ARTICLES (MAGAZINE)

This paper addresses 3-parameter log-normal distribution control charting. Statistical Process Control (SPC) has a primary purpose, which is to identify when special cause conditions occur for timely corrective actions. SPC textbooks and training state that an individuals control chart (X) or an individuals and moving range chart (XmR) control chart is appropriate and should be used when tracking individuals data. With an individuals chart, between subgroup variability affects the magnitude of the control limits from the overall response mean. This paper illustrates how the individuals control chart is not robust to non-normality. When data are not normally distributed (e.g., when there is a boundary condition) the data need a normalizing transformation that is appropriate for the process being evaluated. The log-normal distribution often is a good fit for these situations. However, the log-normal distribution cannot accept negative numbers, which are often needed to describe this type of process response. This paper describes the use of the 3-parameter log-normal distribution in 30,000-foot-level control charting. In this 30,000-foot-level metric reporting methodology, process response is evaluated for regions of stability. Within identified stable regions, a process capability non-conformance estimate can then be reported if a specification exists. If there is a recent region of stability, one can consider the data in this region to be a random sample of the future; hence, a prediction statement can be made. An enterprise can assess its value-chain metrics collectively – where each has 30,000-foot-level reporting – to determine where improvements can be made that positively impact the enterprise financials as a whole. Goals to these metrics would pull for a process improvement or design project creation that positively impacts these 30,000-foot-level metrics.

This paper addresses infrequent failure data control charting. A primary purpose of Statistical Process Control (SPC) is to identify when special cause conditions occur for timely corrective actions. SPC textbooks and training state that a c-chart or u-chart should be used for count data tracking. With a c-chart, the tracked response is the number of counts, which has time-series identified rational sub-groups. A u-chart has a similar tracking but only occurrence rate is track. Described in this paper are technical issues that make the c-chart and u-chart ineffective, especially when the counts (e.g., safety incidents) are very low for the subgrouping (e.g., months). The technical reason for this occurrence is discussed along with an alternative 30,000-foot-level reporting system that addresses these issues. Traditional c-chart and u-chart control charts can lead to inappropriate activities, since the underlying assumptions for these charts are often not valid in the real world. In the 30,000-foot-level metric reporting methodology, which centers on use of the individuals control chart, process response is evaluated for regions of stability, where time between incidents is a response that can be tracked for adding power to the test when there are infrequent count occurrences. Within identified stable regions, a process capability estimate can then be reported. If there is a recent region of stability, one can consider the data in this region to be a random sample of the future; hence, a prediction statement can be made. An enterprise can assess its value-chain metrics collectively – where each has 30,000-foot-level reporting – to determine where improvements can be made that positively impact the enterprise financials as a whole. Goals to these metrics would pull for a process improvement or design project creation that positively impacts these 30,000-foot-level metrics.

This paper addresses technical problems with p-chart and provides an alternative. Statistical Process Control (SPC) has a primary purpose of identifying when special cause conditions occur for timely corrective actions. SPC textbooks and training state that a p-chart should be used for non-conformance rate tracking, when the data are attribute. With a p-chart, there is non-conformance rate reporting that has time-series identified rational sub-groups. Described in this paper are technical issues with the p-chart that can lead to a falsely identifying common cause process variation as though it were special cause. The technical reason for this occurrence is discussed along with an alternative 30,000-foot-level reporting system that addresses these issues. Traditional p-chart control charts can lead to much firefighting, since the underlying assumptions for these charts are often not valid in the real world. In the 30,000-foot-level metric reporting methodology, which centers on use of the individuals control chart, process response is evaluated for regions of stability. Within identified stable regions, a process capability non-conformance estimate can then be reported. If there is a recent region of stability, one can consider the data in this region to be a random sample of the future; hence, a prediction statement can be made. An enterprise can assess its value-chain metrics collectively – where each has 30,000-foot-level reporting – to determine where improvements can be made that positively impact the enterprise financials as a whole. Goals to these metrics would pull for a process improvement or design project creation that positively impacts these 30,000-foot-level metrics.