Traditional risk assessments struggle with these events, necessitating a strategic approach to risk management. Charles Perrow's "normal accidents" theory explains that accidents are inherent in complex systems due to interactive complexity, tight coupling, and catastrophic potential. HILP events are hard to predict due to their novelty and nonlinear propagation. Organizations must adopt AI and machine learning for early anomaly detection, enhancing their ability to identify precursors and mitigate impacts. Proactive risk management and sophisticated early-warning systems can help reduce the potential damage from inevitable failures.
Product & Capabilities
HILP Events on IBM i: Understanding and Mitigating High-Impact, Low-Probability Failures
Omnilogy's Maciej Wielgus highlights the challenges of high-impact, low-probability (HILP) failures in IBM i systems, crucial for many industries. Despite investments in reliability, these systems' complexity makes them prone to unpredictable failures with severe financial and reputational consequences.
May 14, 2026 ·
5 min read · Maciej Wielgus
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