Use of Big Data Analysis to Identify Possible Sources of Supply Chain Disruption Through the DOTMLPFI Method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG42__%2F23%3A00558360" target="_blank" >RIV/60162694:G42__/23:00558360 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.logforum.net/pdf/18_3_5_22.pdf" target="_blank" >https://www.logforum.net/pdf/18_3_5_22.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17270/J.LOG.2022.731" target="_blank" >10.17270/J.LOG.2022.731</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Use of Big Data Analysis to Identify Possible Sources of Supply Chain Disruption Through the DOTMLPFI Method

Popis výsledku v původním jazyce

Backgrounds: The presented research deals with the investigation of how big data analytics can help predict possible disruptive events in supply chains. The supply chain can be considered a complex system with a wide spectrum of possible sources of internal and external disruptions. Since the individual entities of the supply chains operate in a particular environment and interact with this environment, there is a certain level of mutual interdependency. This set of interconnected interactions within the supply chain will be the unit of analysis. Methods: There are many internal and external sources of supply chain disruption, which opens up the potential application of Big Data Analysis (BDA) as an early warning tool. To analyse the possible application of the BDA to identify sources of supply chain disruptions, we conduct a bibliometric analysis to define an appropriate structure for supply chain risk classification as well as appropriate keywords that make data collection quicker and easier. The DOTMLPFI methodology was used to systematically identify the most relevant risks threatening the supply chains. Results: The proposed research approach creates a possible framework to support the operational sustainability and resilience of the supply chain as a system, toward internal and external disruptions. The research results also point out the most explored attributes of supply chain disruption. The conducted bibliometric research and content analysis support the theoretical framework of using BDA as a possible early warning tool, especially for the identification of possible sources of supply chain disruption. The approach of grouping Big Data sources into categories based on DOTMLPFI groups allows to identify the appropriate keywords for their later BDA analysis. The analytical framework provides a starting point for individual supply chain entities to understand risks and systematically collect the appropriate data in the required structure about them. Conclusion: The complexity of supply chains, together with the increasing possibility of digital applications, requires a new analytical framework for evaluating the overall supply chain, with the possible application of new data sources and analytical approaches regarding the risks threatening the chain. DOTMLPFI methodology allows covering all the relevant categories of supply chain risks, and by proposing relevant keywords and data sources it can help companies to find the appropriate open-source, up-to-date information and be prepared for disruptive events.

Název v anglickém jazyce

Use of Big Data Analysis to Identify Possible Sources of Supply Chain Disruption Through the DOTMLPFI Method

Popis výsledku anglicky

Backgrounds: The presented research deals with the investigation of how big data analytics can help predict possible disruptive events in supply chains. The supply chain can be considered a complex system with a wide spectrum of possible sources of internal and external disruptions. Since the individual entities of the supply chains operate in a particular environment and interact with this environment, there is a certain level of mutual interdependency. This set of interconnected interactions within the supply chain will be the unit of analysis. Methods: There are many internal and external sources of supply chain disruption, which opens up the potential application of Big Data Analysis (BDA) as an early warning tool. To analyse the possible application of the BDA to identify sources of supply chain disruptions, we conduct a bibliometric analysis to define an appropriate structure for supply chain risk classification as well as appropriate keywords that make data collection quicker and easier. The DOTMLPFI methodology was used to systematically identify the most relevant risks threatening the supply chains. Results: The proposed research approach creates a possible framework to support the operational sustainability and resilience of the supply chain as a system, toward internal and external disruptions. The research results also point out the most explored attributes of supply chain disruption. The conducted bibliometric research and content analysis support the theoretical framework of using BDA as a possible early warning tool, especially for the identification of possible sources of supply chain disruption. The approach of grouping Big Data sources into categories based on DOTMLPFI groups allows to identify the appropriate keywords for their later BDA analysis. The analytical framework provides a starting point for individual supply chain entities to understand risks and systematically collect the appropriate data in the required structure about them. Conclusion: The complexity of supply chains, together with the increasing possibility of digital applications, requires a new analytical framework for evaluating the overall supply chain, with the possible application of new data sources and analytical approaches regarding the risks threatening the chain. DOTMLPFI methodology allows covering all the relevant categories of supply chain risks, and by proposing relevant keywords and data sources it can help companies to find the appropriate open-source, up-to-date information and be prepared for disruptive events.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

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

LOGFORUM

ISSN

1895-2038

e-ISSN

1734-459X

Svazek periodika

18

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

11

Strana od-do

309-319

Kód UT WoS článku

000866714600004

EID výsledku v databázi Scopus

2-s2.0-85139386056