From data to big data in production research: the past and future trends

被引:135
|
作者
Kuo, Yong-Hong [1 ]
Kusiak, Andrew [2 ]
机构
[1] Univ Hong Kong, Dept Ind & Mfg Syst Engn, Hong Kong, Peoples R China
[2] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA 52242 USA
关键词
production; data; data mining; data-driven models; big data; smart manufacturing; data envelopment analysis; simulation; DATA ENVELOPMENT ANALYSIS; DATA-MINING APPROACH; ENTERPRISE RISK-MANAGEMENT; ANALYTIC HIERARCHY PROCESS; PRACTICAL INACCURATE DATA; MEDIUM-SIZED ENTERPRISES; SUPPLY CHAIN MANAGEMENT; DEA VAR APPROACH; CELL-FORMATION; REAL-TIME;
D O I
10.1080/00207543.2018.1443230
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Data have been utilised in production research in meaningful ways for decades. Recent years have offered data in larger volumes and improved quality collected from diverse sources. The state-of-the-art data research in production and the emerging methodologies are discussed. The review of the literature suggests that production research enabled by data has shifted from that based on analytical models to data-driven. Manufacturing and data envelopment analysis have been the most popular application areas of data-driven methodologies. The research published to date indicates that data mining is becoming a dominant methodology in production research. Future trends and opportunities for data-driven production research are presented.
引用
收藏
页码:4828 / 4853
页数:26
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