Using Predictive Analytics for Incident Prevention

Using Predictive Analytics for Incident Prevention

Data analytics involves examining datasets to answer questions and gain insights for effective planning. Predictive analytics, specifically, uses data to try to determine what might happen in the future should certain actions be taken. This analysis is increasingly associated with artificial intelligence (AI) as machine learning improves AI’s ability to interpret data, but it ultimately requires decisions be made by people to achieve desired outcomes.

Predictive analytics has many potential applications in safety, health, and environmental (SH&E) practice. As described in The Hub’s previous guide, How Artificial Intelligence Impacts Occupational Safety, predictive analytics are useful in making risk assessments and planning safety improvements, especially with regard to machines’ continued operation and the safety of regular work processes.

 

The Importance of Data

Data that is organized in a consistent manner best makes possible effective analysis by both people and AI. To make use of AI tools, it is necessary that safety professionals have risk data saved in a standardized structure within a compatible electronic database. This database acts as a Decision Support System (DSS), defined by the Oxford English Dictionary as “a computer program or other system used to aid in decision-making.”

A variety of these systems are listed on Info-Tech Research Group’s Software Reviews page for Environmental Health and Safety (EHS) Software. Note that not all these systems offer AI analysis. BCSP does not endorse or recommend any one specific system, but provides this information to support you in determining which system is best for your organization.

Once data is standardized in a DSS, it is possible for AI to rapidly process large datasets from various sources, such as historical incident records and real-time worksite or environmental conditions. With the support of a robust database, safety professionals and AI agents can detect patterns and predict potential safety risks, enabling organizations to make data-driven predictions, plan the allocation of time and resources strategically, and prioritize safety initiatives.

 

Predictive Maintenance

Predictive analytics is particularly useful in maintaining the conditions of machines, including those used in manufacturing, conveyance systems, and vehicles. This predictive maintenance can mitigate hazards related to machine failure, such as struck-by incidents resulting from components unexpectedly breaking down during use due to excessive heat, wear, or other factors, resulting in improvements to operations by keeping work uninterrupted and on schedule.

Machines used in production are increasingly joining the ranks of "smart" devices, leveraging advanced sensors to enhance connectivity and functionality. Previously, these sensors required manual readings and localized data collection. Today, they can be integrated into data networks, allowing real-time remote monitoring and analysis.

This transformation is part of the formation of the Industrial Internet of Things (IIoT). The incorporation of workplace machines into the IIoT allows safety professionals and AI systems to collaborate on automated inspections and predictive maintenance, revolutionizing operational efficiency.

AI-powered sensors and monitoring systems collect and analyze real-time data on machine conditions, identifying anomalies or deviations from normal operation. Predictive maintenance takes this capability further, combining historical data, performance metrics, and real-time sensor readings to forecast when a machine is likely to need servicing or may be at risk of failure. With the expertise of safety professionals, this data can be used to evaluate performance, detect wear and tear, and address emerging issues. AI systems, enhanced by machine learning capabilities, can adapt their assessments and refine algorithms to address specific operational needs, highlighting potential concerns before they become critical problems.

In manufacturing, predictive maintenance monitors the condition of machines, identifying signs of degrading performance. For example, vibration sensors can detect imbalances in rotating machinery, while temperature readings and thermal imaging can identify overheating components. This enables maintenance teams to perform timely repairs or replacements, reducing unplanned downtime, minimizing costs, and enhancing equipment reliability.

With regard to conveyance systems, sensors can track wear patterns, alignment, and motor health, reporting this data to the DSS and providing alerts when components like rollers or belts will soon need servicing. This ensures smooth material flow, prevents potentially dangerous breakdowns, and prevents costly stoppages.

Even vehicles can now remotely report engine performance, battery health, and tire conditions to a centralized database that supports fleet safety efforts. These insights enable maintenance teams to service vehicles proactively, extending their lifespan and maintaining safe operation.

By integrating predictive maintenance across workplace machines, organizations can achieve enhanced efficiency, improved safety, and significant cost savings. This comprehensive approach reduces the likelihood of incidents, supports continuous operations, and paves the way for a safer, more productive manufacturing environment.

 

The Human Factor

It is important to note that predictive analytics and AI are related but distinct concepts in data science and decision-making. For example, an experienced safety professional can use regression analysis to determine the relationship between different variables in a dataset and draw conclusions about how changes in one variable affect another. AI might mimic this calculation and do so quickly, but it only has access to the data provided in the database with which it is working. AI tools can be incredibly useful, but the number of factors involved in work processes involving people require a safety professional’s greater experience and observations to fully understand.

With that in mind, safety professionals can use predictive analytics to ensure safety is maintained in the regular work processes in which their team members are engaged. The greater the degree to which a work process is standardized, the greater the accuracy of predictions regarding that process. Using predictive models, a safety professional can analyze past incident reports, near-miss records, and hazard-identification data to identify patterns associated with specific changes to work processes and make improvements.

This may include using data from instances where changes were made to a similar process at another time or place before the implementation of a similar change elsewhere. This could be the addition of a new machine or a change in production line procedure. If noted in the database, a safety professional or AI could compare the data from before and after the initial change and use predictive analysis to determine what implementation of a similar change could mean for the health and safety of those involved.

Predictive analytics can also be used in cases where data from an event that occurred in the past can reasonably be expected to occur again in the future, such as the impact of summer heat or seasonal shifts in product demand in warehouse work.

Thorough data collection and analysis can even be used to make predictions about the effectiveness of implementing safety controls in a work process, should adequate data from comparable situations be available. In this way, predictive analytics can assist safety professionals in making the case for new safety initiatives.

The accuracy of predictive analytics depends on the completeness of the data upon which it is based. The use of AI in this analysis cannot replace human expertise, but it is an increasingly useful tool for making sense of large datasets. In the end, achieving desired SH&E outcomes relies on experience and the actions of the safety team. Safety professionals are essential in effective decision-making and providing the leadership necessary to guide that team to a safer future.

Tags: AI Artificial Intelligence Data Analysis Predictive Analytics

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