Moving Beyond the “5 Why’s”, Fishbone, FMEA, and DoE

(This is the final installment of this Article.  To see full article go to: www.epsrca.com/Inside_View_of_Apollo_RCA)

None of the approaches used in the Analyze phase expose the depth and inter-relationship of the multiple-cause environment in which we live and function with any certainty.

“5 Why’s”:  The “5 Why’s” can show how causes connect; however, it overly simplifies the cause and effect relationship into a linear progression and typically only focuses on the Action causes. This limits the understanding of the event and the corresponding available solution set.  For example, if a lab technician drops and breaks a glass sample bottle of sulfuric acid resulting in a burn to the foot, it wouldn’t be uncommon to see the incident report citing the cause of the burn to be “Human Error-Employee not paying attention”, or “Employee did not conduct task in safe manner”, or “Employee unaware of the hazard”.  While these could be true if it was done intentionally (normally not), we typically see corrective actions that target, or attempt to target controlling the behavior of the “culprit”. Typically these solutions are ineffective at preventing the problem from recurring because they fail to address the conditional cause environment that remains completely intact for all remaining and future lab technicians who perform the same task.  Further, the solution that is intended to control the behavior won’t be effective until it understands the underlying causes of the behavior. 

Fishbone: The Fishbone method provides a useful vehicle for recognizing the sources of cause: Man, Machine, Method, Material, and Environment - there are many variation of the categories used on the Fishbone:  People, Procedure, Hardware and Nature is another.  However, the Fishbone diagram fails to show how all these causes interact with each other.  It also fails to provide verification or “evidence” that the proposed causes are valid.  The fishbone diagram is mainly used to identify vital “X’s” in Six-Sigma™.  Once these vital “X’s” are identified, corrective actions are taking to eliminate, change or control these “X’s”.  While this is a great way to identify causes affecting your process, these “X’s” are typically identified without understating the true correlation to the output “Y”.  The corrective actions are typically taken on the causes that are easy to implement and have a high payoff.  The remaining causes are ignored.

Fishbone is an excellent start to Apollo RCA™.  An Apollo Cause and Effect Chart will show how the causes interact, for example, where a “Method” and “Machine” interacting together, caused by a “Man’s” decision to use a certain “Material” is causing a problem in the “Environment”. 

FMEA:  Failure Modes and Effect Analysis is a procedure and tool that helps identify every possible failure mode of a process or product, to determine its effect on other sub-items and on the required function of the process or product. The FMEA is also used to rank and prioritize the possible causes of failures of a process or product and then determine the frequency and impact of the failure as well as develop and implement preventative actions, with responsible persons assigned to carry out these actions.

While FMEA is a great way to identify causes, and assesses the risk associated with them, it does not tell you how to eliminate, change or control these causes or the causes with high RPN’s.  FMEA is an excellent ending to Apollo RCA™.

DoE:  Consider the time and resource investment to be committed in the data collection, correlation and validation steps.  If a greater level of certainty of the causes can be established early on, less time will need to be spent confirming what is unknown and more time can be spent developing and implementing solutions.  If you don’t have a clear understanding of the causal relationships, you will be forced to expend much more time in DoE or other validation experiments.  You still need to define the Y=f(X) in order to set up your DoE.  DoE is typically not understood by many which cause the Project Leader to revert to a different tool to use during this phase.  Depending on the industry, DoE work can be very costly and cumbersome to complete, far more so than spending a bit more time building an evidence-based cause and effect chart for the problem.  While you may still want to conduct a DoE after you complete an Apollo Cause and Effect chart, since now you will have the function Y=f(X), you will discover that the list of variables requiring intensive data collection and correlation will be significantly less than if you enter into DoE with a simplistic understanding of the causes (variables).

Summary

Over the last several years, companies who have been enjoying the successes and improvements generated through Six-Sigma™ have begun incorporating Apollo RCA™ into their Six-Sigma™ programs.  Our intention is to cause you to evaluate your program in order to determine if you can further improve your Six-Sigma™ results and problem solving skills.  We believe most programs can be improved by optimizing a very important step in the process that has historically been weak and requires unnecessary additional experimentation and data crunching later in the process.

