Modeling metallurgical supply chain resilience using Markov process

Abstract

Metallurgical supply chain consists of raw materials miners, transport, integrated metallurgical enterprise, service centers and local distributors. The overall performance of the supply chain which is subject to disruption from the environment is dependent on performance of its links and its ability to recovery after such event or resilience, respectively. As it is possible to estimate mean time between failures and mean time to recovery for each supply chain link, the whole process can be modeled as a stochastic process using Markov process. This article aims in setting assumptions for a model, creating a model of metallurgical supply chain in the context of supply chain resilience using Markov process which would allow to asses resilience using overall supply chain performance as an indicator.