Prioritizing the Factors for Customer-Oriented New Product Design in Industry 4.0

Year 2020, Volume: 24 Issue: 4, 751 - 769, 01.08.2020

Melike Erdoğan Özge Nalan Bilişik

https://doi.org/10.16984/saufenbilder.681926

Abstract

Customer-oriented new product design is one of the most important processes in the production environment to improve product quality and reliability and maximize their productivity. It is also necessary to consider customer expectations in this process for an effective design. In this paper, we present a methodology which is called Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) for prioritizing criteria which should be considered for an efficient customer-oriented new product design in Industry 4.0 transition primarily. We use Pythagorean Fuzzy Sets (PFSs) to allow experts to make more flexible evaluations and handle the uncertain and vague information in a wider way. We determine five main and eighteen sub-criteria that affect the new product design process and after applying PF-AHP, we find that the most important main-criterion determined as “Production” and sub-criterion determined as “Return on Investment”.

Keywords

Industry 4.0, Multi-Criteria Decising Making, Product Design, PFSs

References

• [1] J. Stark, “Product Lifecycle Management,” 2015, pp. 1–29.
• [2] X. L. Liu, W. M. Wang, H. Guo, A. V. Barenji, Z. Li, and G. Q. Huang, “Industrial blockchain based framework for product lifecycle management in industry 4.0,” Robot. Comput. Integr. Manuf., vol. 63, Jun. 2020, doi: 10.1016/j.rcim.2019.101897.
• [3] V. Alcácer and V. Cruz-Machado, “Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems,” Engineering Science and Technology, an International Journal, vol. 22, no. 3. Elsevier B.V., pp. 899–919, 01-Jun-2019, doi: 10.1016/j.jestch.2019.01.006.
• [4] G. Büyüközkan and O. Feyziog̃lu, “A fuzzy-logic-based decision-making approach for new product development,” Int. J. Prod. Econ., vol. 90, no. 1, pp. 27–45, Jul. 2004, doi: 10.1016/S0925-5273(02)00330-4.
• [5] X. T. Nguyen, V. D. Nguyen, V. H. Nguyen, and H. Garg, “Exponential similarity measures for Pythagorean fuzzy sets and their applications to pattern recognition and decision-making process,” Complex Intell. Syst., vol. 5, no. 2, pp. 217–228, Jun. 2019, doi: 10.1007/s40747-019-0105-4.
• [6] E. Kıyak and A. Kahvecioğlu, “Bulanık mantık ve uçuş kontrol problemine uygulanması,” Havacılık ve Uzay Teknol. Derg., vol. 1, no. 2, pp. 63–72, 2003.
• [7] M. Yucesan and M. Gul, “Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS,” Soft Comput., vol. 24, no. 5, pp. 3237–3255, Mar. 2020, doi: 10.1007/s00500-019-04084-2.
• [8] A. Karasan, E. Ilbahar, and C. Kahraman, “A novel pythagorean fuzzy AHP and its application to landfill site selection problem,” Soft Comput., vol. 23, no. 21, pp. 10953–10968, Nov. 2019, doi: 10.1007/s00500-018-3649-0.
• [9] A. Karasan, E. Ilbahar, S. Cebi, and C. Kahraman, “A new risk assessment approach: Safety and Critical Effect Analysis (SCEA) and its extension with Pythagorean fuzzy sets,” Saf. Sci., vol. 108, pp. 173–187, Oct. 2018, doi: 10.1016/j.ssci.2018.04.031.
• [10] E. Ilbahar, A. Karaşan, S. Cebi, and C. Kahraman, “A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system,” Saf. Sci., vol. 103, pp. 124–136, Mar. 2018, doi: 10.1016/j.ssci.2017.10.025.
