Collect Data on Key Measures
This posting discusses the second step in the Hoerl-Snee Process Improvement Strategy. Refer to the figure in the previous posting for an overview of the process. Use Britz et al (2000) and Hoerl and Snee (2002) as references. 
After understanding and documenting the process, the next step is to collect data on key process and output measures. These key measures can include the overall process performance measure(s) and measures derived from the inputs and outputs of each process step.
For example, the Ricoh team in the Resin Output Variation example, March 21 posting, was concerned with the product yields being greater than theoretical expectations so they collected yield-ratio data. In the Pease Industry example, posted on March 4, the company team wanted to improve quality of their residential entry doors so they collected defect-rate data from their customers. In the automotive door frame example, posted on February 21, the manufacturer wanted to improve the quality of critical dimensions on the welded door frame. They collected data from incoming material and after each processing step, i.e., roll mill, bender and saw. These data consisted mainly of dimensional measurements.
The process may be a sequence of steps required to perform a task with a cycle-time principal performance measure. For example, the process might be the activities required to fill a prescription in a hospital. For each order submitted, the data might include the submittal time, the arrival time at each processing step, the actual step processing time, the completion time for each processing step, and the drug prescribed. In addition, one would need the number of servers at each processing step.
Breyfogle (2003) on page 10 introduces several terms that are useful in identifying important process variables. A Key Process Output Variable (KPOV) in an important output for a process. Another name for this variable is a Critical to Quality Characteristic (CTQ). Key Process Input Variables (KPIVs) are process inputs that affect the KPOVs.
One might ask: how do we select the data to collect? We use a combination of the output of the previous step, Understand the Process, and existing process knowledge. Also, a previous iteration of the Process Improvement Strategy (see the posting on April 4) may have identified some KPIVs. The author has found in his consulting experience that manufacturers of process equipment may have important information regarding the sensitivity of their equipment to process variables. Also, do not forget the internet. A search may reveal research reports indicating the sensitivity of equipment to process variables.
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.
3. Breyfogle, Forrest W. (2003). Implementing Six Sigma: Smarter Solutions Using Statistical Methods, Second Edition, John Wiley & Sons, Inc.
