Enhancing supply chain resilience under disruption: analysis of the farmed data by Monte Carlo simulation

Motivated by the critical need for supply chains to withstand disruptions, this paper investigates the application of three core resilience strategies and their combinations at the network design level. We propose an investigation framework that utilises Monte Carlo simulation to generate disruption rates of varying severity. This allows for the mathematical optimisation of eight different models, each representing a combination of the three core resilience strategies, based on the simulated disruptions. The framework also incorporates farmed structured data analysis to examine the performance and features of each model. Considering disruptions that can affect both suppliers and factories, a detailed comparison is performed between the strategies. Numerical comparisons reveal that the model incorporating all resilience strategies (Multiple Allocation Network with Backup Suppliers and Factories, or MANBSF) outperforms others, potentially achieving zero shortages even under significant disruptions. This comprehensive model allows for multiple allocations of facilities and incorporates backup suppliers and factories. The analysis further suggests that network resilience increases with size, density, and the number of complete paths.