• [11] F. Ülengin, “Ulaşım Problemlerinde Analitik Hiyerarşi Yaklaşımı: İstanbul İçin Bir Uygulama,” in TMMOB İstanbul 2. Kent İçi Ulaşım Kongresi, 1992, pp. 103–121.
• [12] M. M. Jabri, “Personnel selection using INSIGHT - C: An application based on the analytic hierarchy process,” J. Bus. Psychol., vol. 5, no. 2, pp. 281–285, Dec. 1990, doi: 10.1007/BF01014338.
• [13] D. Mourtzis, V. Zogopoulos, and E. Vlachou, “Augmented Reality supported Product Design towards Industry 4.0: A Teaching Factory paradigm,” in Procedia Manufacturing, 2018, vol. 23, pp. 207–212, doi: 10.1016/j.promfg.2018.04.018.
• [14] R. Wagner, B. Schleich, B. Haefner, A. Kuhnle, S. Wartzack, and G. Lanza, “Challenges and Potentials of Digital Twins and Industry 4.0 in Product Design and Production for High Performance Products,” Procedia CIRP, vol. 84, pp. 88–93, 2019, doi: 10.1016/j.procir.2019.04.219.
• [15] D. A. Zakoldaev, A. V. Shukalov, I. O. Zharinov, and O. O. Zharinov, “Computer-aided design of technical documentation on the digital product models of Industry 4.0,” in IOP Conference Series: Materials Science and Engineering, 2019, vol. 483, no. 1, doi: 10.1088/1757-899X/483/1/012069.
• [16] S. A. Carolina, E. R. da Silva, E. P. de Lima, F. Deschamps, and S. E. G. da Costa, “Critical success factors for digital manufacturing implementation in the context of industry 4.0,” Conf. Proc., pp. 199–204, 2017.
• [17] J. Ang, C. Goh, A. Saldivar, and Y. Li, “Energy-Efficient Through-Life Smart Design, Manufacturing and Operation of Ships in an Industry 4.0 Environment,” Energies, vol. 10, no. 5, p. 610, Apr. 2017, doi: 10.3390/en10050610.
• [18] A. Albers, T. Stürmlinger, C. Mandel, J. Wang, M. B. de Frutos, and M. Behrendt, “Identification of potentials in the context of Design for Industry 4.0 and modelling of interdependencies between product and production processes,” Procedia CIRP, vol. 84, pp. 100–105, 2019, doi: 10.1016/j.procir.2019.04.298.
• [19] M. B. Ahmed, C. Sanin, and E. Szczerbicki, “Smart virtual product development (SVPD) to enhance product manufacturing in industry 4.0,” in Procedia Computer Science, 2019, vol. 159, pp. 2232–2239, doi: 10.1016/j.procs.2019.09.398.
• [20] M. Bilal Ahmed, S. Imran Shafiq, C. Sanin, and E. Szczerbicki, “Towards Experience-Based Smart Product Design for Industry 4.0,” Cybern. Syst., vol. 50, no. 2, pp. 165–175, Feb. 2019, doi: 10.1080/01969722.2019.1565123.
• [21] K. Y. Lin, “User experience-based product design for smart production to empower industry 4.0 in the glass recycling circular economy,” Comput. Ind. Eng., vol. 125, pp. 729–738, Nov. 2018, doi: 10.1016/j.cie.2018.06.023.
• [22] A. Asmae, B. A. Hussain, S. Souhail, and Z. El Moukhtar, “A fuzzy ontology-based support for multi-criteria decision-making in collaborative product development,” in 2017 Intelligent Systems and Computer Vision, ISCV 2017, 2017, doi: 10.1109/ISACV.2017.8054953.
• [23] C. Favi, M. Germani, and M. Mandolini, “Development of complex products and production strategies using a multi-objective conceptual design approach,” Int. J. Adv. Manuf. Technol., vol. 95, no. 1–4, pp. 1281–1291, Mar. 2018, doi: 10.1007/s00170-017-1321-y.
