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首页> 外文期刊>International journal of production economics >Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications
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Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications

机译:供应链管理中的数据科学,预测分析和大数据的数据质量:问题简介以及研究和应用建议

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摘要

Today's supply chain professionals are inundated with data, motivating new ways of thinking about how data are produced, organized, and analyzed. This has provided an impetus for organizations to adopt and perfect data analytic functions (e.g. data science, predictive analytics, and big data) in order to enhance supply chain processes and, ultimately, performance. However, management decisions informed by the use of these data analytic methods are only as good as the data on which they are based. In this paper, we introduce the data quality problem in the context of supply chain management (SCM) and propose methods for monitoring and controlling data quality. In addition to advocating for the importance of addressing data quality in supply chain research and practice, we also highlight interdisciplinary research topics based on complementary theory.
机译:当今的供应链专业人员被数据淹没,激发了新的思维方式来思考如何生成,组织和分析数据。这为组织采用和完善数据分析功能(例如数据科学,预测分析和大数据)提供了动力,以增强供应链流程并最终改善绩效。但是,通过使用这些数据分析方法得出的管理决策仅与它们所基于的数据一样好。在本文中,我们在供应链管理(SCM)的背景下介绍了数据质量问题,并提出了监视和控制数据质量的方法。除了提倡在供应链研究和实践中解决数据质量的重要性外,我们还强调基于互补理论的跨学科研究主题。

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