海运业能效数据分析的障碍

Veronica Jaramillo Jimenez, Z. H. Munim, Hyungju Kim, Prasad Perera
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引用次数: 0

摘要

海运业被敦促减少温室气体排放,提高船舶能效。一个潜在且成本相对较低的解决方案是采用数据分析作为辅助手段,以确定需要改进的领域,从而优化船舶性能和燃料消耗。本研究调查了有意利用数据作为运营改进手段的海事组织在数据分析方面遇到的障碍。本研究采用 DELPHI - 最佳最差法 (BWM) 混合方法,对数据分析提高能效的障碍进行识别和排序。研究结果显示,共有 20 个子障碍,分为五个主要障碍。这些障碍分为两大类:组织障碍(包括文化、管理和经济)和技术障碍(包括数据管理和数据分析)。本研究还强调了每个类别中最关键的障碍,揭示了数据治理不足、实施综合系统所需的多个供应商以及合同和限制性条款是阻碍海事领域采用大数据分析的主要障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Barriers to data analytics for energy efficiency in the maritime industry
The maritime industry is urged to reduce greenhouse gas emissions and improve the energy efficiency of ships. A potential and relatively inexpensive solution is to implement data analytics as an aid to identify areas of improvement to optimize ship performance and fuel consumption. This study investigates barriers to data analytics for maritime organizations intending to utilize data as a means of operational enhancement. This study used the DELPHI – Best Worst Method (BWM) hybrid approach to identify and rank the barriers to data analytics for energy efficiency. The results revealed a total 20 sub-barriers grouped into five main barriers. These barriers fall into two overarching categories: Organizational barriers, including Cultural, Managerial, and Economic, and Technological barriers, comprising Data Management and Data Analysis. This study also highlights the most critical barriers within each category, revealing inadequate data governance, multiple suppliers needed to implement a comprehensive system and contracts and restrictive clauses as the dominant barriers that hamper the adoption of big data analytics in the maritime domain.
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