Root Cause Analysis Example

A root cause analysis is a project management methodology that attempts to get to the bottom of a problem. The five whys methodology is an example of a commonly used Six Sigma technique for performing a root cause analysis. Root cause analyses aim to help resolve consumer complaints about quality, fix problems that occur in the production process, or create more productive processes. Once identified, the root cause will help eliminate and prevent problems that occur in the future. When performing a root cause analysis, you break down the failure of a product or process to determine what exactly went wrong. Prevention of problems is much less expensive for your company than repairing the problem.

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When beginning a root cause analysis, it's important to get down to the core of the problem. Here are the important steps of a root cause analysis:

1. You must provide a clear definition of the problem.

2. You need to gather any data concerning the problem and analyze all evidence of the existence of the problem or issue.

3. Once you have solid data, analyze the problem by asking "why." Keep asking "why" until you cannot go any further down the causal relationship ladder and all potential causes have been identified.

4. Once identified, you can then analyze the causes for those that are changeable for prevention of the problem in the future.

5. Brainstorm workable solutions for the team. Eliminate causes that are not necessary to the process and change those that are within your control.

6. Implement the changes through effective creation of a change management plan. Monitor the results.

If necessary, repeat steps one through six to eliminate further causes.

Brenda's company has a problem. For every ten t-shirts her company produces, there is a defect. The screen-printing on the t-shirts is blurry and off-center. Moreover, some of the colors bleed through the fabric. Customers and distributors have been complaining. Brenda will have to perform a root cause analysis in order to get to the bottom of the problem.

She first calls a meeting with key stakeholders in the quality improvement project. With the stakeholders, a problem definition is reached, "The images on the t-shirts are not printing correctly."

Brenda assigns the task of data collection to two team members. They find the following: While 1/10 of t-shirts experiences a serious highly-noticeable defect, 1/4 of all shirts have an image that is off-center, 1/3 of the shirts have images ranging from slightly blurred to extremely blurred, and more than half of the shirts, almost 2/3 have images that bled through the t-shirt fabric in varying severities from barely noticeable to highly noticeable.

Brenda decides this problem can be better analyzed through using a Pareto chart and she creates one in Excel. After examining the data, she decides to focus on the cause of the image bleed.

After determining that the quality management project would focus on the cause of the image bleed on the t-shirts (because almost 2/3 of the t-shirts produced by her company show this defect), Brenda begins to ask "why" to determine the cause of the problem. At the top of a sheet of paper, she writes "2/3s of t-shirts produced bleed through the material from a severity range of barely noticeable to highly noticeable."

Underneath this, she writes, "Why?"

"The t-shirt fabric is too thin." This first response can't be possible, because the company carefully researched the fabric and the ink for the project to ensure the materials would work. So, she looks for an alternate cause and comes up with:

"The ink isn't drying fast enough."

"Why not?" She asks the question, again, to get closer to the root cause of the problem.

"Because the presses are using too much ink." If this is the answer, it would also solve another problem the company has been experiencing, the blurring of images printed on 1/3 of all shirts produced.

Another potential problem at this stage could be that the ink ordered wasn't correct for the project. However, Brenda checks the inventory logs and finds that this isn't the case.

"But why are the presses using too much ink?"

"Because the presses haven't been properly calibrated."

It seems as though this last answer is a contender. Brenda sits down with her project team and constructs a plan for changing the calibration on the machines.

While some root cause analyses will produce results quickly like Brenda's, your team may go through the quality improvement process several times before determining which causes to tweak, change, or eliminate completely. By completing a complete root cause analysis before undertaking any quality improvement project, your team can save money and time by preventing problems, rather than repairing problems.