By incorporating Apollo RCA™ into the Analyze and Improve phase of your Six-Sigma™ program, you should expect the following enhancements:

Clear definition of the function Y=f(X).
Reduction in time and resources spent in;
DoE by reducing the number of experiments performed and in data validation.
Improve phase if the solution does not yield the desired improvement in the “Y”.
100% certainty that you have discovered the vital “X’s” affecting your process.
Control over the special cause in your product or process.
Greater solution choices on the vital “X’s”, leading to more cost effective solutions.

About the Author

    Fadi E. Rahal currently serves as the President of Effective Problem Solving LLC. Rahal has worked in the Energy industry for more than 18 years and additionally has extensive experience leading investigations within all types of businesses around the globe.  Rahal has also institutionalized and implemented Six-Sigma program in concert with Continuous Improvement - Root Cause Analysis programs in various organizations, including GE and Black & Veatch.
As an investigator, Fadi has led countless RCA incident investigations with Fortune 500 companies resulting in millions of dollars in cost savings.  His many investigation areas include:  machine and plant shutdown, machine reliability, EHS incidents, fire and explosion in a mine, employee productivity, product delivery, on-time and on-budget project execution, product sourcing and purchasing, scrap and rework, and customer dissatisfaction.
As a trainer, Fadi has led more than 200 RCA facilitator training sessions and trained thousands of students across North America for such companies as GE, Honeywell, Oncor Electric, REC Silicon, The Delta Group, Moog, Siemens, Carmeuse, Quaker, Tropicana, Kennametal, Ameren, Sonoco, Smith-Aerospace, American Society of Safety Engineers and in the Middle East for such companies as ARAMCO, SABIC, SASREF, SIPCHEM, EQUATE, KPC, KNPC, QAFCO, Oman LNG, and Pakistan Exploration.
Rahal holds a Master of Engineering degree from Rensselaer Polytechnic Institute, Troy, NY. He is a graduate of the Edison Engineering Program and obtained both Black Belt and Master BB certifications, as well as RCA Leader Qualification from General Electric.  In 2009, he received the Master Apollo RCA™ Instructor Certification – the only one ever awarded.

Send feedback to author directly:  fadi.rahal@epsrca.com or info@epsrca.com or visit us at www.epsrca.com

References

Six Sigma Academy, 2002 The Black Belt Memory Jogger GOAL/QPC and Six Sigma Academy; Salem, NH
Harry, Mikel J. Ph.D., 1994 The Vision of Six Sigma:  Tools and Methods for Breakthrough, Fourth Edition, Sigma Publishing Company, Phoenix, AZ, USA.
Gano, Dean 1999 Apollo Root Cause Analysis- A New Way of Thinking Apollonian Publications; Yakima, WA
Gano, Dean 2008 Apollo Root Cause Analysis- A New Way of Thinking Apollonian Publications; Yakima, WA
Gano, Dean 2011 Seven Steps to Effective Problem-Solving And Strategies For Personal Success
Gupta, Praveen 2004 Six Sigma Business Scorecard McGraw-Hill; New York, NY: Apollonian Publications, LLC
Pande, Peter, Robert Neuman, Roland Cavanagh 2000 The Six Sigma Way McGraw-Hill; New York, NY
Rath & Strong, 2002 Six Sigma Pocket Guide Rath and Strong Management  Consultants; Lexington, MA
Rahal, Fadi 2012 Strengthen your Six-Sigma™ program with Root Cause Analysis
Rahal, Fadi 2012 The Inside View of Apollo RCA™ and Six-Sigma™
General Electric:  Our Company:  What is Six Sigma:? http://www.ge.com/en/company/companyinfo/quality/whatis.htm
Motorola:  Six Sigma at Motorola  http://www.qualitydigest.com/dec97/html/motsix.html
i Six Sigma®:  www.isixsigma.com
RealityCharting®:  www.realitycharting.com
Effective Problem Solving LLC:  www.epsrca.com

Key Terms and Definitions

Actions    Causes on the Apollo Cause and Effect chart that are the result of an action by a human, system, equipment or nature.

