Question

Answer the below question:

Write in your own words minimum 500 words

1) Provide examples of how SPC tools are used to root out process control issues.

Note: Please include the References

Answer 1

SPC is method of measuring and controlling quality by monitoring the manufacturing process. Quality data is collected in the form of product or process measurements or readings from various machines or instrumentation. The data is collected and used to evaluate, monitor and control a process. SPC is an effective method to drive continuous improvement. By monitoring and controlling a process, we can assure that it operates at its fullest potential.

Often we focus on average values, but understanding dispersion is critical to the management of industrial processes. Consider two examples:

• If you put one foot in a bucket of ice water (33 degrees F) and one foot in a bucket of scalding water (127 degrees F), on average you'll feel fine (80 degrees F), but you won't actually be very comfortable!
• If you are asked to walk through a river and are told that the average water depth is 3 feet you might want more information. If you are then told that the range is from zero to 15 feet, you might want to re-evaluate the trip.

Statistical tables have been developed for various types of distributions that quantify the area under the curve for a given number of standard deviations from the mean (the normal distribution is shown in this example). These can be used as probability tables to calculate the odds that a given value (measurement) is part of the same group of data used to construct the histogram.

Shewhart found that control limits placed at three standard deviations from the mean in either direction provide an economical tradeoff between the risk of reacting to a false signal and the risk of not reacting to a true signal - regardless the shape of the underlying process distribution.

If the process has a normal distribution, 99.7% of the population is captured by the curve at three standard deviations from the mean. Stated another way, there is only a 1-99.7%, or 0.3% chance of finding a value beyond 3 standard deviations. Therefore, a measurement value beyond 3 standard deviations indicates that the process has either shifted or become unstable (more variability).

The illustration below shows a normal curve for a distribution with a mean of 69, a mean less 3 standard deviations value of 63.4, and a mean plus 3 standard deviations value of 74.6. Values, or measurements, less than 63.4 or greater than 74.6 are extremely unlikely. These laws of probability are the foundation of the control chart.

Now, consider that the distribution is turned sideways, and the lines denoting the mean and ± 3 standard deviations are extended. This construction forms the basis of the Control chart. Time series data plotted on this chart can be compared to the lines, which now become control limits for the process. Comparing the plot points to the control limits allows a simple probability assessment.

We know from our previous discussion that a point plotted above the upper control limit has a very low probability of coming from the same population that was used to construct the chart - this indicates that there is a Special Cause - a source of variation beyond the normal chance variation of the process.

While the initial resource cost of statistical process control can be substantial the return on investment gained from the information and knowledge the tool creates proves to be a successful activity time and time again. This tool requires a great deal of coordination and if done successfully can greatly improve a processes ability to be controlled and analyzed during process improvement projects.