The PEAT ERM system also allows for the creation of Risk Control charts and KRI trends over time (Figure 2.14), and statistical process controls can be applied. Control charts help to visually and statistically determine if a specific risk event is in-control or out-of-control. For instance, if the number of risk events, such as a plant accident, spikes within a certain time period, was that set of events considered expected under statistically normal circumstances or was it an outlier requiring more detailed analysis?
Start by typing in or pasting into the grid some historical data, select the type of chart to show, and type in the variable to test (e.g., VAR7), and hit Run. Multiple charts can be saved for future retrieval by adding in a name and clicking Save As.
The charts’ statistical control limits are computed based on the actual data collected (e.g., the number of risks on a manufacturing factory floor). The number of risk events is taken over time and the upper control limit (UCL) and lower control limit (LCL) are computed, as are the central line (CL) and other sigma levels. The resulting chart is called a control chart, and if the process if out-of-control, the actual defect line will be outside of the UCL and LCL lines. Typically, when the LCL is a negative value, we set the floor as zero. The ERM software presents several control chart types, and each type is used under different circumstances.
- X-chart: used when the variable has raw data values and there are multiple measurements in a sample experiment, multiple experiments are run, and the average of the collected data is of interest.
- R-chart: used when the variable has raw data values and there are multiple measurements in a sample experiment, multiple experiments are run, and the range of the collected data is of interest.
- XmR-chart: used when the variable has raw data values and is a single measurement taken in each sample experiment, multiple experiments are run, and the actual value of the collected data is of interest.
- P-chart: used when the variable of interest is an attribute (e.g., defective or nondefective) and the data collected are in proportions of defects (or the number of defects in a specific sample) and there are multiple measurements in a sample experiment, multiple experiments are run, and the average proportion of defects of the collected data is of interest; and the number of samples collected in each experiment differs.
- NP-chart: used when the variable of interest is an attribute (e.g., defective or nondefective) and the data collected are in proportions of defects (or the number of defects in a specific sample) and there are multiple measurements in a sample experiment, multiple experiments are run, and the average proportion of defects of the collected data is of interest; also, the number of samples collected in each experiment is constant for all experiments.
- C-chart: used when the variable of interest is an attribute (e.g., defective or nondefective) and the data collected are in the total number of defects (actual count in units) and there are multiple measurements in a sample experiment, multiple experiments are run, and the average number of defects of the collected data is of interest; also, the number of samples collected in each experiment are the same.
- U-chart: used when the variable of interest is an attribute (e.g., defective or nondefective) and the data collected are in the total number of defects (actual count in units) and there are multiple measurements in a sample experiment, multiple experiments are run, and the average number of defects of the collected data is of interest; also, the number of samples collected in each experiment differs.