Using Analytics To Improve Workplace Safety: 3 Steps


You likely have a bunch of different spreadsheets, reports, and databases related to safety scattered across your organization. These data sources are compiled by various people in different departments. For example, HR may have information about driver certifications, while customer support keeps records of complaints about drivers and fleet operations holds maintenance records. You might not even know all the sources of possible data pertaining to accidents and injuries, of them, even if your job title contains the word “safety”.

Your organization’s safety record is probably pretty respectable. After all, accidents on the job are decreasing, as described in this news release from the Department of Labor. Check out this chart from the news release:

Safety trend chart
Occupational injuries and illnesses have been declining for more than 10 years

Still, there is no room for complacency. The National Safety Council cites nearly $200,000 in direct and indirect costs associated with a workplace injury that results in a doctor or hospital visit.

How do you apply those disparate and scattered sources of data to reducing the risk of accident and injury in your organization? There are three simple steps to using your data to improve workplace safety.

The first step is bringing together your using existing safety-related data using visual analytics. Just seeing your spreadsheets and databases correlated into intuitive visualizations that everyone can share delivers significant gains in safety. Creating a picture of your data clarifies relationship between data, puts data into a familiar context, and identifies potential problems in data quality.

For instance, the historical safety data for a trucking company may show which drivers were involved in accidents in the last five years. You could find that information easily by sorting a table of data. Placing that same tabular data on a map and a timeline tells you much more about exactly where and when those accidents occurred.

Safety table map
Visualizing data correlations is more effective than hunting for correlations in a table of data

The next step is moving from viewing historical data to responding to today’s events by applying diagnostic analytics to alert users about current unsafe conditions. In the trucking example, adding alerts to a visualization of historical accident data can tell dispatchers when a driver is entering an area of frequent accidents. With that information, the dispatcher might caution the driver to reduce speed and drive carefully.

The third step in using data to improve workplace safety moves beyond the past and present to look into the future. Predictive and prescriptive analytics, based on your historical and real-time data, calculate the likelihood of future hazardous events and locations. The power to predict is the power to avoid or mitigate consequences.

Keeping with our trucking company scenario, predictive and prescriptive analytics may take the form of a prioritize list of tasks to perform to reduce risk and avoid accidents. For example, the dispatcher may change a driver’s rout to avoid areas with frequent accidents.

Safety prescriptive
Analysis of risks leads to recommended actions for lowering risk

The journey from scattered spreadsheets to ubiquitous prescriptive analytics requires dedication, funding, and a vision of a safer workplace. Traveling the full journey may not make sense for every organization. Even so, stepping beyond simple spreadsheets and databases offers value for your employees and the organization as a whole.




Three Ways Analytics Improves Emergency Response


fire office building--web

In previous posts about safety, I’ve talked about how predictive analytics help prevent accidents before they happen. But, analytics are also useful for improving the speed and effectiveness of response when accident do happen.

Here are three ways to apply analytics to emergency response:

Analytics help you identify the real problem

In today’s IoT and Big Data world, you may have multiple systems creating alarms in response to a single accident or event. How do you know what the real problem is? Analytics that bring together alarms from across your enterprise help identify and respond to the real problem.
For instance, your building management system gives you an alarm that sprinklers have turned on in one building of your corporate campus. Does this mean that there is actually a fire, or that someone created a false alarm, or that the sprinkler system has somehow malfunctioned? Without analytics that correlate the sprinkler alarm with other data such as smoke alarms and security video, it’s hard to know.

Analytics help you triage effectively

Alarms tell you that something happened, but by themselves don’t tell you the consequences of what happened. Analytics that include a criticality score for locations, equipment and inventory quickly give you the true magnitude of an accident or event. Knowing the magnitude of consequences also allows you to prioritize your response to multiple simultaneous events.
Continuing with our building fire example, let’s say that a correlated alarm system tells you that there is, indeed, a fire. You immediately want to know the potential consequences of the fire. How many people work in the area? What sort of equipment or inventory is nearby? Are there any guests in the building? Without analytics that rate the consequences of the fire, it’s hard to fully assess the situation.

Analytics help you respond efficiently

Once an accident or event has been properly identified and assessed, there’s no time to waste with inefficient or ineffective response. Analytics that correlate the type of incident with the specific qualifications of first responders and their respective current location means that people arrive on the scene ready to act, instead of ready to assess. The same approach applies to vehicles and equipment that first responders may need to address the situation.
If our building fire is a chemical fire in an inventory warehouse, that situation requires a different type of response compared to an electrical fire in an office building. Without analytics that correlate people, equipment, locations and events, you risk having the wrong people respond with the wrong equipment to handle the situation.

(Image: stanislaw / 123RF Stock Photo)


Analytics, Transportation Safety And Extreme Weather


transportation safety landslide 01

Extreme weather conditions such as floods, landslides, sinking water tables, droughts, ice storms and snow drifts takes a toll on the landscape. Those weather-driven changes to the landscape directly impact rail beds, roadways and bridges, rendering them unsafe to use until conditions change and repairs are made.