• [24] N. Fatchurrohman, S. Sulaiman, S. M. Sapuan, M. K. A. Ariffin, and B. T. H. T. Baharuddin, “A new concurrent engineering - multi criteria decision making technique for conceptual design selection,” in Applied Mechanics and Materials, 2012, vol. 225, pp. 293–298, doi: 10.4028/www.scientific.net/AMM.225.293.
• [25] P. Kumar and P. Tandon, “A paradigm for customer-driven product design approach using extended axiomatic design,” J. Intell. Manuf., vol. 30, no. 2, pp. 589–603, Feb. 2019, doi: 10.1007/s10845-016-1266-2.
• [26] C. Favi, M. Germani, and M. Mandolini, “A Multi-objective Design Approach to Include Material, Manufacturing and Assembly Costs in the Early Design Phase,” in Procedia CIRP, 2016, vol. 52, pp. 251–256, doi: 10.1016/j.procir.2016.07.043.
• [27] A. D. Joshi and S. M. Gupta, “Evaluation of design alternatives of End-Of-Life products using internet of things,” Int. J. Prod. Econ., vol. 208, pp. 281–293, Feb. 2019, doi: 10.1016/j.ijpe.2018.12.010.
• [28] X. Zeng, Y. Zhu, L. Koehl, M. Camargo, C. Fonteix, and F. Delmotte, “A fuzzy multi-criteria evaluation method for designing fashion oriented industrial products,” Soft Comput., vol. 14, no. 12, pp. 1277–1285, 2010, doi: 10.1007/s00500-009-0496-z.
• [29] B. Song, Q. Peng, J. Zhang, and P. Gu, “A fuzzy number based hierarchy analytic method and application in improvement of rehabilitation devices,” Comput. Aided. Des. Appl., vol. 16, no. 2, pp. 369–381, 2019, doi: 10.14733/cadaps.2019.369-381.
• [30] X. Wu and H. Liao, “An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making,” Inf. Fusion, vol. 43, pp. 13–26, Sep. 2018, doi: 10.1016/j.inffus.2017.11.008.
• [31] C. H. Wang, “An integrated fuzzy multi-criteria decision making approach for realizing the practice of quality function deployment,” in IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management, 2010, pp. 13–17, doi: 10.1109/IEEM.2010.5674597.
• [32] T. Buchert, S. Neugebauer, S. Schenker, K. Lindow, and R. Stark, “Multi-criteria decision making as a tool for sustainable product development - Benefits and obstacles,” in Procedia CIRP, 2015, vol. 26, pp. 70–75, doi: 10.1016/j.procir.2014.07.110.
• [33] G. Lian, X. Bai, Y. Shen, D. Liu, and C. Zhu, “Multi-criteria decision making for mechanic product design schemes based on layered ordinal relation analysis,” in Applied Mechanics and Materials, 2012, vol. 110–116, pp. 3990–3996, doi: 10.4028/www.scientific.net/AMM.110-116.3990.
• [34] H. T. Liu, “Product design and selection using fuzzy QFD and fuzzy MCDM approaches,” Appl. Math. Model., vol. 35, no. 1, pp. 482–496, Jan. 2011, doi: 10.1016/j.apm.2010.07.014.
• [35] Y. T. İç and S. Yıldırım, “Çok Kriterli Karar Verme Yöntemleriyle Birlikte Taguchi Yöntemini Kullanarak Bir Ürünün Tasarımının Geliştirilmesi,” Gazi Üniv. Müh. Mim. Fak. Der., vol. 2, no. 2, pp. 447–458, 2012.
• [36] F. Guini, A. El Barkany, A. Jabri, and E. H. Irhirane, “An approach for the evaluation of a product’s process planning during the design phase through a group multi-criteria decision-making,” Int. J. Eng. Res. Africa, vol. 38, pp. 154–162, 2018, doi: 10.4028/www.scientific.net/JERA.38.154.
