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We all love rules of thumb. They make life simpler. In the plant, we often use rules of thumb to quickly size up a problem based upon similar problems that we’ve seen before and apply a solution framework that’s been effective in the past. Unfortunately, these problem-solving rules of thumb can often be off the mark and, in some instances, outright wrong.

This is why, as I wrote in a recent article for The RAM Review, that when it comes to creating a framework for defect management and a culture of defect avoidance, we must institute a “why-why” mindset. But more on that later.


Click Here To Read The Referenced Article,
A Closed-Loop System For Defect Management”


Human-behavior professionals refer to these types of rules of thumb as “heuristics.” We employ different types of them. The “availability” heuristic is one that comes easily to mind, often because the problem assessment and solution framework may recently have been employed effectively or is frequently applicable. The “representative” heuristic is applied when the current situation possesses several attributes that are similar to past scenarios. The “affect” heuristic is another popular rule of thumb, whereby the individual morphs all problems so that they are familiar. In other words, if I’m a hammer man, I work hard to make everything look like a nail.

We’re motivated to rely on heuristics to reduce effort, save time, save money and, in some instances, to reduce complex problems to simple problems. It’s this last motivation that can cause trouble for us in the plant.

The motivation to reduce a complex problem into a simple one for which a familiar solution will fit is usually well-intentioned, be it an individual or group applying the heuristic (Fig. 1). He, she, or they usually want to save time and money for the company, fix the problem, and, often, get the plant up and running again. Still, however well intentioned, wrong is wrong. That’s why we need a “why-why” mindset and culture in the plant.


Fig. 1. Simplified schematic of a heuristic-driven decision process.



Also referred to as five-why analysis or apparent-cause analysis, why-why analysis is a scaled down version of cause analysis. It differs from root-cause analysis (RCA) in more than just terminology.

Why-why analysis is the linear process of asking “why” successively until the person or group reaches the forcing function that produced the outcome. Conversely, RCA starts with the universe of all possible causes (see DOE-NE-1004) and, through careful analysis, eliminates those that didn’t contribute to the current failure or event, leaving a manageable set of causal factors that either did contribute to the event or can’t be logically excluded. (Note: DOE-NE-1004 is free standard that provides a great taxonomy of failure causes for use in RCA and why-why analysis. (As I mentioned in my recent article on a defect-management system, please email me at dtroyer@theramreview.com for a PDF of the document).

Used properly, both why-why analysis and RCA are valuable in the plant, but I believe that instilling a why-why mindset that produces a culture of defect avoidance offers the greatest return on investment associated with the defect-management framework. But why must these valuable analytical tools be reserved for unusual problems?

Kaizen is foundational element of the Toyota Production System (TPS), the basis for Lean Manufacturing. Kaizen is the Japanese work for continuous improvement. It works on the assumption that small incremental improvements over time produce big gains. Simply put, correcting little problems prevents big problems from occurring. A why-why culture is the heart and soul of Kaizen. Thus, why must we wait for something to go wrong before leveraging why-why analysis? Here are just a few examples of “why” that we should be asking and addressing every day in the plant:

♦  Why did this machine fall out of adjustment?

♦  Why was this part hard to get to?

♦  Why was it difficult to apply lubricant to that part?

  Why couldn’t I execute that inspection?

♦  Why do I have to shut down the machine to complete that inspection or PM?

♦  Why was the necessary detail not included in my work preventive or corrective work instruction?

♦  Why wasn’t a helpful tool specified in the work instruction?

♦  Why are we using this part or material when another might be better?

♦  Why don’t I really understand how to perform this task?

While it may not solve 100% of our heuristic, i.e., rule-of -thumb-induced, failures, the why-why mindset and a culture of defect avoidance will substantively reduce their frequency of occurrence. Additionally, a why-why culture naturally leads to a “how-to” culture. Why-why plus how-to adds up to Kaizen and continuous improvement. And don’t limit this mindset and culture to what’s going wrong: Apply it to successes as well.

Asking why something worked can help you make it work better and/or extend it systemically. You know about root cause failure analysis (RCFA), which is applied to significant failure events or bad actors. Why not employ root-cause SUCCESS analysis (RCSA) to systematically analyze successes so that they can be fined tuned and/or applied systemically? I’ll save RCSA for a future post for The RAM Review.TRR



ABOUT THE AUTHOR

Drew Troyer has more than 30 years of experience in the RAM arena. Currently a Principal with T.A. Cook Consultants, he was a Co-founder and former CEO of Noria Corporation. A trusted advisor to a global blue chip client base, this industry veteran has authored or co-authored more than 250 books, chapters, course books, articles, and technical papers and is popular keynote and technical speaker at conferences around the world. Drew is a Certified Reliability Engineer (CRE), Certified Maintenance & Reliability Professional (CMRP), holds B.S. and M.B.A. degrees, and is Master’s degree candidate in Environmental Sustainability at Harvard University. Contact him directly at 512-800-6031 or dtroyer@theramreview.com.

 



Tags: reliability, availability, maintenance, RAM, fasteners, lubrication, alignment, balance, vibration, root-cause analysis, RCA