BB    Black Belts are knowledgeable and skilled in the use of the Six Sigma methodology and tools.  Six-Sigma™ team leaders are responsible for implementing process improvement projects (DMAIC or DFSS) within the business - to increase customer satisfaction levels and business productivity.

CC    Common Cause is a source of failure that is always present as part of the random “Variation” inherent in the process itself.  See also Special Cause.

Conditions    Causes on the Apollo Cause and Effect chart that are the results of pre-existing environmental states.

DFSS    Design for Six-Sigma™ or new product/service introduction.  DFSS is the same as DMADV.  DMADV consists of five interconnected phases: Define, Measure, Analyze, Design, and Verify. DMADV is a data-driven quality strategy for designing products and processes, and it is an integral part of a Six-Sigma™ Quality Initiative.

DPMO    Defects per Million Opportunities (DPMO) is the average number of defects per unit observed during an average production run divided by the number of opportunities to make a defect on the product under study during that run normalized to one million.

DMAIC    DMAIC refers to a data-driven quality strategy for improving processes, and is an integral part of the company's Six-Sigma™ Quality Initiative. DMAIC is an acronym for five interconnected phases: Define, Measure, Analyze, Improve, and Control.

Fishbone    A tool used to solve quality problems by brainstorming causes and logically organizing them by branches. It is also called the Cause & Effect diagram and Ishikawa diagram.

FMEA    Failure Mode and Effect Analysis  A procedure and tool that helps to identify every possible failure mode of a process or product, to determine its effect on other sub-items and on the required function of the product or process. The FMEA is also used to rank and prioritize the possible causes of failures of a product or services and then determine the frequency and impact of the failure as well as develop and implement preventative actions, with responsible persons assigned to carry out these actions.

Gage R&R    Gage Repeatability and Reproducibility, is a statistical tool that measures the amount of variation in the measurement system arising from the measurement device and the people taking the measurement.

MBB    Master Black Belt is Six-Sigma™ Quality expert that are responsible for the strategic implementations within an organization.  A Master Black Belt main responsibility include training and mentoring of Black Belts; helping to prioritize, select and charter high-impact projects; maintaining the integrity of the Six Sigma measurements, improvements and tollgates; and developing, maintaining and revising Six Sigma training materials.

SC    Special Cause is an intermittent, unpredictable or unstable cause that lies outside the process or product acceptable limits.  A source of “quality” failure.

Vital “X”    Vital “X” or vital few derived from the Pareto chart, the term indicates that many defects come from relatively few causes (the 80/20 rule).

5 Why's    The “5 Why’s” typically refer to the practice of asking, five times, why the failure has occurred in order to get to the root cause/causes of the problem. There can be more than one cause to a problem as well. In an organizational context, generally root cause analysis is carried out by a team of persons related to the problem. No special technique is required.

Maximizing Value and Minimizing Resource Consumption

Anyone familiar with both methods will be quick to point out the differences in scope, method and the resource requirements for completion.  Some will argue that Six-Sigma™ is too complex, too resource intensive and not consistently successful within organizational cultures that do not exhibit strong “top down” leadership.  You will also hear that Six-Sigma™ can be applied to anything thus rendering any other problem solving approach unnecessary.  Our position is that for any organization to maximize the potential value realized with their problem solving efforts, a two part strategy will be most effective.

Strategy One:    For complex multi-variable problems, Six-Sigma™ AND Apollo RCA™ should be applied in concert; they are complimentary methods and not conflicting, as many people believe.

Strategy Two:      For any other problem types, or even if the complex problems are significantly lacking in data, Apollo RCA™ may be more effective, especially when considering the resources and time spent collecting additional data to solve the problem.

Upon the initial dissection of complex problems, the cause and effect relationships will rarely be known.  Establishing an evidence-based, cause and effect chart will more quickly help the team clarify the known causes from the possible causes, thus greatly reducing the time spent gathering data and performing statistical evaluations.  Six-Sigma™ relies on “Good” or “Gage R&R” data and on a statistically significant sample size (typically 25 sets of data or more) that will produce reliable conclusion.  Data gathering and statistical evaluations will never be completely eliminated; however, they can be greatly reduced by spending more time up-front developing the cause and effect chart which will focus the team on the causes requiring validation instead of pursuing the many possible theories requiring subsequent and systematic data collection, evaluation and possible dismissal until the remaining causes stand on their own. 

