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Alan Metzel

Six Sigma

Organizing an Ishikawa Chart for Complex Analysis

Introducing the Enhanced Perkin Tracker

Published: Monday, January 23, 2023 - 12:03

Almost seven years ago, Quality Digest presented a short article by Matthew Barsalou titled “A Worksheet for Ishikawa Diagrams.” At the time, I commented concerning enhancements that provide greater granularity. Indicating that he would probably have little time to devote to such a project, Barsalou graciously invited me to expand upon his work. As one of the few positive outcomes of the recent storms that have raged across the United States, I’ve finally completed that task. In thanks to Barsalou—and with tribute to his mentor—I refer to this effort as the “Enhanced Perkin Tracker.”

Story update 1/23/2023: A previous version of this story linked to the wrong Excel file. That Excel link is now correct.

For those who might be unfamiliar with the Ishikawa diagram, it’s a graphic problem-solving tool used to relate multiple potential causes to a single effect in a rational manner. Based on its shape, it’s easy to understand why it’s often referred to as a “fishbone” or “herringbone” diagram.

As Barsalou points out, the Ishikawa diagram is a powerful tool for facilitating brainstorming and, to some degree, organizing ideas. But because no branch, or sub-branch, has any more or less weight than any other, it provides little help in evaluating or tracking various hypotheses (figure 1).

Chart, diagram  Description automatically generated
Figure 1: Typical Ishikawa format

Fortunately, in the vast percentage of occurrences, the potential root causes are relatively few. This makes tracking and managing the process straightforward and proportionately painless. In fact, it’s often the case that only three or four arms of an Ishikawa chart may be populated. With only a handful of options, prioritization presents few conflicts.

But consider the other end of the spectrum. Your launch vehicle rises into the bright blue sky over the Atlantic Ocean. Everything is going perfectly... and then, 139 seconds later, catastrophe strikes. The exceptionally reliable booster has exploded. Something has gone wrong! Somewhere within that finely engineered system, some single point of failure has triggered a rapid unscheduled disassembly event, scattering vehicle and payload across many square miles of the Atlantic. Significant as the immediate economic impact is, the repercussions could be far greater. Can other boosters be trusted? When can flight operations resume?

With myriad possible points of failure and potentially catastrophic economic repercussions, it becomes imperative to determine the root cause and implement corrective actions in the most expeditious and economical manner possible.

To quote Barsalou, “In an ideal world, all hypotheses would be immediately tested, but resources may be limited, so those believed to be highly linked to the problem should be tested first. Hypotheses with a weaker connection to the problem may also have a high priority for evaluation if testing them is quick, easy, and inexpensive.”

With that rationale in mind, imagine, if you will, tracking all the conceivable “what ifs” and “maybes” using only an Ishikawa chart. Of course, there would be untold hosts of supporting documents. Evidence, suppositions, possible tests.... How does one rationally organize this haystack of data and prioritize efforts in a search for the needle therein?

The Enhanced Perkin Tracker shown in figure 2 (an Excel spreadsheet that you can download here) allows the user to consolidate and organize all this information in one document, and provides a consistent method to prioritize and track investigation and testing. Obviously, all those supporting documents aren’t supplanted by the tracker, but at any moment in time, it provides immediate situational awareness.

Text  Description automatically generated with medium confidence
Figure 2: Enhanced Perkin Tracker spreadsheet

With the tracker spreadsheet, hypotheses may be recorded, categorized, and given a numerical rating based on experience and past performance. For each hypothesis, one or more evaluation actions may be added and numerically rated. In the example shown, actions are rated on cost, time required, and perceived likelihood of success. (Likely, quick, and cheap will rate high; unlikely, long duration, and expensive will rate low.)

Individual ratings are multiplied together to provide an overall rating of each hypothesis.

With rankings in hand, action planning can proceed, responsible functions or persons assigned to each action, and appropriate completion dates forecast. As the investigation proceeds, anyone, at any given time, can access a real-time status. Further, should test results indicate the need, ratings may be adjusted, quickly providing revised priorities.

Categories, numerical ratings, and root cause status are all entered using drop-down menus as shown in figure 3. (Please note that, due to size, some dropdowns require scrolling to view the entire list.)

Table  Description automatically generated with low confidence
Figure 3: Drop-down menu data

To maximize utility, note that each drop-down has the capacity for both expansion and modification. For instance, the time selections could be extended or further subdivided for a specific issue. Likewise, the numerical ratings may be modified. Perhaps you would rate experience as “1, 3, 5” or “1, 3, 7,” rather than “1, 2, 3.” Since a blank entry defaults to a rating of 1, a column can be ignored without any modification.

I’m sure that in some areas there could be a desire to add another rating group; existing groups may be easily replaced by others through simple replacement of the appropriate data fields.

