Understanding Crew Estimations for Icebreaker Assistance in Ice-Covered Waters

Abstract

In ice-covered waters merchant vessels often require assistance from icebreakers to avoid navigational hazards such as besetment and hull damage. Given that icebreakers are a limited resource, accurately estimating the need for assistance is crucial for the efficiency and safety of winter navigation. This estimation is non-trivial and involves several interconnected factors, including traffic restrictions, ice conditions, weather conditions, and vessel characteristics. Currently, icebreaker captains depend heavily on their experience to assess this need; however, there is a lack of understanding in how crews on board actually make these estimations. This study aims to present a clearer understanding of the estimation process used by crews. Employing the Critical Decision Method (CDM), we investigate the crew’s goals, the specific features they consider, and their ranking of these features in their estimation process. In-depth interviews were conducted with four participants with extensive seafaring experience, ranging from 15 to 43 years, and varying degrees of involvement in icebreaker operations, from 6 to 18 years. The analysis of the interviews reveals that despite variations among interviewees in feature rankings, there is consistency in identifying key influencing features. The resulting experience-driven key features and rankings are compared with data-driven analysis by Liu et al. (2024). Both methods identify ice conditions, such as ridged ice, as having a significant impact on estimations. However, interviewees place additional emphasis on vessel characteristics such as engine power. This comparison illustrates how experience-driven insights can enhance data-driven analysis which are often limited by the data quality and quantity. The outcomes of the study will contribute to the development of effective decision support tools for winter navigation.

Keywords: Interviews, Icebreaker, Experience-driven, data-driven, Decision Support Technology

DOI: 10.54941/ahfe1006548