Barriers to data analytics for energy efficiency in the maritime industry

Abstract

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.