• [37] R. A. Khan, A. Anand, and M. F. Wani, “A holistic framework for environment conscious based product risk modeling and assessment using multi criteria decision making,” J. Clean. Prod., vol. 174, pp. 954–965, Jan. 2018, doi: 10.1016/j.jclepro.2017.11.005.
• [38] C. H. Wang and J. N. Chen, “Using quality function deployment for collaborative product design and optimal selection of module mix,” Comput. Ind. Eng., vol. 63, no. 4, pp. 1030–1037, Dec. 2012, doi: 10.1016/j.cie.2012.06.014.
• [39] G. Buyukozkan and F. Gocer, “A Novel Approach Integrating AHP and COPRAS Under Pythagorean Fuzzy Sets for Digital Supply Chain Partner Selection,” IEEE Trans. Eng. Manag., 2019, doi: 10.1109/TEM.2019.2907673.
• [40] A. Yildiz, E. Ayyildiz, A. T. Gumus, and C. Ozkan, “A Modified Balanced Scorecard Based Hybrid Pythagorean Fuzzy AHP-Topsis Methodology for ATM Site Selection Problem,” Int. J. Inf. Technol. Decis. Mak., 2020, doi: 10.1142/S0219622020500017.
• [41] A. Kaya, B. Çiçekalan, and F. Çebi, “Location selection for WEEE recycling plant by using Pythagorean fuzzy AHP,” J. Intell. Fuzzy Syst., vol. 38, no. 1, pp. 1097–1106, 2020, doi: 10.3233/JIFS-179471.
• [42] I. Otay and M. Jaller, “Multi-criteria and multi-expert wind power farm location selection using a pythagorean fuzzy analytic hierarchy process,” in Advances in Intelligent Systems and Computing, 2020, vol. 1029, pp. 905–914, doi: 10.1007/978-3-030-23756-1_108.
• [43] A. Karasan, İ. Kaya, M. Erdoğan, and A. Budak, Risk analysis of the autonomous vehicle driving systems by using pythagorean fuzzy AHP, vol. 1029. Springer International Publishing, 2020.
• [44] M. Gul, “Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and colouring unit,” Int. J. Occup. Saf. Ergon., 2018, doi: 10.1080/10803548.2018.1492251.
• [45] M. Yucesan and G. Kahraman, “Risk evaluation and prevention in hydropower plant operations: A model based on Pythagorean fuzzy AHP,” Energy Policy, vol. 126, pp. 343–351, Mar. 2019, doi: 10.1016/j.enpol.2018.11.039.
• [46] R. R. Yager, “Pythagorean fuzzy subsets,” in Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 2013, pp. 57–61, doi: 10.1109/IFSA-NAFIPS.2013.6608375.
• [47] K. T. Atanassov, “Intuitionistic Fuzzy Sets,” 1999, pp. 1–137.
• [48] S. Zeng, J. Chen, and X. Li, “A hybrid method for pythagorean fuzzy multiple-criteria decision making,” Int. J. Inf. Technol. Decis. Mak., vol. 15, no. 2, pp. 403–422, Mar. 2016, doi: 10.1142/S0219622016500012.
• [49] X. Zhang and Z. Xu, “Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets,” Int. J. Intell. Syst., vol. 29, no. 12, pp. 1061–1078, Dec. 2014, doi: 10.1002/int.21676.
• [50] M. Gul and M. F. Ak, “A comparative outline for quantifying risk ratings in occupational health and safety risk assessment,” J. Clean. Prod., vol. 196, pp. 653–664, Sep. 2018, doi: 10.1016/j.jclepro.2018.06.106.
• [51] B. Özkan, İ. Kaya, M. Erdoğan, and A. Karaşan, Evaluating blockchain risks by using a MCDM methodology based on pythagorean fuzzy sets, vol. 1029. Springer International Publishing, 2020.
• [52] S. Gupta, G. S. Dangayach, and A. K. Singh, “Key determinants of sustainable product design and manufacturing,” in Procedia CIRP, 2015, vol. 26, pp. 99–102, doi: 10.1016/j.procir.2014.07.166.
