[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120657-en":3,"doc-seo-120657-105":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":20,"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},120657,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Machine Learning-Based Technique for Predicting Vendor Incoterm (contract) in Global Omnichannel Pharmaceutical Supply Chain","Supply chain management strongly influences business performance and social development, and modern supply chains continue to evolve in competitive, highly dynamic conditions. As complexity increases, robotics, machine learning, and fast information dissemination can act as transformation enablers. While machine learning is widely used, data-driven research in pharmaceutical supply chains remains limited. This paper introduces a machine learning-based model to select vendor Incoterms for direct drop-shipping in a global omnichannel pharmaceutical supply chain and identifies key decision factors.","Machine learning-based technique for predicting vendor incoterm (contract) in global omnichannel pharmaceutical supply chain  \nKUMAR DETWAL, Pankaj, SONI, Gunjan, KUMAR JAKHAR, Suresh, KUMAR SHRIVASTAVA, Deepak, MADAAN, Jitendar and KAYIKCI, Yasanur  \n\u003C [http://orcid.org/0000-0003-2406-3164](http://orcid.org/0000-0003-2406-3164)>  \nAvailable from Sheffield Hallam University Research Archive (SHURA) at: [http://shura.shu.ac.uk/31336/](http://shura.shu.ac.uk/31336/)  \nThis document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it.  \nPublished version  \nKUMAR DETWAL, Pankaj, SONI, Gunjan, KUMAR JAKHAR, Suresh, KUMAR SHRIVASTAVA, Deepak, MADAAN, Jitendar and KAYIKCI, Yasanur (2023) . Machine learning-based technique for predicting vendor incoterm (contract) in global omnichannel pharmaceutical supply chain. Journal of Business Research, 158:  \n113688.  \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)  \nTitle Page  \nMachine Learning-Based Technique for Predicting Vendor Incoterm (contract) in Global Omnichannel Pharmaceutical Supply Chain  \nPankaj Kumar Detwal  \nMalaviya National Institute of Technology Jaipur, India  \n[pankajrayan7@gmail.com](pankajrayan7@gmail.com)  \nGunjan Soni*  \nMalaviya National Institute of Technology Jaipur, India [gsoni.mech@mnit.ac.in](gsoni.mech@mnit.ac.in)  \nSuresh Kumar Jakkar  \nIndian Institute of Management, Lucknow, India  \n[skj@iiml.ac.in](skj@iiml.ac.in)  \nDeepak Kumar Shrivastava  \nIndian Institute of Management, Tiruchirappalli, India  \n[srideepak1978@gmail.com](srideepak1978@gmail.com)  \nJitendar Madaan  \nIndian Institute of Technology, Delhi, India  \n[jmadaan@dms.iitd.ac.in](jmadaan@dms.iitd.ac.in)  \nYasanur Kayikci  \nSheffield Business School, Sheffield Hallam University, Sheffield, UK Science Policy Research Unit, University of Sussex Business School,  \nBrighton, UK  \n[y.kayikci@shu.ac.uk](y.kayikci@shu.ac.uk), [yk327@sussex.ac.uk](yk327@sussex.ac.uk), [yasanur.kayikci@gmail.com](yasanur.kayikci@gmail.com)  \n* Corresponding author  \nManuscript (WITHOUT AUTHOR DETAILS) Click here to view linked References   \nMachine Learning-Based Technique for Predicting  \nVendor Incoterm (contract) in Global Omnichannel  \nPharmaceutical Supply Chain  \nAbstract: The importance of supply chain management to business operations and social  \ngrowth cannot be overstated. Modern supply chains are considerably dissimilar from those  \nof only a few years ago and are still evolving in a vastly competitive environment.  \nTechnology dealing with the rising complexity of dynamic supply chain processes is  \nrequired. Robotics, machine learning, and rapid information dispensation can be supply  \nchain transformation enablers. Quite a few functional supply chain applications based on  \nMachine Learning (ML) have appeared in recent years; however, there has been minimal  \nresearch on applications of data-driven techniques in pharmaceutical supply chains. This  \npaper proposes a machine learning-based vendor incoterm (contract) selection model for  \ndirect drop-shipping in a global omnichannel pharmaceutical supply chain. The study also  \nhighlights the critical factors influencing the decision to select a vendor incoterm during the  \nshipment of pharmaceutical goods. The findings ofthis study show that the proposed model  \ncan accurately predict a vendor incoterm (contract) for given values of input parameters.  \nThis comprehensive model will enable researchers and business administrators to undertake  \ninnovation initiatives better and redirect the resources regarding the direct drop shipping of  \npharmaceutical products.  \nKeywords: data-driven; omnichannel; pharmaceutical supply chain; vendor incoterm  \nmachine learning; direct drop-shipping  \n1. Introduction  \nThe structure of the supply chain","cbCaieYwHUaCXJeS","https://ap.wps.com/l/cbCaieYwHUaCXJeS","pdf",964998,1,27,"English","en",105,"# Introduction\n## Supply chain structure and performance impact\n## Global health supply chains and pandemic-driven disruption\n## Technology-driven and omnichannel approaches","[{\"question\":\"What problem does the paper address in pharmaceutical supply chains?\",\"answer\":\"It addresses the limited research on data-driven techniques for selecting vendor Incoterms (contracts) in global omnichannel pharmaceutical supply chains, especially for direct drop-shipping decisions.\"},{\"question\":\"How does the proposed approach work?\",\"answer\":\"It proposes a machine learning-based vendor Incoterm selection model that uses input parameters to predict the appropriate vendor Incoterm (contract).\"},{\"question\":\"What factors are considered when selecting a vendor Incoterm?\",\"answer\":\"The study highlights critical factors influencing the vendor Incoterm decision during shipment of pharmaceutical goods.\"}]","Machine Learning-Based Technique for Predicting Vendor Incoterm (contract) in Global Omnichannel Pharmaceutical Supply Chain | 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