For instance, heavy rains trigger landslides that block roadways and railways, stopping traffic until the debris is removed and the damage repaired.

Analytics can highlight where and when disasters might strike. Predicting where damage may occur and taking precautions prevents damage to people and property.

Factors that signal a landslide or flood hazard include

  • Transportation corridors in proximity to sloped terrain or bodies of water
  • Vegetation, or lack thereof, on slopes and banks
  • Moisture levels in the soil
  • Past, current and projected weather conditions
  • Condition of the road or railway itself
  • Traffic metrics for the corridor including overall volume of traffic, patterns of traffic flow, and criticality of the corridor in connecting valuable locations

Imagine a situational intelligence approach to transportation safety. An analytics application could correlate, analyze and visualize the factors listed above. That analysis and visualization would enable government transportation officials and railroad operations and planning professionals to see the probability of landslide on a slope adjacent to a right of way. Knowing the probability of a landslide and the magnitude of its impact informs decisions about operations, crisis response and mitigation.

A similar situational intelligence approach applies to floods damaging transportation corridors that are adjacent to bodies of water. Some of the measurements and algorithms would be different, but the much of the output and outcomes from such a system would resemble that from landslides.

When public transportation departments and private transportation companies know that a corridor is at risk, they can take action to avert disasters. Traffic and shipments can be redirected to minimize delays and remain safe. Customers can be notified if their shipments will be delayed and new arrival times. Repair crews can be positioned for faster response to the most critical areas.

Analytics for transportation and weather is especially important in the United States given the current condition of transportation infrastructure. The American Society of Civil Engineers give these systems low marks:

Infrastructure that is in barely passable condition is less resilient to the impact of extreme weather. If we can’t change the weather and don’t have the money to improve the infrastructure, at least we can be smart about how we plan and respond.

(Image courtesy of fotokostic / 123RF Stock Photo)


Saving Lives On The Job With Analytics – It’s Easier Than Curing Cancer


Construction workers blog

A University of California, Davis study calculates that workplace accidents and illnesses cost the United States economy $250 billion annually. That’s more than the direct and indirect costs of cancer. That’s more than diabetes and strokes, combined.

The good news is that workplace accidents and illnesses are easier to prevent than cancer, diabetes and strokes. That’s in part because accidents and illnesses happen at a known place and time: on the job.

That sounds obvious, I realize, and a little trite given the seriousness of the topic. But as we know with situational intelligence, understanding the place and time of an actual or predicted event is powerful.

For example: according to the U.S. Bureau of Labor Statistics, 4,679 workers in the US died from workplace accidents in 2014 (the most recent year for which we have data). That’s an awful number, but also a very general number. It doesn’t tell us who, where or when. Can a situational intelligence approach to safety help detect accidents more quickly and even prevent accidents?

To be more specific, 20 percent of those workplace deaths happened in the construction industry. That tells us a lot more about who, where and when. Furthermore, the BLS reports that the nearly half of all construction deaths are the result of falling or being electrocuted.

Now we have two specific and preventable accident scenarios within a single industry.

Imagine that you’re the safety director for a construction company. Receiving a report every morning of the day’s planned elevated and electrical work gives you insight into where accidents might happen later that day. You’d have time to review safety equipment and procedures with workers before they start their tasks. That’s a good first step towards a safer workplace.

Since we’re now in the Internet of Things era, it’s easy to envision that your workers wear safety vests equipped with GPS and other location technology. Knowing the location of all your workers in real time is another step towards protecting them. The vest tells you when a worker is more than, say, three floors off the ground, or within 10 feet of high-voltage equipment.

Warnings about these potentially dangerous situations are pushed to your laptop and also to your mobile device as you’re out on the site. This information helps you respond faster to accidents, since you know exactly where people are working

Analytics can go further, to help prevent accidents. Knowing that falls and electrocutions are top priorities, you can design new measures to restrict access to dangerous areas. By correlating personnel records with scheduled tasks, you know which workers have the certification and experience to safely work at heights and with electrical systems. If a worker is potentially distracted by daydreams about an upcoming vacation, the personnel records flag that potential hazard for you to address during a safety briefing.

These are just a couple of high-value use cases from the construction industry. Similar use cases exist in nearly any industry where workplace safety can be an issue such as utilities, mining, transportation, agriculture and manufacturing.

Analytics is broadly applicable to reducing the impacts of workplace accidents and illnesses. Those impacts include hard costs associated with insurance premium and claims, litigation, compliance and other expenses that contribute to that $250 billion annual figure. There are also soft costs associated with employee morale, customer dissatisfaction and company reputation.

Improving safety with analytics may be more mundane than curing cancer, but also more achievable.

Image Copyright: hxdbzxy / 123RF Stock Photo