Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection
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Abstract
Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.
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References
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[26] T. E. Teddy, Muhammad Luthfi Akbar, Nola Dita Puspa, and Mesran, “Penerapan Metode MOORA dan Pembobotan ROC Dalam Pemilihan Alat KB,” Journal of Computing and Informatics Research, vol. 2, no. 2, pp. 37–43, Mar. 2023, doi: 10.47065/comforch.v2i2.524.
[27] W. I. Safitri, M. Mesran, and S. Sarwandi, “Penerapan Metode Preference Selection Index (PSI) Dalam Penerimaan Staff IT,” Bulletin of Informatics and Data Science, vol. 1, no. 1, p. 1, May 2022, doi: 10.61944/bids.v1i1.1.
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[2] A. Puška, D. Božanić, M. Nedeljković, and M. Janošević, “Green supplier selection in an uncertain environment in agriculture using a hybrid MCDM model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS model,” Axioms, vol. 11, no. 9, p. 427, 2022, doi: 10.3390/axioms11090427.
[3] I. Đalić, Ž. Stević, C. Karamasa, and A. Puška, “A novel integrated fuzzy PIPRECIA–interval rough SAW model: Green supplier selection,” Decision Making: Applications in Management and Engineering, vol. 3, no. 1, pp. 126–145, 2020.
[4] S. M. Vadivel, D. S. Shetty, A. H. Sequeira, E. Nagaraj, and V. Sakthivel, “A Sustainable Green Supplier Selection Using CRITIC Method,” in International Conference on Intelligent Systems Design and Applications, Springer, 2023, pp. 308–315. doi: 10.1007/978-3-031-27440-4_29.
[5] Sumanto et al., “Improved LOPCOW-SAW Method for Optimal Supplier Selection in Supply Chain Management,” in 2024 12th International Conference on Cyber and IT Service Management (CITSM), 2024, pp. 1–5. doi: 10.1109/CITSM64103.2024.10775429.
[6] A. F. O. Pasaribu, “Decision Support System for Best Supplier Selection Using Simple Additive Weighting and Rank Sum Weighting,” CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics, vol. 1, no. 3, pp. 106–112, 2023.
[7] A. Aytekin, “DETERMINING CRITERIA WEIGHTS FOR VEHICLE TRACKING SYSTEM SELECTION USING PIPRECIA-S,” Journal of process management and new technologies, vol. 10, no. 1–2, pp. 115–124, Jun. 2022, doi: 10.5937/jpmnt10-38145.
[8] I. M. Jiskani, Q. Cai, W. Zhou, X. Lu, and S. A. A. Shah, “An integrated fuzzy decision support system for analyzing challenges and pathways to promote green and climate smart mining,” Expert Syst Appl, vol. 188, p. 116062, 2022, doi: https://doi.org/10.1016/j.eswa.2021.116062.
[9] Z. Guo et al., “An integrated MCDM model with enhanced decision support in transport safety using machine learning optimization,” Knowl Based Syst, vol. 301, p. 112286, 2024, doi: https://doi.org/10.1016/j.knosys.2024.112286.
[10] H. Ayadi, N. Hamani, L. Kermad, and M. Benaissa, “Novel Fuzzy Composite Indicators for Locating a Logistics Platform under Sustainability Perspectives,” Sustainability, vol. 13, no. 7, p. 3891, Apr. 2021, doi: 10.3390/su13073891.
[11] H. Nirinarivelo and R. G. Rasoanaivo, “Multi-criteria Evaluation of Madagascar’s Regions in the Context of Employment Using the CoCoFISo Method,” Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 135–156, Jan. 2025, doi: 10.31181/sdmap21202514.
[12] M. S. Saidin, L. S. Lee, S. M. Marjugi, M. Z. Ahmad, and H.-V. Seow, “Fuzzy Method Based on the Removal Effects of Criteria (MEREC) for Determining Objective Weights in Multi-Criteria Decision-Making Problems,” Mathematics, vol. 11, no. 6, p. 1544, Mar. 2023, doi: 10.3390/math11061544.
[13] D. Alamsyah, A. Herdiansah, H. Wijaya, and H. Rusdianto, “Combination of Rank Reciprocal Method and Composite Performance Index for Promotion Decision Support System,” J-INTECH, vol. 12, no. 1, pp. 23–35, Jun. 2024, doi: 10.32664/j-intech.v12i1.1199.
[14] I. Ramadhan, N. Nugroho, H. Kurniawanto, and J. Warta, “Sistem Pendukung Keputusan Menggunakan Metode WASPAS Untuk Pemilihan Aplikasi Manajemen Bisnis dan Keuangan,” J-INTECH, vol. 12, no. 1, pp. 49–61, Jun. 2024, doi: 10.32664/j-intech.v12i1.1214.
