MODELING METALLURGICAL SUPPLY CHAIN RESILIENCE USING MARKOV PROCESS

1 ČECH Martin
Co-authors:
1 LENORT Radim 1 WICHER Pavel 2 TOLSTYKH Tatiana 2 SHKARUPETA Elena
Institutions:
1 VSB - Technical University of Ostrava, Faculty Ostrava, Czech Republic, EU, martin.cech@vsb.cz
2 Institute of Economics and Industrial Management, The National University of Science and Technology “MISIS”, Moscow, Russia, nshmeleva@misis.ru
Conference:
28th International Conference on Metallurgy and Materials, Hotel Voronez I, Brno, Czech Republic, EU, May 22nd - 24th 2019
Proceedings:
Proceedings 28th International Conference on Metallurgy and Materials
Pages:
1798-1803
ISBN:
978-80-87294-92-5
ISSN:
2694-9296
Published:
4th November 2019
Proceedings of the conference were published in Web of Science and Scopus.
Metrics:
61 views / 24 downloads
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.

Keywords: metallurgy, modeling, supply chain, resilience, Markov process
Scroll to Top