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.