Therefore, if you are faced with problems that are not as complex but still present a challenge, or if you are in a situation where you don’t have a statistically significant sample size of data available, Apollo RCA™ will likely be a more cost and resource effective approach to start with in your quest to solve the problem.

For problems of lesser complexity, the time required to complete the Six-Sigma™ project will greatly exceed the time needed to complete the Apollo RCA™.  If your objective is to implement an effective solution with the least investment of time and money, an Apollo RCA™ will be your best starting point.  While supporting data and evidence will be required for an Apollo RCA™, the rigor applied should be commensurate with the significance of the problem.

Herein lies what we believe to be a major improvement opportunity within the Six-Sigma™ process.   Currently in the Analyze phase of Six-Sigma™ addressing the “Where and why do Defects Occur?”, most Six-Sigma™ processes rely on overly simplistic and incomplete analysis techniques but then expend great effort to prove/disprove the results originating from these incomplete techniques.  Most Six-Sigma™ techniques rely on the “5 Why’s”, “Brainstorming”, “Fishbone”, “FMEA” and, in some variations, guessing at the “probable root causes” are utilized as approaches to understand the effect of the “X’s” on the “Y’s”.  Design of Experiment or DoE is the most powerful tool in the Improve phase of Six-Sigma™, but it is often the tool least used.  Further, most Six-Sigma™ approaches confuse the notion of cause and effect by utilizing the “5 Why’s” or “Fishbone” techniques and calling it a “cause and effect analysis”.  Therefore, there are key differences between the two methodologies which will be examined in following posts.
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Similarities and Differences between Methodologies

When looking at the Apollo RCA™ and Six-Sigma™ anatomy, it is very clear that both methodologies are based on defining the causes or variables associated with the process you are studying and/or trying to control.  Both methodologies share the objectives of discovering the causes affecting the process or product and to take some type of corrective or preventive action to eliminate, change or control these causes in an effort to control your output.

In a Six-Sigma™ project, in order to achieve the desired improvement objectives (which is to eliminate the variation or reduce the defect level to an acceptable specification limit), the Project Leader (MBB or BB) needs to define the “Output” as a function Y = f(X), where “Y” represent the output, and “X” represent the process variables or input.  Defining the variables is an important step in understanding any process and it is often the hardest step in any Six-Sigma™ project.  Until these relationships are defined or understood, Design of Experiment (DoE) cannot be achieved with successful results.  Hence, the Project Leader in many cases will revert to the “5 Why’s”, “Brainstorming”, “Fishbone”, “FMEA”, or to knowledge from a process expert, as means to identify these variables.  But without understating the true correlation between these “X’s” and the “Y”, trying to implement effective solutions is like shooting in the dark.  The Project Leader will typically have to wait to validate the effect of the corrective actions on the “Y” after the process changes have been made.

In the Analyze phase, Six-Sigma™ asks:  “Where and why does the Defect Occur?” and directs you to perform a Root Cause Analysis (RCA).  While this is the correct next step, most Six-Sigma™ programs fail to provide an effective RCA methodology as part of their curriculum.  This gap causes Six-Sigma™ users to jump to solutions without a solid understanding of the causes of the problem.  Identification of solutions is premature at this stage of the process because the problem is still not understood, and there is limited understanding of the causes effecting the output “Y”. 
Stayed tuned for more on this topic....

What is Apollo RCA™?

What is Apollo RCA™?

Apollo RCA™ is a 4 step methodology that looks at the interrelationships between causes and effects to identify opportunities for effective solutions.

What is Apollo RCA™ Strategy?

Step 1:    Define the Problem
Step 2:    Create the Cause and Effect Chart
Step 3:    Identify Effective Solutions
Step 4:    Implement the Best Solution

Problem Definition is the starting point for all Apollo RCA’s.   It identifies the significance of the event and why should you proceed.  The creation of the cause and effect chart allows a visual depiction and understanding of the causes that created the problem.  The solution generation is simple and effective where you challenge each cause on the cause and effect chart for potential solutions.  Once solutions have been identified, implementing those solutions is critical to the success of problem prevention.
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What is Six-Sigma™ Strategy?