[15] D. A. Megawaty, D. Damayanti, S. Sumanto, P. Permata, D. Setiawan, and S. Setiawansyah, “Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection,” JOIV: International Journal on Informatics Visualization, vol. 9, no. 1, 2025, doi: 10.62527/joiv.9.1.2744.
[16] Z. Çolak, “A hybrid MCDM method for enhancing site selection for wind power plants in Turkey,” Energy for Sustainable Development, vol. 82, p. 101536, 2024, doi: https://doi.org/10.1016/j.esd.2024.101536.
[17] S. Wu and R. Niu, “Development of carbon finance in China based on the hybrid MCDM method,” Humanit Soc Sci Commun, vol. 11, no. 1, p. 156, 2024, doi: 10.1057/s41599-023-02558-1.
[18] M. A. Al-Gerafi et al., “Designing of an effective e-learning website using inter-valued fuzzy hybrid MCDM concept: A pedagogical approach,” Alexandria Engineering Journal, vol. 97, pp. 61–87, 2024, doi: https://doi.org/10.1016/j.aej.2024.04.012.
[19] I. Taufik, C. N. Alam, Z. Mustofa, A. Rusdiana, and W. Uriawan, “Implementation of Multi-Attribute Utility Theory (MAUT) method for selecting diplomats,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2021, p. 32055.
[20] Aditia Yudhistira, Setiawansyah, Temi Ardiansah, Sufiatul Maryana, Yuli Yadin, and Risma Oktaviani, “Development of Multi-Attribute Utility Theory Methods in Dynamic Decision Models Using Change-Data Driven,” Evergreen, vol. 11, no. 4, pp. 3279–3289, Dec. 2024, doi: 10.5109/7326962.
[21] U. Akpan and R. Morimoto, “An application of Multi-Attribute Utility Theory (MAUT) to the prioritization of rural roads to improve rural accessibility in Nigeria,” Socioecon Plann Sci, vol. 82, p. 101256, 2022.
[22] M. H. Sadeghiravesh, H. Khosravi, and A. Abolhasani, “Selecting proper sites for underground dam construction using Multi-Attribute Utility Theory in arid and semi-arid regions,” J Mt Sci, vol. 20, no. 1, pp. 197–208, 2023.
[23] A. A. Kusuma, Z. M. Arini, U. Hasanah, M. Mesran, and M. Kom, “Analisa Penerapan Metode Multi Attribute Utility Theory (MAUT) dengan Pembobotan Rank Order Centroid (ROC) Dalam Pemilihan Lokasi Strategis Coffeshop Milenial di Era New Normal,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 3, no. 2, pp. 51–59, 2021.
[24] F. A. AlFaraidy, K. S. Teegala, and G. Dwivedi, “Selection of a Sustainable Structural Floor System for an Office Building Using the Analytic Hierarchy Process and the Multi-Attribute Utility Theory,” Sustainability, vol. 15, no. 17, p. 13087, 2023.
[25] A. Karim, S. Esabella, K. Kusmanto, M. Mesran, and U. Hasanah, “Analisa Penerapan Metode Operational Competitiveness Rating Analysis (OCRA) dan Metode Multi Attribute Utility Theory (MAUT) Dalam Pemilihan Calon Karyawan Tetap Menerapkan Pembobotan Rank Order Centroid (ROC),” Jurnal Media Informatika Budidarma, vol. 5, no. 4, pp. 1674–1687, 2021.
[26] T. E. Teddy, Muhammad Luthfi Akbar, Nola Dita Puspa, and Mesran, “Penerapan Metode MOORA dan Pembobotan ROC Dalam Pemilihan Alat KB,” Journal of Computing and Informatics Research, vol. 2, no. 2, pp. 37–43, Mar. 2023, doi: 10.47065/comforch.v2i2.524.
[27] W. I. Safitri, M. Mesran, and S. Sarwandi, “Penerapan Metode Preference Selection Index (PSI) Dalam Penerimaan Staff IT,” Bulletin of Informatics and Data Science, vol. 1, no. 1, p. 1, May 2022, doi: 10.61944/bids.v1i1.1.
[28] R. I. Borman, D. A. Megawaty, and A. Attohiroh, “Implementasi Metode TOPSIS Pada Sistem Pendukung Keputusan Pemilihan Biji Kopi Robusta Yang Bernilai Mutu Ekspor (Studi Kasus: PT. Indo Cafco Fajar Bulan Lampung),” Fountain of Informatics Journal, vol. 5, no. 1, pp. 14–20, 2020.