Understanding the Patterns that Impact Risk Management

Patterns Impacting Risk Management

It’s a busy season at the oil refinery when a key process suddenly shuts down. A forensic review of operating data reveals the culprit: Gradual, unseen breakdown in the step that “cracks” crude oil into gasoline. The result: a costly delay in production.

But what if plant operators had used big data to find patterns that help predict and prevent such expensive and potentially dangerous ­disruptions?

It’s a question increasingly asked by oil and gas refineries, mines, utilities, chemical and petrochemical plants — even by food and beverage producers eager to manage unexpected incidents, shutdowns, supply chain interruptions, worker injuries and other threats to safe, optimized production.

Despite advances in process automation and safety, siloed data and shortcomings in current risk assessment methods make it hard to spot many problems in advance. As a result, more companies and plant operators are looking to advanced analytics to help better manage risk.

While there’s no one-size-fits all approach, here are five reasons why analytics is transforming risk management.

1.    It Lets You Move from Forensic to Future

Complex enterprises collect millions of real-time data points from plant, process, and other operational systems — part of the emergent Industrial Internet of Things (IIoT). Data analytics uses algorithms and statistical models to deep dive into these vast troves produced by sensors, robots, and a myriad of other connected devices. The revealed patterns and insights enable risk managers to make the journey from hindsight (understanding what happened) to insight (why it happened) to foresight (predictive) to action (what should I do).

The ability to move from static monitoring to proactive risk mitigation and dynamic decision-making improves the management of Operational Risk Management, and Process Safety Management and Environmental Health and Safety.

2.    It’s Becoming a Necessity

Organizations worldwide perceive an increasingly complex risk environment, according to the “2017 Global State of Enterprise Risk Oversight” report by two major accounting institutes in the US and UK.1 In particular, supply chains are highly complex and continuously exposed to a variety of internal and external risks, including cyberattacks.

No surprise that surveys across a variety of industries show rising interest and investments in data analytics by risk and audit executives.

One indicator of surging global interest: The 2016 European Conference on Process Safety and Big Data, which drew huge international crowds to a vast array of topics. The event’s co-sponsors, The Center for Chemical Process Safety and European Process Safety Center, expect even bigger turn-outs this year and at related U.S. events. 2

3.     It Works

A growing body of research and cases shows big data analytics can help mitigate operational risk. Common benefits across a wide variety of industries include: preventing unplanned outages, maintaining asset integrity, avoiding “stock outs,” improving workforce competency and enhancing compliance assurance.

According to a new study published in the journal Sustainability, data analytics can help companies predict and lower many risks, including “social” problems. These include reducing speeding and traffic violations and improving overall worker health and safety.3

At one petroleum company, big data analytics helps significantly reduce operation risk and cost. It does so by preventing drilling blowouts, predicting maintenance and downtime, and using weather and workforce data to avoid creating dangerous work conditions.4

One manufacturer uses big data analytics to reduce the possibility of a stoppage of raw material deliveries in natural disasters. The company analyzes weather statistics for tornadoes, earthquakes, hurricanes, then uses predictive analytics to calculate the probabilities of delays and to identify backup suppliers.5

And once data-driven risk management is in place, technology can build on its impact. For example, a Canadian refinery wanted to know if its process safety management systems were being effectively implemented. DuPont Sustainable Solutions created an integrated system that collects, manages, reports and gives real-time insights to help managers make more informed decisions and better manage interconnected routine and non-routine work tasks.6

4. A Holistic Approach is Better

Many companies start in a specific area, such as process safety. However, data analytics in risk management delivers the biggest, sustained gains when deployed enterprise wide, from supply chain to operations, asset integrity, workforce enablement, engineering, capital projects and environmental health and safety.

“All organizations engage in risk management, but conventional risk management is done in silos, whereas the Enterprise Risk Management approach allows for a holistic overview of risks across silos,” explains Mark Beasley, co-author and director of the ERM Initiative at North Carolina State University.7

5.     You’ll Need Patience and Expert Help

Building expertise in advanced analytics for risk management requires a significant commitment of time and resources. Organizations typically follow a path to maturity that begins with basic analytics (compliance), and then moves to descriptive (historical), predictive (model future outcomes), and finally prescriptive (actions and decisions).

Recent research on risk and compliance finds that companies employing data scientists (as opposed to traditional database specialists) are far more likely to benefit from supply chain analytics.8 Because data scientists are in high demand, and those with process risk management experience even more so, many companies will end up hiring outside experts.

Minimize Pain, Maximize Gain

By illuminating problems before they worsen, risk management analytics helps organizations proactively protect people, assets and operations from injury, damage, and financial loss. Analytics also forms a solid foundation for next-generation risk mitigation, and helps inform an enterprise culture of safety and higher productivity. This integrated approach can be used to manage a variety of risk across the value chain.

DuPont Sustainable Solutions offers a wide range of risk management consulting services and technologies that help organizations become safer, more efficient and more environmentally sustainable.



1 http://www.cgma.org/resources/reports/2017-global-risk-oversight-report.html

2 https://www.aiche.org/conferences/aiche-spring-meeting-and-global-congress-on-process-safety/2017/proceeding/session/big-data-analytics-and-process-safety-i

3 http://www.mdpi.com/2071-1050/9/4/608

4 http://www.ingrammicroadvisor.com/data-center/4-big-data-use-cases-in-the-manufacturing-industry

5 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2678821

6 http://www.dupont.com/products-and-services/consulting-services-process-technologies/brands/sustainable-solutions/sub-brands/operational-risk-management/videos/suncor-collaboration-worker-safety.html

7 https://erm.ncsu.edu/library/research-report/global-survey-execs-reporting-significant-risks-but-less-than-robust-effort

8 http://www.ey.com/Publication/vwLUAssets/EY_-_Big_data:_changing_the_way_businesses_operate/%24FILE/EY-Insights-on-GRC-Big-data.pdf