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This paper addresses x-bar and R control charting issues. For a given process, do you believe that everyone would make the same statement relative to: 1. process control/predictability from a created control chart? 2. process capability to meet its specifications? Not necessarily! Process statements such as these are not only a function of process characteristics but can also be very dependent upon sampling approach. The limits for the x-bar chart are derived from within-subgroup variability, while sampling standard deviation for XmR charts are calculated from between-subgroup variability. 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 x-bar and R control chart is appropriate for situations where there are multiple continuous responses in a subgroup. Described in this paper are technical issues with the x-bar and R chart which can lead to falsely identifying common cause process variation as though it were special cause. The reason for these issues is discussed along with an alternative 30,000-foot-level reporting system that addresses these issues. Traditional x-bar and R 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. 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.