Process Behavior Charts Technique

Performance expectations are the Key Performance Indicators (KPIs) on which you ll focus your design and optimization efforts, and which you will track over time with your Design Scorecards (Technique 39) and/or Process Behavior Charts (Technique 52). 

While the purpose of Design Scorecards is to prevent problems, defects, and errors through superior design, they also enable better problem detection after a new solution (design) is implemented. If you are in detect-and-fix mode, any number of process-optimization techniques may help, such as Process Behavior Charts (Technique 52), Cause Effect Matrix (Technique 54), Mistake Proofing (Technique 49), and Design of Experiments (Technique 50). 

Either way, measurement data needs to be recorded consistently and accurately to help you compare data points over time. If your process is complicated, you ll want to employ Process Behavior Charts (Technique 52) to quickly and visually track when the process goes out of control. 

With an optimized innovation ready for the market, it s time to improve and transition the project to its owners for ongoing operation. Use the Process Behavior Charts and Control Plan techniques during and after this transition. Also use the Cause Effect Diagram and Cause Effect Matrix to diagnose, solve, or at least mitigate any implementation problems encountered. 

Process Behavior Charts are often called control charts, but this convention implies that a control function is performed on the contrary, Process Behavior Charts only perform a monitoring function. The control function is performed by virtue of a good Control Plan (Technique 55). 

Many statistical programs (such as Minitab, SigmaXL, and IMP) automatically calculate control limits for the various types of Process Behavior Charts. If you re curious or want to perform your own calculations, see the resources listed at the end of this technique. 

The major objective in SPC is to use process data and statistical techniques to determine whether the process operation is normal or abnormal. The SPC methodology is based on the fundamental assumption that normal process operation can be characterized by random variations around a mean value. The random variability is caused by the cumulative effects of a number of largely unavoidable phenomena such as electrical measurement noise, turbulence, and random fluctuations in feedstock or catalyst preparation. If this situation exists, the process is said to be in a state of statistical control (or in control), and the control chart measurements tend to be normally distributed about the mean value. By contrast, frequent control chart violations would indicate abnormal process behavior or an out-of-control situation. Then a search would be initiated to attempt to identify the assignable cause or the. special cause of the abnormal behavior... 

Autocorrelation in data affects the accuracy of the charts developed based on the iid assumption. One way to reduce the impact of autocorrelation is to estimate the value of the observation from a model and compute the error between the measured and estimated values. The errors, also called residuals, are assumed to have a Normal distribution with zero mean. Consequently regular SPM charts such as Shewhart or CUSUM charts could be used on the residuals to monitor process behavior. This method relies on the existence of a process model that can predict the observations at each sampling time. Various techniques for empirical model development are presented in Chapter 4. The most popular modeling technique for SPM has been time series models [1, 202] outlined in Section 4.4, because they have been used extensively in the statistics community, but in reality any dynamic model could be used to estimate the observations. If a good process model is available, the prediction errors (residual) e k) = y k)—y k) can be used to monitor the process status. If the model provides accurate predictions, the residuals have a Normal distribution and are independently distributed with mean zero and constant variance (equal to the prediction error variance). 

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