Five tips for reimagining safety traditions
Making use of data science
Data analytics is helping guide decision-making in virtually every industry. Many manufacturing-based companies are embracing data science to optimize their marketing, accelerate their sales and forecast their financials. Yet when it comes to workplace safety, the manufacturing sector has yet to unleash the power of data -- perhaps because of an uninformed picture of what big data truly is. Bringing data into the equation is not just about reinventing old processes with more precision – it’s about asking new questions and undertaking new analyses that challenge the way we think outside the proverbial box and reward companies with exceptional outcomes.
Examining data on the events leading up to workplace incidents, the circumstances surrounding them, and the after-effects, our data science team is beginning to unlock new answers on the prevention of workplace injury and death. After analyzing ten years of data from over 1,000 companies, we have big data driven insights that challenge how companies have been approaching workplace safety up to now.
For manufacturers to best integrate a data-driven approach to safety, there’s a distinct set of guidelines they should follow. Below, we outline five tips for manufacturers looking to reimagine their traditional approaches to health and safety.
1 Measure and predict success, not just failure
We are convinced that the greatest future discoveries will be in figuring out why major injuries and fatalities are not occurring in some workplaces, despite the dangerous work involved. Workplace safety programs should integrate employee engagement approaches, especially those where successful managers achieve high productivity while maintaining high safety ratings. Beyond that, organizations should take predictive analytics lessons from several disciplines including medicine, education, psychology, and organizational development, and determine how to integrate similar approaches to forecast long-term success. More specifically, manufacturers can use data science to understand the underlying reasons for better worker safety in some plants versus others with similar conditions and circumstances.
2 Go the extra mile
In many cases, companies diligently doing more of the same (e.g., training, safety handouts, paper reporting processes) start seeing diminishing returns on these investments over time. By contrast, we found a handful of enterprises that track not just what is legally required but also “near miss” occurrences find enhanced overall safety as a result. These organizations are generally ones that have provided employees with tools and specific applications to document hazards. The positive safety gains at these industry-leading companies is remarkable.
Our data science efforts have confirmed several leading indicators as key elements of an overall approach to safety, including: worker engagement, training, inspections and audits, incidents and near misses, and corrective actions. Undoubtedly this is not an exhaustive list of all leading indicators that may prove helpful to an organization. But of the many variables that we had access to, these consistently made a substantial contribution to safety outcomes across all companies.
3 Lead from the top
Success comes from embracing a culture of safety at the very top of the enterprise. Indeed, accountability must come from leadership, while observation, reporting, and supporting each other’s safety must originate on the ground floor — from those most affected. Our ongoing research speaks to an urgent need to put safety communication into every worker's hands, particularly those who suffer a disproportionate likelihood of injury. These include new employees, contractors and workers without close supervision. The key to safety leadership is that it involves a two-way dialogue. By leadership insisting on the latest technology to keep their workers safe, including mobile communications, organizations can better “democratize” safety.
4 Involve everyone
With safety technology today, organizations can charge all employees — not just safety engineers — with finding the risky behaviors and unchecked hazards that can lead to death, serious injury and minor accidents. Be sure to have diligent systems in place to regularly assess the risk of process failures that can result in machines behaving badly versus people acting poorly. In both instances, recording “near misses” without fear of reprisal is critical. If every employee and contractor is trained and provided the tools to facilitate safe behaviors, organizations can transform the entire safety pyramid and make for a safer workplace.
5 Adopt a maturity curve
As a start, companies must meet standards of Level 1 Regulatory Compliance by documenting and reporting what is required by law. Beyond that, some companies are approaching occupational safety from a Level 2 Performance Perspective, by tracking leading indicators and realizing that safety and productivity go hand in hand. At the top of the curve are a few companies taking a Level 3 Transformational approach by infusing safety mindsets into how they recruit, train, recognize, reward, and promote their employees. Ssafety success for these enterprises is a strategic differentiator -- one most manufacturers should strive to replicate.
To bring about ground-breaking safety gains in manufacturing, we need to adopt new thinking. The infusion of data science to solve real business goals in manufacturing is a game-changer for health and safety. To achieve this goal, we need data-driven insights to shape new solutions that will produce breakthrough results in safety gains.