[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120332-en":3,"doc-seo-120332-105":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},120332,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","PREDICTING LATE DELIVERIES IN INTERNATIONAL HEALTHCARE LOGISTICS USING MACHINE LEARNING MODELS","Healthcare logistics in global healthcare operations ensures timely delivery of essential medical supplies, yet frequent inefficiencies and disruptions reduce reliability during crises such as the COVID-19 pandemic. This project develops machine learning approaches that incorporate external variables, including the Logistics Performance Index (LPI), to improve lead-time predictions and reduce late-delivery risk. It investigates drivers of on-time performance and uses models such as random forest regression and hybrid algorithms like WKM_ID3 to detect delay-critical factors, while LPI supports proactive risk mitigation and more accurate forecasting. Results indicate a data-driven shift from reactive to proactive management and suggest future research on cost-effectiveness, accessibility, sustainability, and geopolitical constraints.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 5-2025\u003Cbr>PREDICTING LATE DELIVERIES IN INTERNATIONAL HEALTHCARE LOGISTICS USING MACHINE LEARNING MODELS\u003Cbr>Jeevan Sai Gali\u003Cbr>California State University-San Bernardino\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Operations and Supply Chain Management Commons |  |\n\nRecommended Citation  \nGali, Jeevan Sai, \"PREDICTING LATE DELIVERIES IN INTERNATIONAL HEALTHCARE LOGISTICS USING MACHINE LEARNING MODELS\" (2025) . Electronic Theses, Projects, and Dissertations. 2182.  \n[https://scholarworks.lib.csusb.edu/etd/2182](https://scholarworks.lib.csusb.edu/etd/2182)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nPREDICTING LATE DELIVERIES IN INTERNATIONAL HEALTHCARE  \nLOGISTICS USING MACHINE LEARNING MODELS  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in  \nInformation Systems and Technology  \nby  \nJeevan Sai Gali  \nPREDICTING LATE DELIVERIES IN INTERNATIONAL HEALTHCARE  \nLOGISTICS USING MACHINE LEARNING MODELS  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nJeevan Sai Gali  \nMay 2025  \nApproved by:  \nDr. Nima Molavi , Committee Chair  \nDr. Sepideh Alavi , Committee member, Co-Reader  \nDr. Dorota Huizinga , Committee member, Co-Reader  \nDr. Conrad Shayo, Director, School of Cyber & Decision Sciences  \n© 2025 Jeevan Sai Gali  \nABSTRACT  \nThe healthcare supply chain plays a pivotal role in ensuring the timely delivery of essential medical supplies, particularly during global crises such asthe COVID-19 pandemic. However, this critical system is often plagued by inefficiencies and disruptions caused by factors such as inadequate infrastructure, natural disasters, geopolitical tensions, and variability in vendor performance. These challenges underscore the need for advanced methodologies to enhance supply chain resilience and operational efficiency. This culmination experience project explores the integration of machine learning models and external variables, such as the Logistics Performance Index (LPI), to optimize lead-time predictions and mitigate risks in global healthcare supply chains.  \nThe research questions are: (Q1) What are the influential factors in healthcare logistics on-time deliveries? (Q2) How can predictive analytics and machine learning models be used to identify the shipments with high chance of late deliveries in healthcare logistics? (Q3) What role does the integration of external variables like the country logistics capabilities play in improving the accuracy of prediction of healthcare logistics performance, and why is this critical? The findings reveal that machine learning models, including random forest regression and hybrid algorithms such as WKM_ID3, effectively identify critical factors influencing delays, such as transportation modes and vendor terms, significantly enhancing prediction accuracy and operational efficiency.  \nFurthermore, the integration of the LPI provides a comprehensive framework for understanding how national logistics capabilities influence delivery timelines, thereby enabling proactive risk mitigation strategies. These findings highlight the transformative potential of data-driven approaches in healthcare logistics.  \nThe study concludes that leveraging predictive analytics allows healthcare supply chains to transition from reactive to proactive management, fostering enhanced resilience an","cbCaicnKC23wUhWV","https://ap.wps.com/l/cbCaicnKC23wUhWV","pdf",1296803,1,85,"English","en",105,"# Abstract\n## Keywords\n## Acknowledgements\n## Dedication","[{\"question\":\"Which problems does the project address in international healthcare logistics?\",\"answer\":\"It addresses inefficiencies and disruptions that cause late deliveries, including infrastructure gaps, disasters, geopolitical tensions, and variability in vendor performance.\"},{\"question\":\"How do the machine learning models help identify late-delivery risk?\",\"answer\":\"The study uses models such as random forest regression and hybrid approaches like WKM_ID3 to detect critical factors behind delays, improving prediction accuracy and operational efficiency.\"},{\"question\":\"What is the role of external variables such as the Logistics Performance Index (LPI)?\",\"answer\":\"The project integrates LPI to capture national logistics capabilities, improving the understanding of how country-level factors affect delivery timelines and enabling proactive risk mitigation.\"}]","PREDICTING LATE DELIVERIES IN INTERNATIONAL HEALTHCARE LOGISTICS USING MACHINE LEARNING MODELS | 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