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Without Proper Context, Your Ladder May Be Leaning Against the Wrong Building

By Greg Alvarado, Chief Editor at Inspectioneering. April 27, 2023
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Stephen Covey, the renowned author and lecturer who penned The 7 Habits of Highly Effective People, often talked about the importance of context. For example, without the right perspective and context, we may find after much hard work building the most beautiful ladder that, at the end of a project, it is leaning against the wrong building. How often have we seen this in our IDMS and EAM program designs? A hypothesis from the same book that I believe is related is that our planning and work should first focus on effectiveness, then efficiencies. Otherwise, the author says, we may be doing the wrong things faster.

Another contextual story from the book is about a father on a commuter train with his children. The kids were a bit wild and unruly. A passenger was offended by the children's behavior and said something to the father. The father replied that he was sorry and that his wife, the mother of the children, had just passed away that morning, and the kids did know how to act as a result of the traumatic experience. Of course, the context of the children’s behavior was much different after that, and the man who complained now became the one offering an apology—context matters. Don’t go off “half-cocked,” so to speak.

I recall reading an article in Harvard Business Review that described the era we are in as one of hyper-specialization, contrasted against the ability to see how things fit together, which is a shrinking talent and is usually learned by experience. The latter is difficult to see from a specialist’s perspective. This leads me to mention the danger of just looking things up on the internet, so to speak, without understanding the nuances of context considerations for the data. Perhaps some of the shortfalls can be justifiably attributed to the lack of mentorship in today’s business environment. Companies tend to look for the sheepskin or certifications and think this is good enough, then “throw them to the wolves” (i.e., toss them into the field). Mentors often supply context.

This lays the groundwork for the importance of context. But one more important thought—the importance of motive. Motive will color our whole perspective around how we approach a project, initiative, or job, as well as how we relate to others. For instance, I have witnessed RBI being used to justify taking equipment out of turnarounds or applying longer inspection intervals for the sole reason of saving money. Of course, this falls far short of being able to take full advantage of an RBI program’s potential benefits, and it produces a limiting mindset. Why? Because the motive is extremely narrow. The objectives of RBI should be to identify risks and areas of vulnerability and to help us understand the ramifications of our decisions (or indecisions) in a systematic, holistic way. By the way, it’s more beneficial to think of RBI “savings” as cost optimization.

So, context and motive are important considerations for almost anything we do. For instance, in my early career as a chemist, part of my daily responsibilities was operating an emission spectrograph. I worked in non-ferrous metallurgy at a large secondary smelter. Effective QA/QC were critical to success. Off-spec products would be returned. It was extremely important for accurate analyses of each batch of material.

In the early 70s, this spectrograph was not computer controlled or calibrated. The spectographer had to lathe each sample carefully, being careful not to introduce any contaminants. Then they had to mount the fresh graphite electrode and sample. The next step was to expose the sample to a high-energy, electrically generated spark. The excitation of the sample via the spark would emit light, which was captured on film. We used the German spectral tables to determine the best wavelengths to use for each element to measure the elemental concentrations via a densitometer. The light emitted by most elements can be seen at various wavelengths, but a very important detail is that the density of the light at those different wavelengths was different for the same element, depending upon the matrix. For example, if measuring Bismuth and other elements, the densities would look different at different wavelengths. Was I wanting to measure the % of Bi in a lead alloy or iron sample? So, the context of the situation, once again, mattered. A little later, we started using computer-controlled quantometers, which used photomultiplier tubes instead of film. They worked fine until they were out of calibration, but that is for another article.

Let’s look at one more example that is more relevant to the Inspectioneering reader. Have you ever seen someone trying to combine RBI results from different risk calculators? In the early days of API RBI technology development, we developed two approaches -- one qualitative and the other semi-quantitative. In trying to be efficient, the prevailing thought was to do the initial RBI assessment with the quicker, less data-intensive, qualitative approach. Then follow up with the semi-quantitative analysis on items that had qualitative risks greater than X, for example. Wanting to be thorough and effective (motive), we performed both types of analysis on all the equipment to evaluate the results of the premise. To our surprise, we learned that some of the equipment had higher calculated risks using the semi-quantitative method/context than the qualitative method/context. So much so that the equipment that should have been calculated using the semi-quantitative method would not have made the cut had we relied on qualitative only. The problem was that they were not calibrated against one another. Some call this normalization. I have seen similar issues with trying to place RBI-generated risks in an HSE risk matrix used for corporate decision-making. There must be a common basis at some level that makes the comparison valid. Remember what Stephen Covey said: work on being effective before efficient; otherwise, we may be doing the wrong thing faster. In our world, we see much rework because of such behavior.

Now, we are diving into “data lakes” to extract more value from data to be more effective risk and reliability prognosticators and managers. An important aspect of this will be the contextualization of data so we can use it correctly, effectively, and safely. Yes, wrong modeling and predictions can lead to negative consequences. This is not to be taken lightly, as we should focus on effectiveness first. In comparing both the qualitative and quantitative approaches above, this was our goal. Sometimes plus or minus 10 degrees matters. Sometimes the range can be much broader.

Safety, mechanical integrity, and business/reliability excellence do not have to be thought of as mutually exclusive. They can and should be complimentary.

Thanks for sticking with me in the odyssey of this article. We covered a lot of ground, especially in the areas of context and motive. I hope you found it enlightening and challenging. We welcome your comments.


Comments and Discussion

Posted by Vahid Rahmanian on May 16, 2023
Thanks for your beautiful lecture. it is so... Log in or register to read the rest of this comment.

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