Evaluating the Performance of the Raw Material Providers based on the Customer-based LARG (CLARG) Paradigm: A Machine Learning-based Method
محورهای موضوعی : Supply chain management and logisticsFardin Rezaei Zeynali 1 , Somayeh Hatami 2 , Ramin Talebi Khameneh 3 , Mohssen Ghanavati-Nejad 4
1 - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 - Lecturer- Sheffield Hallam University
3 - School of Systems and Enterprises, Stevens Institute of Technology, New Jersey, USA
4 - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
کلید واژه: Raw material provider selection, LARG paradigm, Customer-based indicators, Machine learning-based model, Agri-food industry,
چکیده مقاله :
One of the critically important tasks of supply chain managers is to evaluate the performance of the raw material providers, especially in today’s modern and dynamic business environment. In this regard, the current study focuses on the evaluation process of the raw material providers based on some crucial metrics named the customer-based LARG paradigm. For this purpose, based on a real-world case study in the agri-food industry, the main criteria and sub-criteria are determined. Afterward, to evaluate the performance of the potential raw material providers, a machine learning-based method by combining the stochastic best-worst method and weighted decision tree is developed. In general, this research contributes to the literature by proposing an efficient machine learning-based model to investigate the raw material provider selection problem for the agri-food industry based on the customer-based LARG paradigm. The results obtained from the implementation of the developed approach show that the general, leagility, resilience, customer-based, and green criteria are the most significant ones, respectively. Also, among the sub-criteria, “Service level”, “Robustness”, “Cost”, “Quality”, “Manufacturing flexibility”, “Delivery speed”, “Waste management”, and “Restorative Capacity” are specified as the best ones. Additionally, based on the achieved outcomes, the effectiveness, reliability, and validity of the proposed machine learning-based approach are confirmed.
One of the critically important tasks of supply chain managers is to evaluate the performance of the raw material providers, especially in today’s modern and dynamic business environment. In this regard, the current study focuses on the evaluation process of the raw material providers based on some crucial metrics named the customer-based LARG paradigm. For this purpose, based on a real-world case study in the agri-food industry, the main criteria and sub-criteria are determined. Afterward, to evaluate the performance of the potential raw material providers, a machine learning-based method by combining the stochastic best-worst method and weighted decision tree is developed. In general, this research contributes to the literature by proposing an efficient machine learning-based model to investigate the raw material provider selection problem for the agri-food industry based on the customer-based LARG paradigm. The results obtained from the implementation of the developed approach show that the general, leagility, resilience, customer-based, and green criteria are the most significant ones, respectively. Also, among the sub-criteria, “Service level”, “Robustness”, “Cost”, “Quality”, “Manufacturing flexibility”, “Delivery speed”, “Waste management”, and “Restorative Capacity” are specified as the best ones. Additionally, based on the achieved outcomes, the effectiveness, reliability, and validity of the proposed machine learning-based approach are confirmed.
Abbasi, S. (2023). Environmental impact assessment with rapid impact assessment matrix method during the COVID-19 pandemic: A case study in Tehran.
Abdo, H., & Flaus, J.-M. (2016). Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory. International Journal of Production Research, 54(19), 5862–5885.
Agyabeng-Mensah, Y., Baah, C., & Afum, E. (2024). Do the roles of green supply chain learning, green employee creativity, and green organizational citizenship behavior really matter in circular supply chain performance? Journal of Environmental Planning and Management, 67(3), 609–631.
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
Anvari, A. R. (2021). The integration of LARG supply chain paradigms and supply chain sustainable performance (A case study of Iran). Production & Manufacturing Research, 9(1), 157–177.
Aria, S., Torabi, S. A., & Nayeri, S. (2020). A Hybrid Fuzzy Decision-Making Approach to Select the Best online-taxis business. Advances in Industrial Engineering, 54(2), 99–120.
Asadabadi, M. R. (2017). A customer based supplier selection process that combines quality function deployment, the analytic network process and a Markov chain. European Journal of Operational Research, 263(3), 1049–1062.
Ekinci, E., Sezer, M. D., Mangla, S. K., & Kazancoglu, Y. (2024). Building sustainable resilient supply chain in retail sector under disruption. Journal of Cleaner Production, 434, 139980.
Fallahpour, A., Nayeri, S., Sheikhalishahi, M., Wong, K. Y., Tian, G., & Fathollahi-Fard, A. M. (2021a). 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, 1–21.