Define:        What is the problem?
Measure:    What is the frequency of defects?
Analyze:        Where and why does the defect occur?
Improve:    How can we fix the process?
Control:        How can we make the process stay fixed?

Each of the Six-Sigma™ phases contains a set of tools that can be used throughout the entire project.  Each of these tools is designed for a specific objective.  Make sure you are familiar with the tool before you apply it otherwise your result might be unpredictable and not repeatable.  These tools can be used in multiple phases of the Six-Sigma™ project. Below you can see a list of the tools available within each of the Six-Sigma™ phases (note this is not a comprehensive list of tools).

Define:    Process Map, Product Tree, FMEA, QFD, Surveys, and Pareto Chart.
Measure:      Six-Sigma™ Macro, Events/Causals, Histogram, Benchmarking, L1 Worksheet, and Gage R&R.
Analyze:     Fishbone, Brainstorming, 5 Why’s, Run Charts, Hypothesis Test, Pareto, and Regression.
Improve:     ANOVA, Response Surface, DoE, EHS Checklist, ISO checklist, Interactions Plots, and Response Equation.
Control:     Risk Management, Mistake Proofing, SPC Charts, Procedures, Training, and Quality Management Systems.

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What is Six-Sigma™?

Six-Sigma™ is a methodology that consists of a set of tools to address each of the DMAIC steps to manage process variations that cause defects, defined as an unacceptable deviation from the mean, or target, and to systematically work towards reducing the variation and centering the mean, or average, around a target by eliminating those defects.  It was pioneered by Motorola in 1986 and was originally defined as a metric for measuring defects and improving quality and a methodology to reduce defect levels below 3.4 Defects per Million Opportunities (DPMO).  It was initially based on “five” phases: Define, Measure, Analyze, Improve and Control (DMAIC).  Now, Six-Sigma™ has grown beyond defect control, beyond Motorola, and beyond DMAIC.  Each of the DMAIC phases contains many steps that must be completed before proceeding to the next phase by making use of available tools for each phase.  We will focus primary on DMAIC and Apollo RCA™ in further postings.

Creating a link between Six-Sigma™ and Apollo RCA™

Like Apollo RCA™, Six-Sigma™ has become a widely utilized method for solving complex problems across a wide range of industries.  The past successes of Six-Sigma™ within industrial, petro-chemical, aviation technology-driven organizations like Motorola, GE, Honeywell, and Dow Chemical, among others, have demonstrated that Six-Sigma™ can be an effective tool, not just within manufacturing, but also as an enterprise solution yielding substantial results in any part of the organization.

Because both Apollo RCA™ and Six-Sigma™ consistently generate substantial savings and improvements by achieving the end goal of problem elimination, debates over which approach should be used have become more common.  We encounter situations where many people have developed an “either/or” mindset.  “Let’s not duplicate efforts by using both Apollo RCA™ and Six-Sigma™: Let’s just pick one and move on.”  Sound familiar?

The key to finding an effective solution to any problem requires one to first develop a clear understanding of the causes or variables of that problem.  While Six-Sigma™ also supports this premise; it does not provide a methodology or an approach on how to define these causes or variables.  Because of the many tools and techniques within the Analyze and Improve steps, it is easy for the project leader or practitioner to lose this simple notion.  To simplify, every problem one ever solves in life is the result of discovering a cause that one can eliminate, change or control from occurring.  It is really that simple.  The end result is that when we take control of causes, we become masters of problem solving.  This is why developing a clear and deep understanding of cause and effect is so important.

Apollo RCA™ Cause and Effect chart structure follows exactly the function Y = f(X) required in the Six-Sigma™ methodology, and this is why Apollo RCA™ is a must-have addition to the Six-Sigma™ tool set.  Apollo RCA™ should also be the methodology of choice during the Analyze phase of Six-Sigma™ - specifically when dealing with:

A “special cause”

Insufficient or missing data

A sample size that is not statistically significant

For more information go to: https://epsrca.com