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

• Published in: CIRP journal of manufacturing science and technology Vol. 32; pp. 491 - 506
• Main Authors: Treber, Stefan, Benfer, Martin, Häfner, Benjamin, Wang, Lihui, Lanza, Gisela
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2021
• Subjects: Information; Network; Optimization; Production; Simulation; Simulation; Information; Optimization; Production; Network
• ISSN: 1755-5817; 1878-0016; 1878-0016
• DOI: 10.1016/j.cirpj.2020.08.012

•Modelling effects of information flow on the performance of global production networks.•Revealing cause-effect relationships based on multi-method simulation and statistical analysis.•Identifying target configurations for information exchange using surrogate modeling and robust optimization. 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.