Using Predictive Analytics to Reduce Risk in Contractor Selection

Nov 21, 2022
Veriforce Team

When it comes to contractor selection, safety risks are predicated on lagging indicators based on legacy safety records, including the number of fatalities, accidents, and near misses.

Today, predictive analytics and the development of models can further fine-tune your contractor selection process by engaging data analytics. Leveraging accurate predictive modeling gives you an edge in the contractor selection process, providing additional insight into prospective contractors, leading to a better-informed hiring decision.

Improving contractor selection with predictive analytics

Predictive analytics provide operators with a tool to lower risk when onboarding new contractors. The predictive analytics model has the ability to identify risks before the operator selects the contractor or trains them on their own process. The ability to predict a contractor’s safety record when evaluating contractors can be invaluable.

Predictive models can also shorten the timespan from signing an agreement with a new contractor to getting them on the jobsite. By eliminating the prequalification stage most operators use as a final opportunity to evaluate contactors, predictive modeling can save hiring clients both time and money.

Operators, specifically those managing smaller organizations, typically don’t have the resources to devote heavily to the contractor onboarding process. Without a proper vetting process, the risk of workplace accidents rises. Risky contractors endanger not only themselves but an operator’s full-time employees as well. Incidents can result in injuries or fatalities, and lead to work stoppages and higher costs.

Secondary to worker safety for operators is ensuring company goals are met and work is done on time and on budget. Any incident can be costly if operations are shut down, and the liability of remediating the damage can be sky high.

Factors influencing contractor risk level

Predictive safety models seek to understand and map contractor risk factors based on a wide variety of survey questions. A comprehensive model might ask a contractor upward of 400 questions around previous incidents and recordable rates, the contractor’s policies and procedures, the type of services they perform and more.

For example, a contractor that has a hearing protection safety policy and trains on proper hearing protection should fare better in a predictive analytics model than a contractor that does not have such a policy.

Additionally, a painting contractor that isn’t working on scaffolding likely will have a lower predictive rank assigned to it than, say, a wild well control company in the oil and gas industry, based on the inherent danger level of each job.

It is important to note that while some predictive models can predict the probability of an adverse event with incredible accuracy, the models currently cannot predict the severity of an event. 

The next frontier for predictive analytics

When it comes to predictive analytics, models are improving toward an ultimate goal of creating an apples-to-apples comparison of contractors of different sizes while on the jobsite. Without predictive analytics, that can be harder to achieve. For example, larger companies with a higher number of man-hours are often ranked with a higher risk based on the sheer number of workers who could potentially have an incident. Predictive analytics aims to right-size the risk assessment for contractors of all sizes.

As predictive models become more sophisticated and digital maps more mature, severity will be a point of measure for lagging events. Additionally, predictive analytics will be able to engage and analyze data in real time. When this happens, an operator could use the information to anticipate an incident and work with the contractor to proactively prevent it from happening at the moment. In this scenario, rather than docking a contractor for its high-risk ranking in a predictive analytics model, businesses could work with contractors to help them lower their risk.

Compliance Pro, a Veriforce contractor management product, leverages predictive analytics to help businesses choose the most risk averse contractors.

Ready to build a safer workplace by putting this powerful predictive technology to work in your contractor selection process?

About the Authors

Paul Upshaw
VP of Technical Product, Veriforce

Barbara Fadeyi
Sr. Product Manager, Data, Analytics & Reports, Veriforce