Lastly, rating columns may be added by judicious use of cut and paste, and appropriate editing.

Hopefully you will find this tool to be of use—but hopefully never at the 139-second mark!


About The Author

Alan Metzel’s picture

Alan Metzel

Alan Metzel is a senior principal quality engineer at Northrop Grumman Mission Systems, with responsibilities for metrology equipment, methods, and training supporting model design and fabrication, and nondestructive examination. He serves on the Coordinate Metrology Society board of directors, the CMS Certification Committee, and the executive committee of the Cumberland Valley Chapter SAE. He received his Bachelor of Science in mechanical engineering from the University of Tennessee (Knoxville) and has prior experience in high-speed sheet metal manufacturing, gear manufacturing, and building armored vehicles.


Ishikawa Diagrams

Dr Ishikawa had 3 types of Cause-and-Effect Analysis. It is unfortunate, that most say "Fishbone" for Type A and B when in his books he never described that type as such. These are useful for Event Problem indeed. However, as all work happens through processes, Ishikawa's Process Classification or as Don Dewar (RIP) described and simplified it to the Process Cause and Effect, is more appropriate to process-based problems.

Using a so-called Fishbone on a Process Problem is not as he advised and is sub-optimal in cause analysis. He pointed out that if there were too many causes and expanded 5-Why analysis (see AIAG FMEA for Flow Process Chart or Maynard Industrial Engineering HB and editions) on the highest ranked causes for respective arms or "bones", then the situation is too complex (Event). For processes, it is too unstable (Process). Reference to Standardized Work or Procedures in Processes of the Management System is the foundation.

As others have mentioned, if there are too many causes, these also direct one to the PFMEA as they should have been identified. Too much Problem Solving and one knows the PFMEA is wrong. This Perkins Table could be useful, but Dr Wheeler as the Comment described, provides reasons not so.

For weighting or ranking causes, in doing such Ishikawa Diagrams on real projects across diverse industries, the simple tasks of good brainstorming and data collection is required. Post brainstorming, and where needed to clarify the cause if needed with ‘known’ and current 'evidence' for the causes so voted, then some 5-Why Analysis on the highest ranked cause/s followed by a basic data collection plan on the highest ranked causes, works fine and helps team member segregate the causes from symptoms, or what they thought were causes, were not. Useful discussion and article.

Problem with risk priority numbers

Alan, thanks for your contribution. It is clear there is a need for such prioritization in at least some cases, but I am afraid that your proposed solution suffers from the same fundamental mathematical issue that many (most?) FMEA tools, or House of Quality tools suffer from. Namely that you use ordinal scales (for 4 different quantities), and then you multiply them together. It may seem OK at first glance (similar things are done for FMEA's or Houses of Quality), but unfortunately no arithmetic operation can be performed on ordinal variables. This is simply because they do not preserve scale: a rating of 5 is absolutely NOT 25% more than a rating of 4, and a rating of 2 is not twice (as likely, as costly, as ...) as a rating of 1. 1 does not mean 1, it just means lowest. Multiplying lowest by "next lowest" has no meaning.

You may want to read the article published by Donald Wheeler in Quality Digest: "Problems with Risk Priority Numbers" https://www.qualitydigest.com/inside/quality-insider-article/problems-risk-priority-numbers.html, where he describes this practice (of performing arithmetic on ordinal values as "utter and complete nonsense" (his words, not mine).

Now, if you could derive ratio scales for your 4 criteria, the issue would go away (likelihood, in percent, of the item under consideration being the true cause, cost in $ of the experiment, duration in days of the experiment, odds of success in percent of the experiment), you then could, and should multiply these; but multiplying the percentages, the cost, the duration. This would give you an expected cost-duration.


Risk Priority Numbers

While I understand your logic, I prefer to think of the system as dividing each grouping into "n-tiles". When performing this exercise, we are guessing... arriving at a concensus that the probability lies within a range. If merited, simply increasing the ordinal range narrows the range of probailities for each bucket. 

I would also make the arguement that expressing the ranking as a decimal may imply, to the casual user, an unwaranted degree of certitude.  If you were to limit yourself to one decimal place, how does 0.1, 0.2, 0.3,.... differ from 1,2,3,...? And, for a guess, can one differentiate between .10, .11, and .12?

In whatever manner one might choose to use this system, ordinals or decimals may be utilized, or with a simple revision, decimal values entered directly. Whatever values one chooses to use, in the end, we arrive at a ranking to aid in rational planning.

Thanks for the input


I've not seen this before.  It looks like a cross between a Ishakawa Diagram and a FMEA.  This will help in priortizing how your project would move forward.  I'll certainly try to use this on my next project.  Thanks.

Wrong Attachment?

I think you attached the wrong file.

You might be right

Hi Steve, You are right. Thanks for spotting that. The correct file is now attached. -- Quality Digest