Process Improvement Plan: Operations
Process Improvement Plan
Your name
OPS/571
June 14, 2010
Daryl West
Process Improvement Plan The purpose of this paper is to outline the process improvement plan to include the statistical process control identified in week one. To begin, I will explain the control limits, discuss the effect of seasonal factors and the confidence intervals and their usefulness based on the number of data points. For the past five weeks I have been monitoring and reviewing the process of posting the assignments on time as reviewed in the syllabus. The week consists of Monday through Monday postings with four days to post up to two substantive comments, discussion questions and for this class there have been three individual assignments due on week one, week three and week five. The process improvement is to reduce the wait time in starting the required assignments.
Statistical Process Control Statistical Process Control (SPC) is a technique for testing a random sample of output from a process to determine whether the process is producing items within a prescribed range (Chase, Jacobs, & Aquilano, 2005, p. 364). Essentially, process improvement is to monitor how something is conducted and determining if it can be done better. The process I have chosen to monitor is when to start my assignments over the past five weeks. All papers and postings, I expected an “A”; however, not having a complete understanding of the concepts, I did not. The proposed process improvement plan will satisfy the bottleneck in the process of waiting until the end of the week to start discussions and papers to ensure full understanding of the concepts; however, this delay resulted in poorly written papers because it did not give me adequate time to ask questions. The bottleneck is for me to start my discussion questions, add substantive comments to others discussion questions, and apply concepts earlier in the week.
Control Limits The ideal measurement of the process would be to have had more than
Your name
OPS/571
June 14, 2010
Daryl West
Process Improvement Plan The purpose of this paper is to outline the process improvement plan to include the statistical process control identified in week one. To begin, I will explain the control limits, discuss the effect of seasonal factors and the confidence intervals and their usefulness based on the number of data points. For the past five weeks I have been monitoring and reviewing the process of posting the assignments on time as reviewed in the syllabus. The week consists of Monday through Monday postings with four days to post up to two substantive comments, discussion questions and for this class there have been three individual assignments due on week one, week three and week five. The process improvement is to reduce the wait time in starting the required assignments.
Statistical Process Control Statistical Process Control (SPC) is a technique for testing a random sample of output from a process to determine whether the process is producing items within a prescribed range (Chase, Jacobs, & Aquilano, 2005, p. 364). Essentially, process improvement is to monitor how something is conducted and determining if it can be done better. The process I have chosen to monitor is when to start my assignments over the past five weeks. All papers and postings, I expected an “A”; however, not having a complete understanding of the concepts, I did not. The proposed process improvement plan will satisfy the bottleneck in the process of waiting until the end of the week to start discussions and papers to ensure full understanding of the concepts; however, this delay resulted in poorly written papers because it did not give me adequate time to ask questions. The bottleneck is for me to start my discussion questions, add substantive comments to others discussion questions, and apply concepts earlier in the week.
Control Limits The ideal measurement of the process would be to have had more than
References: Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2005). Operations Management for Competitive Advantage (11th ed.). New York, New York: McGraw-Hill Irwin.