Journal Article

Enhancing Port Shipping Synergy Through Bayesian Network: A Case of Major Chinese Ports

Cheng, Siqian, Hu, Zhihua, Huang, Youfang, Hu, Jiankun

2025

Overview

Port shipping collaboration is vital to greener, more resilient trade, yet decisions remain siloed and uncertain. This study develops a Bayesian network model grounded in empirical data from major Chinese ports, aiming to systematically analyze and enhance port shipping collaborative capacity. The methodology integrates expert knowledge and structural learning algorithms to construct a Directed Acyclic Graph (DAG), representing complex multi-stakeholder interactions among port enterprises, shipping companies, customers, and governmental bodies. Through forward and backward probabilistic inference, the study quantifies how coordinated improvements yield substantial synergistic benefits. Five leverage points stand out: customer engagement in green supply chains, perceived service quality, port digital information integration, multilateral trading maturity, and strict policy enforcement. A newly revealed feedback loop between digital integration and enforcement extends Emerson et al.’s collaborative governance framework, highlighting “digital-era connectivity” as a critical governance dimension and offering managers a focused, evidence-based action agenda.

• Publisher: MDPI AG
• Subject: Algorithms / Bayesian analysis / Bayesian network / Bayesian theory / Collaboration / Customer services / Customers / Digital transformation / Efficiency / Enforcement / Evidence-based practice / Feedback loops / Governance / Graphical representations / Infrastructure / Innovations / Knowledge management / Logistics / Machine learning / Optimization / port shipping collaboration / Ports / Probabilistic inference / probabilistic modeling / Probability theory / Shipping / Shipping industry / Stakeholders / Subsidies / Supply chain sustainability / Supply chains