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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

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

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

50201 - Economic Theory

Projekt

<a href="/cs/project/CK03000182" target="_blank" >CK03000182: Výzkum stavebně-technických požadavků na využití národní pozemní infrastruktury TEN-T k řešení krizových situací velkého rozsahu</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

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

ISSN

0353-359X

e-ISSN

1847-2206

Svazek periodika

35

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

HR - Chorvatská republika

Počet stran výsledku

15

Strana od-do

467-481

Kód UT WoS článku

000903265600001

EID výsledku v databázi Scopus

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