1345. Applications of Predictive Analytics in Container Shipping Supply Chains: A Systematic Review
Invited abstract in session WB-32: Green maritime and port logistics-1, stream Maritime and Port Logistics.
Wednesday, 10:30-12:00Room: Maurice Keyworth 1.09
Authors (first author is the speaker)
| 1. | Nader Alshammari
|
| Management School, University of Liverpool | |
| 2. | Dongping Song
|
| School of Management, University of Liverpool | |
| 3. | Yuanjun Feng
|
| Operations & Supply Chain Management, University of Liverpool | |
| 4. | XINJIE XING
|
| Operations and supply chain department, University of Liverpool |
Abstract
Container Shipping Supply Chains (CSSC) encompass the movement of containerized cargo across global maritime networks. CSSCs represent a significant portion of maritime trade’s value, making their resilience and operational efficiency essential to global trade. Predictive Analytics (PA) provide powerful tools to enhance CSSC performance and mitigate disruptions in these complex logistics systems. Despite a rise of PA-focused studies in the CSSC domain, existing research on its applications remains scattered and an overview and synthesis of existing literature is lacking, limiting a comprehensive understanding of the field. To address this, we conducted a systematic literature review (SLR) guided by PRISMA practices, screening 4,652 publications, of which 105 met the eligibility criteria. These publications were classified according to specific CSSC segments, and their implications on logistic performance were analysed by mapping dependent variables to established logistic performance frameworks. PA applications are analysed and discussed in terms of forecasting methodologies, approaches, and validation techniques, highlighting key challenges around data quality, methodological rigor, scalability, and generalizability. By offering a structured assessment of PA applications in CSSC, we hope this SLR can inform academics, practitioners, and policymakers of avenues for future research and practices.
Keywords
- Maritime applications
- Forecasting
- Logistics
Status: accepted
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