Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

被引:50
作者
Lee, In [1 ]
Mangalaraj, George [1 ]
机构
[1] Western Illinois Univ, Coll Business & Technol, Sch Comp Sci, Macomb, IL 61455 USA
关键词
big data analytics; data analytics; supply chain management; sustainability; performances; SCOR model; predictive analytics; PREDICTIVE ANALYTICS; COMPETITIVE ADVANTAGE; LOGISTICS; CAPABILITIES; INFORMATION; PERFORMANCE; INTEGRATION; CHALLENGES; IMPLEMENTATION; FRAMEWORK;
D O I
10.3390/bdcc6010017
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.
引用
收藏
页数:29
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