Statistical process control, is a graphical tool used to monitor on-going performance. Control charts can trace their origins back to Shewhart at Western Electric in the 1920s. Control charts show process variation while work is underway. It provides a means for monitoring the state of the process in real-time, and detecting issues. Control charts establish performance boundaries. They also help us uncover assignable causes of variation, and distinguish these from chance causes. There are over 30 types of control charts in use today. So control charting can be applied to many types of data. They also assist in creating a foundation from which professionals can discuss and address process performance issues. Control charts are applicable for any scenario that varies over time. We particularly are interested in using these types of tools to assess process performance after an improvement has been implemented. Remember, control charting is not just for monitoring, it is also for detection. SPC, or statistical process control, enables real time monitoring. It allows us to assess the state of a process, as well as the effectiveness of a change to that process. SPC can also help us identify process changes. The type of change and the need for a corrective action used properly. SPC can ensure our decisions are statistically valid. Other important benefits include centering of the process, reduction of variation, and improvement in our understanding of the process and design. The components of a control chart include a center line and upper and lower control limits. Quality characteristic values are plotted along the vertical axis. There are two main types of control charts. The first is attribute. Attribute charts measure percent non-conforming or the number of non-conformities in a unit. The other type is variables. These are based on numerical values. With control charting, we can know first when to take collective action. We also know the type of remedial action to take, when to leave and process alone. We also can learn about the process capability. Finally, possible avenues for quality improvement. It also helps us to define a baseline. Before we can eliminate variation, we must recognize that there are really two types of variation at play. Special cause is a signable variation, something that is not inherent in the process. Common cause is due to chance. Variability due to common or chance causes is something inherent in the process. Deming believed that 15% of all problems are due to special causes. Action on the part of management and workers can reduce special causes. Deming also believe that about 85% of all problems are due to common causes. This can only be solve by management since you must change the system. Once you have localized an eliminated assignable causes, we should remove out of control points and revise the center line and control limits. There is no need to maintain control charts if the process demonstrates consistent capability. Instead, focus your attention and resources on other areas. Be aware that control limits on control charts are influenced by the variability in the process. As such, these control limits will change. Today's technology makes this a very manageable endeavor. Control charts are easy to set but are very difficult to maintain. Often, our failure to maintain comes down to our inattention. In other words, failing to make adjustments when the control chart detects an issue. Prioritization or productivity also overshadows control charting. Lack of training can also derail a control charting program. In most cases, sampling is done in time order or by process. We call this type of sampling, the instant of time method. Observations are selected at approximately the same thing, for the population under consideration. If we want to detect a small shift, a larger sample size is needed. Choosing large samples frequently provides the most information, but is not always feasible. We must consider whether we have destructive testing and the cost of sampling.