Ops 571 Statistical Process Control

Chase, Jacobs and Aquilano pose questions such(prenominal) as, How many paint defects are there in the finish of a car? and Have we amend our painting attend by installing a new sprayer? These questions are meant to investigate and consecrate different techniques that we provoke use to improve the case of life. Quality stop non only applies to manufacturing techniques, it ignore also be applied to everyday life. This discussion lead focus on a particular proposition method of quality control called statistical process control that will ensure my forenoon process is effective.One method of quality control can be pursued done process control procedures same statistical process control or SPC. SPC involves testing a random sample of output from a process to determine whether the process is producing items within a preselected range. (Chase, Jacobs & Aquilano, 354) SPC is a method that can be applied to a process in order to proctor or control that process. In week one, I d escribed a personal process of waking up in the morning through to going to work.In addition to my process, I presented several bottlenecks that can slow my process down including the ability of my get down clock working, weather impact on travel time, and availability of gym equipment. In the founts below, I will focus on how alarm failures have affected my morning process. SPC has shown how statistical data can be charted in order to see how my morning process is affected by my bottlenecks and whether or not it is a positive. Goods or services are observed not as variables but as attributes. Attributes are quality characteristics that are classified as either conforming or not conforming to itemation. (Chase, Jacobs & Anquilano, 354) In example one, a sample was taken 10 times over a 30 day cessation in which alarm failures were observed. In order to create a ocular representation of the statistics, we must link the data from the sample. Once the data is gathered, we can pr ovide a solution to create a control chart. Control charts are used as a component of total quality in order to monitor processes. Green, Toms, Stinson, 37) First, we calculate the fraction of defective alarms from the sample in order to gain a total and a centerline for our graph. p = substance number of defects from all samples/ tour of samples ? Sample size p = 25/ 10 ? 30 = . 08333 Next, we can calculate the standard deviation. Sp = vp (1 p)/ n Sp = v . 08333 (1 . 08333) / 30 = . 05050 Example 1Sample Number of Days Days Alarm Failed to Work Fraction Defective 1 30 2 . 06667 2 30 2 . 06667 3 30 3 . myriad 4 30 3 . ten thousand 5 30 2 . 06667 6 30 4 . 13333 7 30 3 . 10000 8 30 2 . 06667 9 30 2 . 6667 10 30 2 . 06667 Total 300 25 . 08333 Sample Standard Deviation . 05050 Finally, the control limits are used to measure attributes with a single decision of yes or no, good or bad, and positive or negative. This simple decision can be translated into a graph with upper and lower control limits. If the sample is plotted and stays in between the limits, then the sample is considered good or working properly. Should a sample mean or proportion fall outside the control limits or a series of mean or proportions pose a non-random pattern the process is deemed out-of-control. (Green, Toms, Stinson, 37) In order to turn the chart into a graph, we will assume to calculate the upper control limits (UCL), the lower control limits (LCL) and z. z is the number of standard deviations for a specific confidence. In this example, we will use the z-value of 3 in order to represent a 99. 7% confidence (Chase, Jacobs, & Anquilano, 356). This means that when that the confidence interval falls outside the control limits, there is a 99. 7% chance that there is something wrong with the process that must be corrected. Green, Toms, Stinson, 37) Though not perfect, a confidence of 99. 7% is useful. The SPC must also take into consideration the number of data points as well. The m ore data that is available the stronger your confidence intervals are. UCL = p + z Sp UCL = p + 3Sp UCL = . 08333 + 3(. 05050) = . 23483 LCL = p z Sp LCL = p 3Sp LCL = . 08333 ? 3(. 05050) = -. 06817 In the control chart, the data from the sample stays in between the controls. This means that my process in the morning is working properly and is effective.Now, it is important to look to the future trends in order to predict seasonal factors. A seasonal factor is the amount of correction needed in a time series to fructify for the season of the year. (Chase, Jacobs & Anquilano, 533) Seasonal factors may affect the samples by taking into consideration factor based on seasons or time periods. The alarm clock that is used to wake me up in the morning is not dependent on any factors of time or season. Statistical process control is one elan to control quality and make sure goals are attained.Statistical methods show that the samples taken can create visual representations that conclu de my alarm clock is an effective method to starting my morning process. This ensures that it is operating at its fullest potential. REFERENCES Chase, R. B. , Jacobs, F. R. , Aquilano, N. J. operations management for competitive advantage (11th ed). New York McGraw Hill/Irwin. Green Jr. K, Toms L, Stinson T. STATISTICAL PROCESS ascendancy APPLIED WITHIN AN EDUCATION SERVICES ENVIRONMENT. Academy Of Educational Leadership Journal serial online. June 201216 (2)33-46.