Learning from Near-miss events


Those at-risk scenarios that have come to pass are near-miss events.

A simple definition would suggest that such events that were potentially dangerous and could’ve materialized into a major incident or accident – define near-misses.

Previously we’ve seen the importance of acknowledging near-miss and establishing a platform for reporting them appropriately. A sound and democratic way of sourcing near-miss information from the workforce via proactive app based reporting.

However, the crux of digital reporting system lies in the way ‘data’ is being used. Data analytics comes to our rescue in this pursuit, enabling us to interpret data and apply it at the workplace. This is only possible when the reporting platforms allow analytical engines to convert information into insights.

There is life beyond the reported near-miss events. This ‘life’ sits within the reported data that is sourced from the different users and even multiple sites. These are the lives that have been potentially saved for not only that particular near-miss event reported, but also for the fact that its insight has offered a chance for the EHS manager to study it, learn from it and define better risk barriers and management strategies to prevent incidents from occurring.

Zero vision defines workplace safety via an applied prevention philosophy (rather than being a mythical numerical target). The next step that organizations should focus after digital reporting platforms is their synchronous relationship with making sense of the EHS ‘big data’ as it aggregates over time.

Here is a reliable methodology to Learn from near-miss events that can be instituted via the following:

  • Defining a Risk Matrix (RM) that is further broken down into risk categories and seriousness color codes. This helps the EHS manager to study near-miss reports more systematically assigning ‘context’ to such events. Risk matrices aren’t unfamiliar territory for any EHS or facility personnel and they can even be built into the app platforms being used.
  • The careful study and analysis of events is therefore broken down into likelihood, consequence and urgency.
  • These factors allow the EHS management team to apply the understood ‘risk context’ into defined operational aspects such as:
    • Poorly written procedures or lack of training
    • Process complexity or personnel issues
    • Age of plant and equipment
    • Environmental problems
    • Random events
  • At the end of such an exercise, prevention and correction methodologies can be determined, applied and EHS management becomes more secure.

It is often thought that software based monitoring, reporting and vigilance would make our lives easier. However, it is entirely up to us to embrace their full potential by studying and analyzing the data aggregated.

With Near-miss reporting based insights available on mobiles and hand-held devices, EHS personnel can visit the site of occurrence of near-misses and investigate.

Potentially high risk near-miss events present challenges to not only the manpower but also the mechanized assets, often both at the same time. An RM based systematic approach provides action to the vigilance for which the near-miss app is responsible.

This approach in combination with the other software platforms such as inspection, observation and audit management cement the risk protection and mitigation strategies.

With the emergence of AI based machine learning as a prominent technology in several different streams, possible integration with risk management and such reporting platforms cannot be ruled out.

Learning from near-miss history is an important exercise. Standards and regulations ensure that there are ‘rules’ by which the organization needs to comply and function.

Learning and applying those learnings from near-miss data ensures better risk barriers protecting the operations. Organizations should not only look for safety apps that have great features but also offer robust analytical engines based on EHS fundamentals.

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