Big data analytics for operations management in engineer-to -order manufacturing

被引：16
|
作者：
Kozjek, Dominik [1 ]
Vrabic, Rok [1 ]
Rihtarsic, Borut [2 ]
Butala, Peter [1 ]
机构：
[1] Univ Ljubljana, Fac Mech Engn, Askerceva 6, Ljubljana 1000, Slovenia
[2] Litostroj Power Doo, Litostrojska Cesta 50, Ljubljana 1000, Slovenia
来源：
51ST CIRP CONFERENCE ON MANUFACTURING SYSTEMS | 2018年 / 72卷
关键词：
Engineer-to-order manufacturing; Operations management; Data analytics; Industrial data; Data mining; Big data; SYSTEMS; FUTURE;
D O I：
10.1016/j.procir.2018.03.098
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Manufacturing data offers big potential for improving management of manufacturing operations. The paper addresses an approach to data analytics in engineer-to -order (ETO) manufacturing systems where the product quality and due-date reliability play a key role in management decision making. The objective of the research is to investigate manufacturing data which are collected by a manufacturing execution system (MES) during operations in an ETO enterprise and to develop tools for supporting scheduling of operations. The developed tools can be used for simulation of production and forecasting of potential resource overloads. 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems.
引用
收藏
页码：209 / 214
页数：6
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