Britz, Emerling, et al (2000, p 118) and Hoerl and Snee (2002, p 203) describe the Is–Is Not Analysis which helps narrow the search for a root cause. It does that by documenting the problem. The analysis documents where, what, when and who are associated with the problem. In addition, the analysis documents where, what, when and who are not associated with the problem symptoms. This documentation suggests further action to discover the root cause.
Pease Industries Example
Smith and Adams (2001) give an example of Is-Is Not analysis being important in identifying a root cause when other approaches failed. Pease Industries is a large Midwestern manufacturer of residential entry doors. This was done as part of a Lean Six Sigma project. A line that was placing rosin and glass inserts for more expensive residential entry doors had a 16% defect rate. They formed a team consisting of operators, managers, and consultants. The work flow used a batch processing system. The team reduced non value-added activity, eliminated batch processing, and re-assigned operators who were no longer needed. The line now had one-piece flow. The defect rate decreased to 11% from 16%. In addition, line productivity increased by 62%.
This improvement still left an 11% defect rate in the decorative glass inserts for a wooden entry door. The defect was a consistent hairline imperfection where liquid resin should have met the edge. They called this defect a “shrink line.” Engineers and managers felt that humidity and temperature variations in the mold department were the root cause. The team collected data and did a regression analysis. The dependent variable was the number of defects and the independent variables were temperature, humidity, and an interaction term involving both temperature and humidity. (I hope they used Poisson regression rather than ordinary least squares.) The result was no correlation between the independent variables and the number of defects.
Team members including engineers, quality managers, an operator, and a consultant went to the shop floor to personally collect data. Using recorded data, they examined defect occurrence by the following factors: part type, monthly occurrence, and day of the week. Stratification is the analysis of data by these factors. To their surprise the following figure shows that defect occurrence was highest on Monday and declined through the week. A Chi-Square test showed the day of week was statistically significant.
Next, the team constructed a Cause-and-Effect diagram giving all possible causes of the defect. Then the team performed an Is-Is Not analysis. The used the data they collected to do the analysis. Their statistical analysis of the data supported not only what circumstances were associated with the defect but the circumstances that were not associated with the defect. For example, resin, swirls, bad resin mixes and laminations were not problems. They examined what the problem was versus what it was not, when it happened and when it did not, and where it happened versus where it did not. They went back to the Cause-and-Effect diagram and eliminated possible causes.
Their conclusion was that they had dirty molds. Molds were cleaned on certain days. After implementing controls, they estimated the annual savings to be $1,050,000.
The example illustrates the potential benefits of using an Is-Is Not analysis. However, a strict application of Statistical Thinking would have employed the Is-Is Not analysis prior to doing regression analyses.
References
- Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
- Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxburry.
- Smith, B. and E. Adams (2001). LeanSigma: Advanced Quality. Annual Quality Congress.
A major benefit of the Is-Is Not Analysis is its documentation of circumstances leading to the problem as well as those not associated with the problem. In the Pease Industries example, the Is-Is Not Analysis allowed the team to eliminate potential causes in the Cause-and-Effect Diagram. Hoerl and Snee (2002, p 204) suggest that the results of the analysis be displayed in a table also showing possible causes and further action.
