企业资源规划(ERP)的未来:利用人工智能

Gaurav Kumar
{"title":"企业资源规划(ERP)的未来:利用人工智能","authors":"Gaurav Kumar","doi":"10.32628/cseit24104112","DOIUrl":null,"url":null,"abstract":"A large pharmaceuticals corporation utilizing a complex IT infrastructure such as SAP ERP typically faces a substantial volume GMP and Serialization data annually, numbering in the hundreds of thousands. These inquiries, whether initiated over the phone or online via platforms like integration, seek assistance with various issues. Enterprise resource planning (ERP) software streamlines business processes by integrating technology, services, and human resources across interconnected applications. This research proposes implementing an intelligent system to streamline volume of the data and analyzation for the SAP ERP. This system aims to automate responses to user queries, reducing the time required for issue investigation and resolution, and enhancing user responsiveness. Employing machine learning algorithms, the system efficiently interprets and classifies text across multiple categories, facilitating accurate question comprehension. Additionally, it utilizes a specialized framework to retrieve relevant evidence, ensuring the delivery of optimal responses. Furthermore, its conversational AI capabilities enable the creation of chatbots, fostering collaborative problem-solving among user groups in real-time.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"109 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Future of Enterprise resource planning (ERP): Harnessing Artificial Intelligence\",\"authors\":\"Gaurav Kumar\",\"doi\":\"10.32628/cseit24104112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A large pharmaceuticals corporation utilizing a complex IT infrastructure such as SAP ERP typically faces a substantial volume GMP and Serialization data annually, numbering in the hundreds of thousands. These inquiries, whether initiated over the phone or online via platforms like integration, seek assistance with various issues. Enterprise resource planning (ERP) software streamlines business processes by integrating technology, services, and human resources across interconnected applications. This research proposes implementing an intelligent system to streamline volume of the data and analyzation for the SAP ERP. This system aims to automate responses to user queries, reducing the time required for issue investigation and resolution, and enhancing user responsiveness. Employing machine learning algorithms, the system efficiently interprets and classifies text across multiple categories, facilitating accurate question comprehension. Additionally, it utilizes a specialized framework to retrieve relevant evidence, ensuring the delivery of optimal responses. Furthermore, its conversational AI capabilities enable the creation of chatbots, fostering collaborative problem-solving among user groups in real-time.\",\"PeriodicalId\":313456,\"journal\":{\"name\":\"International Journal of Scientific Research in Computer Science, Engineering and Information Technology\",\"volume\":\"109 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Scientific Research in Computer Science, Engineering and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32628/cseit24104112\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32628/cseit24104112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

使用 SAP ERP 等复杂 IT 基础设施的大型制药企业每年通常要面对大量的 GMP 和序列化数据,数量高达数十万。这些咨询,无论是通过电话还是通过集成等平台在线发起,都是为了寻求各种问题的帮助。企业资源规划(ERP)软件通过在相互关联的应用程序中整合技术、服务和人力资源来简化业务流程。本研究建议实施一个智能系统,以简化 SAP ERP 的数据量和分析。该系统旨在自动回复用户查询,减少问题调查和解决所需的时间,提高用户响应速度。该系统采用机器学习算法,可有效解释和分类多类别文本,便于准确理解问题。此外,它还利用专门框架检索相关证据,确保提供最佳回复。此外,它的对话式人工智能功能还能创建聊天机器人,促进用户群之间实时协作解决问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Future of Enterprise resource planning (ERP): Harnessing Artificial Intelligence
A large pharmaceuticals corporation utilizing a complex IT infrastructure such as SAP ERP typically faces a substantial volume GMP and Serialization data annually, numbering in the hundreds of thousands. These inquiries, whether initiated over the phone or online via platforms like integration, seek assistance with various issues. Enterprise resource planning (ERP) software streamlines business processes by integrating technology, services, and human resources across interconnected applications. This research proposes implementing an intelligent system to streamline volume of the data and analyzation for the SAP ERP. This system aims to automate responses to user queries, reducing the time required for issue investigation and resolution, and enhancing user responsiveness. Employing machine learning algorithms, the system efficiently interprets and classifies text across multiple categories, facilitating accurate question comprehension. Additionally, it utilizes a specialized framework to retrieve relevant evidence, ensuring the delivery of optimal responses. Furthermore, its conversational AI capabilities enable the creation of chatbots, fostering collaborative problem-solving among user groups in real-time.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信