Yıl 2020,
, 751 - 769, 01.08.2020
Melike Erdoğan
,
Özge Nalan Bilişik
Kaynakça
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Prioritizing the Factors for Customer-Oriented New Product Design in Industry 4.0
Yıl 2020,
, 751 - 769, 01.08.2020
Melike Erdoğan
,
Özge Nalan Bilişik
Öz
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”.
Kaynakça
- [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.
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- [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.
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