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CanQualify's Predictive Safety Insights for 2026

CanQualify has recently unveiled a groundbreaking research framework aimed at revolutionizing safety performance measurements in the upcoming year. This framework signifies a pivotal shift from traditional, historical metrics to a more advanced approach that leverages real-time, predictive intelligence for risk evaluation.

For decades, organizations have predominantly relied on metrics like Total Recordable Incident Rate (TRIR) and Experience Modification Rate (EMR) to gauge safety performance. While these historical benchmarks have served their purpose, they often fail to highlight critical early warning signs that may precede accidents. This new framework emphasizes that incorporating predictive analytics into safety management can identify risks with 25-40% greater accuracy compared to solely using lagging indicators.

The foundational research was developed through an exhaustive analysis of a multi-year dataset that integrates contractor records, data from wearable technologies, and industry benchmarks. In addition, it taps into less commonly utilized metrics such as near-miss occurrences, fatigue levels among workers, training adherence, and payment behaviors. When these indicators are evaluated collectively, they can unveil patterns that provide a more holistic view of safety risks.

Advantages of the New Framework

Aaron Harker, the Vice President of Operations at CanQualify, expressed the importance of evolving from a solely historical perspective. “While traditional metrics remain relevant, we realize that the most challenging scenarios often emerge well before an incident transpires. Predictive analytics grant us the ability to recognize these trends early, enabling us to take action before issues arise.”

Key Insights from the CanQualify Predictive Safety Framework

1. Leading Indicators: Uncover risks that traditional metrics overlook, revealing important patterns in training engagement, near-miss statistics, employee fatigue, and financial health, thus offering a clearer picture of risks.
2. Enhanced Detection: Predictive models can surface emerging risk trends weeks or even months ahead of when they typically appear in incident reports.
3. Real-Time Intelligence: The framework highlights a growing acceptance of safety tools designed to automatically identify shifts in conditions and provide timely alerts or recommendations. These tools serve not as a replacement for human oversight, but as additional resources to bolster safety efforts.
4. Hybrid Approach: The research indicates that integrating traditional benchmarks with predictive analytics yields the most effective outcomes, thus advocating for a balanced perspective rather than choosing one method over the other. Organizations employing both methodologies have reported a decrease in incidents, improved planning precision, and heightened stability across their supply chains.

Robert Hacker, VP of Sales and Marketing at CanQualify, emphasizes that the future of safety does not lie in allowing analytics to make decisions autonomously. Instead, the focus is on equipping safety teams with clearer and more reliable information than ever before.

The complete framework, titled 'From Reactive to Predictive: The 2026–2030 Roadmap for AI-Driven Risk Intelligence,' is now available for public viewing at CanQualify's website.

About CanQualify

Founded with a commitment to advance safety practices, CanQualify is a modern prequalification and supply chain risk management platform. It empowers organizations to transition from a compliance-driven approach to proactive, data-informed safety intelligence, protecting both personnel and operational integrity. For further details, you can visit CanQualify's industry trends page.