (2012), 249–264.
[48]
Dohmen T.J., Falk A., Huffman D., Sunde U., Schupp J. and Wagner G.G., Individual risk attitudes: New evidence from a large, representative, experimentally-validated survey, Experimentally-Validated Survey, Social Science Electronic Publishing (2005) IZA Discussion Paper No. 1730. Available at SSRN: https://ssrn.com/abstract=807408.
[49]
Harrison G.W., Lau M.I. and Rutström E.E., Estimating risk attitudes in Denmark: A field experiment, Scandinavian Journal of Economics 109(2) (2007), 341–368.
[50]
Gong Z., Zhang N. and Chiclana F., The optimization ordering model for intuitionistic fuzzy preference relations with utility functions, Knowledge-Based Systems 162 (2018), 174–184.
[51]
Jung E.J. and Kim J.H., Optimal investment strategies for the HARA utility under the constant elasticity of variance model, Insurance: Mathematics and Economics 51(3) (2012), 667–673.
[52]
Muravev D. and Mijic N., A Novel Integrated Provider Selection Multicriteria Model: The BWM-MABAC Model, Decision Making: Applications in Management and Engineering (1) (2020), 60–78.
[53]
Pinto L., Kaynak E., Chow C.S. and Zhang L.L., Ranking of choice cues for smartphones using the Best–Worst scaling method, Asia Pacific Journal of Marketing and Logistics 31(1) (2019), 223–245.
[54]
Huang H.C., Overview of antenna designs and considerations in 5G cellular phones, In 2018 International Workshop on Antenna Technology (iWAT) (2018 March) pp. 1–4.
[55]
Moslehpour M. and Le Huyen N.T., The influence of perceived brand quality and perceived brand prestige on purchase likelihood of iPhone and HTC mobile phone in Taiwan, Research in Business and Management 1(1) (2014), 62–77.
[56]
Behzadian M., Otaghsara S.K, Yazdani M. and Ignatius J., A state-of the-art survey of TOPSIS applications, Expert Systems with Applications 39(17) (2012), 13051–13069.
[57]
Velasquez M. and Hester P.T., An analysis of multi-criteria decision making methods, International Journal of Operations Research 10(2) (2013), 56–66.
[58]
Liu C., Wu W.Z., Xie W. and Zhang J., Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China, Chaos, Solitons & Fractals 141 (2020), 110429.
[59]
Yin K., Wang P. and Li X., The multi-attribute group decision-making method based on interval grey trapezoid fuzzy linguistic variables, International Journal of Environmental Research and Public Health 14(12) (2017), 1561.
[60]
Liu P., Chu Y. and Li Y., The multi-attribute group decision-making method based on the interval grey uncertain linguistic generalized hybrid averaging operator, Neural Computing and Applications 26(6) (2015), 1395–1405.
[61]
Roy J., Pamučar D. and Kar S., Evaluation and selection of third-party logistics provider under sustainability perspectives: an interval valued fuzzy-rough approach, Annals of Operations Research (2019), 1–46.
[62]
Zhu G.N., Hu J. and Ren H., A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments, Applied Soft Computing 91 (2020), 106228.
[63]
Fazlollahtabar H., Smailbašić A. and Stević Ž., FUCOM method in group decision-making: Selection of forklift in a warehouse, Decision Making: Applications in Management and Engineering 2(1) (2019), 49–65.
[64]
Žižović M. and Pamucar D., New model for determining criteria weights: Level Based Weight Assessment (LBWA) model, Decision Making: Applications in Management and Engineering 2(2) (2019), 126–137.
Index Terms
- A BWM-based approach for customer-oriented product development with insufficient information and its application to 5 G smartphone design
Index terms have been assigned to the content through auto-classification.
Recommendations
A new hesitant fuzzy QFD approach
Display Omitted A new hesitant fuzzy MCDM method based on linguistic term sets is proposed for QFD.The proposed method is relatively more efficient than the existing QFD approaches.It can aggregate the linguistic assessments of more than one decision ...
A paradigm for customer-driven product design approach using extended axiomatic design
AbstractIn the present times, due to increase in customer demands, products complexity is on the rise. This calls for the designers to strike a balance between a wide range of design alternatives and a large set of conflicting criteria. Hence, to take a ...
Customer-oriented product design using an integrated neutrosophic AHP & DEMATEL & QFD methodology
AbstractWith the increasing product variety, companies aim to become better than their competitors by providing a superior product developed with a customer-oriented product design approach and a quality strategy. In order to achieve this, ...
Highlights- A customer-oriented product design approach is developed and applied for car seats.
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© 2021 – IOS Press. All rights reserved.
Publisher
IOS Press
Netherlands
Publication History
Published: 01 January 2021
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 29 Jan 2025