Analyze Common-Cause Variation Examples (Disaggregation)
This posting gives two additional examples illustrating the Analyze Common-Cause Variation step, step 6, in the Hoerl-Snee process improvement strategy. Refer to the posting on 5/18/2008 for a description of this step. Both examples include disaggregation as a tool.
· Disaggregation – Stratification. The posting on 2/18/2008 describes statistical thinking by a Midwest manufacturing firm to reduce waiting times by customers. The company’s goal was to have 95% of incoming customer calls answered by a customer service representative in less than 2 minutes. Based on a process flowchart, team collected service time data for each step in the process. That is disaggregation. The team also collected data for estimating the distribution of incoming calls by time of the day. That is stratification by the time of day. They used these data as inputs to a simulation of the call answering process. They used the simulation construct staffing levels by the hour of the day. The construction and use of the simulation illustrates step 7, Study Cause & Effect.
· Disaggregation – Regression Analysis. The posting on 2/21/2008 describes statistical thinking by a manufacturer of automotive door frames. The purpose was to eliminate a problem meeting dimensional specifications of the finished product. Shop floor personnel thought that variations in the incoming raw material characteristics caused the problem meeting dimensional specifications. The team defined important quality characteristics for each step in the process. They included quality characteristics of the incoming material. The manufacturer collected data listing the important quality characteristics as well as the final part dimensions. A regression analysis showed no effect by the incoming material characteristics. Moreover, it identified several quality characteristics having a significant effect on finished product dimensions. The regression analysis also showed that the left and right door frames had significantly different variation for two quality characteristics. These results motivated corrective action and eliminated the need for rework. In this example, the team did not need to employ step 7, Study Cause & Effect.
