人工知能とビッグデータを用いた海上サプライチェーンの最適化
OPTIMIZING MARITIME SUPPLY CHAINS USING ARTIFICIAL INTELLIGENCE AND BIG DATA (原題)
Dumitru-Cătălin Vasile
🤖 gxceed AI 要約
日本語
本論文は、AIとビッグデータ分析が海上サプライチェーンを航海・港湾・保守・可視化の4層で最適化する可能性を検討する。ETA予測誤差0.25〜5%、コンテナ取扱時間最大25%削減、アイドル設備15〜20%削減などの報告成果を整理し、AISデータ・機械学習・デジタルツインを結ぶ4層フレームワークを提案。黒海最大のコンスタンツァ港を事例に段階的導入ロードマップを示し、データ品質やサイバーセキュリティ等の障壁も分析する。
English
This paper reviews how AI and big data analytics optimize maritime supply chains across four layers: voyage/route optimization, port operations, predictive maintenance, and end-to-end visibility. It synthesizes reported gains (ETA errors ~0.25–5%, container handling time cuts up to 25%, idle-equipment reductions 15–20%) and proposes a four-layer framework linking AIS data, ML models, and digital twins to supply-chain KPIs. The framework is mapped to the Port of Constanța with a staged roadmap, and adoption barriers (data quality, cybersecurity, infrastructure, capability) are analyzed.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
海運は日本企業のScope 3排出と物流コストに直結する。AIによる港湾・航路最適化は、SSBJのScope 3算定精度向上や物流効率化を通じた脱炭素に寄与しうるが、本論文はルーマニア港湾が事例で日本への直接示唆は限定的。
In the global GX context
Maritime logistics is a hard-to-abate sector central to global Scope 3 emissions and supply-chain resilience. This paper's AI-driven optimization framework offers a template for ports and operators to cut emissions and costs, aligning with IMO decarbonization goals and emerging disclosure requirements on logistics emissions.
👥 読者別の含意
🔬研究者:AIとビッグデータを海運物流のKPI最適化に適用する際の4層フレームワークと実証設計の参考になる。
🏢実務担当者:港湾・海運オペレーターがAI導入ロードマップを検討する際の、期待効果と導入障壁の整理に有用。
🏛政策担当者:港湾インフラ整備やデータ標準化、サイバーセキュリティ政策を検討する際の基礎資料となる。
📄 Abstract（原文）
Maritime transport carries over 80% of global merchandise trade by volume, yet the sector faces mounting pressure from chokepoint disruptions, port congestion, freight rate volatility and decarbonization mandates. This paper examines how the convergence of artificial intelligence (AI) and big data analytics can optimize maritime supply chains across four operational layers: voyage and route optimization, port and terminal operations, predictive asset maintenance, and end-to-end visibility. Drawing on a structured review of recent peer-reviewed literature and industry evidence, the study synthesizes reported performance gains, including vessel estimated-time-of-arrival prediction errors in the range of roughly 0.25–5% depending on the dataset and method, container handling-time reductions of up to 25%, and idle-equipment reductions of 15–20% in digital-twin-enabled terminals. A four-layer conceptual framework is proposed that links Automatic Identification System (AIS) data streams, machine-learning models and digital twin simulations to measurable supply-chain key performance indicators. The framework is then mapped to the Port of Constanța, the largest port on the Black Sea, as an illustrative application from which a staged optimization roadmap is derived, building on the port's current Port Community System programme; a design for the empirical validation of the framework is also specified. The paper analyses the principal barriers to adoption, namely data quality and interoperability, cybersecurity exposure, infrastructure readiness and organizational capability, and outlines a phased implementation pathway for port authorities and shipping operators. The findings indicate that AI and big data are structural enablers of resilient, efficient and lower-emission maritime logistics rather than incremental tools.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.53464/jmte.02.2026.11first seen 2026-09-19 05:41:33 · last seen 2026-09-22 05:10:52
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