IV.C.6 Process Variation
Variation is inherent; it exists in all things. No two entities in the world have exactly the same measurable characteristics. The variation might be small and unnoticeable without the aid of precise and discriminative measuring instruments, or it might be quite large and easily noticeable. Two entities might appear to have the same measurement because of the limitations of the measuring device.
Factors Affecting Variation
Everything is the result of some process, so the chance for some variation in output is built into every process. Because material inputs are the outputs of some prior process, they are subject to variation, and that variation is transferred to the outputs. Variation will exist even in apparently identical processes using seemingly identical resources. Even though a task is deﬁned and performed in the same manner repeatedly, different operators performing the same task or the same operator performing the same task repeatedly introduce variation. The precision and resolution of the measuring devices and techniques used to collect data also introduce variation into the output data.
Variation can result from changes in various factors, normally classified as follows:
1. People (worker) inﬂuences
2. Machinery inﬂuences
3. Environmental factors
4. Material inﬂuences
5. Measurement inﬂuences
6. Method inﬂuences
The resulting total variation present in any product is a result of the variations in these six main sources. Because the ramiﬁcations of variation in quality are enormous for managers, knowing a process’s capabilities (see Section 7) prior to production provides for better utilization of resources. Operating costs are reduced when inspection, rework, safety stock storage, and troubleshooting are eliminated. Proper management requires a deep appreciation of the existence of variation, as well as an understanding of its causes and how they can be corrected.
Quality Management BOK Reference
IV Quality Management Tools
IV.C Measurement: Assessment and Metrics
IV.C.6 Process variation - Analyze data to distinguish between common and special cause variation.
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