How Predictive Analytics Is Revolutionizing EHS Management

1. How Predictive Analytics Is Revolutionizing EHS Management Environmental, Health, and Safety (EHS) management is changing fast; data-based models are changing how organizations view safety and compliance in the workplace. Predictive analytics in the EHS field is likely the most important organizational change imaginable; in which predictive models using data analytics, machine learning (ML), artificial intelligence (AI), and big data are used to prevent an unforeseen incident or compliance issue before it can happen. Predictive mode modeling uses data analytics to target focus for predictive modeling, and prevent and incident, or compliance issue by learning from the past. The Shift from Reactive to Proactive Safety Management EHS programs have always relied on management strategies that are mostly reactive; the response to incidents when they happen. The reactive model has proven costly, from the amount of stoppage time incurred and, more seriously, injuries caused. With the creation of predictive analytics in EHS organizations are moving to a new strategy of management that is proactive and seeks to focus on predicting safety data and foreseeing the future possible hazards and taking advance preventative actions. Understanding Predictive Analytics in EHS Predictive analytics in EHS involves the use of past and current data, along with AI algorithms, to predict future safety risks. These resources look back on employee actions,

2. equipment performance, operating conditions, and past incidents to find trends to determine the where and when the risk will occur. As an example, predictive safety tools can notify safety managers about risk areas on a jobsite by looking for themes in incident reports, maintenance reports, and environmental data. This allows companies to implement targeted actions, from supplemental training to upgrading equipment. Benefits of Safety Data Prediction 1. Reduced Workplace Incidents With the power of safety data prediction, organizations can predict and avert incidents before they occur. With lightweight data-informed prediction, organizations can identify and correct potential unsafe conditions before an incident occurs. 2. Improved Compliance and Reporting Applied predictive analytics in EHS make compliance easier to achieve. The predictive analytics system will be able to track regulatory requirements automatically and will predict where violations are likely to occur, thus allowing organizations to intervene before an incident occurs. 3. Enhanced Decision-Making Safety teams are able to gain better insight and deeper understanding through predictive safety tools in order to make better decisions. Managers can be able to address the areas of concern, and apply their time and resources to appropriate risk mitigation areas. Role of AI in Workplace Safety Artificial intelligence has become a core component of contemporary EHS strategies and its continued development in workplace safety remains on the forefront. AI systems are informed by data and use intelligent algorithms and machine learning models to learn from that data and develop predictive capacity while improving over time. For example, AI can monitor security cameras and detect unsafe behavior, as when a worker does not have on personal protective equipment. AI can also monitor sensor data and discover a piece of machinery is overheating, or monitor air quality. When these predictive safety measures are implemented, AI adds the potential for advancements of preventative action.

3. Key Applications of Predictive Safety Tools 1. Real-Time Risk Assessment One of the biggest advantages of predictive safety tools is real-time risk identification. These systems collect data on systemic problems or behaviors 24-7 to identify risks once they are postulated. 2. Worker Health Monitoring Wearables and IoT sensors are recording data on workers physical condition - heart rate, fatigue level, etc. Data linked to behaviors and physical condition is digging into human conditions and previous risks to help to setup AI in workplace safety that could potentially: prevent heat stress, or overexertion from an injury or even illness that isn't not immediately identifiable. 3. Maintenance Forecasting Predictive analytics in EHS can provide a clear indication of when equipment is likely to fail (even at some point in the future we are going to have to understand this is max potential before it breaks down), so companies can actively maintain equipment potential failures before they occur, therefore improving safety and enhancing operational efficiencies. Challenges and Considerations

4. Although predictive analytics has its benefits, there may still exist barriers to its adoption in EHS. As just one example, data privacy and ethical use of monitoring is a cornerstone issue when analyzing employees, so the organization needs to no what is compliant within their data protection requirements when utilizing AI in workplace safety. Also, while predicative safety measures can be effective, the organization also needs to invest in the essential infrastructure, technology and training, and while the cost in implementation/style of work may be significant, the final safety and efficiency dividends from their use will outweigh their fickle cost. Future of Predictive Analytics in EHS As our technology improves, the previous "predictive" analytics in EHS will only improve and become more widespread. With advancements in AI, machine learning technology, and the use of connected IoT devices, EHS professionals will eventually be able to more precisely predict safety data and apply it to more industries. With innovations in AI in workplace safety, we could have systems that initiate safety systems without the need of humans to intervene. For example, to address a toxic gas leak, we can put the system on auto mode and the system will set off the alarms, shut down machinery, and activate evacuation protocols. Conclusion Predictive analytics is a game-changer for organizations, as it allows them to rethink their definition of health and safety in an entirely new way. Organizations are able to consider safety data prediction, use of AI in workplace safety, predictive safety tools that are technology enabled, and help organizations recognize how to move from reactive models to predictive models - all to save lives, save money, saving the Department of Labor or other regulators time on site, or saving attorneys money by improving compliance. If your organization is ready to invest in the future of EHS, Salomi has the technology that is right for your organization. With predictive analytics, organizations can anticipate risk, ensure compliance, and create a safer workplace for employees and customers.