Theoretical Framework: Bow-Tie Analysis & SC Resilience

The study is grounded in supply chain risk management theory, using the bow-tie model as a central analytical tool to understand and categorise mitigation strategies.

1. The Bow-Tie Model

This model visually maps the path from potential causes of a risk event to its potential consequences, with barriers acting as safeguards.

• Left Side (Fault Tree): Identifies potential causes or threats (e.g., severe storm, mechanical failure).
• Central Knot (Event): The undesired incident itself (e.g., ship becoming ice-bound, collision).
• Right Side (Event Tree): Outlines potential consequences of the event (e.g., delay, spill, loss of life).
• Barriers:
  • Preventive Barriers: Aim to prevent the event from occurring (e.g., ice-class hull, accurate weather routing).
  • Protective Barriers: Aim to mitigate impacts after the event occurs (e.g., icebreaker escort, efficient SAR response).

2. Supply Chain Risk in a Network Context

Risk is viewed as the disruption of flows (information, resources, products, finances) between interdependent nodes in a network. The Arctic context amplifies these risks due to:

• Extreme isolation and long distances between nodes.
• Lack of redundant infrastructure (ports, communications).
• Uncertain and dynamic data (bathymetry, ice charts, weather).

3. A Resilience-Based Perspective

Moving beyond pure risk prevention, the study incorporates resilience thinking—the system’s adaptive capacity to deal with, recover from, and learn from disruptions. For Arctic SCs, this means building the ability to:

• Anticipate changes in ice and weather conditions.
• Monitor the operational environment in real-time.
• Respond effectively when preventive barriers fail.
• Learn from incidents to improve future reliability.

This framework shifts the focus from merely avoiding events to building robust systems capable of maintaining function amidst inevitable Arctic hazards.

Methodology & Data

Research Design & Case Selection

The study uses a qualitative, case-based methodology to provide in-depth insights into the operational realities of Arctic shipping:

• Geographic Focus: Baffin Bay and the waters surrounding Greenland.
• Case Categories: Three types of maritime supply chain activities were analyzed:
  1. Community Supply Vessels: Serving remote settlements (e.g., Royal Arctic Line in Greenland).
  2. Transit Traffic (Container Ships): Exploring the feasibility of routes like the Northwest Passage.
  3. Bulk Carriers & Tankers: Supporting extractive industries (e.g., mining projects, LNG transport).
• Data Sources: Publicly available examples, company reports (e.g., Royal Arctic Line), government documents (Greenlandic, Danish Arctic Command, Canadian Coast Guard), incident reports, and scientific literature.

Analytical Procedure: Applying the Bow-Tie

1. Hazard Identification:
Arctic-specific hazards were categorized into four themes (Technical, Safety, Environmental, Reputational) based on literature and case evidence (see Table 1 in the paper).

2. Barrier Analysis:
For each case and hazard, the researchers identified:

• Preventive Barriers: Technologies implemented to prevent hazards (e.g., Azipod propulsion, ice-class hulls, satellite monitoring).
• Protective Barriers: Organisational and infrastructural capacities to respond to events (e.g., icebreaker fleet availability, SAR helicopter coverage, spill response plans).

3. Gap Identification:
The effectiveness and geographic distribution of these barriers were evaluated to identify critical vulnerabilities in the supply chain network, particularly in the high Arctic regions of northwest Canada and Greenland.

Key Findings

1. Critical Hazards to Reliability

• Dynamic Ice Conditions: Sea ice and icebergs are the most pervasive threats, capable of blocking access to ports (e.g., Ittoqqortoormiit in 2020) and transit routes unexpectedly.
• Severe Weather & Icing: Polar lows, fog, and marine icing impair navigation and equipment, with icing posing a direct threat to vessel stability and safety systems.
• Infrastructural Deficits: Many communities lack basic port facilities, requiring slow and weather-dependent barge operations. Communication and navigational data are often poor.
• Geographic Concentration of Risk: The most severe conditions and the newest shipping lanes are in areas farthest from support infrastructure.

