Sherlock Holmes and Root Cause Analysis: Lessons in RCA from a Famous Fictitious

Detective

Matthew Barsalou

Statistical Problem Resolution Master Black Belt

BorgWarner Turbo Systems Engineering GmbH

Worms, Germany

matthew.barsalou@gmail.com

SUMMARY

Sherlock Holmes as a quality Guru? Not being in the field of quality may exclude him from

consideration as a Guru: however, the stories of Sherlock Holmes provides many tidbits of advice

that a root cause analysis investigator can apply when investigating a failed product or process.

This paper presents the relevant Sherlock Holmes quotes together with practical advice for

applying the concepts on an actual root cause analysis and finishes by showing how to translate

many working hypotheses into actionable items.

KEYWORDS

Root cause analysis, hypotheses, empiricism, Sherlock Holmes, Cause-and-effect diagram, RCA

helix

INTORUDCTION

Although not a quality Guru, the stories of Sherlock Holmes provide tidbits of advice applicable

to root cause analysis (RCA). This advice can be applied when investigating the failure of a

product or process.

Sir Arthur Conan Doyle, 1859-1930, was a British physician who created Sherlock Holmes

(Lycett 2008). His Sherlock Holmes character is the archetype for detectives. In addition to

Doyle’s original stories of Sherlock Holmes, the fictional detective has been presented in many

forms of media. There have been many films from 1916 silent film Sherlock Holmes (BBC 2014)

to 2009’s Sherlock Holmes (Warner Brothers 2009). There are also TV series' based on Sherlock

Holmes (BBC 2016) and the detective has also appeared in radio serials (Archive.org), animation

(TV.com 2016), and video games (World of Spectrum2014).

Doyle published 56 short stories and 4 novels 1886-1893 and 1903-1927. Countless authors,

including Mark Twain (Twain 2013) have also tried their hand at writing Sherlock Holmes

stories. However, the original stories by Doyle contain many tips that can guide us in performing

root cause analysis in the field of quality.

THE LESSONS OF SHERLOCK HOLMES

The Copper Beeches

“Data! data! data!” he cried impatiently. “I can't make bricks without clay (Doyle 2003d p. 501).”

An all too common mistake during an RCA is to ideally jump to conclusions before data

is available. Often, this may be the result of prior experience such as when a comparable failure

has been seen before. Using prior knower is not of itself a problem; declaring the root case

identified and moving straight to corrective actions is the problem. There may be insufficient data

on hand at the start of an RCA, but this need not be a problem.

Is production data available from the time the failed part was produced? If so, check the

data to see if anything out of the ordinary happened. Is there no data available? Then inspect the

part as soon as it is available. Perhaps the part is being shipped from the customer and

unavailable at the moment; in such situations, it may be prudent to attempt to recreate the failure

under controlled conditions to gain a preliminary understanding of the issue.

Silver Blaze

"It is one of those cases where the art of the reasoner should be used rather for the sifting of

details than for the acquiring of fresh evidence (Doyle 2003i p. 522).”

In a long and complicated RCA there may be a point in time in which many reports have

been collected, but the root cause remains unknown. This is the time to briefly stop and perform a

deep review of the information currently available. It is possible that no one measurement or

laboratory report provides a direct indication as to the root cause; however, a full review of all

documents may provide a big-picture view with indications as to where the root cause may truly

be.

If measurement data is available, it should be subjected to a statistical analysis. For

example, multiple reports with the measurement of ten parts might not show anything of potential

relevance; however, a trend may be present if the entire data set is analyzed together.

The Sign of Four

"No, no: I never guess. It is a shocking habit,--destructive to the logical faculty. What seems

strange to you is only so because you do not follow my train of thought or observe the small facts

upon which large inferences may depend (Doyle 2003h p. 219).”

Any “guess” should be based on the facts on hand. Is there any conclusion which would

explain the many little details that are available? For example, one or two facts alone may not

lead to any conclusion; but, taken as a whole, the facts may contain the key to unlocking the root

cause. Such a guess should be viewed as a tentative hypothesis; assume it is true for the sack of

testing, and then test the hypothesis. It is OK to be wrong in such situations as the testing may

yield new information that drives the investigation forward.

The Adventure of Black Peter

"Oh, yes, my dear Watson, I am perfectly satisfied. At the same time Stanley Hopkins's methods

do not commend themselves to me. I am disappointed in Stanley Hopkins. I had hoped for better

things from him. One should always look for a possible alternative and provide against it. It is the

first rule of criminal investigation (Doyle 2003b p. 899).”

