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Process Improvement Plan

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Process Improvement Plan
OPS/571
March 26, 2012 Process Improvement Plan
Processes exist in all organizations and the true success of any company is the ability to examine processes and implement an improvement plan. The organizations that strive to improve processes can improve quality control, increase profits and performance, lower costs, and create a more efficient organization. Implementing a process improvement plan requires management to identify, analyze and improve existing processes to meet new goals and objectives.
Taking the time to analyze a process, either in business or personally, allows individuals the opportunity to find shortcuts or improvements. Organizations find the benefits making processes more efficient, which results in profit, individuals will benefit by making life easier and more enjoyable. The objective of this paper is to analyze the process identified in Week Ones project of examining a daily process that may requirement improvement, apply statistical process control, determine the effect of any seasonal factors, and apply confidence intervals to the data points collected over the last four weeks.
Process Description
The process from Week Ones project is ‘Making Dinner’ during the week days. The daily task of making dinner for a family takes time and commitment, if time can be saved in making dinner the added benefit is spending the saved time towards other daily activities or family time. This process consists of preparing food, which requires washing and chopping, cooking, setting the table, and cleanup after dinner. The after dinner cleanup consists of clearing the table, washing dishes and wiping the counter tops. The weekly grocery shopping is completed once a week on Sunday with preselected menus.

Statistical Process Control
“Statistical process control (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, 2006, p. 354). As organizations seek to become more effective, statistical process control can be a valuable tool to provide an output for a specific process of whether it is functioning within a predetermined range. Organizations need to engage in testing and fine tuning their processes to stay competitive because keeping a process efficient will ultimately provide in an overall savings in operational costs.
Statistical process control considers two types of sample measurements, sampling by attribute and sampling by variable, depending on the type of data. The data for ‘Making Dinner’ fits in the attribute measurements, “Attributes are quality characteristics that are classified as either conforming or not conforming to specification” (Chase, Jacobs, & Aquilano, 2006, p. 354). The decision will be either yes or no, using the p chart with upper control limit (UCL) and lower control limit (LCL), calculated by the total number of defects from all samples over number of samples multiplied by sample size.
Control charts are explained as, “These are time-sequenced charts showing plotted values of a statistic including a centerline average and one or more control limits” (Chase, Jacobs, & Aquilano, 2006, p. 330). Statistical process control can help monitor a process behavior by utilizing a process control chart. The control chart allows data to be recorded and unusual events can be observed. The chart can distinguish between two types of process variation, common variation and assignable variation (Chase, Jacobs, & Aquilano, 2006). The control chart displayed in Appendix C, reflects the process ‘Making Dinner’ and some investigation will need to be completed because of sudden change in level 14.
Seasonal Factors
“A seasonal factor is the amount of correction needed in a time series to adjust for the season of the year” (Chase, Jacobs, & Aquilano, 2006, p. 532). In the process ‘Making Dinner’ the seasonal factor is the availability of fresh fruit and veggies in the summer, which can determine the menu. The change in menu can have an effect on the overall time because more fresh fruits and veggies will require more preparation time as well as cooking outside on the grill. Outside cooking may lower the cleanup time because the appliances indoors are not being used, so the forecast will need to include the seasonal factors. Once a seasonal factor is determined the calculation for forecasting is on past seasonal data as well as the trend and seasonal index. Decomposition of data and forecasting using least squares regression are the more formal techniques used when there is seasonal activity (Chase, Jacobs, & Aquilano, 2006).
Confidence Intervals
“We say that a process is capable when the mean and standard deviation of the process are operating such that the upper and lower control limits are acceptable relative to the upper and lower specification limits” (Chase, Jacobs, & Aquilano, 2006, p.350). The confidence interval quantifies the accuracy of the population and the selected confidence interval for the process ‘Making Dinner’ data will be 99 percent. This means that 99 percent of the sample means are expected to fall within these control limits, within 99 percent confidence interval (Chase, Jacobs, & Aquilano, 2006). Appendix A and B display the data and the descriptive statistics on the data for the process of ‘Making Dinner’. The solution for the control chart requires first to calculate the overall fraction of defective from all samples. Next the calculation of the sample standard deviation, which is provided in Appendix A, and finally the calculation of the z-value along with the 99 percent confidence that the process is within the upper and lower limits (Chase, Jacobs, & Aquilano, 2006). Reviewing the lower confidence interval at 55.03 and the upper confidence interval at 80.76 on Appendix A indicates that 11 out of 19 pass, 8 of the 19 fall out of the range.
Conclusion
Examining a process with the right tools and techniques is essential for an organization and can benefit personally an individual by eliminating unwanted time spent on a daily process. The results of the process ‘Making Dinner’ clearly need more investigation due to the large number of days that fell out of the lower and upper confidence intervals. The process is a simple process however there are a number of variables that can skew the total time per day. Cooking time and menus are both factors to consider per examination of the data. It is also reasonable to take into consideration sources of error for future forecasting. The sample size is small and bias errors can occur from not including the right variables or mistaken shifts in the seasonal demand from where it normally occurs (Chase, Jacobs, & Aquilano, 2006). There needs to be more data collected for the process ‘Making Dinner’ and the seasonal factor should be considered as well before a determination is finalized.

References
Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2006). Operations management for competitive advantage (11th ed.). New York, NY: McGraw Hill/Irwin.

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