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(industrial engineering, faculty of engineering, Universitas Negeri Yogyakarta, Indonesia)(2) Chairani Fauzi
(Master of Technology Management, Institute of Informatics and Business Darmajaya, Bandar Lampung, Indonesia)(3) Sri Lestari
(Master of Technology Management, Institute of Informatics and Business Darmajaya, Bandar Lampung, Indonesia)*corresponding author
AbstractSupplier segmentation is a strategic activity for businesses. It involves dividing suppliers into distinct categories and managing them differently. Various supplier typologies based on different dimensions and factors are available in the existing literature. By highlighting two main characteristics the skills and the desire of suppliers to work with a specific company this article integrates many typologies. Almost all of the supplier segmentation criteria stated in the literature are covered by these dimensions. These dimensions can be defined utilizing a multi-criteria decision-making process for each specific case. To account for the inherent ambiguities and uncertainties in human judgment, a fuzzy Analytic Hierarchy Process (AHP) is suggested as part of the technique. This approach makes use of fuzzy preference relations. A broiler firm uses the suggested process to divide up its suppliers. A categorization of vendors according to two aggregated criteria is the end outcome. Lastly, we offer some suggestions for future research, draw some conclusions, and talk about some techniques to address distinct sectors
KeywordsSupplier segmentation Supply chain management Fuzzy AHP Fuzzy preference relations
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DOIhttps://doi.org/10.29099/ijair.v7i1.1.1103 |
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References
A. Saha, D. Pamucar, O. F. Gorcun, and A. R. Mishra, “Warehouse site selection for the automotive industry using a fermatean fuzzy-based decision-making approach,” Expert Syst. Appl., vol. 211, Jan. 2023, doi: 10.1016/j.eswa.2022.118497.
Y. Yang, X. Wang, and Z. Xu, “The multiplicative consistency threshold of intuitionistic fuzzy preference relation,” Inf. Sci., vol. 477, pp. 349–368, Mar. 2019, doi: 10.1016/j.ins.2018.10.044.
J. Aguarón and J. M. Moreno-Jiménez, “The geometric consistency index: Approximated thresholds,” Eur. J. Oper. Res., vol. 147, no. 1, pp. 137–145, May 2003, doi: 10.1016/S0377-2217(02)00255-2.
M. Serda et al., “Synteza i aktywno?? biologiczna nowych analogów tiosemikarbazonowych chelatorów ?elaza,” Uniw. ?l?ski, vol. 7, no. 1, pp. 343–354, 2013, doi: 10.2/JQUERY.MIN.JS.
Y. Xu, X. Shang, J. Wang, W. Wu, and H. Huang, “Some q-rung dual hesitant fuzzy Heronian mean operators with their application to multiple attribute group decision-making,” Symmetry, vol. 10, no. 10, 2018, doi: 10.3390/SYM10100472.
E. Herrera-Viedma, F. Herrera, F. Chiclana, and M. Luque, “Some issues on consistency of fuzzy preference relations,” Eur. J. Oper. Res., vol. 154, no. 1, pp. 98–109, Apr. 2004, doi: 10.1016/S0377-2217(02)00725-7.
M. Zhou, Z. Wang, and Y. Shen, “Simultaneous fault estimation and fault-tolerant tracking control for uncertain nonlinear discrete-time systems,” Int. J. Syst. Sci., vol. 48, no. 7, pp. 1367–1379, May 2017, doi: 10.1080/00207721.2016.1258596.
H. Wu, I. Yuji, and H. Ban, “Reverse Radial Bias: Temporal Orientation Bias Compensation in Early Visual Areas Revealed by MEG,” J. Vis., vol. 18, no. 10, p. 716, Sep. 2018, doi: 10.1167/18.10.716.
R. M. Rodríguez, Á. Labella, M. Sesma-Sara, H. Bustince, and L. Martínez, “A cohesion-driven consensus reaching process for large scale group decision making under a hesitant fuzzy linguistic term sets environment,” Comput. Ind. Eng., vol. 155, May 2021, doi: 10.1016/j.cie.2021.107158.
Y. Xu, F. J. Cabrerizo, and E. Herrera-Viedma, “A consensus model for hesitant fuzzy preference relations and its application in water allocation management,” Appl. Soft Comput. J., vol. 58, pp. 265–284, Sep. 2017, doi: 10.1016/j.asoc.2017.04.068.
Y. Zhang, Z. Xu, and H. Liao, “A consensus process for group decision making with probabilistic linguistic preference relations,” Inf. Sci., vol. 414, pp. 260–275, Nov. 2017, doi: 10.1016/j.ins.2017.06.006.
Á. Labella, H. Liu, R. M. Rodríguez, and L. Martínez, “A Cost Consensus Metric for Consensus Reaching Processes based on a comprehensive minimum cost model,” Eur. J. Oper. Res., vol. 281, no. 2, pp. 316–331, Mar. 2020, doi: 10.1016/j.ejor.2019.08.030.
P. K. Biswas and S. Liu, “A hybrid recommender system for recommending smartphones to prospective customers,” Expert Syst. Appl., vol. 208, Dec. 2022, doi: 10.1016/j.eswa.2022.118058.
Z. Zhang and C. Wu, “A decision support model for group decision making with hesitant multiplicative preference relations,” Inf. Sci., vol. 282, pp. 136–166, Oct. 2014, doi: 10.1016/j.ins.2014.05.057.
G. Crawford and C. Williams, “A note on the analysis of subjective judgment matrices,” J. Math. Psychol., vol. 29, no. 4, pp. 387–405, 1985, doi: 10.1016/0022-2496(85)90002-1.
J. J. Buckley, “Fuzzy hierarchical analysis,” Fuzzy Sets Syst., vol. 17, no. 3, pp. 233–247, 1985, doi: 10.1016/0165-0114(85)90090-9.
P. Grošelj and G. Dolinar, “Group AHP framework based on geometric standard deviation and interval group pairwise comparisons,” Inf. Sci., vol. 626, pp. 370–389, May 2023, doi: 10.1016/j.ins.2023.01.034.
Z. Zhang and W. Pedrycz, “Goal Programming Approaches to Managing Consistency and Consensus for Intuitionistic Multiplicative Preference Relations in Group Decision Making,” IEEE Trans. Fuzzy Syst., vol. 26, no. 6, pp. 3261–3275, 2018, doi: 10.1109/TFUZZ.2018.2818074.
P. F. Lee, W. S. Lam, W. H. Lam, and W. K. Muck, “Multi-Criteria Decision Analysis on the Preference of Courier Service roviders with Analytic Hierarchy Process Model,” J. Adv. Res. Appl. Sci. Eng. Technol., vol. 35, no. 2, pp. 94–103, May 2024, doi: 10.37934/ARASET.35.2.94103.
Z. Xu, “Intuitionistic preference relations and their application in group decision making,” Inf. Sci., vol. 177, no. 11, pp. 2363–2379, Jun. 2007, doi: 10.1016/j.ins.2006.12.019.
H. N. Ting, Y. M. Choo, and A. A. Kamar, “Classification of asphyxia infant cry using hybrid speech features and deep learning models,” Expert Syst. Appl., vol. 208, Dec. 2022, doi: 10.1016/j.eswa.2022.118064.
E. Ilbahar, A. Kara?an, 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.
B. G. Gebregiorgis, G. M. Takele, K. D. Ayenew, and Y. E. Amare, “Prevalence of hospital-acquired infections (HAIs) and associated factors in Ethiopia: A systematic review and meta-analysis protocol,” BMJ Open, vol. 10, no. 12, Dec. 2020, doi: 10.1136/BMJOPEN-2020-042111.
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