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Driving Sustainability in Workplace Injury Prevention Programs

Driving Sustainability in Workplace Injury Prevention Programs

Using mobile technology and predictive analytics to predict and prevent injuries can help you create a sustainable, world-class EHS program.

As a safety leader, do you have buy-in and engagement in your injury prevention program across your organization? If not, you probably are struggling with a program that has hit a plateau, or worse, is floundering. If you cannot get buy-in from frontline employees and executive management, your program runs the risk of being perceived as just another “flavor of the month.”

The best way to drive sustainability is through results. If you can show that your program is reducing injury rates, or ensuring that already reduced rates stay low, people actively will support it. This creates a “virtuous cycle,” where increased participation begets reduced injury rates and vice versa.

examples of injury prevention programs

The four charts in Fig. 1 are actual examples of injury prevention programs at four different companies. These companies have achieved this virtuous cycle state of continually increased participation (the increasing blue lines in Figure 1) and reduced injury rates (the decreasing red lines in Figure 1). Do you think these companies have sustainability problems? No way!

How do programs get to this enviable state? How do they get this virtuous cycle started? Often, and specifically in the four examples above, they rely on mobile technology and safety software systems to make it easier for their stakeholders to:

a) Collect workplace safety data,
b) Perform advanced and predictive analytics against that data and
c) Report the results of their analysis.

These reported results easily can be delivered to those best equipped to execute injury prevention activities and then communicate their success to drive additional participation and sustainability. Once these foundational elements are in place, companies are ready to prime the pump on their virtuous cycle and achieve results similar to those in the four charts above.

Automating Data Collection

One way to reduce workplace injuries is to predict them and then prevent them from ever occurring. But before we can employ predictive analytics to identify areas of high risk, we need data. Often, the easiest way to collect workplace safety data is through smartphones and tablet computers configured with workplace safety checklists.

Many frontline employees, either personally or through their IT departments, are carrying smartphones and mobile computers around their jobsite. Smartphone and mobile computer usage has become so common that the barriers to adoption – even by long-tenured employees – dramatically have fallen. Angry Birds, Words with Friends and Facebook are not just fun apps, they are helping our employees become more comfortable using mobile computing.

While some leaders believe their employees still are not ready to adopt mobile technology in safety, in reality, many are expecting and even crying out for this type of technology. However, for those companies that still have technology adoption issues, the oldfashioned paper checklist can be automated using scanning technologies. Regardless of the technology used, the data collection step must be simple and support broad participation.

Predictive Analytics

In their book Competing on Analytics, Thomas Davenport and Jeanne Harris show that top performing companies and business functions use advanced and predictive analytics to optimize their resources. Business functions such as marketing, supply chain and finance currently employ advanced and predictive analytics. Why wouldn’t safety?

advanced and predictive analytics

In safety, we are trying to optimize our often-scarce resources to prevent injuries. As the graphic in Fig. 2 (adapted from Davenport and Harris’ book) shows, advanced and predictive analytics is a key step to optimization or, in this case, injury prevention.

Historically, safety data simply were stuffed into filing cabinets never to be heard from again. Over time, companies started to dump this data into Excel, Access or even homegrown databases.

However, these rudimentary software systems only employed basic analytics (the blue section of the pyramid in Fig. 2) and left a gap to injury prevention. While on the surface these systems seemed like an improvement over paper, they simply replaced actual filing cabinets with digitized filing cabinets.

Today, any safety software system worth deploying should at least provide advanced analytics capabilities (the green section of the pyramid shown in Fig. 2) to help close the gap to injury prevention. But to truly achieve an optimized program akin to what Davenport and Harris espouse in their book, predictive analytics are required. A study by researchers at Carnegie Mellon University – the same team that helped build the Watson supercomputer that won on the game show Jeopardy and now is being used to help doctors make medical diagnoses – found that analytics models can predict workplace injuries with accuracy rates of 80 to 97 percent. The four companies depicted in Fig. 1 found that by predicting injuries, they could prevent them. Their results speak for themselves and drive ongoing sustainability.

Actionable Information

The final step in achieving workplace injury prevention results similar to those shown in Fig. 1 is getting actionable information into the hands of employees and leaders who can drive change. A company might have a fantastic safety software system that employs advanced and predictive analytics, but if it cannot communicate that information to its stakeholders, it’s like a tree falling in the forest with no one around to hear it.

Sustainable injury prevention programs are driven by robust reporting capabilities that can disseminate the information resulting from the advanced and predictive analytics in the green and yellow sections of the analytics pyramid in Fig. 2. Urgent, highrisk safety issues need to be reported through automated alerts, while longerterm preventive measures that require changes to a company’s safety culture or processes must be reported through statistical analyses, forecasting, extrapolation and even predictive modeling.

Disseminating short- and long-term corrective and preventive actions to stakeholders helps ensure that safety professionals can drive the process and culture change required to reduce workplace injuries and drive sustainability.

dvanced and predictive analytics in safety software systems

Closing the Loop

Many business leaders today subscribe to the W. Edwards Demming school of decision-making: “In God we trust, all others bring data.” Whether they are funding a workplace safety program or personally supporting and driving it, they need to know that safety professionals can collect the data, analyze it and communicate it across the organization to drive change. This only can be done through the sustained support of frontline employees to collect the data, and then operations and executive management to act on the data and trumpet the results to drive further participation and sustainability.

Until recently, managing these steps was a monumental task. Now, mobility and advanced and predictive analytics in safety software systems make it much easier. As safety software systems continue to innovate, safety professionals can move from being analytically impaired to analytical competitors, as described in Davenport and Harris’ book. It is at this point that the authors suggest we gain deep strategic insights and enterprise-wide intelligence resulting from a fact-based culture and broad, C-level support that is critical to both results, and ongoing participation and sustainability – the virtuous cycle of successful workplace injury prevention programs.

Griffin Schultz is general manager of Predictive Solutions Corp. and can be reached at [email protected] or 412-490-1996.

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