Big data in Supply Chain Management - Applications, Challenges and Benefits

被引:0
作者
Kynast, Moritz [1 ]
Marjanovic, Olivera [2 ]
机构
[1] Univ Munster, Munster, Germany
[2] Univ Sydney, Sydney, NSW, Australia
来源
AMCIS 2016 PROCEEDINGS | 2016年
关键词
Big Data; Supply Chain Management; Applications; Benefits; Challenges; PREDICTIVE ANALYTICS; DATA SCIENCE; TECHNOLOGIES; DESIGN;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper focuses on Big Data (BD) applications, challenges and benefits (ACBs) in Supply Chain Management (SCM). While BD-related research has attracted a growing number of Business Intelligence and Analytics (BI&A) researchers, SCM-specific research on BD is yet to receive their full attention. By combining relevant frameworks from SCM and BI&A, this paper proposes a new research framework for BD in SCM. The combined framework is then used to identify, classify and analyze ACBs of BD in SCM, using insights from a multi-disciplinary literature review from the fields of BI&A and SCM. Based on the main research findings the paper also suggests further SCM-specific research related to BD, by identifying synergies across business functions and value dimensions of SCM.
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收藏
页数:10
相关论文
共 58 条
[1]  
[Anonymous], 2016, J F ASS INFORM SYSTE
[2]  
[Anonymous], 2011, BIG DATA NEXT FRONTI
[3]  
[Anonymous], 2014, CISC VIS NETW IND GL
[4]  
[Anonymous], 2013, INT I ANAL
[5]  
[Anonymous], EUROPEAN J INFORM SY
[6]  
Bange C., 2013, BARC
[7]  
Brown B., 2011, MCKINSEY QTRY
[8]  
Bughin J., 2011, MCKINSEY QTRY
[9]   Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research [J].
Chae, Bongsug .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2015, 165 :247-259
[10]   Data-intensive applications, challenges, techniques and technologies: A survey on Big Data [J].
Chen, C. L. Philip ;
Zhang, Chun-Yang .
INFORMATION SCIENCES, 2014, 275 :314-347