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  • You have reached the RCA - Root Cause Analysis Dictionary within an evolving Business Excellence dictionary. In this dictionary, you will find not only the meaning of a term but also a discussion on practical application of the term. You can see the entire Dictionary here. Business Excellence dictionary is growing with addition of two new terms every week. To take part in making of the dictionary, you may try to reply to the latest question mentioned here


    This page contains links to various discussion topics that relate to RCA - Root Cause Analysis.  This page was last updated on 11th November 2019. Please click on the links below to see the definition, and best reply to the application question. 

    • Hypothesis Testing- Discussion - Hypothesis testing is one of the powerful methods used in Six Sigma methodology. In the pursuit of excellence, how important is hypothesis testing? In which phases of an improvement project, is it likely to be used?
    • Causation- Discussion - Correlation does not prove causation. Assuming continuous data for both, is it safe to say that proven cause effect relationship certainly results in strong correlation between the cause and effect variables? 
    • Common Cause and Special Cause- Discussion - How do you differentiate special cause variation (also called Black Noise) from common cause variation? Why is this differentiation important? Explain how misjudging one of these as the other can create problems in the real world.
    • 8D Problem Solving- Discussion - While DMAIC is a more popular approach as compared to 8D Problem Solving, would you prefer to use 8D over DMAIC in some situations? Why/ Why not? 
    • Fishbone Diagram, Ishikawa Diagram, Why-Why Analysis- Discussion - What are some of the common ways by which Fishbone Diagram or Why-Why Analysis is misused? 
    • Guide to Hypothesis Testing- Discussion - Review of guidelines for selection of the right hypothesis test.
    • Unusual Observation - Discussion - A statistically unusual observation or an outlier is of special interest many a times. However, an outlier is ignored or removed from data set sometimes. What are the factors that help one decide if an outlier is to be considered and investigated or if it can be ignored safely? 
    • Hidden Factory- Discussion - Rolled throughput yield (RTY) is the probability that a single unit can pass through the entire process without defects. Can you multiply the individual throughput yields at each process step to obtain the overall, rolled throughput yield? Since, many organizations focus more on “What gets outside the door”, how does RTY (as a metric) highlight the so-called “hidden factory”? 
    • MBWA- Discussion - Gemba Walk was developed at Toyota while Management By Walking Around (MBWA) traces its origin to HP. Two world class companies using two methodologies where managers walk the shop-floor. Is it just a case of having different names or are the two fundamentally different?
    • Mistake Proofing- Discussion - What does Mistake Proofing intend to achieve? Does it eliminate possibility of human error? or does it allow human error but prevent the conversion of error into defect? 
    • Outlier- Discussion - What is the need to identify an outlier in a data-set? What are the methods and approaches that are useful for identifying outliers? 
    • Rational Sub-grouping- Discussion - What would an excellence practitioner lose if he does not utilise the concept of rational subgrouping in the pursuit of process improvement? 
    • Regression Analysis- Discussion - How can you check if you have taken enough samples for carrying out a Regression Analysis?
    • Sampling Errors - Discussion - Let us consider a situation where drawing/testing large number of samples is disadvantageous and the Lean Six Sigma analyst has decided to use limited samples. What are the precautions that the analyst should take so as to limit biased and unbiased sampling errors ? Explain with suitable examples.
    • R Squared- Discussion - What is the usage of R Squared and R Squared Adjusted as used in Regression Analysis. Please explain with example(s).
    • Small Sample Size- Discussion - In many industries, it is costly to do trials while establishing solution for a problem. Verifying improved process capability with very few samples is not easy. What are the approaches for decision making with a few samples?
    • Stable Process- Discussion - A capable process is one that produces an output that meets customer specifications. A stable process is one that has controlled variation and operates within the control limits. Give examples of situations when a capable and stable process may suddenly be rendered incapable. 
    • Tribal Knowledge- Discussion - What are the methods for unlocking, capturing and harnessing “Tribal Knowledge”? How much importance should one attribute to this endeavour? 
    • Verification and Validation- Discussion - In the DMADV roadmap of Lean Six Sigma, the last phase is Verify/ Validate. Explain the difference in Verification and Validation using examples from different domains/ industries.
    • Y = F(X)- Discussion - Y = F(X) indicates that we need to improve inputs and improve the process to get better outputs than what we got earlier. Is it possible to let one of the two remain at a below industry average level (inputs or process) and focus heavily on the other one to generate excellent results?
    • Genchi Genbutsu - Discussion - While Genchi Gembutsu seems to make perfect sense in manufacturing, how do you infer its relevance in highly intangible services like trouble shooting in Software Development or issues found in e-commerce platforms? You may like to provide more examples of relevance from other service sector.
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