1st ICAI 2020

International Conference on Automotive Industry 2020

Mladá Boleslav, Czech Republic

Step 7. The developed and verified model in step 6 is the starting point for the application phase, which consists in conducting simulation tests. The aim of the tests is to calculate vulnerability measures for all potential possible risky scenarios. Step 8. The basis for making a decision about accepting or rejecting the solution are the results obtained in step 7. If the level of vulnerability measure are acceptable, it can be assumed that the logistic network is sufficiently well protected from threats and hazards. If, however, the test results are not satisfactory, then we should go back to step 5 and make changes to the system design, and then perform further research (steps 6 to 8) for the changed configuration of the network. This procedure should be repeated until we receive fully satisfactory results. 3. Case study – vulnerability assessment The above-described indicators for assessing the vulnerability of supply chains have been determined in simulation studies. The subject of simulation research was a steel mill located in Central Europe. The plant is supplied with large amount of raw material required for a production, of which the strategic one is an iron ore. Annually, delivered and consumed is about seven million tons of various raw materials. The most important due to the weight of goods as well as the cost of deliveries is the iron ore. The delivery can be realized in different ways. Typically, supplier A (from Ukraine) covers 80% of the demand for raw materials, while the remaining 20% provides supplier B (from Brazil). In case of supply disruptions by one of the above mentioned suppliers, the demand for raw materials may be covered by delivery from a supplier C (from Serbia), but this involves higher transport costs and longer time of delivery. Disturbances tend to be rare random events, however, they have a significant impact on the functioning continuity of the logistics system, especially at the operational level. In order to investigate the effect of such disturbances on the continuity of production process, the simplified simulation model of the logistics system of raw materials supply using the AnyLogic software was constructed. The software allows to connect different techniques of the systemmodeling, namely the continuous simulation using the method of system dynamics modeling (SDM), discrete event simulation (DES) and agent-based modeling (ABM). Parameters used in the simulation model cover a period of 100 days business activity and were carried out by one of the various possible scenarios. The scenarios for disruptive events presented in Table 1 have been analyzed using computer simulation.

Table 1: The scenarios for disruptive events Scenario Before disruption (% of delivery)

After disruption (% of delivery) Sup. A Sup. B Sup. C Sup. A Sup. B Sup. C

S1 S2 S3

80% 20% none 80% 20% none 80% 20% none

none none none

20% 80% 100% none 40% 60%

Source: Own elaboration

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