Resilient and sustainable supplier selection: an integration of SCOR 4.0 and machine learning approach

References

• Abdulla, A., Baryannis, G., & Badi, I. (2019). Weighting the key features affecting supplier selection using machine learning techniques. International Conference on Transport and Logistics. University of Niš. December, 1–6. https://doi.org/10.20944/preprints201912.0154.v1
   Google Scholar
• Alavi, B., Tavana, M., & Mina, H. (2021). A dynamic decision support system for sustainable supplier selection in circular economy. Sustainable Production and Consumption, 27(February), 905–920. https://doi.org/10.1016/j.spc.2021.02.015
   Google Scholar
• Allgurin, A., & Filip, K. (2018). Exploring machine learning for supplier selection-A case study at bufab Sweden AB. 1–81.
   Google Scholar
• Amindoust, A. (2018). A resilient-sustainable based supplier selection model using a hybrid intelligent method. Computers & Industrial Engineering, 126(September), 122–135. https://doi.org/10.1016/j.cie.2018.09.031
   Google Scholar
• Amiri, M., Hashemi-Tabatabaei, M., Ghahremanloo, M., Keshavarz-Ghorabaee, M., Zavadskas, E. K., & Banaitis, A. (2020). A new fuzzy BWM approach for evaluating and selecting a sustainable supplier in supply chain management. International Journal of Sustainable Development and World Ecology, 00(00), 1–18. https://doi.org/10.1080/13504509.2020.1793424
   Google Scholar
• Ayyildiz, E., & Taskin Gumus, A. (2021). Interval-valued pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0. Complex & Intelligent Systems, 7(1), 559–576. https://doi.org/10.1007/s40747-020-00221-9
   Web of Science ®Google Scholar
• Badi, I., & Ballem, M. (2018). Supplier selection using rough bwm-mairca model : A case study in pharmaceutical supplying in libya. Decision Making: Applications in Management and Engineering, 1(2), 1–18. https://doi.org/10.31181/dmame1802016b
   Google Scholar
• Batta, M. (2020). Machine learning algorithms - a review. International Journal of Science and Research (IJ international Journal of Science and Research (IJ, 9(1), 381. https://doi.org/10.21275/ART20203995
   Google Scholar
• Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. In Artificial Intelligence Review (Vol. 54, Issue 3). Springer Netherlands. https://doi.org/10.1007/s10462-020-09896-5
   Google Scholar
• Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
   Web of Science ®Google Scholar
• Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020). A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. IEEE Access, 8, 90225–90265. https://doi.org/10.1109/ACCESS.2020.2992341
   Web of Science ®Google Scholar
• Craighead, C. W., Ketchen, D. J., & Darby, J. L. (2020). Pandemics and supply chain management research: Toward a theoretical toolbox*. Decision Sciences, 51(4), 838–866. https://doi.org/10.1111/DECI.12468
   PubMed Web of Science ®Google Scholar
• Delipinar, G. E., & Kocaoglu, B. (2016). Using SCOR model to gain competitive advantage: A literature review. Procedia - Social and Behavioral Sciences, 229, 398–406. https://doi.org/10.1016/j.sbspro.2016.07.150
   Google Scholar
• Didier, M., Broccardo, M., Esposito, S., & Stojadinovic, B. (2018). A compositional demand/supply framework to quantify the resilience of civil infrastructure systems (Re-CoDes). Sustainable and Resilient Infrastructure, 3(2), 86–102. https://doi.org/10.1080/23789689.2017.1364560
   Web of Science ®Google Scholar
• Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. https://doi.org/10.48550/arXiv.1810.11363
   Google Scholar
• Es-Satty, A., Lemghari, R., & Okar, C. (2020). Supply chain digitalization overview SCOR model implication. 2020 13th International Colloquium of Logistics and Supply Chain Management, LOGISTIQUA 2020, 2–4. https://doi.org/10.1109/LOGISTIQUA49782.2020.9353936
   Google Scholar
• Ghadimi, P., Wang, C., Lim, M. K., & Heavey, C. (2019). Intelligent sustainable supplier selection using multi-agent technology: Theory and application for industry 4.0 supply chains. Computers & Industrial Engineering, 127(September 2018), 588–600. https://doi.org/10.1016/j.cie.2018.10.050
   Google Scholar
• Ghamari, R., Mahdavi-Mazdeh, M., & Ghannadpour, S. F. (2021). Resilient and sustainable supplier selection via a new framework: A case study from the steel industry. In Environment, development and sustainability (Vol. 24, Issue 8). Springer Netherlands. https://doi.org/10.1007/s10668-021-01872-5
   Google Scholar
• He, J., & Zhu, J. (2022). Evaluation on risk factors of elderly services from the perspective of integrated SCOR model. International Journal of Information Systems in the Service Sector, 14(1), 1–18. https://doi.org/10.4018/IJISSS.2022010102
   Web of Science ®Google Scholar
• Hossain, N. U. I., Nur, F., & Jaradat, R. M. (2016). An analytical study of hazards and risks in the shipbuilding industry. In Proceedings of the international annual conference of the American society for engineering management (pp. 1–8). American Society for Engineering Management.
