Process Behavior Charts and

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. 

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). 

The general sequence for constructing Process Behavior Charts is the same for all types of data, but there is some variation depending upon whether attribute data (data you can count) or variable data (data on a scale) are involved. We ll show you the steps and some details for each type of Process Behavior Chart. 

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. 

Here you are looking for rule violations, which are Process Behavior Chart readings that indicate the process is out of control and needs help. 

One of the most common types of Process Behavior Charts for variable data is the Xbar/R chart, or average and range chart. While the overall procedure for constructing this chart is the same as for the C-chart, a few additional calculations are necessary due to the nature of variable data. 

The SkiBlades defect rate was mostly steady, with one exception on day 13. A team of process experts then discovered a correlation between fluctuations in the temperature of the resin curing oven and the defective pulls that broke away from the boots. At the team s recommendation, the temperature controller was replaced with a more modem unit, and the oven temperature was recorded and monitored using a Process Behavior Chart. 

Two additional Process Behavior Chart rules come into play when dealing with variable data. For these rules, the area between the process mean and the control limits is divided into thirds, that is, la, 2a, and 3a zones, as shown in Exhibit 52.3. (a stands for standard deviation, the variance or spread of a given data set). 

For more information and Process Behavior Chart calculations, see ... 

The National Institute of Standards and Technology (NIST) has a good online resource for Process Behavior Charts called the Engineering Statistics Handbook. The portion on Process Behavior Charts can be found at ... 

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. 

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... 

The chemistry of imidosulfinates is vastly unexplored [10] and nothing is known to date about the behavior of 0-allyl imidosulfinates in particular. By comparison with the known process C (Chart 1), the sulfinate-sulfone rearrangement (Sect. 2.2), it seems rather promising to try a new sulfoximine synthesis based on that rearrangement. 

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). 

If the process is out-of-control, the next step is to find the source cause of the deviation (fault diagnosis) and then to remedy the situation. Fault diagnosis can be conducted by associating process behavior patterns to specific faults or by relating the process variables that have significant deviations from their expected values to various equipment that can cause such deviations as discussed in Chapter 7. If the latter approach is used, univariate charts provide readily the information about process variables with significant deviation. Since multivariate monitoring charts summarize the information from many process variables, the variables that inflate... 

Multivariate SPM methods with PCs can employ various types of monitoring charts. If only a few PCs can describe the process behavior in a satisfactory manner, biplots could be used as visual aids that are easy to interpret. Such biplots can be generated by projecting the data to two dimensional surfaces as PC versus PC2, PC versus SPE, and PC2-SPE as illustrated in Figure 5.1. 

Since yMst is a random variable, SPM tools can be used to detect statistically significant changes. histXk) is highly autocorrelated. Use of traditional SPM charts for autocorrelated variables may yield erroneous results. An alternative SPM method for autocorrelated data is based on the development of a time series model, generation of the residuals between the values predicted by the model and the measured values, and monitoring of the residuals [1]. The residuals should be approximately normally and independently distributed with zero-mean and constant-variance if the time series model provides an accurate description of process behavior. Therefore, popular univariate SPM charts (such as x-chart, CUSUM, and EWMA charts) are applicable to the residuals. Residuals-based SPM is used to monitor lhist k). An AR model is used for representing st k) ... 

Fig. 2.14. Control chart with patterns of anomalous behavior. UCL and LCL indicate the control limits. The central line corresponds to the process mean.

If a shift has occurred in the central tendency of the process, the process data will cluster mound the new mean. Such shifts are often caused by changes in raw materials or process settings. In most all cases, such process behavior is not desirable and can be detected quickly with traditional control charts. 

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