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Journal of Intelligent Manufacturing

Erschienen in:

29.09.2021

Integrated approaches of BWM-QFD and FUCOM-QFD for improving weighting solution of design matrix

verfasst von: Hamid Reza Fazeli, Qingjin Peng

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 3/2023

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Abstract

Quality Function Deployment (QFD) is commonly used to translate customer requirements into design specifications in the product conceptual design phase. House of Quality (HOQ) is an essential part of QFD to map different requirements of product solutions using a design relationship matrix. In traditional approaches, the way of deciding weights in the relationship matrix can lead to a distortion in ranking of technical measures. Although some methods like Analytic Hierarchy Process have been proposed to improve this problem, they require additional data collection and process. This paper introduces two effective approaches based on Best–Worst Method and Full Consistency Method (FUCOM) to decide weights of technical measures in the relationship matrix of HOQ. The novel integrated approach of FUCOM-QFD is developed for rapid weight allocation in the relationship matrix. The proposed BWM-QFD and FUCOM-QFD approaches use consistency ratio and total deviation measures in reliable decision-making. They not only reduce data collection, but also improve inconsistency problems. The vector-based pairwise comparison and consistency measure are two important features of the proposed approaches. A case study demonstrates improvement in the efficiency and accuracy of weighing solutions for technical measures compared to the existing methods.

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Abdel-Basset, M., Manogaran, G., Mohamed, M., & Chilamkurti, N. (2018). Three-way decisions based on neutrosophic sets and AHP-QFD framework for supplier selection problem. Future Generation Computer Systems, 89, 19–30. https://doi.org/10.1016/j.future.2018.06.024CrossRef

Aggogeri, F., Mikolajczyk, T., & O’Kane, J. (2019). Robotics for rehabilitation of hand movement in stroke survivors. Advances in Mechanical Engineering, 11(4), 1–14. https://doi.org/10.1177/1687814019841921CrossRef

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2015). An Introduction to Management Science: Quantitative Approaches to Decision Making: Cengage Learning.

Avikal, S., Singh, R., & Rashmi, R. (2020). QFD and Fuzzy Kano model based approach for classification of aesthetic attributes of SUV car profile. Journal of Intelligent Manufacturing, 31(2), 271–284. https://doi.org/10.1007/s10845-018-1444-5CrossRef

Ayağ, Z. (2016). An integrated approach to concept evaluation in a new product development. Journal of Intelligent Manufacturing, 27(5), 991–1005. https://doi.org/10.1007/s10845-014-0930-7CrossRef

Bagassi, S., De Crescenzio, F., & Piastra, S. (2020). Augmented reality technology selection based on integrated QFD-AHP model. International Journal on Interactive Design and Manufacturing (IJIDeM), 14(1), 285–294. https://doi.org/10.1007/s12008-019-00583-6CrossRef

Boser, Q. A., Dawson, M. R., Schofield, J. S., Dziwenko, G., & Hebert, J. S. (2018). Defining the design requirements for an assistive powered hand exoskeleton. bioRxiv, doi:https://doi.org/10.1101/492124.

Brokaw, E. B., Black, I., Holley, R. J., & Lum, P. S. (2011). Hand Spring Operated Movement Enhancer (HandSOME): A Portable, Passive Hand Exoskeleton for Stroke Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(4), 391–399. https://doi.org/10.1109/TNSRE.2011.2157705CrossRef

Chan, F. T. S., & Chan, H. K. (2010). An AHP model for selection of suppliers in the fast changing fashion market. The International Journal of Advanced Manufacturing Technology, 51(9), 1195–1207. https://doi.org/10.1007/s00170-010-2683-6CrossRef

Chaudha, A., Jain, R., Singh, A. R., & Mishra, P. K. (2011). Integration of Kano’s Model into quality function deployment (QFD). The International Journal of Advanced Manufacturing Technology, 53(5), 689–698. https://doi.org/10.1007/s00170-010-2867-0CrossRef

