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Cost-Benefit Analysis
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Most projects require a business case or cost case to help justify the need for them Costbenefit analysis is a relatively simple and widely used technique for deciding whether to make a change (for example, staying with an existing process or tool or going with one project versus another) As its name suggests, you simply list the benefits of a particular course of action as well as the savings or improvements realized and then subtract the costs associated with that action Hopefully the benefits far outweigh the cost, thus making the project an easy sell Cost-benefit analysis really helps decision makers choose the projects they want (or need) to sponsor
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Details of Cost of Quality (COQ)
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Cost of quality (COQ) refers to the total cost of all efforts related to quality throughout the product life cycle The decisions made during the project can impact the operation costs of quality as well (for example, the type of equipment or material used to build the product may not have a long life cycle or may require higher maintenance in the long run)
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8: Project Quality Management
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When calculating COQ, you also need to consider failure cost (cost of poor quality), potential cost of rework, cost of scrap, lost productivity (work stoppage), and so on Costs can usually be broken into one of two categories: cost of conformance or cost of nonconformance Here are some examples of the cost of conformance:
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Prevention costs (money spent during the project to avoid defects or failures)
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Training Document processes Equipment Time to do it right the first time Testing Destructive testing loss (for example, car safety testing) Inspections
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Appraisal costs (to assess quality of the product)
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And here are some examples of the cost of nonconformance:
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Internal failure costs (failures found by the project)
Rework Scrap Lost business Warranty work Liability
External failure costs (failures found by the customer)
Control Charts
Control charts are a great tool to determine if a process or product is performing to expected levels They can be used to track a variety of products, repetitive activities, cost and schedule variances, volume of output, and even the impact of changes to the project Control charts or graphs can be used to show specific targets with upper and lower control limits tied to the acceptable requirements of the product If the product is not meeting the acceptable range (within approved control limits) in the specifications, there may be fallout (throwaway) parts and/or penalties associated with
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the out-of-limit parts or products The control (specification) limits usually represent three standard deviations on either side of the centerline (or mean) of a normal distribution of data plotted in the control chart (see Figure 8-1) When a process is within acceptable (control or warning) limits (generally 3 sigma), it is said to be in control and doesn t need to be adjusted When the process is outside the control limit, it should be monitored If you see a run (seven consecutive points or events outside, above or below, the control limit), that indicates the process is out of control and adjustments may be required
Standard Deviation
When managing quality on a project, you may encounter the term standard deviation People who have taken a statistics class in school are familiar with the term and might even understand it Standard deviation (or sigma) is a measure of the range or area of a normal distribution (bell curve) from the mean (average) Sigma is taken from both sides of the mean and shows as a minus ( ) on the left side and a plus (+) on the right side (see Figure 8-2) The following explanation comes from Wikipedia: In statistics, standard deviation is a simple measure of the variability or dispersion of a data set A low standard deviation indicates that the data points tend to be very close to the same value (the mean), while high standard deviation indicates that the data are spread out over a large range of values For example, the average height for adult men in the United States is about 70 inches, with a standard deviation of around 3 inches This means that most men (about 68%, assuming a normal distribution) have a height within 3 inches of the mean (67 inches 73 inches), while almost all men (about 95%) have a height within 6 inches of the mean (64 inches 76 inches) If the standard deviation were zero, then all men would be exactly 70 inches high If the standard deviation were 20 inches, then men would have much more variable heights, with a typical range of about 50 to 90 inches [13]
Remember that half of a normal distribution curve is on the left side of the mean and half is on the right side of the mean If you are planning to take the PMP exam, you should memorize the following ranges: 1 sigma (or standard deviations) = 6826%, which is the percentage of data points (occurrences) that fall between the two control limits 2 sigma (or standard deviations) = 9546% 3 sigma (or standard deviations) = 9973% 6 sigma (or standard deviations) = 9999985%
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