   Google Scholar
• Huang, T., Chen, Z., Wang, S., & Jiang, D. (2021). Efficiency evaluation of key ports along the 21st-century maritime silk road based on the DEA–SCOR model. Maritime Policy & Management, 48(3), 378–390. https://doi.org/10.1080/03088839.2020.1773558
   Web of Science ®Google Scholar
• Hwang, Y. D., Lin, Y. C., & Lyu, J. (2008). The performance evaluation of SCOR sourcing process—the case study of Taiwan’s TFT-LCD industry. International Journal of Production Economics, 115(2), 411–423. https://doi.org/10.1016/J.IJPE.2007.09.014
   Web of Science ®Google Scholar
• Ishizaka, A., Khan, S. A., Kheybari, S., & Zaman, S. I. (2022). Supplier selection in closed loop pharma supply chain: A novel BWM–GAIA framework. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04710-7
   Google Scholar
• Islam, S., Amin, S. H., & Wardley, L. J. (2021). Machine learning and optimization models for supplier selection and order allocation planning. International Journal of Production Economics, 242, 108315. https://doi.org/10.1016/J.IJPE.2021.108315
   Web of Science ®Google Scholar
• Jain, V., Kumar, S., Mostofi, A., & Arab Momeni, M. (2022). Sustainability performance evaluation of the E-waste closed-loop supply chain with the SCOR model. Waste Management, 147, 36–47. https://doi.org/10.1016/J.WASMAN.2022.05.010
   PubMed Web of Science ®Google Scholar
• Judith, H., & Daniel, K. (2018). Machine learning for dummies. Journal of the American Society for Information Science, 35(5). https://doi.org/10.1002/asi.4630350509
   Google Scholar
• Lima-Junior, F. R., & Carpinetti, L. C. R. (2016). Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management. International Journal of Production Economics, 174, 128–141. https://doi.org/10.1016/j.ijpe.2016.01.023
   Web of Science ®Google Scholar
• Lu, Q., Goh, M., & De Souza, R. (2016). A SCOR framework to measure logistics performance of humanitarian organizations. Journal of Humanitarian Logistics and Supply Chain Management, 6(2), 222–239. https://doi.org/10.1108/JHLSCM-09-2015-0038
   Web of Science ®Google Scholar
• Luitzen, D. B., Eva, L., & Pierangela, M. (2001). A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management, 80(3), 66–73. https://doi.org/10.1002/ad.1077
   Google Scholar
• Mañay, L. O. R., Guaita-Pradas, I., & Marques-Perez, I. (2022). Measuring the supply chain performance of the floricultural sector using the SCOR model and a multicriteria decision-making method. Horticulturae, 8(2), 168. https://doi.org/10.3390/horticulturae8020168
   Web of Science ®Google Scholar
• Mirkouei, A., & Haapala, K. R. (2014). Integration of machine learning and mathematical programming methods into the biomass feedstock supplier selection process. FAIM 2014 - Proceedings of the 24th International Conference on Flexible Automation and Intelligent Manufacturing: Capturing Competitive Advantage via Advanced Manufacturing and Enterprise Transformation, January, 443–450. https://doi.org/10.14809/faim.2014.0443
   Google Scholar
• Munson, A. D., & Santa-Eulalia, L. A. D. (2014). A Systematic literature review of the supply chain operations reference (SCOR) model application with special attention to envrionnmental issues a systematic literature review of the supply chain operations reference (SCOR) model application with spec. January. https://doi.org/10.13140/RG.2.1.3618.7606
   Google Scholar
• Mustafa, S. K., Engin, E., Belkız, T., & Turan, P. (2020). Supplier selection with machine learning algorithms. Logistics 4 0, (Issue December 2020). https://doi.org/10.1201/9780429327636-3
   Google Scholar
• Nazim, R., & Yaacob, R. A. I. R. (2017). Criteria for supplier selection: An application of AHP-SCOR integrated model (ASIM). International Journal of Supply Chain Management, 6(3), 284–290.
