大数据分析在供应链管理中的应用:来自专家访谈的发现

P. Brandtner, Chibuzor Udokwu, Farzaneh Darbanian, T. Falatouri
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引用次数: 11

摘要

几乎在每个环境中生成的数据量都在增加,这为各种组织应用领域提供了巨大的潜力,其中之一就是供应链管理(SCM)。应用这种新型数据,即大数据(BD)的可能性和用例是巨大的,在这一领域已经进行了大量的研究。本文旨在从实践者的角度,而不是从学术的角度,确定对商业发展的理解和应用。通过采用专家访谈,主要目的是确定(i)从供应链管理从业者的角度对大数据的定义,(ii)目前的供应链管理活动和流程中已经在实践中使用了业务流程,(iii)在供应链管理实践中看到的业务流程的潜在未来应用领域,以及(iv)业务流程应用的主要障碍。结果表明,大数据是指具有高容量和各种来源的复杂数据集,无法用传统方法处理,需要数据专家知识和SCM领域知识才能在组织实践中使用。目前的应用包括在物流和供应链管理中建立透明度,改善需求计划或支持供应商质量管理。受访专家一致认为,BD在未来供应链管理中具有巨大的潜力。一个共同的愿景是实现供应链的实时透明度,基于识别的数据模式预测供应链行为的能力,以及在做出决策之前预测对供应链的影响的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of Big Data Analytics in Supply Chain Management: Findings from Expert Interviews
The increased amount of data being generated in virtually every context provides huge potential for a variety of organisational application fields, one of them being Supply Chain Management (SCM). The possibilities and use cases of applying this new type of data, i.e. Big Data (BD), is huge and a large body of research has already been conducted in this area. The current paper aims at identifying the understanding and the applications of BD not from an academic but a practitioners’ point of view. By applying expert interviews, the main aim is to identify (i) a definition of Big Data from SCM practitioners’ point of view, (ii) current SCM activities and processes where BD is already used in practice, (iii) potential future application fields for BD as seen in SCM practice and (iv) main hinderers of BD application. The results show that Big Data is referred to as complex data sets with high volumes and a variety of sources that can't be handled with traditional approaches and require data expert knowledge and SCM domain knowledge to be used in organisational practical. Current applications include the creation of transparency in logistics and SCM, the improvement of demand planning or the support of supplier quality management. The interviewed experts coincide in the view, that BD offers huge potential in future SCM. A shared vision was the implementation of real-time transparency of Supply Chains (SC), the ability to predict the behavior of SCs based on identified data patterns and the possibility to predict the impact of decisions on SCM before they are taken.
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