Chaudhuri, A., & Bhattacharyya, M. (2009). A combined QFD and integer programming framework to determine attribute levels for conjoint study. International Journal of Production Research, 47(23), 6633–6649. https://doi.org/10.1080/00207540802350757CrossRef

Chen, D.-k., Wang, Y.-q., Jin, N., & Liu, D.-h. The Application of QFD and Information Entropy in Improved Design for the Manned Capsule. In E. Qi (Ed.), In Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation, Paris, 2016// 2016 (pp. 371–379): Atlantis Press

Chen, L.-H., & Chen, C.-N. (2014a). Normalisation models for prioritising design requirements for quality function deployment processes. International Journal of Production Research, 52(2), 299–313. https://doi.org/10.1080/00207543.2013.812813CrossRef

Chen, L.-H., & Chen, C.-N. (2014b). A QFD-Based Mathematical Model for New Product Development Considering the Target Market Segment. Journal of Applied Mathematics, 2014, 594150. https://doi.org/10.1155/2014/594150CrossRef

Dace, E., Stibe, A., & Timma, L. (2020). A holistic approach to manage environmental quality by using the Kano model and social cognitive theory. Corporate Social Responsibility and Environmental Management, 27(2), 430–443. https://doi.org/10.1002/csr.1828CrossRef

Dai, J., & Blackhurst, J. (2012). A four-phase AHP–QFD approach for supplier assessment: A sustainability perspective. International Journal of Production Research, 50(19), 5474–5490. https://doi.org/10.1080/00207543.2011.639396CrossRef

Ding, Z., Zhu, M., Wu, Z., Fu, Y., & Liu, X. (2018). Combining AHP-entropy approach with gis for construction waste landfill selection—A case study of Shenzhen. International Journal of Environmental Research and Public Health, 15(10), 2254.CrossRef

Feizi, F., Karbalaei-Ramezanali, A., & Tusi, H. (2017). Mineral Potential Mapping Via TOPSIS with Hybrid AHP–Shannon Entropy Weighting of Evidence: A Case Study for Porphyry-Cu, Farmahin Area, Markazi Province. Iran. Natural Resources Research, 26(4), 553–570. https://doi.org/10.1007/s11053-017-9338-3CrossRef

Forman, E. H., & Selly, M. A. (2001). Decision by objectives: how to convince others that you are right. World Scientific.CrossRef

Franceschini, F. (2016). Advanced Quality Function Deployment. CRC Press.

Fu, Y., Li, M., Luo, H., & Huang, G. Q. (2019). Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making. Robotics and Autonomous Systems, 122, 103304. https://doi.org/10.1016/j.robot.2019.103304CrossRef

Gupta, A. K., & Modi, B. A. (2018). Selection of sustainable technology for VOC abatement in an industry: An Integrated AHP–QFD approach. Journal of the Institution of Engineers India Series A, 99(3), 565–578. https://doi.org/10.1007/s40030-018-0294-7CrossRef

Haber, N., Fargnoli, M., & Sakao, T. (2020). Integrating QFD for product-service systems with the Kano model and fuzzy AHP. Total Quality Management & Business Excellence, 31(9–10), 929–954. https://doi.org/10.1080/14783363.2018.1470897CrossRef

Hauser, J. R. (1993). How Puritan-Bennett used the house of quality. Sloan Management Review, 34(3), 61–70.

Heo, P., Gu, G. M., Lee, S.-J., Rhee, K., & Kim, J. (2012). Current hand exoskeleton technologies for rehabilitation and assistive engineering. International Journal of Precision Engineering and Manufacturing, 13(5), 807–824. https://doi.org/10.1007/s12541-012-0107-2CrossRef

Herman, M. W., & Koczkodaj, W. W. (1996). A Monte Carlo study of pairwise comparison. Information Processing Letters, 57(1), 25–29. https://doi.org/10.1016/0020-0190(95)00185-9CrossRef

Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625CrossRef

Ho, W. (2008). Integrated analytic hierarchy process and its applications—A literature review. European Journal of Operational Research, 186(1), 211–228. https://doi.org/10.1016/j.ejor.2007.01.004CrossRef

Hsu, T.-H., & Lin, L.-Z. (2006). QFD with fuzzy and entropy weight for evaluating retail customer values. Total Quality Management & Business Excellence, 17(7), 935–958. https://doi.org/10.1080/14783360600598223CrossRef

Huang, J., You, X.-Y., Liu, H.-C., & Si, S.-L. (2019). New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory. International Journal of Production Research, 57(5), 1283–1299. https://doi.org/10.1080/00207543.2018.1470343CrossRef

Iqbal, J., Khan, H., Tsagarakis, N. G., & Caldwell, D. G. (2014a). A novel exoskeleton robotic system for hand rehabilitation – Conceptualization to prototyping. Biocybernetics and Biomedical Engineering, 34(2), 79–89. https://doi.org/10.1016/j.bbe.2014.01.003CrossRef

Iqbal, Z., Nigel, P. G., Govinderaju, K., & Campbell-Allen, N. (2014b). Statistical comparison of final weight scores in quality function deployment (QFD) studies. International Journal of Quality & Reliability Management, 31(2), 184–204. https://doi.org/10.1108/IJQRM-06-2013-0092CrossRef

Jenab, K., Pourmohammadi, H., & Sarfaraz, M. (2014). An i-AHP&QFD warranty model. Benchmarking an International Journal, 21(6), 884–902. https://doi.org/10.1108/BIJ-01-2013-0017CrossRef

Karapetrovic, S., & Rosenbloom, E. S. (1999). A quality control approach to consistency paradoxes in AHP. European Journal of Operational Research, 119(3), 704–718. https://doi.org/10.1016/S0377-2217(98)00334-8CrossRef

Kim, S., & Yoon, B. (2012). Developing a process of concept generation for new product-service systems: A QFD and TRIZ-based approach. Service Business, 6(3), 323–348. https://doi.org/10.1007/s11628-012-0138-xCrossRef

Kowalska, M., Pazdzior, M., & Krzton-Maziopa, A. (2018). Implementation of QFD method in quality analysis of confectionery products. Journal of Intelligent Manufacturing, 29(2), 439–447. https://doi.org/10.1007/s10845-015-1120-yCrossRef

Kutschenreiter-Praszkiewicz, I. (2013). Application of neural network in QFD matrix. Journal of Intelligent Manufacturing, 24(2), 397–404. https://doi.org/10.1007/s10845-011-0604-7CrossRef

Li, Y.-L., Chin, K.-S., & Luo, X.-G. (2012). Determining the final priority ratings of customer requirements in product planning by MDBM and BSC. Expert Systems with Applications, 39(1), 1243–1255. https://doi.org/10.1016/j.eswa.2011.07.133CrossRef

Li, Y.-L., Du, Y.-F., & Chin, K.-S. (2018). Determining the importance ratings of customer requirements in quality function deployment based on interval linguistic information. International Journal of Production Research, 56(14), 4692–4708. https://doi.org/10.1080/00207543.2017.1417650CrossRef

Li, Y.-L., Tang, J.-F., Chin, K.-S., Jiang, Y.-S., Han, Y., & Pu, Y. (2011). Estimating the final priority ratings of engineering characteristics in mature-period product improvement by MDBA and AHP. International Journal of Production Economics, 131(2), 575–586. https://doi.org/10.1016/j.ijpe.2011.02.003CrossRef

Liu, A., Xiao, Y., Ji, X., Wang, K., Tsai, S.-B., Lu, H., et al. (2018). A novel two-stage integrated model for supplier selection of green fresh product. Sustainability, 10(7), 2371.CrossRef

Ma, H., Chu, X., Xue, D., & Chen, D. (2019). 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), 623–639. https://doi.org/10.1007/s10845-016-1269-zCrossRef