   Google Scholar
• Oroojeni Mohammad Javad, M., Darvishi, M., & Oroojeni Mohammad Javad, A. (2020). Green supplier selection for the steel industry using BWM and fuzzy TOPSIS: A case study of Khouzestan steel company. Sustainable Futures, 2(October 2019), 100012. https://doi.org/10.1016/j.sftr.2020.100012
   Google Scholar
• Pamučar, D., Ecer, F., Cirovic, G., & Arlasheedi, M. A. (2020). Application of improved best worst method (BWM) in real-world problems. Mathematics, 8(8), 1342. https://doi.org/10.3390/MATH8081342
   Web of Science ®Google Scholar
• Park, J., Shin, K., Chang, T. W., & Park, J. (2010). An integrative framework for supplier relationship management. Industrial Management & Data Systems, 110(4), 495–515. https://doi.org/10.1108/02635571011038990
   Web of Science ®Google Scholar
• Pilkington, S. F., & Mahmoud, H. N. (2016). Using artificial neural networks to forecast economic impact of multi-hazard hurricane-based events. Sustainable and Resilient Infrastructure, 1(1–2), 63–83. https://doi.org/10.1080/23789689.2016.1179529
   Web of Science ®Google Scholar
• Raut, R. D., Bhasin, H. V., & Kamble, S. S. (2012). Supplier selection using integrated multi-criteria decision-making methodology. International Journal of Operational Research, 13(4), 359–394. https://doi.org/10.1504/IJOR.2012.046223
   Google Scholar
• Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/J.OMEGA.2014.11.009
   Web of Science ®Google Scholar
• Roshan, V., Stewart, J. H. M., Joseph, R., & Stewart, H. M. (2022). Optimal ratio for data splitting. Stat Anal Data Min: The ASA Data Sci Journal, 15(4), 15. https://doi.org/10.1002/sam.11583
   Web of Science ®Google Scholar
• Sepehri, S. (2020). Supplier selection and relationship management : An application of machine learning techniques. Ontario, Canada: Brock University. https://dr.library.brocku.ca/handle/10464/14831
   Google Scholar
• Spieske, A., Gebhardt, M., Kopyto, M., & Birkel, H. (2022). Improving resilience of the healthcare supply chain in a pandemic: Evidence from Europe during the COVID-19 crisis. Journal of Purchasing and Supply Management, 28(5), 100748. https://doi.org/10.1016/J.PURSUP.2022.100748
   Web of Science ®Google Scholar
• Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231
   Web of Science ®Google Scholar
• Tavana, M., Fallahpour, A., DiCaprio, D., & Santos-Arteaga, F. J. (2016). A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Systems with Applications, 61, 129–144. https://doi.org/10.1016/J.ESWA.2016.05.027
   Web of Science ®Google Scholar
• Wendler-Bosco, V., & Nicholson, C. (2020). Port disruption impact on the maritime supply chain: A literature review. Sustainable and Resilient Infrastructure, 5(6), 378–394. https://doi.org/10.1080/23789689.2019.1600961
   Web of Science ®Google Scholar
• Wilson, V. H., Prasad, A. N. S., Shankharan, A., Kapoor, S., & Rajan, J. A. (2020). Ranking of supplier performance using machine learning algorithm of random forest. International Journal of Advanced Research in Engineering and Technology, 11(5), 298–308. https://doi.org/10.34218/IJARET.11.5.2020.031
   Google Scholar
• Wu, C., & Barnes, D. (2018). Design of agile supply chains including the trade-off between number of partners and reliability. International Journal of Advanced Manufacturing Technology, 97(9–12), 3683–3700. https://doi.org/10.1007/S00170-018-2205-5
   Web of Science ®Google Scholar
• Zangoueinezhad, A., Azar, A., & Kazazi, A. (2011). Using SCOR model with fuzzy MCDM approach to assess competitiveness positioning of supply chains: Focus on shipbuilding supply chains. Maritime Policy & Management, 38(1), 93–109. https://doi.org/10.1080/03088839.2010.533715
   Web of Science ®Google Scholar
• Zeydan, M., Çolpan, C., & Çobanoģlu, C. (2011). A combined methodology for supplier selection and performance evaluation. Expert Systems with Applications, 38(3), 2741–2751. https://doi.org/10.1016/j.eswa.2010.08.064
   Web of Science ®Google Scholar
• Zhang, X. -D. (2017). Chapter 6 machine learning. A Matrix Algebra Approach to Artificial Intelligence, 45(13), 223–235.
   Google Scholar
• Zhang, R., Li, J., Wu, S., Meng, D., & Deng, Y. (2016). Learning to select supplier portfolios for service supply chain. Plos One, 11(5), e0155672. https://doi.org/10.1371/journal.pone.0155672
   PubMed Web of Science ®Google Scholar
• Zhaoa, P., Jia, S., & Xuea, Y. (2021, October). Resilient-sustainable supplier selection and order allocation under multi-tier supplier network. Researchgate.Net, https://www.researchgate.net/profile/Shoufeng-Ji/publication/355788632_Resilient-sustainable_supplier_selection_and_order_allocation_under_multi-tier_supplier_network/links/617e09c4eef53e51e10c4b17/Resilient-sustainable-supplier-selection-and-order-alloca
   Google Scholar
• Zhao, L., Qi, W., Zhu, M., & Xie, L. (2021). A study of supplier selection method based on SVM for weighting expert evaluation. Discrete Dynamics in Nature and Society, 2021, 1–11. https://doi.org/10.1155/2021/8056209
   Google Scholar