Comparing conventional and distributed approaches to simulation in a complex supply-chain health system

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Description/Abstract

Decision making in modern supply chains can be extremely daunting due to their complex nature. Discreteevent
simulation is a technique that can support decision making by providing what-if analysis and evaluation
of quantitative data. However, modelling supply chain systems can result in massively large and complicated
models that can take a very long time to run even with today’s powerful desktop computers. Distributed
simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers
to run models.

To investigate this claim, this paper presents experiences in implementing a simulation model with a ‘conventional’ approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period.

However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations