In the past, we simply looked at lag indicators to measure safety performance. Utilizing a root cause analysis methodology, human factors are identified as the primary causes of workplace events, injuries, and fatalities.
Safety professionals now have access to more data than ever before, including predictive, leading, and sensor data, and they recognize that risk is caused by more than just the human aspect.
Machine learning and AI technologies generate so much data that it’s difficult to see the forest for the trees. It is now the responsibility of a team of specialists to monitor, measure, prioritize, and understand where their safety measures will have the greatest impact.
They require data-driven technologies to precisely identify core causes, assess patterns, and determine which proactive approaches are most effective in driving safety performance.
When asked how they want to use data to improve safety performance, health and safety managers say they wish to:
Predict workplace injuries.
Monitor and benchmark their safety culture.
Improve their compliance.
They tie safety to productivity.
What are you going to do with all of this data?
Data collection is currently a top goal for many organizations. This necessitates identifying any underused and underanalyzed data that you can. Because the data will be used to calculate the safety performance score, it must be complete and “clean.”
Data from enterprise resource planning systems, equipment maintenance systems, third-party information from weather reports, geospatial information from images, training records, and near-miss event reports are examples of data sources.
When the data is ready, the next step is to analyze and interpret the differences between leading and trailing indicators using:
A descriptive context in which data is utilized to describe what occurred.
A diagnostic tool capable of explaining why something occurred (by looking at leading and lagging indicators)
Predictive analytics is the use of variables to test the hypothesis of what will happen.
The data is presented in a prescriptive manner in order to determine tactical instructions on the measures to be taken to avert an incident.
Fortunately, technology is here to assist. On the market, there are several data analytics solutions and business intelligence services that predict safety outcomes and prospective safety hazards.
When concerns are identified, correction can begin with focused activities and devoted resources. These results can be used to improve operational efficiencies and safety performance. According to research, organizations still have a long way to go before integrating leading indicators when measuring safety performance.