Statistical Thinking to Improve Quality

This posting continues the resin output variation example described to illustrate the Hoerl-Snee process improvement strategy.   This example appears in Britz et al (2000) and in Hoerl and Snee (2002).   The Ricoh team is focusing on reducing the variability in resin output quantity.   The previous post ended with a description of cause & effect diagram the team constructed to list potential sources of variability.
Based this diagram the team regarded the following potential causes as most likely to be the largest contributors to output variation:

1. The procedure for dividing the resin after phase 2 (potentially the cause of differences between line A and B).
2. The solvent feed ratio.
3. The weighing process, i.e., final (automatic) and in-process (manual).

After attacking the first potential cause, the team found that some resin remained in the reaction tank after sending the materials to the two lines.  That meant that line B had less input and therefore less output.   After changing the dividing procedure, the team found no significant difference between the outputs of the two lines.

The output quantities still had too much variation.   The team turned to the second potential cause, i.e., the solvent feed ratio.  The following figure shows a scatter plot indicating that increasing solvent feed ratio is correlated with increasing output.   In calculating the regression line the team regarded the high output occurring at a feed ratio slightly less than 1, as an outlier.   This correlation violated the team’s knowledge of the underlying process.   They investigated the measurement of the feed ratio, and they found that the ratio measurement was affected by the length of time the solvent was in the tank.   They changed the procedure to insure that the solvent had stabilized prior to measurement.   They collected more data to measure the impact of this change and found less variation in the measured feed ratio and no correlation between the measured feed ratio and the output quantity.

The output variation still did not meet their targets shown in the previous posting.   The next posting will present their analysis of the weighing process.   This posting shows two cycles of PDCA which differs from the sequential process suggested by DMAIC.   From a DMAIC viewpoint the team went through two cycles of Analyze-Improve.

References

1. Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
2. Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxbury.