Canadian Occupational Safety

26 Canadian Occupational Safety | www.cos-mag.com A n electrical contractor in Alberta decided about four years ago to introduce pre- dictive analytics software to improve its safety performance. Over a three-year period, the com- pany identified a direct correlation between increased employee par- ticipation and reduced recordable incidents. With the new program, employees were identifying 10,000 to 15,000 hazards annually, up from just a few hundred per year. "The company built their own lead- ing indicator, which tells them how many safety activities employees are doing per the number of hours they work," says Josh LeBrun, president and chief operating officer at eCompliance in Toronto. "It's almost the opposite of a lagging indicator. It's based on all the activities a person is doing." Predictive analytics can help an organization significantly reduce incidents, and that effectiveness is encouraging the technology's drive in new directions, from smart per- sonal protective equipment (PPE) to machine maintenance. In collecting data for a predic- tive model, the safety team should look at a broad range of data, says Andrew McHardy, senior manager, infrastructure and capital projects practice, at Toronto-based Deloitte Canada. While incident records form a core source of information — when an incident occurred, who was involved, severity, etc. — the team also needs to look at other company systems that contain relevant data points, such as training, operations, maintenance, overtime, shift sched- ules and nature of the task. Valuable information may also come from outside of the organization. "There's a lot of open-source information that tells us about demo- graphics, the environment, weather, seasons, industry," he says. "It's some of those, from a safety point of view, non-traditional data sources that can often be the most valuable... To drive and build out statistical models that are meaningful, we need to think about data in a big way." One purpose of the eCompliance software is to collect observation data. Employees are encouraged to identify all hazards they see, providing infor- mation on leading indicators. Workers use a mobile app to report hazards when they see them. In addition to their description, they can include a photo of the hazardous area in their message to the safety manager. Infor- mation collected is more timely and more accurate than that acquired by having a small safety team walk around and observe people working, LeBrun says. Moreover, much more data is collected. "We have customers who have 200, 300, 400 employees in their organi- zation that are identifying 15,000 to 20,000 hazards a year. It allows for some really interesting and impactful data," he says. The application also collects infor- mation on actual incidents. "We can start to correlate the lead- ing indicator data with the events and provide information to companies on what is likely to precipitate an event. Or, looking at other companies within their industry, if workers are doing certain things, do they have a higher probability or lower probability of incident?" LeBrun says. "We're seeing a ton of hazards that could theoretically result in an incident, so we're going to get ahead of that." The next step is to analyze the data with a view to identifying accu- rately the factors affecting safety performance. "Safety is obviously about human behaviour. It's about culture. And there's a certain element of bias that creeps into decision-making. Analytics gives us an objective lens on that to be able to challenge that and reinforce a risk-based approach where we're really trying to understand the factors that affect performance and quantify them where possible," McHardy says. Identifying these factors and their significance allows the safety team to build a hierarchy of factors and start to be predictive. "Then you focus not just on lagging indicators but look at leading indica- tors. You can then project forward and anticipate what mitigations or inter- ventions in your initiatives in your safety program could make the great- est impact, whether that's more of what you're already doing or making refinements to your safety program," McHardy says. Once decisions are made, it's impor- tant to share the predictive data with employees so they understand the reasons for the decisions. This way, a safety team can increase worker buy-in and the likelihood of better results. With safety analytics models, the value is in the interpretability and being able to use them as tools for culture change, understand root causes and what's driving these models, says Alik Sokolov, manager, financial advi- sory, at Deloitte Canada. "If all you can tell is that someone's at risk, but you don't know why, how do you action that in the field? You can't just prevent the top 20 per cent of your riskiest employees from working. You need to be able to tackle the root cause. And what makes those models action- able is understanding the root cause." DIFFERENT PERSONALITIES Another type of predictive software works by analyzing personality and is based on the idea of a direct connec- tion between certain behavioural types and safety. For example, The Predic- tive Index software, sold by Whitby, Ont.-based Predictive Success, assesses employees for five types of behaviour that affect a person's tendency to act safely: dominance, extraversion, patience, formality and judgment. "The software measures your five behaviours on a subconscious level — how your subconscious feels you should act. This is your makeup. You're hard-wired this way, so it's hard to cheat. It also measures how you consciously think you need to modify those behaviours," says Eric Irwin, managing principal. Employees answer questions based on an ideal behavioural model — the behaviours best suited to the task. The software draws on data col- lected through a number of studies, Irwin adds. People high on "dominance," defined as assertive, independent, self- confident and driven to win, tend to be more focused on themselves and more likely to take risks in order to win. A truck dispatcher, for example, may tell a driver to get a delivery to its destination in five hours when he knows it will likely take eight. In contrast, people low on domi- nance are unselfish and focus on harmony, collaboration and protect- ing their team against risk. "That's who they are. They are natu- rally driven to protect their team more than they are to achieve any particular results. They'll put the safety of their team ahead of results. From a safety point of view, you want to be lower on dominance," says Irwin. Another significant behaviour is for- mality, the degree to which someone follows rules. People high on formal- ity need to follow rules and structure. They hate making mistakes. Behavioural assessments are designed to help organizations decide which employees should be promoted into supervisor roles and what behav- iours need to be modified. They help identify which worker will make the kind of supervisor who will oversee a team that works safely, as well as which workers and supervisors need coaching on how to work more safely. New data sources – and the ability to connect them – are leading to innovative uses for predictive analytics in health and safety By Linda Johnson

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