A critical mistake during an RCA is to form a hypothesis and then seek to defend it instead of

challenging it. It is not too difficult to produce evidence in support of an incorrect hypothesis; an

inadvertent finger on the scale or accidentally pressing too hard on calipers can produce the

desired, yet worse than useless, result. Strong belief in an incorrect hypothesis can lead to failing

to identify the root cause which in turn leads to a failure to implement the type of corrective

actions needed to prevent a reoccurrence.

It may be advantages to look for evidence that refutes our belief (Heffernan 2011). Seeking

evidence that refutes our favorite hypothesis can result either in rejecting an incorrect hypothesis

or if we fail to find contradictory evidence, we will have more support for the hypothesis.

Subjecting a hypothesis subjected to rigorous testing results in a much stronger and more robust

hypothesis. A hypothesis that has survived many challenges is more likely to be a correct

hypothesis.

The Valley of Fear

“I can see only two things for certain at present -- a great brain in London, and a dead man in

Sussex. It's the chain between that we are going to trace (Doyle 1986d pp. 163-164).”

Finding the connection between two seemingly unrelated events may lead to the cause of the

failure being investigated. For example, suppose a part is returned broken and rusted and it

should have survived several more years in service. A brake and rust seem unrelated, but perhaps

the rust weakened the material until the point where it broke. Or, perhaps the brake was due to a

material problem and the brake let in moisture, which in turn resulted in the rust. Following

separate lines of evidence until they converge at the cause may also be helpful (Barsalou 2015).

A Study in Scarlet

“You don't seem to give much thought to the matter in hand,” I said at last, interrupting Holmes’

musical disquisition. “No data yet,” he answered. “It is a capital mistake to theorize before you

have all the evidence. It biases the judgment (Doyle 2003a p. 23).”

Non-empirical preconceived notions may lead to seeking conformation of your hypothesis, while

disregarding contradictory evidence. Preliminary actions such as inspecting suspect product can

be taken before full information is available, but data is needed before concrete hypotheses can

be formed. Suppose a customer issues complaint for “length of X is too long.” Parts can be

inspected to prevent the shipment of out of specification parts, but more details are needed to

address the root cause of the issue. Perhaps the machine was set up wrong resulting in a long part.

On the other hand, this could be a set-up part with telltale set-up damage on the end. More

information is needed before a good hypothesis can be formed; in this case, a quick look at the

part itself would reveal key information. Instead of blindly hypothesizing, get more information.

In this situation, asking the customer a few questions could establish the length is 1.4mm outside

of specification and the end looks damaged.

The Problem of Thorn Bridge

“It seemed a certainty when first it flashed across my mind in the cell at Winchester, but one

drawback of an active mind is that one can always conceive alternative explanations which would

make our scent a false one (Doyle 1967c p. 159).”

Logic alone will not solve the problem; people can always contemplate an alternative explanation

that refutes the first idea or come up with elaborate defenses for a favorite hypothesis. The only

way to is through the use of empiricism. Instead of second-guessing a hypothesis, go test it and

see what the data supports or refutes.

The Adventure of the Empty House

“Ah! My dear Watson, there we come into those realms of conjecture where the most logical

mind may be at fault. Each may form his own hypothesis upon the present evidence, and yours is

as likely to be correct as mine (Doyle 2003f p. 799).”

We can all conjecture; but we can’t really know without verifying our conjectures. The good Dr,

Watson would probably be the one who leads a team in creating a prioritization matrix to vote on

the most popular root cause. The detective novel version of this would be “The team voted, the

janitor did it.” Voting on the most popular root cause is not necessarily wrong; vote on how well

the evidence is explained by the hypothesis, prioritize the hypotheses based on the voting, and

then begin an empirical evaluation of each hypothesis based on the priority it was assigned.

The Adventure of the Norwood Builder

“There, that's enough,” said Lestrade. “I am a practical man, Mr. Holmes, and when I have got

my evidence I come to my conclusions. If you have anything to say you will find me writing my

report in the sitting-room (Doyle 2003f p. 799).”

Detective Lestrade is the kind of quality engineer who starts filling out an 8D report at the first

hint of a potential cause. His conclusion would be based on “It looks like X, so it must be X.”

Quality engineer Holmes would actually take the time to ensure the conclusions are indeed

correct. Lestrade has a hypothesis, but how can he know if it is indeed correct? The only way to

do this though an empirical evaluation. The objective is not necessarily to be correct; rather, to

quickly reject incorrect hypotheses so that the investigator can quickly move past the red-herrings

and arrive at the true root cause.