Fallahpour, A., Nayeri, S., Sheikhalishahi, M., Wong, K. Y., Tian, G., & Fathollahi-Fard, A. M. (2021b). 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
Fallahpour, A., Wong, K. Y., Rajoo, S., Fathollahi-Fard, A. M., Antucheviciene, J., & Nayeri, S. (2021). An integrated approach for a sustainable supplier selection based on Industry 4.0 concept. Environmental Science and Pollution Research, 1–19.
Foley, L., Ball, L., Hurst, A., Davis, J., & Blockley, D. (1997). Fuzziness, incompleteness and randomness; classification of uncertainty in reservoir appraisal. Petroleum Geoscience, 3(3), 203–209.
ForouzeshNejad, A. A. (2023). Leagile and sustainable supplier selection problem in the Industry 4.0 era: a case study of the medical devices using hybrid multi-criteria decision making tool. Environmental Science and Pollution Research, 30(5), 13418–13437.
Ghazvinian, A., Feng, B., Feng, J., Talebzadeh, H., & Dzikuć, M. (2024). Lean, Agile, Resilient, Green, and Sustainable (LARGS) Supplier Selection Using Multi-Criteria Structural Equation Modeling under Fuzzy Environments. Sustainability, 16(4), 1594.
Hosseini, Z. S., Flapper, S. D., & Pirayesh, M. (2022). Sustainable supplier selection and order allocation under demand, supplier availability and supplier grading uncertainties. Computers & Industrial Engineering, 165, 107811.
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.
Li, Y., Diabat, A., & Lu, C.-C. (2020). Leagile supplier selection in Chinese textile industries: a DEMATEL approach. Annals of Operations Research, 287(1), 303–322.
Liang, D., Fu, Y., & Garg, H. (2024). A novel robustness PROMETHEE method by learning interactive criteria and historical information for blockchain technology-enhanced supplier selection. Expert Systems with Applications, 235, 121107.
Liu, W., Fan, H., & Xia, M. (2022). Credit scoring based on tree-enhanced gradient boosting decision trees. Expert Systems with Applications, 189, 116034.
Moshkov, M. (2021). Decision trees based on 1-consequences. Discrete Applied Mathematics, 302, 208–214. https://doi.org/10.1016/j.dam.2021.07.017
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.
Nayeri, S., Sazvar, Z., & Heydari, J. (2023). Towards a responsive supply chain based on the industry 5.0 dimensions: A novel decision-making method. Expert Systems with Applications, 213, 119267.
Nayeri, S., Torabi, S. A., Tavakoli, M., & Sazvar, Z. (2021). A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network. Journal of Cleaner Production, 127691.
Pamucar, D., Yazdani, M., Obradovic, R., Kumar, A., & Torres‐Jiménez, M. (2020). A novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection problem. International Journal of Intelligent Systems, 35(12), 1934–1986.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.
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.
Sahu, A. K., Sharma, M., Raut, R. D., Sahu, A. K., Sahu, N. K., Antony, J., & Tortorella, G. L. (2023). Decision-making framework for supplier selection using an integrated MCDM approach in a lean-agile-resilient-green environment: evidence from Indian automotive sector. TQM Journal, 35(4), 964–1006. https://doi.org/10.1108/TQM-12-2021-0372/FULL/HTML
Salleh, N. H. M., Abd Rasidi, N. A. S., & Jeevan, J. (2020). Lean, agile, resilience and green (LARG) paradigm in supply chain operations: a trial in a seaport system. Australian Journal of Maritime & Ocean Affairs, 12(4), 200–216.
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.
Shao, Y., Barnes, D., & Wu, C. (2022). Sustainable supplier selection and order allocation for multinational enterprises considering supply disruption in COVID-19 era. Australian Journal of Management, (November 2021), 031289622110669. https://doi.org/10.1177/03128962211066953
Siddiquee, M., Shaha, P., & Hasin, A. (2024). Greening the pillars of pharmaceuticals: Sustainable supplier selection in emerging economies. Journal of Future Sustainability, 4(3), 159–168.
Tavakoli, M., Ghanavati-Nejad, M., Tajally, A., & Sheikhalishahi, M. (2023). LRFM—based association rule mining for dentistry services patterns identification (case study: a dental center in Iran). Soft Computing, 1–16.
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.
Wang, S., Li, H., Li, J., Zhang, Y., & Zou, B. (2018). Automatic analysis of lateral cephalograms based on multiresolution decision tree regression voting. Journal of healthcare engineering, 2018.
Zekhnini, K., Chaouni Benabdellah, A., & Cherrafi, A. (2023). A multi-agent based big data analytics system for viable supplier selection. Journal of Intelligent Manufacturing, 1–21.
