TY - JOUR
T1 - Big data analytics in production and distribution management
AU - Yin, Yunqiang
AU - Chu, Feng
AU - Dolgui, Alexandre
AU - Cheng, T. C.E.
AU - Zhou, M. C.
N1 - Funding Information:
This paper was supported in part by the National Natural Science Foundation of China [grant number 71971041]; by the Outstanding Young Scientific and Technological Talents Foundation of Sichuan Province [grant number 2020JDJQ0035]; and by the Major Program of National Social Science Foundation of China [grant number 20&ZD084].
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022/11
Y1 - 2022/11
N2 - Production and distribution are two key constituents of a supply chain. In view of the growing availability of data and advances in big data analytics techniques, there have been more and more applications of data analytics to deal with the problems in production and distribution management. With this in mind, we proposed a special issue on ‘Big Data Analytics in Production and Distribution Management' to report the latest development in this field. In this editorial, we first introduce the background and examine the existing review works on the applications of data analytics to operations management. We then introduce the papers accepted in the issue, and discuss how different types of big data analytics techniques are applied to production and distribution management, including demand forecasting, production scheduling, distribution management, manufacturing management, and supply chain management. Finally, we conclude the paper with a discussion of future research.
AB - Production and distribution are two key constituents of a supply chain. In view of the growing availability of data and advances in big data analytics techniques, there have been more and more applications of data analytics to deal with the problems in production and distribution management. With this in mind, we proposed a special issue on ‘Big Data Analytics in Production and Distribution Management' to report the latest development in this field. In this editorial, we first introduce the background and examine the existing review works on the applications of data analytics to operations management. We then introduce the papers accepted in the issue, and discuss how different types of big data analytics techniques are applied to production and distribution management, including demand forecasting, production scheduling, distribution management, manufacturing management, and supply chain management. Finally, we conclude the paper with a discussion of future research.
KW - Big data analytics
KW - distribution management
KW - manufacturing management
KW - production management
KW - supply chain management
UR - http://www.scopus.com/inward/record.url?scp=85141957159&partnerID=8YFLogxK
U2 - 10.1080/00207543.2022.2130589
DO - 10.1080/00207543.2022.2130589
M3 - Editorial
AN - SCOPUS:85141957159
SN - 0020-7543
VL - 60
SP - 6682
EP - 6690
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 22
ER -