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
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