Enhancing internal supply chain management in manufacturing through a simulation-based digital twin platform

被引:3
|
作者
Cimino, Antonio [1 ]
Longo, Francesco [2 ]
Mirabelli, Giovanni [2 ]
Solina, Vittorio [2 ]
Veltri, Pierpaolo [2 ]
机构
[1] Univ Salento, Dept Innovat Engn, Salento, Italy
[2] Univ Calabria, Dept Mech Energy & Management Engn, Cosenza, Italy
关键词
Digital twin; Internal supply chain; Simulation; Manufacturing industry; Automated simulation modelling; Production planning; CYBER-PHYSICAL SYSTEMS; INDUSTRY; 4.0; INTEGRATION;
D O I
10.1016/j.cie.2024.110670
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Digital Twin (DT) technology is profoundly changing the manufacturing landscape and supply chain management with its ability to create real-time digital replicas of physical processes, allowing for enhanced monitoring and optimized decision-making. However, the analysis of scientific literature reveals that further efforts are needed to spread the use of Industry 4.0 technologies, in the specific context of Internal Supply Chains (ISCs). The main aim of this study is to design, develop test and validate a multi-plant Simulation-Based DT production planning platform for ISCs management. A modular architecture is adopted, and the focus is on a SimulationBased Digital Twin module, which uses an object-oriented structure and enables what-if analyses, involving several scheduling rules and ISC configurations. The proposed solution ensures flexibility and scalability, two crucial features in a constantly evolving market environment. The platform is tested and validated through a case study, involving a corporate group in the Oil & Gas manufacturing sector, which needs to improve the ISC performance, under a Make-To-Order production strategy. The comparison with a baseline scenario, where the platform is not adopted, shows that the proposed approach can significantly reduce the average flow time, the average tardiness, the number of late orders. This study has important practical implications because enables proactive and smart decision-making, aimed at resource optimization and continuous improvement, through predictive analytics and scenario analysis.
引用
收藏
页数:18
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