[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124983-en":3,"doc-seo-124983-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124983,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Evaluating the performance of the raw material providers based on the customer-based LARG (CLARG) paradigm - a machine learning-based method","Evaluating the performance of raw material providers focuses on how supply chain managers assess supplier capability in a modern, dynamic business environment. The study applies the customer-based LARG paradigm to define main and sub-criteria using a real-world agri-food case. A machine learning approach combines the stochastic best-worst method with a weighted decision tree to assess candidate providers and support selection decisions. Results identify general, leagility, resilience, customer-based, and green criteria as most influential, and highlight leading sub-criteria including service level, robustness, cost, quality, manufacturing flexibility, delivery speed, waste management, and restorative capacity. The approach is validated through confirmed effectiveness, reliability, and validity.","Evaluating the performance of the raw material providers based on the customer-based LARG(CLARG) paradigm : a machine learning-based method  \nZEYNALI, Fardin Rezaei, HATAMI, Somayeh, KHAMENEH, Ramin Talebi and GHANAVATI-NEJAD, Mohssen  \nAvailable from Sheffield Hallam University Research Archive (SHURA) at: [https://shura.shu.ac.uk/34529/](https://shura.shu.ac.uk/34529/)  \nThis document is the Accepted Version [AM]  \nCitation:  \nZEYNALI, Fardin Rezaei, HATAMI, Somayeh, KHAMENEH, Ramin Talebi and GHANAVATI-NEJAD, Mohssen (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) . [Article]  \nCopyright and re-use policy  \nSee [http://shura.shu.ac.uk/information.html](http://shura.shu.ac.uk/information.html)  \nSheffield Hallam University Research Archive  \n[http://shura.shu.ac.uk](http://shura.shu.ac.uk)  \nEvaluating the performance of the raw material providers based on the customer-based LARG(CLARG) paradigm : a machine learning-based method  \nZEYNALI, Fardin Rezaei, HATAMI, Somayeh, KHAMENEH, Ramin Talebi and GHANAVATI-NEJAD, Mohssen  \nAvailable from Sheffield Hallam University Research Archive (SHURA) at: [https://shura.shu.ac.uk/34153/](https://shura.shu.ac.uk/34153/)  \nThis document is the author deposited or published version.  \nPublished version  \nZEYNALI, Fardin Rezaei, HATAMI, Somayeh, KHAMENEH, Ramin Talebi and GHANAVATI-NEJAD, Mohssen (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) . [Article]  \nCopyright and re-use policy  \nSee [http://shura.shu.ac.uk/information.html](http://shura.shu.ac.uk/information.html)  \nSheffield Hallam University Research Archive  \n[http://shura.shu.ac.uk](http://shura.shu.ac.uk)  \nEvaluating the Performance of the Raw Material Providers based on the Customer-based LARG (CLARG) Paradigm: A Machine Learning-based Method  \nFardin Rezaei Zeynali  \nSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran  \n[Fardin.rezaei@ut.ac.ir](Fardin.rezaei@ut.ac.ir)  \nSomayeh Hatami  \nSheffield Hallam University, Sheffield, South Yorkshire, England.  \n[Somayeh.Hatami@shu.ac.uk](Somayeh.Hatami@shu.ac.uk)  \nRamin Talebi Khameneh  \nSchool of Systems and Enterprises, Stevens Institute of Technology, New Jersey, USA  \n[ratalebik@stevens.edu](ratalebik@stevens.edu)  \n[Mohssen Ghanavati-Nejad](Mohssen Ghanavati-Nejad)  \nSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran  \n[Mohssen.ghanavati@ut.ac.ir](Mohssen.ghanavati@ut.ac.ir)  \nAbstract  \nOne 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 realworld 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 learningbased 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, customerbased, and green criteria are the most significant ones, respectively. Also, among the sub-criteria,“Service level”, “Robustness”, “Cost”, “Quality”, “Manufacturing flex","cbCaiscKKErT42ZZ","https://ap.wps.com/l/cbCaiscKKErT42ZZ","pdf",1196961,1,21,"English","en",105,"# Introduction\n## Raw material provider evaluation in supply chain management\n## Purpose and context of the customer-based LARG (CLARG) paradigm\n# Methodology\n## Stochastic best-worst method\n## Weighted decision tree and machine learning integration\n# Results and discussion\n## Most significant criteria\n## Best-performing sub-criteria\n## Effectiveness, reliability, and validity","[{\"question\":\"What problem does the study address in supply chain management?\",\"answer\":\"It addresses evaluating the performance of raw material providers, a critical task for ensuring quality, cost, and reliable supply in modern business environments.\"},{\"question\":\"How does the customer-based LARG (CLARG) paradigm contribute to the evaluation?\",\"answer\":\"It provides a set of customer-based main criteria and sub-criteria to structure the supplier performance evaluation in the agri-food case.\"},{\"question\":\"What machine learning-based method is used to evaluate potential providers?\",\"answer\":\"The method combines a stochastic best-worst approach with a weighted decision tree to rank and evaluate candidate raw material providers.\"}]","Evaluating the performance of the raw material providers based on the customer-based LARG (CLARG) paradigm - 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