出口服务的自动信息检索:基于人工智能的出口决策支持工具开发的第一个项目发现

David Aufreiter, Doris Ehrlinger, Christian Stadlmann, Margarethe Uberwimmer, Anna Biedersberger, Christina Korter, Stefan Mang
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引用次数: 0

摘要

在服务化的过程中,制造企业用新的工业和知识服务来补充他们的产品,这带来了不确定性和风险的挑战。除了需要调整内部因素外,服务的国际销售也是一项重大挑战。本文介绍了一项国际研究项目的初步结果,该项目旨在帮助先进制造商决定向国外市场出口他们的服务产品。在这个项目的框架内,开发了一个工具,通过基于自然语言处理和机器学习的自动生成市场信息来支持管理人员的服务输出决策。本文提出了一个基于人工智能的市场信息解决方案的发展路线图。它描述了研究过程的步骤,分析相关行业合作伙伴的问题陈述,选择目标国家和市场,定义工具范围的参数,将不同的服务产品及其组件分类,并开发注释方案,为人工智能解决方案生成可靠和集中的训练数据。本文在基本步骤中展示了良好的实践,并强调了在人工智能支持的未来研究项目中工作的研究人员和管理人员要避免的常见陷阱。最后,本文旨在支持和激励研究人员和管理者在服务化领域发现人工智能的应用和研究机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument
On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.
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