The Defects-Per-Unit metric is a fundamental Six Sigma concept. It means that for a process producing U units of output, with D number of defects observed, then on average, each unit of manufactured product will contain (D/U) such defects.
A.Defects - The number of times a process output does not meet the specifications laid out for its performance.
B.Units - The number of units of process output.
There are two main types of defects. Uniform defects appear within a unit of product, while Random defects are intermittent and unrelated. DPU calculations are based on an assumption of random defects.
1. Once defect data has been collected, the preferred method of calculating parameters is to use a spreadsheet such as MS Excel, or alternatively, Minitab.
2. The formulae for calculating defects-per-unit, given numbers of defects and units of production, are presented below.
DPU = (number of defects)/(number of units)
To compute the number of defects per opportunity (DPO) in order to calculate the probability of defect free units produced by the process.
Reference: The Vision of Six Sigma: A Roadmap for Breakthrough
A. Defects per Opportunity;
B. Number of defects affecting the units produced by the process;
C. Total number of opportunities per
D.Number of opportunities per unit;
E.Number of units per characteristic.
A critical characteristic is an opportunity for defect. One opportunity is an opportunity only if it is measured (i.e. only active opportunities are considered).
1.Select critical characteristics;
2.For each characteristic, count the number of active opportunities;
3.Count the number of defects per characteristic;
Note: although there may be many opportunities for defects, focus on what is critical to the customer. Count what you measure.
If there are 15 blocks on a form that needs completion, but only 10 are CTS and inspected, then there are 10 opportunities for a defect.
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A Quality Control Plan is a documented description of the activities needed to control a process or product. The objective of a QCP is to minimize variation.
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The Weibull distribution is applicable to make population predictions around a wide variety of patterns of variation.