Luthfiansyah, M.F. and Masruroh, N.A. (2021) Evaluation of Supply Chain Network Resilience Level in Pre-disruption and Post-disruption Scenario. In: 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 13-16 December 2021, Singapore.
Full text not available from this repository. (Request a copy)Abstract
Disruption in the supply chain network is unavoidable. Although with a minimal probability, disruption can result in enormous losses due to disruption of flow in an interconnected system. This paper presents a simulation study to evaluate the impact of disruptions. Ten supply chain networks from six different types of industries are used as the case studies. Monte Carlo simulation is used to simulate the randomized disruption scenarios. The resilience is measured using four parameters: density, centrality, connectivity, and network size. The performance of the supply chain networks was evaluated with the mean and CVaR. The result shows that supply chain networks with high initial resilience are not necessarily the most resilient because initial resilience does not correlate with the difference in resilience when exposed to disruption. Furthermore, all networks experience the highest decrease of resilience value due to connectivity parameters. Therefore, the recommendation given is adding new nodes (DC/hubs/manufacturer) as buffer nodes or relationships between existing nodes (between supplier/DC/manufacturer/retailer). Disruption scenarios are not based on each zone and are assumed to have the same characteristics as research limitations. © 2021 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 2 |
Uncontrolled Keywords: | Intelligent systems; Supply chains; Value engineering; Case-studies; Conditional Value-at-Risk; Minimal probability; Monte Carlo's simulation; Network resilience; Network size; Simulation studies; Structural parameter; Supply chain network; Supply chain network resilience; Monte Carlo methods |
Subjects: | T Technology > TJ Mechanical engineering and machinery |
Divisions: | Faculty of Engineering > Mechanical and Industrial Engineering Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 25 Oct 2024 03:19 |
Last Modified: | 25 Oct 2024 03:19 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8589 |