The Cause and Effect Diagram graphically portrays the potential causes of an effect.   The causes are grouped into categories.   Common categories are manpower (personnel), materials, methods and machines.   When the diagram uses these specific categories we might call the diagram a 4M diagram.  Depending on the effect, the diagram might display other categories.   The diagram is also known as an Ishikawa diagram since Dr. Ishikawa devised its first use of the diagram.   Another name for the diagram is a Fishbone diagram because of its appearance.   Recording the results of a brainstorming session is a typical use for the diagram.   A project might use a brainstorming session to generate a list of potential causes of an effect or a quality problem.  

We will continue the case study reported by Gijo (2005) to illustrate the Cause and Effect diagram.   The previous post presented a Pareto chart for a machine shop showing that the grinding operations generated most of the rejections experienced by the shop. They estimated grinding machine capability based on a sample of 40 parts.   The estimated P_pk for this sample was .49.  This result verified the lack of grinding machine capability. 

The posting on 5/1/2008 defines the process capability indices C_p and C_pk.   Process capability indices assume the process is stable.   When we have lack evidence that the process is stable, we call the capability index a performance index and use the same equation.   The index P_pk is a process performance index.

Selected individuals participated in a brainstorming session to generate a set of potential causes of grinding machine rejections.    The following figure shows the resulting causes. 

After further study, project members selected four factors for further analysis based on designed experiments.   These factors were Feed Rate, Wheel Speed, Work Speed, and Wheel Grade.   Analysis of the experimental results identified “optimum” levels for the four factors.  The estimated P_pk  at the optimum factor levels was 1.25 based on a sample of 40 parts.   This showed significant improvement.

References

1. Gijo, E. V. (2005). "Improving Process Capability of Manufacturing Process by Application of Statistical Techniques." Quality Engineering 17(2): 309-315.