Aim/purpose – The aim of this paper is to present a strategy that allows companies to recover from disasters, when creating a supply chain. Furthermore, it also shows the impact on the company’s resources that are used in the implementation of the strategy in case of small and big disasters. Thanks to the proposed solution, it is possible to analyze each company individually, as well as in groups, at any given time.
Design/methodology/approach – The results were obtained based on a numerical analysis which was performed with the use of MATLAB software. The tests were carried out separately for five companies, as each of them may expect a disaster on any different day. However, the selection of the day when crises occur is carried out in accordance with the probability determined by scientific research.
Findings – The research showed that companies using their resources can continue to fulfill their functions as a link in the Supply Chain despite the fact that they react differently to small disasters compared to big ones. This difference occurs since small disasters in contrast to big ones appear in every company much more often. Consequently, it is more difficult for companies to build their wealth in the case of small disasters. The advantage of the proposed approach is that one can freely test which strategy can cause the least losses for the company as well as for the entire supply chain.
Research implications/limitations – The analysis carried out shows that companies wishing to develop in conditions of unexpected disasters, that cannot be predicted, should regularly increase their assets because they are needed to implement a strategy that allows them to maintain an appropriate operational level. This approach provides tools that enable the selection of strategies with variable parameters, freely determined during the scientific research.
Originality/value/contribution – The paper presents a graphical analysis of the change in the value of resources of a supply chain company over one year period. Such an analysis may be useful for any company that creates a supply chain during the COVID-19 crisis period, which is an unpredictable disaster. The adoption of a Gaussian Pseudo Random Number Generator turned out to be useful as it creates crises days while simulation studies allow us to generate experiments for different data configurations. This paper provides an analysis of small and large disasters separately, which is an approach not presented in the literature.
and 