核心原则 统计过程控制 区分两种类型的过程变异。普通原因变异是指稳定且可预测的过程中固有的、随机的“噪声”。特殊原因变异,或称可归因原因变异,源于外部可识别的因素,表明过程不稳定。目标是消除特殊原因并减少普通原因变异。

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核心原则 统计过程控制 区分两种类型的过程变异。普通原因变异是指稳定且可预测的过程中固有的、随机的“噪声”。特殊原因变异,或称可归因原因变异,源于外部可识别的因素,表明过程不稳定。目标是消除特殊原因并减少普通原因变异。
The distinction between common and special causes of variation is the bedrock upon which Statistical Process Control is built. Walter A. Shewhart, while working at Bell Labs, recognized that variation is inherent in any process, but not all variation is equal. He categorized it into two types. Common cause variation is the cumulative effect of many small, unavoidable causes. It is a natural part of the process and, when only common causes are present, the process is considered stable, in-control, and predictable within statistical limits. Examples include slight variations in material properties, ambient temperature, or machine vibration. This variation creates a distribution that is typically normal.
In contrast, special cause variation (also called assignable cause) arises from specific, identifiable circumstances that are not part of the process’s usual operation. These causes are intermittent, unpredictable, and result in a process that is out of statistical control. Examples could be a new, untrained operator, a faulty batch of raw material, a machine malfunction, or a change in measurement method. The key objective of SPC is to first detect the presence of special causes using tools like control charts. Once detected, these causes can be investigated and eliminated, bringing the process back into a state of statistical control. Only after a process is stable (i.e., free of special causes) can one meaningfully work on reducing the common cause variation to improve 过程能力.
普通原因变异和特殊原因变异
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