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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

50204 - Business and management

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2024

Confidentiality

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

Name of the periodical

Operations and Supply Chain Management

ISSN

1979-3561

e-ISSN

2579-9363

Volume of the periodical

17

Issue of the periodical within the volume

1

Country of publishing house

ID - INDONESIA

Number of pages

31

Pages from-to

1-31

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85192077842