![]() ![]() ![]() ![]() Mathematically, the calculation of control limits looks like: Adding (3 x σ to the average) for the UCL and subtracting (3 x σ from the average) for the LCL.Estimating the standard deviation, σ, of the sample data.This is also referred to as process dispersion.Ĭontrol limits (CLs) ensure time is not wasted looking for unnecessary trouble – the goal of any process improvement practitioner should be to only take action when warranted. Upper and lower control limits (UCL and LCL) are computed from available data and placed equidistant from the central line.A central line (X) is added as a visual reference for detecting shifts or trends – this is also referred to as the process location.A control chart begins with a time series graph.There are three main elements of a control chart as shown in Figure 3. 3 Figure 2: Natural Process Degradation Elements of a Control Chart Control charts are robust and effective tools to use as part of the strategy used to detect this natural process degradation (Figure 2). Companies typically begin some type of improvement effort when a process reaches the state of chaos (although arguably they would be better served to initiate improvement plans at the brink of chaos or threshold state). All processes will migrate toward the state of chaos. Figure 1: Four Process StatesĮvery process falls into one of these states at any given time, but will not remain in that state. Here, the process is not in statistical control and produces unpredictable levels of nonconformance. The fourth process state is the state of chaos. The lack of defects leads to a false sense of security, however, as such a process can produce nonconformances at any moment. In other words, the process is unpredictable, but the outputs of the process still meet customer requirements. The brink of chaos state reflects a process that is not in statistical control, but also is not producing defects. Although predictable, this process does not consistently meet customer needs. This type of process will produce a constant level of nonconformances and exhibits low capability. This process is predictable and its output meets customer expectations.Ī process that is in the threshold state is characterized by being in statistical control but still producing the occasional nonconformance. This process has proven stability and target performance over time. When a process operates in the ideal state, that process is in statistical control and produces 100 percent conformance. Processes fall into one of four states: 1) the ideal, 2) the threshold, 3) the brink of chaos and 4) the state of chaos (Figure 1). If the process is unstable, the process displays special cause variation, non-random variation from external factors.Ĭontrol charts are simple, robust tools for understanding process variability. A process is in control when based on past experience it can be predicted how the process will vary (within limits) in the future. When a process is stable and in control, it displays common cause variation, variation that is inherent to the process. The descriptions below provide an overview of the different types of control charts to help practitioners identify the best chart for any monitoring situation, followed by a description of the method for using control charts for analysis. The most common application is as a tool to monitor process stability and control.Ī less common, although some might argue more powerful, use of control charts is as an analysis tool. Control charts have two general uses in an improvement project. ![]()
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