The Adventure of the Blue Carbuncle

“I can see nothing,” said I, handing it back to my friend. “On the contrary, Watson, you can see

everything. You fail, however, to reason from what you see. You are too timid in drawing your

inferences (Doyle 2003c p. 378).”

Once observations have been made, decide what could account for the observations. This may

not be the root cause, but it can be the push needed to move the investigation along. There may

be multiple reasons a failure happened such as when a diameter is out of specification. Was the

tool worn? Was the correct drawing used? Did a scrapped part accidentally get shipped? Consider

each possibility as a hypothesis and prioritize based on how well the hypothesis describes the

issue and how probably the cause is believed to be. Then evaluate each hypothesis in order of

prioritization.

The Adventure of the Sussex Vampire

"One forms provisional theories and waits for time or fuller knowledge to explode them (Doyle

1967b p.106)."

Sometimes a tentative hypothesis may be clear, but not fully supported by the data. In such

situations it may be necessary to collect more data before taking other actions. If there is no data

to collect, perform an experiment to generate data. For example, attempting to recreate the failure

under controlled conditions may provide a better understanding of an issue.

The Adventure of the Sussex Vampire

"It has been a case for intellectual deduction, but when this original intellectual deduction is

confirmed point by point by quite a number of independent incidents, then the subjective

becomes objective and we can say confidently that we have reached our goal. I had, in fact,

reached it before we left Baker Street, and the rest has merely been observation and confirmation

(Doyle 1967b p. 112)."

Individual pieces of evidence taken alone may not lead to a clear concession; but when looked at

as a while, they may provide strong support for a hypothesis such as when multiple lines of

evidence converge at one point (Barsalou 2015). Naturally, such a hypothesis must be confirmed

either through direct observation or though conformation testing.

The Adventure of the Cardboard Box

"We approached the case, you remember, with an absolutely blank mind, which is always an

advantage. We had formed no theories. We were simply there to observe and to draw inferences

from our observations (Doyle 1986a p. 332).”

It is best to begin the investigation without preconceived notions. Deciding that you know the

root cause before data is available leads to a risk of inadvertently overlooking key details or

ignoring contradictory evidence. It is natural to think you know the cause if the failure looks

remarkably similar to one that has happened repeatedly in the past; however, this situation may

be different so you must look at the evidence before deciding on a root cause.

The Boscombe Valley Mystery

“‘There is nothing more deceptive than an obvious fact,’ he answered, laughing (Doyle 2003g p.

310).”

The answer to the problem may be obvious, but is it the correct answer? Be sure that obvious

hypotheses are tested as rigorously as the not so obvious ones.

The Adventure of Wisteria Lodge

“Is it beyond the limits of human ingenuity to furnish an explanation which would cover both of

these big facts? If it were one which would also admit of the mysterious note …., why, then it

would be worth accepting as a temporary hypothesis. If the fresh facts which come to our

knowledge all fit themselves into the scheme, then our hypothesis may gradually become a

solution (Doyle 1986c p. 302).”

It may be necessary to form a working hypothesis that best explains the valuable evidence. This

hypothesis may prove to be incorrect, but that is OK. Such a hypothesis is the beginning and not

the end of the investigation. The working hypothesis should then be evaluated. Does the

evaluation support the hypothesis? If not, it should be rejected. Does the hypothesis appear to be

partially correct? If so, it can be revised. Did we learn something new during the evaluation? If

so, perhaps we can use this new information to form a new working hypothesis.

The Adventure of the Devil's Foot

"Let us walk along the cliffs together and search for flint arrows. We are more likely to find them

than clues to this problem. To let the brain work without sufficient material is like racing an

engine. It racks itself to pieces. The sea air, sunshine, and patience, Watson--all else will come

(Doyle 1986b p. 427).”

There are times when it is best to take a break from the investigation and work on something

different. This gives the mind the chance to work in the background. Dropping the problem at

hand and doing something different gives you a chance to refresh and regain your concentration

(Ariga & Lleras 2011).

The Adventure of the Devil's Foot

"I have not all my facts yet, but I do not think there are any insuperable difficulties. Still, it is an

error to argue in front of your data. You find yourself insensibly twisting them round to fit your

theories (Doyle 1986c p. 303)."

A preconceived notion may lead to interpreting all data in terms of the preconceived notion

instead of forming conclusions based on what the data indicates. This leads to interpreting all

data in terms of the preconceived notion. This results in potently ignoring contradictory evidence

and not looking at the data and asking “What is the data telling me?”

