Process Improvement Plan - Essay Example

The data that I was able to gather in the process of measuring the times that it took to perform these activities will serve as example in the succeeding discussion on Statistical Process Control. Statistical process control is the process of applying appropriate statistical measures to measure and analyze the variations or differences in the behavior that are present in a particular process (Oakland, 2007). The use of statistical process control has gained much popularity in today’s technological age because it has greatly improved the performance and corresponding outputs of a great number of processes.

In applying statistical process control, one must have sufficient amount of data from which the required information will be derived. In the particular process that I chose, the important data would be the amount of time that it takes to complete each activity that is undertaken in preparation for going to work, together with the amount of time that it takes to travel to work when taking a particular route. Table 1. Recorded data for Identified Process.   Monday Tuesday Wednesday Thursday Friday Alarm set 5:00AM 5:00AM 5:00AM 5:00AM 5:00AM Time taken to complete pre-departure activities (in minutes) 78 91 100 47 39 Time left home 6:18 AM 6:31 AM 6:40 AM 5:47 AM 5:39 AM Travel time (in minutes) 142 134 140 118 121 Time arrived at work 8:40 AM 8:45 AM 9:00 AM 7:45 AM 7:40 AM Arguably, the best method for implementing statistical process control is through the use of control charts and observing the behavior of the data with regards to the control limits (Doty, 1996).

Control charts are simply visual representations of the data points while the control limits are the imaginary lines within which the data points must lie to be considered acceptable (Stapenhurst, 2005). In order to calculate the control limits, it is first necessary to calculate the mean (x-bar) and standard deviation (sigma) of the data points. To get the Upper Control Limit, a multiple of the standard deviation (either 1-sigma, 2-sigma, 3-sigma) is added to the mean. Correspondingly, the Lower Control Limit is calculated by subtracting the same multiple of the standard deviation from the mean (Oakland, 2007).

In the example, the mean time for completing my pre-departure activities is 71 minutes, with standard deviation of 27 minutes. Using the 1-sigma rule, the lower control limit is 44 minutes while the upper control limit is 98 minutes. Based on this information, we see that the Thursday set of activities falls below the lower control limit while the Wednesday set of activities falls above the upper control limit. In addition, the mean travel time is 131 minutes, with standard deviation of 11 minutes.

Thus, using the same formula mentioned above, the lower control limit is 120 minutes while the upper control limit is 142 minutes. Travel times falling within these two values are considered acceptable. Thus, the route taken for Thursday falls beyond the control limits. Human activities and sometimes, machine operations, tend to follow particular patterns that may be attributed to seasonal factors (Stapenhurst, 2005). These seasonal factors may be held responsible for the observed fluctuations in a control chart.

Unfortunately, the given example only supplies information for one week thus, not much information regarding the effects of seasonal factors may be derived. However, personal experience would reveal that my pre-departure