AI-POWERED PREDICTIVE ANALYTICS FOR INTELLECTUAL PROPERTY RISK MANAGEMENT IN SUPPLY CHAIN OPERATIONS: A BIG DATA APPROACH

Md Abdur Rauf, Md Majadul Islam Jim, Md Mahfuzur Rahman, Md Tariquzzaman
{"title":"AI-POWERED PREDICTIVE ANALYTICS FOR INTELLECTUAL PROPERTY RISK MANAGEMENT IN SUPPLY CHAIN OPERATIONS: A BIG DATA APPROACH","authors":"Md Abdur Rauf, Md Majadul Islam Jim, Md Mahfuzur Rahman, Md Tariquzzaman","doi":"10.62304/ijse.v1i04.184","DOIUrl":null,"url":null,"abstract":"The rapid advancement of technology and the increasing complexity of global supply chains have heightened the need for robust intellectual property (IP) risk management strategies. This study explores the application of artificial intelligence (AI) and big data analytics in enhancing IP risk management within supply chains. A comprehensive literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, identifying 578 records through database searches and an additional 90 records through other sources. After removing duplicates, 568 records were screened, with 196 full-text articles assessed for eligibility. Ultimately, 135 articles were included in the final synthesis. The findings reveal that AI-driven predictive analytics significantly enhance the detection and mitigation of IP risks by analyzing large volumes of data from various sources, such as patent filings, market trends, and social media. Big data analytics tools like Hadoop and Spark facilitate real-time monitoring and early identification of potential IP threats, providing a comprehensive view of the supply chain landscape. Several successful case studies across different industries, including pharmaceuticals, electronics, and fashion, demonstrate the practical applications of these technologies in addressing IP risks. However, the review also highlights several challenges, including data quality, scalability, model interpretability, data privacy, and integration with legacy systems. Despite these challenges, the benefits of AI and big data analytics in IP risk management are substantial, enabling organizations to protect their intellectual assets more effectively. The study underscores the need for future research to address these challenges and explore innovative solutions to maximize the potential of AI and big data analytics in IP risk management. By investing in the necessary infrastructure and expertise, organizations can enhance their resilience and maintain a competitive edge in the global market.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"10 12","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Mainstream Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.62304/ijse.v1i04.184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The rapid advancement of technology and the increasing complexity of global supply chains have heightened the need for robust intellectual property (IP) risk management strategies. This study explores the application of artificial intelligence (AI) and big data analytics in enhancing IP risk management within supply chains. A comprehensive literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, identifying 578 records through database searches and an additional 90 records through other sources. After removing duplicates, 568 records were screened, with 196 full-text articles assessed for eligibility. Ultimately, 135 articles were included in the final synthesis. The findings reveal that AI-driven predictive analytics significantly enhance the detection and mitigation of IP risks by analyzing large volumes of data from various sources, such as patent filings, market trends, and social media. Big data analytics tools like Hadoop and Spark facilitate real-time monitoring and early identification of potential IP threats, providing a comprehensive view of the supply chain landscape. Several successful case studies across different industries, including pharmaceuticals, electronics, and fashion, demonstrate the practical applications of these technologies in addressing IP risks. However, the review also highlights several challenges, including data quality, scalability, model interpretability, data privacy, and integration with legacy systems. Despite these challenges, the benefits of AI and big data analytics in IP risk management are substantial, enabling organizations to protect their intellectual assets more effectively. The study underscores the need for future research to address these challenges and explore innovative solutions to maximize the potential of AI and big data analytics in IP risk management. By investing in the necessary infrastructure and expertise, organizations can enhance their resilience and maintain a competitive edge in the global market.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
以人工智能为动力的供应链运营中知识产权风险管理预测分析:大数据方法
技术的飞速发展和全球供应链的日益复杂,使人们更加需要强有力的知识产权(IP)风险管理策略。本研究探讨了人工智能(AI)和大数据分析在加强供应链知识产权风险管理中的应用。研究采用系统综述和元分析首选报告项目(PRISMA)方法进行了全面的文献综述,通过数据库搜索确定了 578 条记录,并通过其他来源确定了另外 90 条记录。去除重复内容后,共筛选出 568 条记录,并对其中的 196 篇全文进行了资格评估。最终,135 篇文章被纳入最终综述。研究结果表明,人工智能驱动的预测分析通过分析来自专利申请、市场趋势和社交媒体等各种来源的大量数据,大大提高了发现和降低知识产权风险的能力。Hadoop 和 Spark 等大数据分析工具有助于对潜在的知识产权威胁进行实时监控和早期识别,从而提供一个全面的供应链视图。医药、电子和时尚等不同行业的一些成功案例研究,展示了这些技术在应对知识产权风险方面的实际应用。不过,回顾也强调了一些挑战,包括数据质量、可扩展性、模型可解释性、数据隐私以及与传统系统的集成。尽管存在这些挑战,但人工智能和大数据分析在知识产权风险管理方面的优势是巨大的,能使企业更有效地保护其知识资产。这项研究强调,今后需要针对这些挑战开展研究,探索创新解决方案,最大限度地发挥人工智能和大数据分析在知识产权风险管理中的潜力。通过对必要的基础设施和专业知识进行投资,企业可以增强其应变能力,并在全球市场中保持竞争优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A FEASIBILITY STUDY ON UNDERGROUND INFRASTRUCTURE IMPLEMENTATION TO ENHANCE DHAKA’S ELECTRICAL GRID RELIABILITY AI-POWERED PREDICTIVE ANALYTICS FOR INTELLECTUAL PROPERTY RISK MANAGEMENT IN SUPPLY CHAIN OPERATIONS: A BIG DATA APPROACH Housebuilding Finance in the United States: From Budgeting to Funding A FRAMEWORK FOR LEAN MANUFACTURING IMPLEMENTATION IN THE TEXTILE INDUSTRY: A RESEARCH STUDY A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN ENHANCING CYBERSECURITY THREAT DETECTION AND RESPONSE MECHANISMS
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1