4. Attribute sampling vs. Variable sampling
While attribute sampling means that the result could be either conforms or does not conform and it checks whether an item is defective or not, variable sampling is when the result is rated on a continuous scale. In other words, variable sampling is about checking “how much”, “how good”, or “how bad” and attribute sampling is, no doubt, a yes or no answer. Consider this example, whether the color is black or not, is attribute data, meanwhile its shade of gray is variable data.
5. Tolerances (specification limits) vs. Control limits
Tolerances or specification limits specified the range of acceptable results that your customer has given you, it represents the “voice of the customer.” Control limits, however identify the boundaries of common variation in a statistically stable process or process performance, or we can say control limits represent the “voice of the process”. As if to exemplify, if you are producing aluminium dowels, the control limits of this production process are 14.9cm and 15.1cm. The specification limits or tolerances are 14.8 and 15.2. Inspection shows that the last 5 dowels produced are 15.2cm long. Then what do you do? The answer in this case is to deliver to the customer.
6. Continuous Improvement
The underlying basis for quality improvement is the plan-do-check-act (PDCA) cycle that Walter Shewhart developed and J. Edwards Deming refined and publicized in the 1950s. In addition, quality improvement initiatives such as total quality management (TQM), Six Sigma, and Lean Six Sigma may improve both the quality of project management, as well as the quality of the end product, service, or result. For example: "Your process is performing to six sigma". This means that 99.99985% of your output is without defect, and for the PMP® exam you have to know the statistical percentages for 1, 2, 3 and 6 sigma:
• 1 sigma: 68.27%
• 2 sigma: 95.45%
• 3 sigma: 99.73%
• 6 sigma: 99.999985%
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