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首页> 外文期刊>Journal of business logistics >Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential
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Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential

机译:供应链管理中的数据科学,预测分析和大数据:当前状态和未来潜力

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摘要

While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large-scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large-scale survey, and offer insight into its future potential via the discussion of how a research university is training next-generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.
机译:尽管经常使用数据科学,预测分析和大数据作为流行语,但对这些领域的严格学术研究才刚刚出现。在这篇具有远见的文章中,我们讨论了最近在供应链管理(SCM)专业人员中针对这些主题进行的大规模调查的结果,并补充了我们在开发,实施和管理预测分析中第一个硕士学位课程中的经验。因此,我们通过大规模调查有效地提供了对该领域当前状态的评估,并通过讨论研究型大学如何培训下一代数据科学家的方式提供了对该领域未来潜力的见识。具体而言,我们报告了SCM中预测分析的当前使用情况,潜在动机以及可感知的收益和障碍。此外,我们重点介绍了成功的数据科学家所需的技能,并举例说明了如何在课程中实施预测性分析。依托SCM迄今为止收集的最大的预测分析用户数据集之一,以及我们在预测分析领域首批硕士学位课程之一的经验,我们打算及时对该领域进行评估,以说明其未来潜力,并激励该领域的其他研究和教学进展。

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