Trend 9: On the road to zero harm has been saved
Cover image by: Stephanie Dalton Cowan
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COVID-19 has put safety in the spotlight for everyone, driving heightened awareness of our actions and movements as we go about our daily lives. While the focus on safety is obviously not new for the mining industry, conditions are now in place to move the dial toward a goal of zero harm through the use of predictive analytics and wearables. In doing so, however, companies will likely need to integrate different data pools and systems, while more proactively driving industry collaboration. If this does not happen, we may still be highlighting the potential for improvement a few years from now, without having seen much progress.
While safety has always been central to mining, COVID-19 has highlighted that it is essential to maintaining employee and community trust. As a result, companies are now going beyond putting robust internal controls in place and are investing in intensive training. Many are also taking steps to move workers out of harm’s way through the accelerated rollout of automation and robotics solutions.
To move the dial on safety outcomes, however, the industry should embrace a new generation of integrated and predictive systems. The spread of COVID-19 may have smoothed the way for wearables by making people more comfortable with tracking and tracing mechanisms. However, wearables are likely just a first step.
To take this to the next level, mining companies would need to create programs designed to prevent safety incidents before they occur. The ability to pool data to drive increasingly complex analytics now makes it possible to move from historical safety analysis to predictive solutions. The key is to leverage this confluence of issues to usher in a new wave of safety systems that put companies on the path to zero harm. This means harnessing the power of safety analytics in a more integrated way than in the past.
There is little question that the next generation of advanced analytics and artificial intelligence (AI) has the potential to move significantly toward zero harm. With the right data, analytics can help companies go beyond a simple analysis of past events to identify potential future scenarios that create a higher risk of an incident occurring. These predictive models can help prevent safety incidents before they occur.
One of the sticking points for advanced analytics, however, involves aggregating the right data. Many companies have learned the hard way that simply collecting massive amounts of safety data is insufficient. Most mining companies have in-depth reports tracking the number of worker injuries sustained, the frequency rate of safety incidents, and many other metrics. But this data is all collected after the fact. Companies serious about monitoring conditions to proactively prevent incidents need greater insight into the circumstances and drivers of those incidents.
While this begins with the ability to recognize early warning signs, it does not stop there. “There are numerous stories of companies around the world that were forewarned of a potential safety hazard but that failed to take appropriate action to prevent it,” say Karla Velasquez, Mining & Metals partner, Deloitte Peru. “This underscores the very real need for some companies to improve their internal controls.”
To reach zero harm, most companies must consequently go far beyond their current practices. What additional tools and training are needed? Do certain working conditions enhance safety outcomes? Are there particular behaviors companies should encourage or avoid? Insights to consider related to these questions and more can be found in data that already exists (figure 1).
However, many companies struggle to access this level of data. That’s partly because it often resides in disconnected systems. “As mining companies move toward integrated nerve centers and begin to build enterprisewide data lakes, they’ll be able to harness the data necessary to run advanced predictive models,” explains Shak Parran, partner, Consulting, Deloitte Canada.
By allowing companies to combine vast amounts of data, rather than viewing each in isolation, an integrated approach can help them uncover hidden patterns of behavior or conditions that contribute to incidents. At the same time, predictive models can position them to target high-risk operational scenarios and employee groups in order to intervene before these incidents occur.
A key here will be for miners to unify their disparate data sources to provide everyone across the organization with access to consistent, reliable, and always available data. If data ownership remains informal, with undefined accountability and inconsistent data standards, safety analytics simply can’t yield the benefits it promises. Real insight hinges on mature data governance, which may see mining companies appointing chief data officers or other executives responsible for establishing data standards across the organization. Turning the potential for a zero-harm future into reality may also require mining companies to look beyond their own internal data sources.
“To successfully predict the risk level of activities, many disparate data sources are required—sometimes even from several companies,” Shak Parran adds. “This underscores the need for greater cross-industry collaboration.”
A mining company was looking to improve its already strong safety record through the use of data and analytics. To help it prepare for its safety analytics journey, we:
As a result of this engagement, the company was able to execute diagnostics at several of its mine sites and design interventions to facilitate both short-term safety gains and long-term strategic changes. With improved data quality, the company now also generates more accurate insights and has been able to centralize a vast amount of distinct data sources into one common source of truth—positioning it to clearly identify instances where safety initiatives may need to be improved, dynamically select and predict the impact of factors on incident occurrence, and recognize high-risk activities in advance.
One way in which companies have been looking to collect safety data is through wearable technologies. As the march of technological innovation continues apace, countless wearable devices have been developed to help improve health and safety outcomes. These span a wide range, with an array of hard hats, watches, clothing, eyeglasses, and more, designed to deliver various benefits—from collision avoidance and environmental monitoring to fatigue management and personal injury reduction.
While a strong business case exists for many of these devices, mining companies looking to boost their safety performance frequently run into challenges as they begin to use this technology. The primary issue is again a lack of integration. Rather than providing a view of safety performance in alignment with the shift toward integrated operations throughout the enterprise, these solutions remain disconnected and siloed.
“The main reason these technologies are likely struggling to gain traction is because they each operate as stand-alone solutions,” says Gerhard Prinsloo, partner, Consulting, Deloitte Canada. “In some cases, they even compete with one another.”
One way in which companies have been looking to collect safety data is through wearable technologies.
The inability to link disparate safety systems together can limit the utility of each individual device and result in cost escalation as new wearables are added to the mix. Additionally, without one integrated solution, workers might end up outfitted in a number of stand-alone devices.
Most critically, however, a lack of integration can prevent companies from using these devices to achieve their strategic health and safety objectives. It’s one thing to use a point solution for a narrow application, such as radio frequency identification (RFID) tags to track people’s movement through the site to optimize workforce mobilization. It’s another thing entirely to extend that solution into an operating environment. That’s especially true when not all technologies can be used in high-risk zones and where there is no open platform that enables enterprisewide analytics.
Lack of interoperability is only one of the issues companies face as they look at implementing wearable solutions. Other concerns involve:
“To break this deadlock, it’s important to bring technology developers together in an experimental culture to encourage the sharing of intellectual property,” says Rakesh Surana, Mining & Metals leader, Deloitte India. “The ultimate aim should be the development of an integrated solution that can aggregate safety data collected from wearables into a single dashboard with the ability to drill down to a single individual.”
There is also a role for the wider industry to play by coming together and driving collaboration in some key areas. For example, industry associations continue to work on defining common standards of interoperability for these wearable devices. Furthermore, there may be an opportunity for the industry to more systematically pool safety and related data to understand trends and aid the development of more systematic and predictive insights.
The solution may be closer than we think. In many ways, COVID-19 has made data sharing more feasible. If mining companies can now work together to share data, we could be on the verge of building truly predictive safety models, designing a fully integrated wearable solution, and laying the foundation for the next generation of predictive safety systems.