Edge Computing Infrastructure for Real-Time Maritime Business Decision Support and Operational Efficiency
DOI:
https://doi.org/10.55123/ijisit.v3i1.61Keywords:
Edge Computing, Maritime Business Operations, Real-Time Analytics, Distributed Systems, Decision SupportAbstract
Maritime business operations generate massive data volumes from vessel tracking systems, port logistics networks, cargo documentation, and supply chain coordination, yet centralized cloud computing architectures create latency barriers preventing real-time decision-making critical for time-sensitive operations including vessel routing optimization, port congestion management, and cargo scheduling coordination. This research presents the design and validation of edge computing infrastructure distributing data processing closer to maritime operational environments enabling low-latency analytics, autonomous decision support, and bandwidth-efficient operations at STIP Jakarta's Maritime Business Simulation Center. Employing design science research methodology with qualitative stakeholder evaluation, the study engaged maritime business instructors (n=10), shipping company managers (n=12), and port operations professionals (n=8) through structured interviews examining system performance, decision quality improvements, and operational efficiency gains. The edge computing architecture deployed distributed processing nodes at vessel, port, and logistics facility locations processing real-time operational data locally while synchronizing strategic insights to central cloud infrastructure. Thematic analysis revealed strong support for edge computing deployment, identifying critical themes of latency reduction, bandwidth optimization, and operational resilience enhancement. Pilot implementation across 8-month period demonstrated 87% latency reduction (from 450ms to 58ms average query response), 73% bandwidth consumption decrease enabling operations in limited connectivity environments, and 64% improvement in decision timeliness supporting operational cost reductions estimated at $47,000 monthly across simulated 50-vessel fleet operations, contributing validated edge computing architectures and empirical evidence supporting distributed intelligence deployment in maritime business contexts addressing real-time operational requirements and connectivity constraints.
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Copyright (c) 2026 Marihot Simanjuntak, Brenhard Mangatur Tampubolon, Winarno Winarno
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