Digital Twin Technology, Predictive Analytics, and Sustainable Project Management in Global Supply Chains for Risk Mitigation, Optimization, and Carbon Footprint Reduction through Green Initiatives

This review explores the integration of digital twin technology, predictive analytics, and sustainable project management to enhance global supply chain efficiency, resilience, and environmental sustainability. Digital twins provide real-time virtual representations of physical supply chain systems, enabling predictive analytics to identify potential disruptions and optimize decision-making processes. By combining these advanced technologies with sustainable project management practices, such as circular supply chains and green logistics, organizations can proactively address risks while reducing their carbon footprint. The focus on data- driven insights and scenario analysis facilitates informed risk mitigation and resource optimization. The integration of frameworks like the Triple Bottom Line emphasizes the importance of balancing economic, social, and environmental objectives in project management. This approach aims to improve supply chain performance, drive sustainability efforts, and create a resilient logistics network that adapts effectively to market uncertainties and environmental challenges.

Authors

Publication Details

Journal
International Journal of Innovative Science and Research Technology (IJISRT)
Published
2024-11-23
DOI
https://doi.org/10.38124/ijisrt/ijisrt24nov1344
Citations
36
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
12.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital Twin Technology, Predictive Analytics, and Sustainable Project Management in Global Supply Chains for Risk Mitigation, Optimization, and Carbon Footprint Reduction through Green Initiatives

Joy Onma Enyejo, Toyosi Motilola Olola, Babatunde Olusola Awotiwon, Chidimma Judith Ihejirika et al.
36 citations
International Journal of Innovative Science and Research Technology (IJISRT)
Digital Transformation in Industry
12.21
article

Digital Twin Technology, Predictive Analytics, and Sustainable Project Management in Global Supply Chains for Risk Mitigation, Optimization, and Carbon Footprint Reduction through Green Initiatives

Joy Onma Enyejo, Toyosi Motilola Olola, Babatunde Olusola Awotiwon, Chidimma Judith Ihejirika, Ololade Peter Fajana, Irene Sele Jok
article en
36 citations

Abstract

This review explores the integration of digital twin technology, predictive analytics, and sustainable project management to enhance global supply chain efficiency, resilience, and environmental sustainability. Digital twins provide real-time virtual representations of physical supply chain systems, enabling predictive analytics to identify potential disruptions and optimize decision-making processes. By combining these advanced technologies with sustainable project management practices, such as circular supply chains and green logistics, organizations can proactively address risks while reducing their carbon footprint. The focus on data- driven insights and scenario analysis facilitates informed risk mitigation and resource optimization. The integration of frameworks like the Triple Bottom Line emphasizes the importance of balancing economic, social, and environmental objectives in project management. This approach aims to improve supply chain performance, drive sustainability efforts, and create a resilient logistics network that adapts effectively to market uncertainties and environmental challenges.

International Journal of Innovative Science and Research Technology (IJISRT)
Openalex Percentile: Top 1%
Digital Transformation in Industry
12.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.