By Scott McKenzie


RLG’s Maintenance Excellence™ methods directly address and reduce natural human biases contradicting effective maintenance practices.

Studies by Behavioural Economists in recent years have highlighted many natural human biases in the areas of decision making and risk tolerance; indeed, there are more than 20 known biases. This series of articles will focus on specific biases, how they relate to directly impact maintenance decision making and risk identification, and how to alleviate them.

The First Bias – The Availability Heuristic: We equate facts with ease of remembering

This bias is well known in behavioral circles. Its premise is that individuals are more likely to believe something is true if it more easily comes to mind, especially if one specific example can be recalled.

This has three implications for maintenance teams:

1. If an individual wasn’t directly involved with a maintenance issue (for example a specific equipment failure), then their opinion will be that the likelihood of a future failure is lower than what it really is; and
2. If an individual was directly involved in a maintenance issue which had a high impact on that individual (high urgency, high consequence, or high emotional impact) then they will likely remember this easily and therefore assign a higher failure risk than what is reality.

This means that people will tend to disagree on the likelihood of failure, or the impact of that failure (or both) based upon their personal experience. Furthermore, they won’t be aware of this bias or will downplay its effect upon them. It’s counter-intuitive. Everyone believes they are correct, and have specific examples to highlight why they are right, but the fact is everyone is likely wrong to some degree.

This bias is magnified in shift work and job-sharing maintenance positions because each of these multiplies the number of people involved, and so the number of subjective experiences and opinions is higher for the same issues. 

WHAT CAN BE DONE?

The consequence of this bias is that the culture in which risk discussions are held is equally important as the risk being discussed. To ignore culture means that the person who argues the best will have their opinions and views promoted. Alternatively, the most senior person may have their views pushed through even if they are wrong.

On a recent project, we reduced the impact of this bias through three methods. First, we raised awareness of this Availability Heuristic bias within the team which was discussing risk of equipment failures. We used personal examples to engage and involve the team to help them understand how this bias comes up daily for each of them, and how it can skew our collective ability to effectively evaluate risk.

Second, we created ground rules for discussions which emphasized the practice that every viewpoint had to be clearly supported with facts and data. If it couldn’t, then that viewpoint was still gathered but labeled as being unsupported and given a lower weighting in the evaluation.

Finally, since risk is a combination of both the consequence and the likelihood of a failure:

Risk = Consequence of failure x likelihood of failure occurrence;

we created a risk matrix which allowed the team to objectively rank each of these components based upon facts (and not remembered experience). The team could then focus their discussions to identifying which risk category most closely matched the actual failure data they had in front of them.

The result was a risk evaluation which was more accurate, more repeatable (less reliant on individual personalities), and faster to gain consensus.

RLG’s Maintenance Excellence™ (Mx™) program reduces the impact of this (and other) negative effects which naturally occur in any workplace. Operating Rhythm™ is specifically designed to bring data and facts into the daily conversation, and reduce disparities between Operations teams, Maintenance teams, Inspection Teams, shifts and rotations. Our performance coaching approach increase engagement and involvement of people in problem solving based upon data and facts, and raises awareness about how human biases can get in the way of good maintenance practices. Our experts put in place systematic and structured routines to protect against these behavioral barriers to performance.