LAPSE:2023.2319
Published Article
LAPSE:2023.2319
A Review of Digital Transformation on Supply Chain Process Management Using Text Mining
February 21, 2023
Industry 4.0 technologies are causing a paradigm shift in supply chain process management. The digital transformation of the supply chains provides enormous benefits to organizations by empowering collaboration among multiple internal and external organizations and systems. This study presents a narrative review explaining the existing knowledge on digital transformation in supply chain process management using text mining. It summarizes the existing literature to explain the current state of the art in supply chain digitalization. This comprehensive review identifies the most important topics and technologies and determines the future trends in this emerging field. We investigate the articles published in Web of Science and Scopus databases and use text mining techniques (clustering and topic modeling) on the article contents. Using VOS viewer, a bibliometric analysis of 395 articles with 12,700 references is analyzed. The contents of the articles are explored using text mining approaches. The synthesized results reveal that the most important topics in digital transformation are “sustainable supply chain management” and “circular economy and industry 4.0 technologies”. The study further discovers big data, data analytics, blockchain, artificial intelligence, machine learning, and the Internet of Things as the most critical technologies for facilitating supply chain digital transformation. Finally, an overlay heatmap analysis of the research articles found that digital transformation, supply chain management, industry 4.0, decision-making, and sustainability are emerging trends in supply chain digitalization.
Keywords
analytics, Big Data, digital transformation, Industry 4.0, supply chain management, text mining
Suggested Citation
Tavana M, Shaabani A, Raeesi Vanani I, Kumar Gangadhari R. A Review of Digital Transformation on Supply Chain Process Management Using Text Mining. (2023). LAPSE:2023.2319
Author Affiliations
Tavana M: Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA 19141, USA; Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, 331 [ORCID]
Shaabani A: Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran 14896-84511, Iran [ORCID]
Raeesi Vanani I: Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran 14896-84511, Iran
Kumar Gangadhari R: Industrial Engineering and Manufacturing Systems, National Institute of Industrial Engineering, Mumbai 400087, India [ORCID]
Journal Name
Processes
Volume
10
Issue
5
First Page
842
Year
2022
Publication Date
2022-04-24
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr10050842, Publication Type: Review
Record Map
Published Article

LAPSE:2023.2319
This Record
External Link

doi:10.3390/pr10050842
Publisher Version
Download
Files
[Download 1v1.pdf] (5.8 MB)
Feb 21, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
104
Version History
[v1] (Original Submission)
Feb 21, 2023
 
Verified by curator on
Feb 21, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.2319
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version