Risk filtering, ranking, and management framework using hierarchical holographic modeling.

Area of Science:

• Systems Engineering
• Risk Management
• Operations Research

Background:

• Large-scale systems require robust methodologies for risk scenario identification and management.
• Existing approaches may not adequately address the complexity and scale of potential risks.
• Systematic risk assessment is crucial for ensuring operational effectiveness and safety.

Purpose of the Study:

• To introduce a comprehensive eight-phase methodological framework for risk scenario management in large-scale systems.
• To provide a structured approach for identifying, prioritizing, assessing, and managing diverse risk scenarios.
• To apply the framework to operations other than war, demonstrating its practical utility.

Main Methods:

• Development of a hierarchical holographic model (HHM) for scenario identification.
• Phased filtering and ranking using bi-criteria and multi-criteria evaluations.
• Quantitative assessment based on likelihood, consequence, resiliency, robustness, and redundancy.
• Risk management option identification, cost-benefit analysis, and risk reduction estimation.
• Safeguarding against missing critical items and incorporating operational feedback for refinement.

Main Results:

• The eight-phase methodology provides a systematic process for handling numerous risk scenarios.
• Qualitative screening is effective initially, followed by quantitative assessments for prioritized scenarios.
• The framework allows for the integration of system user responsibilities and interests.
• Phase VII ensures consideration of previously filtered-out scenarios, enhancing comprehensiveness.

Conclusions:

• The proposed eight-phase methodology offers a philosophical and systematic approach to risk scenario management.
• This framework moves beyond simple scenario ranking to consider the totality of all risks.
• The methodology is adaptable and can be refined through operational feedback for continuous improvement.