Process_Improvement_Plan

Running Head:  PROCESS IMPROVEMENT PLAN  Process Improvement Plan University of Phoenix Felisa Bonner OPS/571 Operations Management Professor Art Close March 8, 2010 Process Improvement Plan The process improvement plan suggests a way to improve upon a process to eliminate wasted time. Getting to work on time is a valued because not doing so may cause termination of services. This is a plan to refine the structure of a process, as opposed to explaining problems singularly. This is done by the statistical process control, control limit, and confidence intervals. Statistical Process Control Statistical process control (SPC) entails using random samples to assess and examine the differences in a process (Chase, 2006). Statistical quality control is about being able to improve the quality of a process which includes SPC, a variation decrease, process capacity breakdown, and a process enhancement plan. Statistical process control (SPC) is a way to monitor the process behavior procedures (Chase, 2006). The behavior that was monitored is getting dressed for work every morning. There is no possible way to get to bed early without finishing daily responsibilities and waking up earlier would cause sleep deprivation. So, bargaining with the times of waking up and go to bed is not an option. Hence, shortening the dressing time will help the process of being to work on time. The bottleneck to this process is ironing clothes. This process will eliminate some of the wasted time.   To explain how to get the outcome of the specific process under control or even improved, it is suggested that one should shorten the ironing process. If the ironing is done on Sunday before the week starts then it will eliminate up to six minutes from the process. This process will consist of ironing enough clothes for the entire week. Also, lay the clothes out the night before so, that there is no need for extra decisions in the morning. Another way to cut down the time is to buy instant breakfast foods. Pop-tarts are a great substitute to cooking. This would cut off another 14 minutes of the process. Taking control of these processes helps cut the total process down to about 14 to 18 minutes per day. Control Limits Use the control limits to evaluate whether or not the variations are out of stink or abnormal, statistical process control techniques are used in the control charts. The point variation from the start to finish is then shown. The improvements should also be seen in your chart. [pic] | |Day 1 |Day2 |Day 3 |Day 4 |Day 5 |Sample Means | | | | | | | | | |Week 1 |0 |35 |45 |35 |35 |30 | |Week 2 |0 |35 |0 |29 |22 |17.2 | |Week 3 |23 |24 |23 |14 |24 |21.6 | |Week 4 |14 |16 |13 |15 |14 |14.4 | | |Total Mean | | |20.8 | Figure 2 In figure 2 the Control Limits for the mean: Upper Control Limit = 29.3 minutes Lower Control Limit =12.31 minutes Mean =20.80 minutes On figure 1 the mean (Xbar) is the pink line above. It shows the calculation that identifies the average amount of variation in the Key Performance Indicator (KPI). In the chart above it is 20.08. The UCL (Upper Control Limit) is the yellow line. It shows the calculation that identifies the higher limits of variability in the KPI. In the chart above it is 29.3. The LCL (Lower Control Limit) is bluish green. It shows the calculation that identifies the lower limits of variability in your KPI. In the chart above it is 13.31 (Kaushik, 2007).  Look at figure 1 on week 1 and the beginning of week 2 to see how it clearly shows that control is outside the limits and warrants a solution. “Not only that but if a series of data points fall outside the control limits then it is a bigger red flag in terms of something highly impactful going awry” (Kaushik, 2007).  Figure 2 shows the average day’s delay per week in minutes. As you can see in figure 1 the process intervention begins to help at the end of week 2. Then by week 4 the process was controlled. Confidence Interval The confidence interval is the series of value which extends from the lower confidence limit to the upper confidence limit. “This range is expected to cover the population parameter of concern, such as the population mean, with a degree of certainty which is specified up front” (Charusombat, 1997). The charts constructs the confidence interval which shifts through the course of time, then tracks how every new period’s figures will fall within range so that decisions are prepared concerning how the process is going (Schumacher, 2009). If there is a confidence level of 99% then the confidence interval of this process is ±7.29 minutes and the range for the true population mean is 13.51 minutes to 28.09 minutes. Conclusion The statistical process control assists in achieving and maintaining a process supply that doesn’t modify in conditions of the means and variances. If the mean or variability changes within the process it is because of the control limits on the control charts. “A process that is in statistical control, however, may not be producing services or products according to their design specifications because the control limits are based on the mean and variability of the sampling distribution, not the design specifications” (Schumacher, 2009). The confidence interval tells what the range the mean of population falls in. The charts and processed information shows how improvements can be made. When the correct measurements are modified to supply added reliable performance, new figures are then composed; the average times are now 18 minutes with a standard deviation of 4.81 for the two weeks of intervention. References: Charusombat, U. & Sabalowsky, A. (1997). What are Confidence Intervals , Tolerance Intervals and Prediction Intervals' Retrieved March 13, 2010, from, http://www.cee.vt.edu/ewr/environmental/teach/smprimer/intervals/interval.html#whatc Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2006) Operations management for competitive advantage (11th ed). New York: McGraw Hill/Irwin. Kaushik, A. (2007 January 17). Leverage Statistical Control Limits. Retrieved March 13, 2010, from, http://www.kaushik.net/avinash/2007/01/excellent-analytics-tip-9-leverage-statistical-control-limits.html Schumacher, E. (2009). Statistical Process Control. Retrieved March 13, 2010, from http://www.trinity.edu/eschumac/HCAI5320/HCAI5221%20TC3.pdf ----------------------- Figure 1 Chart Average Weekly Delays 0 5 10 15 20 25 30 35 40 45 1 2 3 4 5 Weeks Average Delay Per Week Average Delay Xbar UCL LCL --- UCL = 29.3 --- X= 20.8 ---LCL = 12.31