A Clear View

1 minute read

Ankur Pariyani, Nancy Zarrow, Ulku Oktem, and Deborah Grubbe

Methods to reduce cognitive bias in industrial operations

The chemical processing industry, including chemical manufacturing, oil refining, and gas production, has experienced astounding advancements in the ability to collect and store information at every point in their processes, yet the ability to pinpoint problems continues to elude many.

The weak link in the data chain

Today, a modern industrial plant monitors hundreds of parameters, generating upwards of 10 to 50 million data points every day, that can be tracked and analysed for current and historical patterns. However, with such an overwhelming amount of data at hand, operating teams are struggling to uncover meaningful, actionable insight. The answer may not lie in more analytics, but a better way to comprehend the analytics – converting this ‘big data’ into ‘smart data’ to help draw conclusions easily and make intelligent decisions quickly.

With many tools and analytic engines at hand, humans are often still part of the final steps of deciphering and prioritising what the data informs them to do. This means natural biases in filtering information play a significant, and sometimes costly, role. Incident investigations and new research studies have shown that the ‘answers’ are available, but are often disguised as disparate information hidden in the reams of data. Operating team members, responsible for everything from identifying issues and deciding on corrective actions, to running the plants reliably, are faced with a monthly inventory of over 1 billion recorded data points for just 500 process variables. Decision science research shows that when individuals or teams are bombarded with large amounts of information and are required to make decisions within a short period of time, they often resort to mental shortcuts to save time and energy. This often introduces cognitive bias during the judgement process, which can lead to inaccurate interpretation and irrationality. In such scenarios, individuals simply discredit information that does not support their views and heuristics, without realising and evaluating their underlying assumptions. Even the most experienced human operators and engineers are not impervious to these weaknesses in the daily environment.



Keywords: cognitive bias, operations risks, precursors, big data, operational excellence, early risk detection