July 24th 2015 Posted at Information Theory
A process is the value-added transformation of inputs to outputs. The inputs and outputs of a process can involve machines, materials, methods, measurement, people, and the environment. Each of the inputs is a source of variability. Variability in the output can result in poor service and poor product quality, both of which often decrease customer satisfaction. Control charts, developed by Walter Shewhart in the 1920s, are commonly used statistical tools for monitoring and improving processes. A control chart analyzes a process in which data are collected sequentially over time. You use a control chart to study past performance, to evaluate present conditions, or to predict future outcomes. You use control charts at the beginning of quality improvement efforts to study an existing process (such charts are called Phase 1 control charts).
Information gained from analyzing Phase 1 control charts forms the basis for process improvement. After improvements to the process are implemented, you then use control charts to monitor the processes to ensure that the improvements continue (these charts are called Phase 2 control charts). Different types of control charts allow you to analyze different types of critical-to-quality variables—for categorical variables, such as the proportion of hotel rooms that are nonconforming in terms of the availability of amenities and the working order of all appliances in the room; for discrete variables such as the number of hotel guests registering complaints in a week; and for continuous variables, such as the length of time required for delivering luggage to the room. In addition to providing a visual display of data representing a process, a principal focus of a control chart is the attempt to separate special causes of variation from common causes of variation.
Special causes of variation represent large fluctuations or patterns in data that are not part of a process. These fluctuations are often caused by unusual events and represent either problems to correct or opportunities to exploit. Some organizations refer to special causes of variation as assignable causes of variation. Common causes of variation represent the inherent variability that exists in a process. These fluctuations consist of the numerous small causes of variability that operate randomly or by chance. Some organizations refer to common causes of variation as chance causes of variation.
Control charts allow you to monitor a process and identify the presence or absence of special causes. By doing so, control charts help prevent two types of errors. The first type of error involves the belief that an observed value represents special cause variation when it is due to the common cause variation of the process. Treating common cause variation as special cause variation often results in over adjusting a process. This over adjustment, known as tampering, increases the variation in the process. The second type of error involves treating special cause variation as common cause variation. This error results in not taking immediate corrective action when necessary. Although both of these types of errors can occur even when using a control chart, they are far less likely.
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