Determining the impact of unexpected disruptions to a complex supply chain
This final year project deals with simulating disruptions in a multi-echelon supply chain and the objective is to study the propagation of such disruptions and also the factors affecting such disruptions. Different simulation techniques were reviewed and studied before discrete event simulation (DE...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/68445 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This final year project deals with simulating disruptions in a multi-echelon supply chain and the objective is to study the propagation of such disruptions and also the factors affecting such disruptions.
Different simulation techniques were reviewed and studied before discrete event simulation (DES) was chosen as inventory levels are of interest in this project and inventory levels in a supply chain evolve over time due to discrete events.
A FlexSim model was created to study the propagation of disruptions in a supply chain. Verification was done by checking the structure, code and animated flow of the entities. As real life data was not available, validation was carried out based on data provided by SIMTech that was derived using an time based Input-output model (I-O model that had been developed and validated previously by SIMTech). After the FlexSim model had been verified and validated, disruptions were introduced at various locations in the supply chain so that the propagation of disruptions can be studied.
Based on the results of the simulation model, critical nodes were identified and the factors leading to these nodes being critical were studied, analyzed and given weights. The analysis showed that trading volume and BOM are important factors that render nodes critical. The results also show that the disruption experienced by the end consumer does not change much when there are additional disruptions downstream of critical nodes. However, the disruption propagated to the end consumer fluctuates differently when there are additional disruptions downstream of critical nodes. These findings allow managers to prioritize nodes when planning and to take mitigating actions to manage the effects of disruptive events. |
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