Cause and Effect Matrix aka X-Y Diagram aka Correlation Matrix

What is a cause-and-effect matrix?

A Cause and Effect Matrix is a Six Sigma tool used to determine the key process input variables (KPIVs) based on priorities of customer outputs (KPOVs). In other words, it reveals perceived strength of influence between process input variables and customer outputs during the root cause analysis, based on team judgement.

The Cause and Effect Matrix aims to prioritize key inputs (Xs) based on how strongly they are believed to influence customer outputs (Ys). This is done using a structured scoring method, but it is not a mathematical or statistical computation of correlation.

Cause and effect analysis, process flow map, and information about the voice of the customer are the prerequisites for a Cause and Effect Matrix.

A cause-and-effect matrix can be used to examine and document relationships between input and output variables. This method is also very similar to the Quality Function Deployment. It provides a structured way to capture the team’s subjective judgments about the importance of each KPIV.

When Would You Use an X-Y Diagram in the DMAIC Process?

The Cause and Effect Matrix is a great tool for prioritizing a long list of possible things and is especially useful in the Measure phase of the DMAIC project. This method is also used to determine the primary factors for experiments in DOE and also to determine the goals of the Matrix Diagram & FMEA.

The input variables might influence the outcome in a process, but not all input variables are equally important. You should use a structured prioritization method to focus on the most important inputs that impact customer outputs, based on team assessments. The Cause and Effect Matrix helps to identify such key inputs.

How Do You Use It?

Step 1: First, identify the customer requirements, or in other words, understand the voice of the customer. This can be done by conducting surveys, focus groups, and other means to collect their priorities. Place those priorities at the top of the X-Y diagram.

Step 2: Assign a priority factor for each of the customer outputs. Generally, use a 1-10 scale, with 1 being the low priority and 10 being the high priority to the customer.

Step 3: List all possible key input variables or the improvement factors of the process in each row; those are the Xs in the X-Y diagram   

Step 4: Assess the relationship between key input variables and the customer outputs, and then rank each input variable accordingly. We recommend using a geometric progression scale (0,1,3 and 9), with 0 being no impact, 1 – low impact, 3 – medium impact, and 9 – strong impact or strong perceived influence on the output.

Step 5: Cross-multiply the customer output priority numbers with correlation rankings and sum each row at the extreme right column.

Step 6: Finally, determine the rank based on the highest sum total and highlight the critical few variables. This will help to identify the areas for improvement.

An Example of an X-Y Diagram.

XYZ coffee shop franchise located in the San Diego downtown area has reported falling sales for the past six months. A Six Sigma team conducts a root cause analysis, and they also want to see the key process inputs that are impacting the process.

• Conduct customer surveys and identify key customer requirements.

• Assign a priority factor for each of the customer outputs.

• List all possible key input variables.

• Assess the relationship between key input variables and the customer outputs and rank each input variable accordingly.

• Cross-multiply the customer output priority numbers with correlation rankings and sum each row.

• Ex: for coffee flavor = 9*9+3*6+3*3+9*3+1*4 = 139

• Finally, determine the rank based on the highest sum total and highlight the critical few variables.

Benefits of Cause and Effect Matrix

• It helps to include customer inputs for decision making.
• Visually depict the prioritized relationships between key input variables and the customer outputs.
• The priority ranking method helps to take the decision based on the score rather than individual opinions.
• Data collection costs may be reduced by ignoring non-key process inputs.
• Helps to list all the input variables required for the process.

Final Note:

The Cause and Effect Matrix is a qualitative tool, not a statistical analysis. The scores represent expert or team judgment about how much influence each input has on customer outcomes. If you need to compute actual correlation, consider tools like regression analysis or DOE (Design of Experiments).

Cause and Effect Matrix Videos

Cause & Effect Matrix Excel Template

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