Development of a Resilient and Agile Model for Evaluating Medical Equipment Suppliers Based on Machine Learning Algorithms
Aryana Amiri
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Keywords: Medical equipment, Supplier evaluation, Resilience, Agility, Machine learning algorithms,
Abstract :
Medical equipment suppliers play an indispensable role in sustaining healthcare supply chains because of their direct influence on patient health and safety. This study introduces a data-driven and intelligent framework for evaluating supplier performance with an emphasis on resilience and agility. To construct the framework, a set of critical indicators—namely quality consistency, response time, delivery speed, total cost, and regulatory compliance—was identified as the foundation for evaluation. These indicators were analyzed through the application of multiple machine learning algorithms to ensure accuracy, robustness, and interpretability. Comparative analysis showed that the CatBoost algorithm delivered the most reliable outcomes, achieving approximately 92% accuracy while maintaining balanced performance across key metrics such as F1 score, Precision, and Recall. Other methods, including Support Vector Machines (SVM) and Decision Trees, demonstrated moderate results but did not match CatBoost’s superior predictive capability. A sensitivity analysis was further conducted to uncover the most influential determinants of supplier performance. Findings revealed that quality consistency, responsiveness, and timely delivery were the strongest drivers, while cost and compliance played secondary yet important roles. These insights underscore that supplier evaluation cannot rely solely on traditional cost-based metrics but must integrate dynamic performance factors that directly affect resilience and agility. The study highlights the managerial implications of adopting data-driven evaluation models. Beyond improving accuracy in supplier assessment, such frameworks enable healthcare managers to allocate resources more effectively, select resilient partners, and mitigate supply risks. Consequently, the proposed model contributes to enhancing both efficiency and sustainability across healthcare supply chains.
Abbasian, M., & Jamili, A. (2025). A Hybrid Machine Learning Approach to Evaluate and Select Agile-Resilient-Sustainable Suppliers Considering Supply Chain 4.0: A Real Case Study. Process Integration and Optimization for Sustainability, 1–19.
Alamroshan, F., La’li, M., & Yahyaei, M. (2022). The green-agile supplier selection problem for the medical devices: a hybrid fuzzy decision-making approach. Environmental Science and Pollution Research, 29(5), 6793–6811. https://doi.org/10.1007/s11356-021-14690-z
Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing Journal, 12(6), 1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023
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(February), 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
Davoudabadi, R., Mousavi, S. M., Mohagheghi, V., & Vahdani, B. (2019). Resilient supplier selection through introducing a new interval-valued intuitionistic fuzzy evaluation and decision-making framework. Arabian Journal for Science and Engineering, 44(8), 7351–7360.
Dong, Q., & Yuan, Y. (2025). Data‐driven distributionally robust supplier selection and order allocation problems considering carbon emissions. International Transactions in Operational Research, 32(2), 1119–1145.
Fallahpour, A., Nayeri, S., Sheikhalishahi, M., Wong, K. Y., Tian, G., & Fathollahi-Fard, A. M. (2021). A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian Palm oil industry. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-12491-y
GhanavatiNejad, M., Tavakoli, M., Sheikhalishahi, M., Aydın, N., & Aria, S. S. (2025). An integrated smart framework for fast-moving consumer goods online market logistics: a digital twin framework. Journal of Industrial and Production Engineering, 1–17.
Harikrishnan, R., Shivdas, A., & Ananthu, S. (2025). Redefining supplier selection in healthcare sector: a novel framework for supplier classification. Journal of Health Organization and Management.
Javan-Molaei, B., Tavakkoli-Moghaddam, R., Ghanavati-Nejad, M., & Asghari-Asl, A. (2024). A data-driven robust decision-making model for configuring a resilient and responsive relief supply chain under mixed uncertainty. Annals of Operations Research, 1–38.
Lo, H.-W. (2023). A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing. Advanced Engineering Informatics, 56, 101998.
Modares, A., Emroozi, V. B., Roozkhosh, P., & Modares, A. (2025). A Bayesian best-worst approach with blockchain integration for optimizing supply chain efficiency through supplier selection. Supply Chain Analytics, 9, 100100.
Molaei, B. J., Ghanavati-Nejad, M., Tajally, A., & Sheikhalishahi, M. (2025). A novel stochastic machine learning approach for resilient-leagile supplier selection: a circular supply chain in the era of industry 4.0. Soft Computing, 1–22.
