Operational Risk Management Gets Smart with AI

The way most industrial organizations manage risks in their operations is akin to driving a car forward while looking in the rearview mirror. This approach is useful for understanding what’s already happened but is of limited value in seeing what’s ahead and taking preventive action accordingly. The essential solution is to shift from a posture of risk hindsight to one of foresight and the ability to predict and prevent adverse events rather than respond after the fact.

Among the many lessons of the COVID-19 pandemic is that better risk management systems could have helped many organizations respond faster and more effectively. This is not to suggest that better private sector risk management systems could have prevented the crisis, only that they could have reduced disruption by preventing some adverse consequences and minimizing others.

Fortunately, as industrial organizations continue to strengthen their risk management processes and systems, help is on the way in the form of new digital technology enablers. This blog discusses the risk management execution gap that’s prevalent across industry and how Intelligent Risk Management (IRM) is part of the solution. We’ll also give some examples of real-world IRM solutions that are reducing risk, improving safety, and increasing productivity.

Intelligent Risk Management

There is a disconnect between how industrial organizations want to manage operational risk, and their capability to do so. Our research shows that management of operational risk in concert with improving operational and sustainability performance is the top strategic objective for EHS management. Yet, only 37% of industrial organizations report having established a formal risk management framework for operations. And, less than half indicate they have adopted risk management capabilities for any of the elements of closed-loop risk management, i.e., identification, assessment, prioritization, control measures, and monitoring.

In many industrial operations, effective risk management has been undermined by non-standardized processes and fragmented systems for managing and analyzing data. As companies implement Industrial Transformation (IX) initiatives, new and emerging digital technologies are being successfully used to enable a new approach: Intelligent Risk Management (IRM). IRM leverages new sources of data, including Industrial Internet of Things (IIoT) data from sensor-equipped assets and people, and advanced analytics tools to predict what will happen next and take preventive actions accordingly. A key enabler of a “predict and prevent” approach is Artificial Intelligence (AI). Let’s look at several real-world examples of how AI technologies are being applied to mitigate operational risk more effectively.

Real World AI-enabled Solutions

Artificial Intelligence (AI) is one of the most significant technology trends across the industrial, business, consumer, and public sectors. The application of AI and Machine Learning (ML) holds great potential to enable Intelligent Risk Management, and it’s already delivering real-world results. Here are several examples of AI-enabled commercial product offerings taking risk management to new levels:

Serious Injury and Fatality (SIF) prevention (Benchmark ESG™ | Gensuite®) - Many industrial organizations have implemented programs to reduce serious injuries and fatalities (SIFs) as part of targeted efforts to focus on risks with the highest potential consequences. Commercially available AI and ML advanced analytics solutions have been successfully applied to such programs. A good example is the AI-based pSIF analysis and advisory tool from Benchmark ESG (formerly Gensuite). This solution analyzes large data sets related to accidents, injuries, near-misses, and behavior-based safety observations to identify events and scenarios that have the potential to lead to a SIF (pSIFs). This solution has enabled diversified manufacturer Heico Companies to shift to a more predictive risk management posture based on actionable insights targeting risk reduction efforts for the greatest impact in preventing adverse events.

Ergonomic risk assessment (Kinetica Labs) - Work-related Musculoskeletal Disorders (MSDs) are a leading cause of injury and disability in the industrial workforce. Kinetica Labs has developed an innovative software solution to improve the time-consuming process of performing ergonomic risk assessments to prevent MSDs. Their product offering is a cloud-based motion capture app based on a computer vision and Artificial Intelligence (AI) engine. Ergonomists and safety/risk control professionals record a video of a work task and upload it to the app. The software analyzes the video, calculates biomechanical parameters, and assesses risk according to major ergonomic risk methodologies. The output is accurate, actionable data, available much faster, and easier than traditional assessment methods. Multiple assessments can be combined into task and job risk profiles to drive organization-wide improvements.

Regulatory compliance automation (ehsAI) - Identifying specific compliance requirements from environmental permits and other complex regulatory documents has long been a necessary but labor-intensive and error-prone undertaking. A compliance automation solution from ehsAI (acquired by Intelex in 2020) uses AI technology to help solve this problem. The cloud-based platform applies Natural Language Processing (NLP) algorithms to interpret and extract EHS requirements. This greatly speeds up and improves the accuracy of the process of converting unstructured data (text) contained in voluminous regulatory documents to a set of actionable compliance obligations. In turn, the output can be exported into an EHS software system to manage requirements, tasks, and ensure timely execution. The benefit is a reduction of the cost of compliance and a reduction of the risk of non-compliance to negatively impact operational performance.

Toward Predictive Risk Management

The digital technologies of Industrial Transformation (IX) offer the opportunity for a fundamental shift to Intelligent Risk Management. The integrated capabilities of IRM enable a shift from “risk hindsight”— using analytics to describe what went wrong and why — to “risk foresight” — understanding what will go wrong next and how to prevent it. The distinction is shifting analytics from descriptive and diagnostic to predictive and prescriptive, applied to operational safety and risk management. AI is an important enabler of IRM, but it is just one component of a total solution to transform risk management within the context of IX. The examples given above are focused in scope and may be good steps on the way to building a unified risk management system with predictive capabilities.

The views and statements expressed in this blog represent the opinions of the author(s) based on his/her/their industry experience and interpretation of any information collected using the methods described in our Research Integrity. All product and company names are trademarks™ or registered® trademarks of their respective holders; use of them does not imply any affiliation with or endorsement by them.