• [53] J. K. Choi, L. F. Nies, and K. Ramani, “A framework for the integration of environmental and business aspects toward sustainable product development,” J. Eng. Des., vol. 19, no. 5, pp. 431–446, Oct. 2008, doi: 10.1080/09544820701749116.
• [54] C. Chandrakumar, A. K. Kulatunga, and S. Mathavan, “A multi-criteria decision-making model to evaluate sustainable product designs based on the principles of design for sustainability and fuzzy analytic hierarchy process,” in Smart Innovation, Systems and Technologies, 2017, vol. 68, pp. 347–354, doi: 10.1007/978-3-319-57078-5_34.
• [55] A. Karasan, M. Erdogan, and E. Ilbahar, “Prioritization of production strategies of a manufacturing plant by using an integrated intuitionistic fuzzy AHP & TOPSIS approach,” J. Enterp. Inf. Manag., vol. 31, no. 4, pp. 510–528, Jul. 2018, doi: 10.1108/JEIM-01-2018-0001.
• [56] S. Boral, I. Howard, S. K. Chaturvedi, K. McKee, and V. N. A. Naikan, “An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA,” Eng. Fail. Anal., vol. 108, p. 104195, Jan. 2020, doi: 10.1016/j.engfailanal.2019.104195.
• [57] B. Das and S. C. Pal, “Combination of GIS and fuzzy-AHP for delineating groundwater recharge potential zones in the critical Goghat-II block of West Bengal, India,” HydroResearch, vol. 2, pp. 21–30, Dec. 2019, doi: 10.1016/j.hydres.2019.10.001.
• [58] S. Kaganski, J. Majak, and K. Karjust, “Fuzzy AHP as a tool for prioritization of key performance indicators,” in Procedia CIRP, 2018, vol. 72, pp. 1227–1232, doi: 10.1016/j.procir.2018.03.097.
• [59] P. Dehraj and A. Sharma, “An empirical assessment of autonomicity for autonomic query optimizers using fuzzy-AHP technique,” Appl. Soft Comput. J., vol. 90, p. 106137, May 2020, doi: 10.1016/j.asoc.2020.106137.
• [60] C. Acar, A. Beskese, and G. T. Temur, “Sustainability analysis of different hydrogen production options using hesitant fuzzy AHP,” Int. J. Hydrogen Energy, vol. 43, no. 39, pp. 18059–18076, Sep. 2018, doi: 10.1016/j.ijhydene.2018.08.024.
• [61] A. Khoshi, H. Shams Gooshki, and N. Mahmoudi, “The data on the effective qualifications of teachers in medical sciences: An application of combined fuzzy AHP and fuzzy TOPSIS methods,” Data Br., vol. 21, pp. 2689–2693, Dec. 2018, doi: 10.1016/j.dib.2018.10.165.
• [62] W. K. K. Hsu, S. H. S. Huang, and W. J. Tseng, “Evaluating the risk of operational safety for dangerous goods in airfreights – A revised risk matrix based on fuzzy AHP,” Transp. Res. Part D Transp. Environ., vol. 48, pp. 235–247, Oct. 2016, doi: 10.1016/j.trd.2016.08.018.
• [63] N. F. Pan, “Fuzzy AHP approach for selecting the suitable bridge construction method,” Autom. Constr., vol. 17, no. 8, pp. 958–965, Nov. 2008, doi: 10.1016/j.autcon.2008.03.005.
• [64] C. Kahraman, A. Süder, and İ. Kaya, “Fuzzy Multicriteria Evaluation of Health Research Investments,” Technol. Econ. Dev. Econ., vol. 20, no. 2, pp. 210–226, Feb. 2014, doi: 10.3846/20294913.2013.876560.
• [65] T. Y. Hsieh, S. T. Lu, and G. H. Tzeng, “Fuzzy MCDM approach for planning and design tenders selection in public office buildings,” Int. J. Proj. Manag., vol. 22, no. 7, pp. 573–584, Oct. 2004, doi: 10.1016/j.ijproman.2004.01.002.
• [66] T. S. Liou and M. J. J. Wang, “Ranking fuzzy numbers with integral value,” Fuzzy Sets Syst., vol. 50, no. 3, pp. 247–255, Sep. 1992, doi: 10.1016/0165-0114(92)90223-Q.