Nayeri, S., Khoei, M. A., Rouhani-Tazangi, M. R., GhanavatiNejad, M., Rahmani, M., & Tirkolaee, E. B. (2023). A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system. Engineering Applications of Artificial Intelligence, 124, 106511.
Nessari, S., Ghanavati-Nejad, M., Jolai, F., Bozorgi-Amiri, A., & Rajabizadeh, S. (2024). A data-driven decision-making approach for evaluating the projects according to resilience, circular economy and industry 4.0 dimension. Engineering Applications of Artificial Intelligence, 134, 108608.
Qian, L., Chen, Z., Huang, Y., & Stanford, R. J. (2023). Employing categorical boosting (CatBoost) and meta-heuristic algorithms for predicting the urban gas consumption. Urban Climate, 51, 101647.
Rastgoo, A., & Khajavi, H. (2023). A novel study on forecasting the Airfoil self-noise, using a hybrid model based on the combination of CatBoost and Arithmetic Optimization Algorithm. Expert Systems with Applications, 120576.
Rezaei, S., Ghalehkhondabi, I., Rafiee, M., & Namdar Zanganeh, S. (2020). Supplier selection and order allocation in CLSC configuration with various supply strategies under disruption risk. Opsearch, 57, 908–934.
Rezaei Zeynali, F., & Tajally, A. (2024). Designing a sustainable waste chain network for banana farm under uncertainty: a case study. Journal of Optimization in Industrial Engineering, 37(2), 175.
Rostami, O., Tavakoli, M., Tajally, A., & GhanavatiNejad, M. (2023). A goal programming-based fuzzy best–worst method for the viable supplier selection problem: a case study. Soft Computing, 27(6), 2827–2852.
Sahebjamnia, N., Goodarzian, F., & Hajiaghaei-Keshteli, M. (2020). Optimization of multi-period three-echelon citrus supply chain problem. Journal of Optimization in Industrial Engineering, 13(1), 39–53.
Sahoo, S. K., & Goswami, S. S. (2024). Green supplier selection using MCDM: A comprehensive review of recent studies. Spectrum of Engineering and Management Sciences, 2(1), 1–16.
Sazvar, Z., Tavakoli, M., Ghanavati-Nejad, M., & Nayeri, S. (2022). Sustainable-resilient supplier evaluation for high-consumption drugs during COVID-19 pandemic using a data-driven decision-making approach. Scientia Iranica.
Sheykhizadeh, M., Ghasemi, R., Vandchali, H. R., Sepehri, A., & Torabi, S. A. (2024). A hybrid decision-making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: a case study of a pharmaceutical industry. Environment, Development and Sustainability, 26(12), 30969–30996.
Sonar, H., Gunasekaran, A., Agrawal, S., & Roy, M. (2022). Role of lean, agile, resilient, green, and sustainable paradigm in supplier selection. Cleaner Logistics and Supply Chain, 4, 100059.
Sultan, M., & Akram, M. (2025). An extended multi-criteria decision-making technique for hydrogen and fuel cell supplier selection by using spherical fuzzy rough numbers. Journal of Applied Mathematics and Computing, 71(2), 1843–1886.
Tajally, A., Vamarzani, M. Z., Ghanavati-Nejad, M., Zeynali, F. R., Abbasian, M., & Bahengam, A. (2025). A hybrid machine learning-based decision-making model for viable supplier selection problem considering circular economy dimensions. Environment, Development and Sustainability, 1–33.
Tavakoli, M., Tajally, A., Ghanavati-Nejad, M., & Jolai, F. (2023). A Markovian-based fuzzy decision-making approach for the customer-based sustainable-resilient supplier selection problem. Soft Computing, 1–32.
Yazdani, M., Torkayesh, A. E., Stević, Ž., Chatterjee, P., Ahari, S. A., & Hernandez, V. D. (2021). An interval valued neutrosophic decision-making structure for sustainable supplier selection. Expert Systems with Applications, 183, 115354.
Zeynali, F. R., Hatami, S., Khameneh, R. T., & Ghanavati-Nejad, M. (2024). Evaluating the performance of the raw material providers based on the customer-based LARG (CLARG) paradigm: a machine learning-based method. Journal of Optimization in Industrial Engineering, 2(17).
