What is a Cause and Effect Matrix?
Cause and effect matrix is a six sigma tool uses to prioritize the key process input variables (KPIVs) based on priorities of customer outputs (KPOVs). In other words it establish the correlation between process input variables to the customer’s outputs during root cause analysis.
Cause and effect matrix also called X-Y diagram, Prioritization matrix and correlation matrix. The objective of cause and effect matrix is to mathematically compute the correlation between key process input variables (X’s) and Customer outputs (Y’s).
A cause-and-effect matrix can be used to evaluate and document relationships between input and output variables. This method is also very much similar to the Quality Function Deployment. It objectively evaluates the team’s subjective opinion about the KPIVs.
When Would You Use an X-Y Diagram in the DMAIC Process?
Cause and Effect matrix is a great tool for prioritizing a long list of possible things and especially uses in the Measure phase of the DMAIC project . This method also used to determine the primary factors for experiments in DOE and also to determine the objective of the FMEA.
In a process all the input variables might influence the outcome, but all the input variables are not equally important. Hence develop a mathematical model to concentrate the important input variables with respect to the customers output. Cause and effect matrix helps to identify such key input variables.
How Do You Use It?
Step 1: First identify the customer requirements or in other words understand the voice of customer. This can be collected by conducting surveys, focus groups and other means to collect their priorities. Place those priorities in at the top of the X-Y diagram
Step 2: Assign priority factor for each of the customer outputs. Generally use 1-10 scale, where 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 to the customer outputs and rank each input variables accordingly. Recommended to use geometric progression scale (0,1,3 and 9) where 0 being no impact, 1-low impact,3-medium impact and 9- Input has strong impact or correlation on output
Step 5: Cross multiply the customer output priority numbers with correlation rankings and sum the each row at 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 improvement areas.
An Example of an X-Y Diagram.
XYZ coffee shop franchise at San Diego downtown area reporting sales de-growth for the past 6 months. Six sigma team conducted root cause analysis and also they want to see the key process inputs that are impacting the process.
- Conducts customer survey and identified key customer requirements
- Assign priority factor for each of the customer outputs.
- List all possible key input variables
- Assess the relationship between key input variables to the customer outputs and rank each input variables accordingly
- Cross multiply the customer output priority numbers with correlation rankings and sum the 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 correlation between key input variable to the customer out puts
- Priority ranking method helps to take the decision based on score rather than individual opinions
- Data collection cost may reduce by ignoring non key process inputs
- Helps to list all the input variables required for the process