Increase Supply Chain Resilience by Applying Early Warning Signals within Big-Data Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG42__%2F23%3A00558929" target="_blank" >RIV/60162694:G42__/23:00558929 - isvavai.cz</a>

Result on the web

<a href="https://hrcak.srce.hr/ojs/index.php/ekonomski-vjesnik/article/view/24686" target="_blank" >https://hrcak.srce.hr/ojs/index.php/ekonomski-vjesnik/article/view/24686</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.51680/ev.35.2.17" target="_blank" >10.51680/ev.35.2.17</a>

Result language

angličtina

Original language name

Increase Supply Chain Resilience by Applying Early Warning Signals within Big-Data Analysis

Original language description

Purpose: The current environment of globally interconnected supply chains, the dynamics of changes and potential threats significantly reduce the time for a possible response. At the same time, there is a growing demand for information necessary to mitigate the consequences. To minimise the damage and increase the resilience of supply chains, it is necessary to identify sources of threats promptly, the extent of possible damage and the possibility of preventing or minimising their impact. The aim of the paper is to structure supply chain threats and search for appropriate datasets for prevention. Methodology: The paper analyses the state-of-the-art through a comprehensive literature review and demonstrates secondary data about how free-access business data can be used as Early Warning Signals to forecast supply chain disruptive events, with a particular focus on international maritime transportation. Results: It was confirmed that companies can access many open datasets, and collecting and aggregating these data can improve their preparedness for future disruptive events. As the most important issue, the authors defined the selection of proper datasets and interpreting results with foresight. Conclusions: Identifying and analysing the relevant Early Warning Signals by companies to prevent supply chain disruptions are essential for keeping their supply chains sustainable and their resilience on a sufficient level. It was proved that general business indicators (PMI delivery time, container capacity, inflation rate, etc.) can help to signal the increasing possibility of maritime traffic problems in ports and container unavailability as usual supply chain disruption types in recent years. Therefore, the companies in the supply chains need to find, collect and analyse the appropriate data, which are, in some cases, free and available. However, it is a substantial task for data analysts to identify the most relevant data and work out the analytical methodology which can be applied as Early Warning Systems (EWS).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50201 - Economic Theory

Project

<a href="/en/project/CK03000182" target="_blank" >CK03000182: Research of construction-technical requirements for the use of TEN-T ground infrastructure to solve large-scale crisis situations</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Ekonomski vjesnik/Econviews - Review of Contemporary Business, Entrepreneurship and Economic Issues

ISSN

0353-359X

e-ISSN

1847-2206

Volume of the periodical

35

Issue of the periodical within the volume

2

Country of publishing house

HR - CROATIA

Number of pages

15

Pages from-to

467-481

UT code for WoS article

000903265600001

EID of the result in the Scopus database

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