[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120744-en":3,"doc-seo-120744-105":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},120744,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine learning in drug supply chain management during disease outbreaks - a systematic review","Drug supply chain management during disease outbreaks faces structural complexity from multiple stakeholders, upstream production constraints, and downstream demand shifts linked to outbreak characteristics. This systematic review analyzes research published in the last five years on machine-learning approaches for forecasting disease spread and examines how predictive parameters relate to drug supply chain decisions. Using Publish or Perish and Kitchenham methodology, 71 eligible articles are grouped into three outbreak-disease groupings, showing parameter–management correlations, with limited focus on drug supply risk management.","Machine learning in drug supply chain management during disease outbreaks: a systematic review  \nGunadi Emmanuel1,2, Arief Ramadhan3, Muhammad Zarlis4, Edi Abdurachman5, Agung Trisetyarso5  \n1Computer Science Doctoral Program, Bina Nusantara University, Jakarta, Indonesia 2Faculty of Science and Technology, Universitas Katolik Musi Charitas, Palembang, Indonesia 3School of Computing, Telkom University, Bandung, Indonesia  \n4Department of Information System Management, Bina Nusantara University, Jakarta, Indonesia 5Department of Computer Science, Bina Nusantara University, Jakarta, Indonesia  \nArticle history:  \nReceived Mar 1, 2023 Revised Apr 13, 2023 Accepted Apr 15, 2023  \nKeywords:  \nDisease outbreaks Drug supply chain Endemic Machine learning Prediction  \nCorresponding Author:  \nThe drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks.  \nThis is an open access article under the CC BY-SA license.  \nGunadi Emmanuel  \nComputer Science Doctoral Program, Bina Nusantara University Jakarta, Indonesia  \nEmail: [gunadi.emmanuel@binus.ac.id](gunadi.emmanuel@binus.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe coronavirus disease (COVID-19) pandemic event sparked research on forecasting the spread of disease outbreaks to a greater extent than in the past. The danger of COVID-19 urges the fulfillment of the need for COVID-19 drugs. The challenge for drug manufacturers and supply chain management is to meet the large and sudden demand for drugs [1] . The endpoint of drug supply chain management is on the side of the pharmacy user or patient. According to food and drug administration (FDA) [2], a long drug supply chain will complicate drug supply management and even become one of the triggers for drug shortages. Singh et al. [3] revealed that the pharmaceutical supply chain has a complexity that involves various stakeholders ranging from manufacturers, distributors, customers, information service providers, and the government as a regulatory agency. In addition, supply chains face environmental uncertainty, capacity planning, and inventory management [4] . Responding to the complexity of the drug supply chain, Lozano-Diez et al. [5] suggest taking advantage of technological developments.  \nThe use of technology is not only in production planning, distribution processes, and storage. There must be integral management from upstream to downstream, namely primary manufacturing, secondary manufacturing, distribution centers, wholesalers, and retailers/hospitals [6], [7] . In this industrial era 4.0, all  \nfactors in supply chain activities are interrelated and should be integrated [6] . Downstream supply chain activities identifying and fulfilling user demands are directly related to activities in the distribution and manufacturing department [8] . Meanwhile, Karmaker and Ahmed [1] mentioned five indicators of drug supply resilience in drug supply activities: supply chain risk orientation, visibility, flexibility, supply agility, and collaborat","cbCaisFmgvQ8PZPw","https://ap.wps.com/l/cbCaisFmgvQ8PZPw","pdf",623200,1,17,"English","en",105,"# Introduction\n## Drug supply chain complexity during outbreaks\n## Role of information sharing and demand forecasting\n## Machine learning for outbreak spread prediction","[{\"question\":\"What problem does the study address in drug supply chains during disease outbreaks?\",\"answer\":\"It addresses the complexity of matching production and distribution with sudden, outbreak-driven drug demand, where gaps and uncertainty can trigger shortages.\"},{\"question\":\"How was the systematic review conducted?\",\"answer\":\"The review used the Publish or Perish 8 application to screen articles and applied Kitchenham’s systematic review methodology to meet inclusion and keyword search criteria.\"},{\"question\":\"What do the findings suggest about machine learning and drug supply management?\",\"answer\":\"The findings group outbreaks into three broad categories and show that predictive parameters used with machine learning correlate with drug supply management in COVID-19-related cases, while drug supply risk management is less involved.\"}]","Machine learning in drug supply chain management during disease outbreaks - 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