Effective health, safety, and environment (HSE) management is more crucial than ever. The need for legislative compliance is becoming more stringent, and public expectations are high. We have also seen that even a single HSE incident can disrupt business operations or even damage corporate reputation.
Separate tools and systems for managing HSE issues have evolved over the years, but now these are being integrated into the business in a structured way to provide organizations with the assurance that critical HSE risks are recorded and managed effectively.
New HSE engagement programs and digital tools have created huge amounts of reports, all of which need to be reviewed and analyzed. Most organizations have a wide range of data sets that are rarely integrated, making it difficult to create one mineable repository with permits to work, job-hazard analyses, audits and inspection results, observations, and near-miss reports. Often, the most useful information stays locked away in the free text description of the incident, where the clues lie as to direct and root causes at the heart of an incident.
Data also often is not targeted correctly to detect and monitor the significant risks. Along with using data science to learn and analysis risk, real-time monitoring of the key risks also is crucial.
When reporting an incident, employees are asked to categorize the type of incident. The categorization helps analyze the incident and run trends, but approximately 20% of employees chose the “other” category when reporting, making it difficult to understand what they’re reporting.
Implementation of some of these systems includes incident reporting and recording, with a view to capturing records of incidents and using insights to prevent similar incidents in the future. This widespread practice has given rise to a lot of historic safety data. This means that, currently, most organizations are at a transitional maturity level, transitioning from a traditional, paper-based HSE process to a digitalized HSE date-capture system.
So, what can be done with this ever-growing safety data, and how can existing data be used? Can greater insight be extracted so your traditional or transitional HSE systems can evolve to a leading-edge system?
Key Challenges in HSE Reporting
HSE strategy and reporting are still often very ad hoc, very manual, and nonstandardized. While companies are capturing and recording incidents, they often struggle to ingest and understand all of the safety data and clearly identify emerging risks and safety issues, and these companies often find it challenging to put mitigation strategies in place for their most common issues to prevent them being repeated.
Some of the more common challenges in HSE management include:
Difficulty in standardizing, collecting, and digitizing safety data reinforces the existing practice of operating from a rear-view-mirror mindset instead of identifying upcoming risks and evolving the existing HSE systems.
A pressing need exists to distinguish between data and insight. Most firms have lots of HSE data, but much of it is not insightful or used to monitor the safety barriers and control measures. Much of the safety data is measuring and reporting the wrong things (i.e., low-risk activities).
Advancing HSE Systems to the Leading Edge
Businesses face many challenges in meeting the goals of HSE management, regulatory compliance, and business improvement. To meet these goals, the need is increasing to manage the risks with a human-centered approach at a corporate and strategic level, which entails gathering and using employee input and feedback into the HSE analytics for risk assessment, risk trade-off decisions, and risk management.
Businesses and organizations now can use the advances in digital data and analytics to evolve their current HSE systems from traditional or transitional to leading-edge HSE management system that combines data-driven insights with human intelligence to deliver advanced and predictive HSE solutions.
Using Digitized Capture To Enhance HSE Systems
With advancements in technology and data sciences, plus an availability of big data, powerful computing and enhanced algorithms with machine learning and artificial intelligence (AI), the power of organizational data can now be leveraged to create a leading-edge, predictive approach for HSE strategy. This is done by
• Integrating data across silos and formats: Digitization can ensure a seamlessly integration of siloed data sets, across formats into a data lake, (a storage repository that holds a vast amount of raw data in its native format until it is needed), and can draw additional insights from existing organizational data.
• Enriching current data with additional sensor data: Richer context allows for greater insights and predictions of risks. While reporting gives obvious inputs, additional images and body-condition monitoring helps provide actionable, and often real-time, insights into HSE risks and incident management.
• Applying predictive analytics: By using advanced data and digital tools, the direct cause of incidents can be identified and analyzed at scale to obtain a clearer picture of where to focus HSE efforts; this could include visual recording and sensors to capture body condition. These insights help identify issues that were previously unknown and can prevent safety incidents.
• Using AI: Between 50 and 80% of accidents are driven by or related to human; however, human behavior has been traditionally difficult to measure. By using AI—such as natural-language processing for text analytics, vision analytics, and vital-signs analytics to monitor fatigue and heat exhaustion—the hidden insights can be identified and the risk setting can be understood, along with people’s behavior, providing previously unobtainable granularity of actionable insights in HSE processes. This allows for the deployment of efficient HSE strategies that can target human interactions.
• Using dashboards: New dashboard technologies can help identify, investigate, and communicate key insights to help decision-making and awareness-building efforts. Adding advanced analytics to the mix creates a powerful tool to identify emerging risks and patterns.
HSE management is more crucial than ever. Public expectations are high on business expectations, and just one HSE incident can cause major disruption and major damage to corporate reputation. HSE already is on a transformational route for many companies, which are digitalizing a lot of their reporting and managing safety data digitally. This, however, remains often very siloed.
By adding AI to HSE management, companies can integrate data to provide one view of the HSE across the organization. HSE managers now can ensure that they focus their HSE efforts in the right place, reducing incident rates and providing assurance that critical HSE risks are recorded and managed effectively.
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