Today, literally millions of safety observations will be made around the world. Thousands of workers will consciously or unconsciously make decisions regarding their behaviors, their coworkers' behaviors and the environment of their work site. Some will write these observations down and others won't. Some will summarize these observations and submit them to a home office, where they will be neatly filed or summarized in a monthly PowerPoint slide. Some will stick them in a folder. Others will simply throw them away. Until recently, very few people knew that those scribbles, checks and notes would enable them to single out a worksite on the brink of an injury. On top of that, they could have this knowledge without ever setting foot on a site a thousand miles away and still be right about 80% of the time.
In order to use observations to reduce injuries we have to solve two problems. First, we need to identify the risk in time to do something about it. Second, we need to know enough about the type of risk we have in order to deploy appropriate resources for prevention. Unfortunately, many of the methods we use to identify risk today have significant limitations. These limitations require us to look more closely at field observations as a superior alternative.
For the past two years, we have employed the most rigorous and advanced analytical models available to scrutinize safety observations from 76 companies. The end result is a wealth of insight that will be useful for any organization interested in lowering the cost of sustaining their zero injury culture or breaking through the common plateaus en route to world class safety performance. We confirmed that the leading indicators in these observations reveal a lot about observers and their worksites. Our research identifies observers who are in need of training or are not culturally aligned with other team members. In addition, we are able to isolate and validate work areas or projects that are more at risk for a number of reasons.
In this paper, we will share our experience in tapping into these observations and tackling the age-old issues associated with field observations everywhere. We will explain why there is a need to measure the quality of an observer and their observations and share a few ideas about what this might mean for your company. Building upon the link between observations and project risk, we will describe how we developed a predictive model that demonstrates a significant correlation between safety observations and claims. The average correlation to claims for the companies in the study was 78% although in some cases this model has a 90% correlation to claims with an error margin of around 3%.
