The Future of Enterprise resource planning (ERP): Harnessing Artificial Intelligence

Gaurav Kumar
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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.
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企业资源规划(ERP)的未来:利用人工智能
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。
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