5 Root Cause Analysis Tools for More Effective Problem-Solving
Next to defining a problem accurately, root cause analysis is one of the most important elements of problem-solving in quality management. That’s because if you’re not aiming at the right target, you’ll never be able to eliminate the real problem that’s hurting quality.
So which type of root cause analysis tool is the best one to use? Manufacturers have a range of methods at their fingertips, each of which is appropriate for different situations. Below we discuss five common root cause analysis tools, including:
- Pareto Chart
- Fishbone Diagram
- Scatter Diagram
- Failure Mode and Effects Analysis (FMEA)
Download our free Root Cause Analysis 101 Guidebook Read 14 quality metrics every executive should know
1. Pareto Chart
A Pareto chart is a histogram or bar chart combined with a line graph that groups the frequency or cost of different problems to show their relative significance. The bars show frequency in descending order, while the line shows cumulative percentage or total as you move from left to right.
The Pareto chart example above is a report from layered process audit software that groups together the top seven categories of failed audit questions for a given facility. Layered process audits (LPAs) allow you to check high-risk processes daily to verify conformance to standards. LPAs identify process variations that cause defects, making Pareto charts a powerful reporting tool for analyzing LPA findings.
Pareto charts are one of the seven basic tools of quality described by quality pioneer Joseph Juran. Pareto charts are based on Pareto’s law, also called the 80/20 rule, which says that 20% of inputs drive 80% of results.
Learn how to create Pareto charts in this post or download the Pareto Chart Tip Sheet and Sample Excel File
The 5 Whys is a method that uses a series of questions to drill down into successive layers of a problem. The basic idea is that each time you ask why, the answer becomes the basis of the next why. It’s a simple tool useful for problems where you don’t need advanced statistics, so you don’t necessarily want to use it for complex problems.
One application of this technique is to more deeply analyze the results of a Pareto analysis. Here’s an example of how to use the 5 Whys:
Problem: Final assembly time exceeds target
- Why is downtime in final assembly higher than our goal? According to the Pareto chart, the biggest factor is operators needing to constantly adjust Machine A
- Why do operators need to constantly adjust Machine A? Because it keeps having alignment problems
- Why does Machine A keep having alignment problems? Because the seals are worn
- Why are Machine A’s seals worn? Because they aren’t being replaced as part of our preventive maintenance program
- Why aren’t they being replaced as part of our preventive maintenance program? Because seal replacement wasn’t captured in the needs assessment
Of course, it may take asking why more than five times to solve the problem—the point is to peel away surface-level issues to get to the root cause.
Learn more about the 5 Whys method in this blog post or download our free 5 Whys worksheet
3. Fishbone Diagram
A fishbone diagram sorts possible causes into various categories that branch off from the original problem. Also called a cause-and-effect or Ishakawa diagram, a fishbone diagram may have multiple sub-causes branching off of each identified category.
Learn more about how to use a fishbone diagram in this blog post and download our free set of fishbone diagram templates
4. Scatter Plot Diagram
A scatter plot or scatter diagram uses pairs of data points to help uncover relationships between variables. A scatter plot is a quantitative method for determining whether two variables are correlated, such as testing potential causes identified in your fishbone diagram.
Making a scatter diagram is as simple as plotting your independent variable (or suspected cause) on the x-axis, and your dependent variable (the effect) on the y-axis. If the pattern shows a clear line or curve, you know the variables are correlated and you can proceed to regression or correlation analysis.
Download a free tip sheet to start creating your own scatter diagrams today!
5. Failure Mode and Effects Analysis (FMEA)
Failure mode and effects analysis (FMEA) is a method used during product or process design to explore potential defects or failures. An FMEA chart outlines:
- Potential failures, consequences and causes
- Current controls to prevent each type of failure
- Severity (S), occurrence (O) and detection (D) ratings that allow you to calculate a risk priority number (RPN) for determining further action
When applied to process analysis, this method is called process failure mode and effects analysis (PFMEA). Many manufacturers use PFMEA findings to inform questions for process audits , using this problem-solving tool to reduce risk at the source.
No matter which tool you use, root cause analysis is just the beginning of the problem-solving process. Once you know the cause, the next step is implementing a solution and conducting regular checks to ensure you’re holding the gain and achieving sustainable continuous improvement.
Download our free Root Cause Analysis 101 Guidebook
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It is the heart of problem-solving. Root cause analysis is the second important element of problem-solving in quality management. The reason is if you don't know what the problem is, you can never solve the exact problem that is hurting the quality. Manufacturers have a variety of problem-solving tools at hand.
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Manufacturers have a range of methods at their fingertips, each of which is appropriate for different situations. Below we discuss five common root cause analysis tools, including: Pareto Chart. The 5 Whys. Fishbone Diagram. Scatter Diagram. Failure Mode and Effects Analysis (FMEA)
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Capterra: 4.7/5 (2,000+ reviews) 2. Intelex Root Cause Analysis Software. via Intelex. The Root Cause Analysis Software from Intelex is a SaaS tool designed to help companies create better environment, health, safety, and quality (EHSC) procedures. It uses various methodologies to get to the root cause of the problem.
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To use this root cause analysis tool, you plot the independent variable (or suspected cause) on the x-axis while your dependent variable (the effect) is plotted on the y-axis. If the pattern shows a clear line or curve, you know the variables are correlated. If needed, you can continue to more sophisticated regression or correlation analyses.
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Pareto Analysis. Pareto Analysis, or the 80/20 rule, posits that roughly 80% of effects come from 20% of causes. This technique is particularly useful for prioritizing tasks, making it a staple in both managerial decision-making and problem solving course curricula. By focusing on the critical few causes, this analysis aids in resource ...
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