ANALYZE STAGE

 This phase is about - What does your data tell you? Is often intertwined with the Measure Phase. Our data collection team consist people who have collected  different sets of data and additional data. As the team reviewed the data collected during the Measure Phase, they have decided to adjust the data collection plan to include additional information. Then team analyzed both the data and the process to narrow down and verify the root causes of remeasures occurring at the MicroVu process.

Root Cause Analysis:

Root cause analysis (RCA) is a method of problem solving that tries to identify the root causes of faults or problems. A root cause is a cause that once removed from the problem fault sequence, prevents the final undesirable event from recurring.

Once we have the branches labeled, we began brainstorming possible causes and attached them to the appropriate branches. We have also used 5why's to support our session.

After our team's brainstorming session we have finished our fish-bone diagram to visualized all potential X's, in the next step we  further identified the key findings of potential causes. 

 

Key findings on MicroVu Process Analysis:

In our Key Findings we have distinguished three major causes of the remeasures problem and our support team will be working on improving these three effects on remeasure. A large number of potential root causes (process inputs, X) of the project problem were identified via root cause analysis. However the top 3 potential root causes were selected using multi-voting tool for further validation. 

 

The purpose of this step was to identify, validate and select root cause for elimination. A data collection plan was created and data were collected to establish the relative contribution of each root causes to the project metric, Y. This process was repeated until "valid" root causes could be identified. 

We have:

• Listed and prioritized potential causes of the problem
• Prioritized the root causes (key process inputs) to pursue in the Improve step
• Identified how the process inputs (Xs) affected the process outputs (Ys). Data was analyzed to understand the magnitude of contribution of each root cause, X, to the project metric, Y.
• Detailed process maps could also be created to help pin-point where in the process the root causes reside, and what might have been contributing to the occurrence.