2. Technological (Preventive) Barriers: Advances and Limitations

• Ship Design: Innovations like azimuth thrusters, double-acting ships (sailing stern-first in ice), and higher ice classes (e.g., Arc7) have improved capability.
• The Implementation Gap: Many vessels operating in the region are old (1980s/90s) and cannot be easily upgraded. New technology is expensive and not economically justified for all operators.
• Environmental Tech: Shift towards hybrid/LNG power and bans on heavy fuel oil are emerging as reputational and environmental preventive barriers.
• Case Example – Failure: Ironbark’s 2018 attempt to sail into Citronen Fjord (83°N) failed due to unanticipated ice buildup, highlighting that technology alone cannot guarantee access.

3. Organizational (Protective) Barriers: Severe Shortages

• Icebreaker Shortfall: Canada has limited icebreakers relative to its vast Arctic coastline. Greenland has no dedicated icebreakers, relying on ship ice-class or foreign assistance.
• Inadequate SAR Coverage: Helicopter SAR in the Canadian Northwest Passage is based at distant “forward operating locations.” Greenland’s few helicopters are based on the populated west coast. This violates the Polar Code’s ideal of a 5-day max rescue time in some high Arctic areas.
• Case Example – Consequence: In 2018, stretched Canadian Coast Guard resources due to southern operations caused multi-week delays for community resupply in Nunavut.

4. The Traffic-Infrastructure Mismatch

While ship traffic is increasing and moving further north (e.g., tripling in Baffin Bay region from 1990-2015), investment in the protective “safety net” has not kept pace. The core infrastructure gap leaves the entire supply chain system vulnerable to routine Arctic hazards.

Implications & Future Research

Theoretical Contributions

• Applies Bow-Tie to Arctic SCs: Demonstrates the model’s utility for structuring complex, multi-hazard risk environments.
• Integrates Resilience: Argues for viewing Arctic SC reliability through a resilience lens (adaptive capacity) rather than just a risk prevention lens.
• Highlights Network Vulnerability: Emphasizes that reliability depends on the weakest link in a network of infrastructure, not just on individual ship capability.

Practical Implications for Operators & Governments

For Shipping Companies & Industries:

• Invest in appropriate ice-class technology, but recognize its limits against extreme and dynamic Arctic conditions.
• Factor significant schedule buffer and high operating costs into Arctic business models. “Just-in-time” is not viable.
• Engage proactively with authorities on emergency planning, given the limited response assets.

For Arctic Nations (Canada, Denmark/Greenland):

• Strategic Infrastructure Investment: Prioritize icebreaker and SAR asset placement closer to emerging high-traffic or high-risk areas in the northern Arctic, not just southern hubs.
• Develop Port Infrastructure: Support the development of basic port facilities in key communities and industrial sites to reduce reliance on risky barge operations.
• Enhance Coordination: Strengthen the practical implementation of international Arctic SAR and pollution response agreements.
• Support the Polar Code: Use and further develop the IMO Polar Code as a baseline for strengthening both preventive (ship design) and protective (operational) barriers.

Limitations & Future Research

• Case-Based Generalisability: Findings are based on specific cases in Baffin Bay/Greenland; other Arctic regions (e.g., Russian Arctic with its heavier icebreaker investment) may differ.
• Data Availability: Fragmented and limited public data on precise traffic patterns and incident rates in the region.
• Dynamic Baseline: Rapid climatic and economic changes mean the risk landscape is constantly evolving.

Future Research Directions:

• Quantify the economic cost of unreliability (delays, cancellations) for Arctic communities and industries.
• Model optimal placement strategies for icebreakers and SAR assets based on projected future traffic patterns.
• Study the effectiveness of specific new technologies (e.g., hybrid power, advanced ice forecasting) in real-world Arctic operations.
• Explore governance models for funding and maintaining the “public good” of Arctic safety infrastructure amidst increasing commercial use.