Madzík, P. (2018). Increasing accuracy of the Kano model—a case study. Total Quality Management & Business Excellence, 29(3–4), 387–409. https://doi.org/10.1080/14783363.2016.1194197CrossRef

Mei, Y., Liang, Y., & Tu, Y. (2018). A Multi-granularity 2-tuple QFD method and application to emergency routes Evaluation. Symmetry, 10(10), 484.CrossRef

Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). 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, 205–225. https://doi.org/10.1016/j.omega.2019.01.009CrossRef

Mistarihi, M. Z., Okour, R. A., & Mumani, A. A. (2020). An integration of a QFD model with Fuzzy-ANP approach for determining the importance weights for engineering characteristics of the proposed wheelchair design. Applied Soft Computing, 90, 106136. https://doi.org/10.1016/j.asoc.2020.106136CrossRef

Moslem, S., Gul, M., Farooq, D., Celik, E., Ghorbanzadeh, O., & Blaschke, T. (2020). An integrated approach of best-worst method (BWM) and triangular fuzzy sets for evaluating driver behavior factors related to road safety. Mathematics, 8(3), 414.CrossRef

Mou, N., Wang, C., Yang, T., & Zhang, L. (2020). Evaluation of development potential of ports in the yangtze river delta using FAHP-entropy model. Sustainability, 12(2), 493. https://doi.org/10.3390/su12020493CrossRef

Olewnik, A., & Lewis, K. (2008). Limitations of the House of quality to provide quantitative design information. International Journal of Quality & Reliability Management, 25(2), 125–146. https://doi.org/10.1108/02656710810846916CrossRef

Oña, E. D., Cano-de la Cuerda, R., Sánchez-Herrera, P., Balaguer, C., & Jardón, A. (2018). A review of robotics in neurorehabilitation: towards an automated process for upper limb. Journal of Healthcare Engineering, 2018, 9758939. https://doi.org/10.1155/2018/9758939CrossRef

Pal, D. K., Ravi, B., & Bhargava, L. S. (2007). Rapid tooling route selection for metal casting using QFD–ANP methodology. International Journal of Computer Integrated Manufacturing, 20(4), 338–354. https://doi.org/10.1080/09511920600883229CrossRef

Pamučar, D., Stević, Ž, & Sremac, S. (2018). A new model for determining weight coefficients of criteria in mcdm models: full consistency method (FUCOM). Symmetry, 10(9), 393.CrossRef

Raharjo, H. (2013). On normalizing the relationship matrix in quality function deployment. International Journal of Quality & Reliability Management, 30(6), 647–661. https://doi.org/10.1108/02656711311325601CrossRef

Raharjo, H., Brombacher, A. C., & Xie, M. (2008). Dealing with subjectivity in early product design phase: A systematic approach to exploit Quality Function Deployment potentials. Computers & Industrial Engineering, 55(1), 253–278. https://doi.org/10.1016/j.cie.2007.12.012CrossRef

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009CrossRef

Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001CrossRef

Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158–169. https://doi.org/10.1016/j.tranpol.2018.05.007CrossRef

Shahin, A., & Akasheh, S. (2017). Classifying customer requirements using Kano model and Kano map: The case of hospital services. International Journal of Productivity and Quality Management, 21(4), 500–515. https://doi.org/10.1504/ijpqm.2017.085257CrossRef

Shahin, A., & Ebrahimi, S. (2020). Revising the interrelationship matrix of house of quality by the Kano model. The TQM Journal. https://doi.org/10.1108/TQM-11-2019-0267CrossRef

Shannon, C. E. (2001). A mathematical theory of communication. SIGMOBILE Mob. Comput. Commun. Rev., 5(1), 3–55. https://doi.org/10.1145/584091.584093CrossRef

Shvetsova, O. A., Park, S. C., & Lee, J. H. (2021). Application of quality function deployment for product design concept selection. Applied Sciences, 11(6), 2681.CrossRef

