Application of Analytics in Supply Chain Management from Industry and Academic Perspective

被引:5
作者
Kumar, Anup [1 ]
Shrivastav, Santosh Kumar [2 ,3 ]
Oberoi, Sarbjit Singh [1 ]
机构
[1] Inst Management Technol Nagpur, Nagpur, Maharashtra, India
[2] Inst Management Technol, Ghaziabad, Uttar Pradesh, India
[3] Inst Management Technol, Ghaziabad 201001, Uttar Pradesh, India
关键词
Data analytics; pedagogy; supply chain analytics; digital transformation; BIG DATA ANALYTICS; PREDICTIVE ANALYTICS; LOGISTICS; DEMAND; FUTURE;
D O I
10.1177/23197145211028041
中图分类号
F [经济];
学科分类号
02 ;
摘要
Information and communication technology (ICT) has been the backbone of businesses for some time now. ICT adoption has rendered data availability in vertically connected organizations. In recent times, the rise of analytics has paved the way to rethink the structure and working of vertically connected firms within a supply chain. Big data analytics (BDA) for instance, has become a prominent tool to analyse unstructured data. This study explores the application and benefit of various types of analytics such as descriptive analytics, predictive analytics and prescriptive analytics in the process of supply chain management (SCM). Additionally, the study also looks at ways by which analytics could integrally be included within the SCM curriculum in higher education to prepare future supply chain analyst. Notably, the article is positioned for bridging the gap between the use of analytics in SCM, both from academia and the industry perspective.
引用
收藏
页码:503 / 516
页数:14
相关论文
共 37 条
[1]  
ALSAKRAN HO, 2014, LIFE SCI J, V11, P918
[2]  
Beamon B., 1999, LOGISTICS INFORM MAN, V12, DOI [10.1108/09576059910284159, DOI 10.1108/09576059910284159]
[3]   The Evolution of Analytics and Implications for Industry and Academic Programs [J].
Bowers, Melissa R. ;
Camm, Jeffrey D. ;
Chakraborty, Goutam .
INTERFACES, 2018, 48 (06) :487-499
[4]   Supply chain inventory management and the value of shared information [J].
Cachon, GP ;
Fisher, M .
MANAGEMENT SCIENCE, 2000, 46 (08) :1032-1048
[5]  
Campbell G. M., 2009, PRODUCTION INVENTORY, V45, P7
[6]  
Chopra S., 2007, SUMMA SUMMARUM MANAG, P265, DOI [DOI 10.1007/978-3-8349-9320-5_22, 10.1007/978-3-8349-9320-5_22]
[7]  
Crainic T.G., 2003, Handbook of Transportation Science, International Series in Operations Research Management Science, P451, DOI [10.1007/0-306-48058-1_13, DOI 10.1007/0-306-48058-1_13]
[8]  
Funaki K., 2009, State of the Art Survey of Commercial Software for Supply Chain Design
[9]   Big data and predictive analytics for supply chain and organizational performance [J].
Gunasekaran, Angappa ;
Papadopoulos, Thanos ;
Dubey, Rameshwar ;
Wamba, Samuel Fosso ;
Childe, Stephen J. ;
Hazen, Benjamin ;
Akter, Shahriar .
JOURNAL OF BUSINESS RESEARCH, 2017, 70 :308-317
[10]   Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda [J].
Hazen, Benjamin T. ;
Skipper, Joseph B. ;
Ezell, Jeremy D. ;
Boone, Christopher A. .
COMPUTERS & INDUSTRIAL ENGINEERING, 2016, 101 :592-598