Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F24%3A63576078" target="_blank" >RIV/70883521:28120/24:63576078 - isvavai.cz</a>

Výsledek na webu

<a href="https://journal.oscm-forum.org/publication/article/data-analytics-in-supply-chain-management-a-state-of-the-art-literature-review" target="_blank" >https://journal.oscm-forum.org/publication/article/data-analytics-in-supply-chain-management-a-state-of-the-art-literature-review</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31387/oscm0560411" target="_blank" >10.31387/oscm0560411</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Popis výsledku v původním jazyce

In recent years, there has been a growing surge of interest in the application of data analytics (DA) within the realm of supply chain management (SCM), attracting attention from both practitioners and researchers. This paper presents a comprehensive examination of recent implementations of DA in SCM. Employing a systematic literature review (SLR), we conducted a meticulous analysis of over 354 papers. Building upon a prior SLR conducted in 2018, we identify contemporary areas where DA has been applied across various functions within the supply chain and scrutinize the DA models and techniques that have been employed. A comparison between past findings and the current literature reveals a notable upsurge in the utilization of DA across most SCM functions, with a particular emphasis on the prevalence of predictive analytics models in contemporary SCM applications. The findings of this paper offer a detailed insight into the specific DA models and techniques currently in use across various SCM functions. Additionally, a discernible increase in the adoption of mixed or hybrid DA models is observed. Fowever, several research gaps persist, including the need for more attention to real-time DA in SCM, the integration of publicly available data, and the application of DA to mitigate uncertainty in SCM. To address these areas and guide future research endeavors, the paper concludes by delineating six concrete research directions. These directions offer valuable avenues for further exploration in the field.

Název v anglickém jazyce

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Popis výsledku anglicky

In recent years, there has been a growing surge of interest in the application of data analytics (DA) within the realm of supply chain management (SCM), attracting attention from both practitioners and researchers. This paper presents a comprehensive examination of recent implementations of DA in SCM. Employing a systematic literature review (SLR), we conducted a meticulous analysis of over 354 papers. Building upon a prior SLR conducted in 2018, we identify contemporary areas where DA has been applied across various functions within the supply chain and scrutinize the DA models and techniques that have been employed. A comparison between past findings and the current literature reveals a notable upsurge in the utilization of DA across most SCM functions, with a particular emphasis on the prevalence of predictive analytics models in contemporary SCM applications. The findings of this paper offer a detailed insight into the specific DA models and techniques currently in use across various SCM functions. Additionally, a discernible increase in the adoption of mixed or hybrid DA models is observed. Fowever, several research gaps persist, including the need for more attention to real-time DA in SCM, the integration of publicly available data, and the application of DA to mitigate uncertainty in SCM. To address these areas and guide future research endeavors, the paper concludes by delineating six concrete research directions. These directions offer valuable avenues for further exploration in the field.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Operations and Supply Chain Management

ISSN

1979-3561

e-ISSN

2579-9363

Svazek periodika

17

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

ID - Indonéská republika

Počet stran výsledku

31

Strana od-do

1-31

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85192077842