Sivasamy, K., Arumugam, C., Devadasan, S. R., Murugesh, R., & Thilak, V. M. M. (2016). Advanced models of quality function deployment: A literature review. Quality & Quantity, 50(3), 1399–1414. https://doi.org/10.1007/s11135-015-0212-2CrossRef

Suarez-Escobar, M., Gallego-Sanchez, J. A., & Rendon-Velez, E. (2017). Mechanisms for linkage-driven underactuated hand exoskeletons: Conceptual design including anatomical and mechanical specifications. International Journal on Interactive Design and Manufacturing (IJIDeM), 11(1), 55–75. https://doi.org/10.1007/s12008-015-0297-9CrossRef

Tian, Z.-P., Wang, J.-Q., Wang, J., & Zhang, H.-Y. (2018). A multi-phase QFD-based hybrid fuzzy MCDM approach for performance evaluation: A case of smart bike-sharing programs in Changsha. Journal of Cleaner Production, 171, 1068–1083. https://doi.org/10.1016/j.jclepro.2017.10.098CrossRef

Ucler, C. (2017). Brainstorming the cryoplane layout by using the iterative AHP-QFD-AHP approach. Aviation, 21(2), 55–63. https://doi.org/10.3846/16487788.2017.1344138CrossRef

van de Poel, I. (2007). Methodological problems in QFD and directions for future development. Research in Engineering Design, 18(1), 21–36. https://doi.org/10.1007/s00163-007-0029-7CrossRef

Van der Loos, H. F. M., Mahoney, R., & Ammi, C. (2004). 27 Great expectations for rehabilitation mechatronics in the coming Decade. In Z. Z. Bien & D. Stefanov (Eds.), Advances in rehabilitation robotics: human-friendly technologies on movement assistance and restoration for people with disabilities (pp. 427–433). Heidelberg: Springer.CrossRef

Venkata Subbaiah, K., Yeshwanth Sai, K., & Suresh, C. (2016). QFD–ANP approach for the conceptual design of research vessels: A case study. Journal of the Institution of Engineers (india): Series C, 97(4), 539–546. https://doi.org/10.1007/s40032-016-0321-2CrossRef

Wasserman, G. S. (1993). ON how to prioritize design requirements during the QFD planning process. IIE Transactions, 25(3), 59–65. https://doi.org/10.1080/07408179308964291CrossRef

Wu, H.-Y., & Lin, H.-Y. (2012). A hybrid approach to develop an analytical model for enhancing the service quality of e-learning. Computers & Education, 58(4), 1318–1338. https://doi.org/10.1016/j.compedu.2011.12.025CrossRef

Yazdani, M., Kahraman, C., Zarate, P., & Onar, S. C. (2019). A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis. Expert Systems with Applications, 115, 474–485. https://doi.org/10.1016/j.eswa.2018.08.017CrossRef

Zare Mehrjerdi, Y. (2010). Quality function deployment and its extensions. International Journal of Quality & Reliability Management, 27(6), 616–640. https://doi.org/10.1108/02656711011054524CrossRef

Zhang, F., Yang, M., & Liu, W. (2014). Using integrated quality function deployment and theory of innovation problem solving approach for ergonomic product design. Computers & Industrial Engineering, 76, 60–74. https://doi.org/10.1016/j.cie.2014.07.019CrossRef

Zhang, H., Yin, C., Qi, X., Zhang, R., & Kang, X. (2017). Cognitive best worst method for multiattribute decision-making. Mathematical Problems in Engineering, 2017, 1092925. https://doi.org/10.1155/2017/1092925CrossRef

Zhao, H., Yao, L., Mei, G., Liu, T., & Ning, Y. (2017). A fuzzy comprehensive evaluation method based on AHP and entropy for a landslide susceptibility map. Entropy, 19(8), 396.CrossRef

Titel: Integrated approaches of BWM-QFD and FUCOM-QFD for improving weighting solution of design matrix
verfasst von: Hamid Reza Fazeli
Qingjin Peng
Publikationsdatum: 29.09.2021
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 3/2023
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-021-01832-w

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