Robust optimization of information flows in global production networks using multi-method simulation and surrogate modelling

被引：4
作者：
Treber, Stefan [1 ]
Benfer, Martin [1 ]
Haefner, Benjamin [1 ]
Wang, Lihui [2 ]
Lanza, Gisela [1 ]
机构：
[1] Karlsruhe Inst Technol, Wbk Inst Prod Sci, Kaisterstr 12, D-76131 Karlsruhe, Germany
[2] KTH Royal Inst Technol, Dept Prod Engn, Brinellvagen 68, S-11428 Stockholm, Sweden
来源：
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY | 2021年 / 32卷
关键词：
Production; Information; Network; Simulation; Optimization; SUPPLY CHAIN PERFORMANCE; MANUFACTURING NETWORKS; ENGINEERING CHANGES; SHARING STRATEGIES; TRANSPARENCY; SYSTEMS; MULTIPLE;
D O I：
10.1016/j.cirpj.2020.08.012
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Low information exchange in global production networks results in long response time to disruption and negative performance impact. Digitalization enables a more intensive information exchange. This paper analyses the performance of order management, quality problem resolution and engineering change management in production networks with respect to different disruptions and information flows. Cause-effect relationships are revealed based on a multi-method simulation model and statistical experiments. Using surrogate modelling and robust optimization, a target picture for information exchange is determined. The benefits of the approach are demonstrated using a case study for the production of metal-plastic parts for the automotive supplier industry. (C) 2020 CIRP.
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页码：491 / 506
页数：16
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