The Adventure of the Blanched Soldier

“……when you have eliminated all which is impossible, then whatever remains, however

improbable, must be the truth. It may well be that several explanations remain, in which case one

tries test after test until one or other of them has a convincing amount of support (Doyle 1967c p.

57).”

A difficult analysis may be greatly simplified by eliminating possible explanations. It may also be

easier to confirm something is not the root cause than to find the actual root cause. Such an action

results in a shorter list of suspects. Suppose there was a murder investigation with ten potential

suspects. The investigation would be much easier if we could conclusively prove that six of the

suspects were out of town when the murder happened. We won’t have the name of the guilty

party, but suddenly, we have less candidates and therefore, less alibis to check out. The

investigation has been simplified.

The Valley of Fear

"The temptation to form premature theories upon insufficient data is the bane of our profession

(Doyle 1986d p. 163).”

From such a quote, one could be inclined to think Mr. Holmes was a quality engineer. Often,

there is an individual who automatically jumps to a conclusion as soon as the investigation has

started. Maybe they are an expert of they have seen such an issue before. They may even be

correct, but we can only be certain if we look at the data and then draw conclusions.

The Adventure of the Blue Carbuncle

“Chance has put in our way a most singular and whimsical problem, and its solution is its own

reward (Doyle 2003c p. 396).”

An RCA should be seen as an intellectual challenge. The most difficult problems may require the

application of every tool and method we can muster and may push our abilities to the limits,

thereby increasing our current limits and abilities. The more difficult the problem, the harder we

must think and the greater the end reward: Growth and development.

RCA HEXIX AS AN APPROACHTO ROOT CAUSE ANALYSIS

A systematic and structured approach is needed when performing an RCA and the RCA helix

provides this. The RCA helix provides a simple and easy to apply process for the investigation of

quality failures. The concept is generic enough to apply to many types of issues ranging from

inadequate service to the failure of manufactured product. This concept combines aspects of

Box’s iterative inductive-deductive process (Box, Hunter & Hunter 2005) and Deming’s plan-do-

check-act (Deming 1989).

The first step is to form a tentative hypothesis using the available data. More data should be

collected if insufficient data is available. The hypothesis is then evaluated empirically with the

results used to modify the hypothesis if it fails the evaluation. If the hypothesis passes, it should

then be subjected to conformation testing before improvements are identified (See Figure 1). The

process starts over if the hypothesis is rejected and information gained during the evaluation may

be used to modify the hypothesis.

Figure 1: RCA Helix ©2016 Matthew Barsalou

A cause-and-effect diagram should be used to consolidate multiple hypotheses. This is especially

useful at the beginning of an issue when many compeating hypothesies must be evaluated.

Displaying them graphically can also be helful when brainstorming potential causes of a problem

in a team as the team can see all of the ideas that have been presented. Figure 2 shows a simple

cause-and-effect diagram for a murder mystery.

Figure 2: Cause-and-effect diagram based on The Adventure of the Bruce-Partington Plans

The various hypotheses are displayed in the cause-and-effect diagram, but it is unclear what

actions should be taken so the hypotheses should be transferred to a spreadsheet such as the one

shown in Figure 3.

Figure 3: Cause-and-effect spreadsheet

Figure 2 depicts a typical cause-and-effect diagram for an industrial problem; in this case,

unwanted iron in the propduct. These items are then transferrd into a speedsheet such as the one

in Figure 5. If necessary, the hypothesis can be expended with additional details for clarification

such as changing “Calibration” in the cause-andeffect diagram to “Calibration incorrect resulting

in bad readings causing incorrect mixture.” The team then prioritizes the items based on how well

they explain the physics of the failure. Actions are then identified for the highest priority items

and a responsible person is nominated and a target compleationd date is set. After evaluation, the

results are then summarized.

Figure 4: Cause-and-effect diagram

Reprinted with permission from Quality Press © 2005 ASQ, www.asq.org

No further redistribution without permission.

Figure 5: Cause-and-effect spreadsheet

CONCLUSSION

An RCA should be driven by hypotheses that seek to explain the failure using both the evidence

at hand and new data generated though testing and evaluation. The use of a cause-and-effect

diagram provides the team with a graphical depiction of the many hypotheses that are under

consideration when analyzing a complex issue. Transferring the cause-and-effect diagram to a

spreadsheet turns a graphical depiction of hypotheses into an action item list with a summary of

results; thereby making the issue easier to manage and providing a quick overview of results.

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