What if you were driving somewhere, and could only see backwards in the rear-view mirror? Sounds scary, but this is pretty much how traditional Environmental, Health and Safety (EHS) metrics like incident and injury rates have been used for decades to manage performance. Such lagging indicators measure what occurred in the past. They give you a good view of where you’ve been, but not where you’re going.
With the advent of new Big Data technology and predictive analytics tools, EHS business leaders now have the opportunity to intelligently analyze large amounts of safety, business, and operational data to identify predictive, or leading, indicators, of future safety performance. And to better use these leading indicators to drive continuous improvement.
Lagging indicators: what’s wrong with the old standbys?
Lagging safety indicators are useful – up to a point. They do a good job of documenting historical safety and EHS results in hard numbers like recordable incident and lost-time injury rates, number and cost of environmental releases, etc. They’re helpful in measuring the outcomes of your EHS management system. This kind of lagging indicator is also easy to calculate with readily available data, and they are easily compared across various organizational levels, and even between companies and industries.
But they are reactive in that they only measure the failures that occurred. Measuring failure is of limited use in managing future performance. While it’s useful to know “what happened”, continuous improvement depends on knowing why events occurred, and even better, being able to predict what will happen next. Knowing “why?” and “what’s next?” enables resources to be better allocated to optimize risk reduction efforts and prevent incidents proactively.
Leading indicators are now more feasible to implement
Leading indicators are being used more and more in combination with traditional lagging indicators to drive performance improvement. Leading indicators measure progress and accomplishments toward achieving desired safety outcomes, rather than the outcome itself.
They are typically harder to measure than lagging indicators, but are relatively easy to influence. Some examples are: performance of audits and inspections, corrective action completion, training, and safety observations.
The use of leading indicators of EHS performance is not new. But implementation has been challenging because there are no agreed upon standard leading metrics, and they can be difficult to track and compare across different organizations. It is also hard to even identify which metrics are consistently predictive of outcomes. Big Data technology is helping to overcome these challenges.
Big Data Technology and Predictive Analytics as an Enabler
Big Data technology now makes it cost-effective to collect large quantities of data from disparate systems and analyze it in new and powerful ways. Instead of analyzing safety and incident data in isolation, they can be combined with various business and operational data sources to identify the factors that correlate with EHS outcomes. For example, data from incident investigations can be analyzed along with HR records and equipment maintenance activities.
In one real-world case, a global manufacturer was able to use such a predictive analytics approach to determine that injury rates were correlated with the amount of overtime labor, and with on-time performance of preventive maintenance work. These factors are monitored as leading indicators, and corrective actions taken appropriately, with the end result of reduced injury rates.
Most organizations are using leading indicators to some extent today to manage EHS performance. The availability of new Big Data technologies including predictive analytics tools opens the door for broader use of such predictive metrics. Here are several tips for further incorporating them into your programs:
- Add more leading indicators to the mix- the traditional lagging indicators are still valid in measuring absolute EHS outcomes. A balanced portfolio of metrics with leading indicators will give confidence that actions being taken today such as audits, training, maintenance, etc. will improve outcomes tomorrow.
- Leverage non-EHS data to identify the right leading indicators – part of the promise of Big Data is the ability to gather and analyze data from many different sources, then analyze it to identify the factors that are predictive of outcomes. This means the ability to not only better analyze and act on EHS data, but to do so in combination with operational and business data.
- Keep it simple and actionable- As you further incorporate leading indicators into your EHS management system, try to keep it simple. This means giving priority to metrics already being tracked, avoiding new data collection efforts. Also make sure that leading indicators really provide actionable information. If an indicator goes out of the acceptable range, can and will appropriate actions be taken to reduce risk and improve safety? Otherwise it could just become just another data collection exercise.