Statistical Process Control - Research Paper Example

Statistical Process Control Statistical process control improvement defines the procedure that requires improvement with the aim of improving the process. The current process needs to be defined before another plan to establish a better alternative with the target of reducing time and hence cutting on the overall cost of undertaking the exercise. The most important issue is proper communication that ensures that sound implementation is done to see if the process is feasible (Mesbah, at al. 2002). During week one and week two a table was created to show the amount of time taken for creating daily workloads and reviewing progress/completion on daily workloads. The main purpose of the tabulated data was to establish a more efficient way to complete this process, to find out any potential bottleneck and to eliminate the wasted time. This work seeks to discuss the control limits that will constitute the calculation of the data that will be used to estimate them, explore the effects of any other extraneous factors using each of the process performance data and introduce the role of statistical tests of significance like the concept of confidence interval (Persse, 2008). The usefulness of such statistical techniques will also be applied based on the amount of data points. Statistical scholars and researchers have established that statistical process control constitutes the testing of a random sample of any output from a procedure to establish whether the exercise produces variables within a range that is preselected. In week two, obstacles were identified during the morning preparation routine. The bottlenecks were found to be as a result of lack of technology and automation in the process. Specifically, a bottleneck was created at three main points, the time spent waiting on reports to generate, reviewing the reports and then auditing completed work. This is to say that the longer the amount of time spent in such duties, the shorter is the time allocated for the actual work on files. One of the open options to overcome this challenge is to allocate overtime hours every day so that the tasks can be undertaken with some time allowance to absorb the expected lag time. A more realistic and cost effective solution to the bottleneck would be to implement technology and automation to make the work more streamlined and eliminate the waiting period created in the current system. Some constraints’ theorists like Goldratt, argues that the best remedial measure against a problem dependent on problem solving skills. The techniques provide a layout of procedure that helps in identification of the constraints, eliminating them and seeking out other feasible alternatives (Persse, 2008). In other words, once the time taken to review data is shortened, then the next target is to reduce the time of carrying out the morning routine duties. It is imperative that the significance of statistical tools in solving real life challenges like the one in question are taken into consideration (Mesbah, at al. 2002). Some of the measures of central tendency and dispersion that are vital in computing the tabulated data to establish a solution include mean and standard deviation. Data Summary for the two weeks in hours Week One Day Creating daily workloads (Minutes) Reviewing progress/completion (Min) Monday 38 37 Tuesday 38 30 Wednesday 41 34 Thursday 30 29 Friday 26 37 During the first week, the data was collected for five days covering from Monday to Friday. During the week, the data collected was based on the duration of time required to prepare the daily workloads as well as to audit the progress/completion of the daily workloads. Over the five days, the duration taken in preparing the workloads for each of the five days range from 26 minutes to a maximum of 41 minutes. This gives the range of 15 minutes as the spread of the workload creation time. On the other hand, during the same week, the manager had to spend some time reviewing the completion and the progress of the daily accounts. The minimum time taken in reviewing the work progress is 29 minutes on Thursday while Monday reported the longest duration of 37 minutes. The difference between the minimum and the maximum is 8 minutes which represents the range for the first week. Week Two Day Creating daily workloads (Minutes) Reviewing progress/completion (Min) Monday 40 42 Tuesday 35 42 Wednesday 33 36 Thursday 30 39 Friday 38 40 In the second week, the minimum workload creation time is 30 minutes with a maximum of 40 minutes recorded on the first day of the week. The range for workload creation time is 10 minutes (40-30 = 10). Reviewing work progress report at the end of each of the working days has a maximum duration period of 42 minutes and a minimum of 36 minutes, resulting into 6 minutes range for the week. Range is important aspects of statistical control process since it is the measure of dispersion that accounts for the spread between the maximum and the minimum values. Variable Week 1 Week 2 Workload creation Work Review Workload creation Work Review Mean 34.6 33.4 35.2 39.8 Max. 41 37 40 42 Min. 26 29 30 36 Range 15 8 10 6 Standard Dev. 6.31 3.78 3.96 3.95 Variance 39.81 14.29 15.68 15.60 During the first week, the average duration for workload creation was 34.6 minutes while that of work reviewing during the same week was 33.4 minutes. This average value measures the mean of the time taken to prepare the daily workloads and audit the progress/completion. The preparation time for week one had a standard deviation of 6.31 with a variance of 39.81. This shows that the workload creation time varies above or below the mean value of 34.6 by 6.31 minutes. The work review period for week one had a standard deviation 3.78 with a variance of 14.29. It is important to set out control standards in the processing of conducting research. For instance, in week one, the control standard would set at 2 units above or below the standard value. This implies that nearly 67.67% of the values fall within this range. However, the smaller the variation the more accurate and reliable the outcome is, hence, the variation should be made as minimal as possible (Persse, 2008). This is an indication that the domains within which the measured values fall are determined by the statistical process. In the second week, the average time for preparing the workload is 35.2 minutes while the mean duration for review of the daily records is 39.8 minutes. The standard deviation below or above the mean for the workload preparation for week two is found to be 3.96 with a corresponding variance of 15.68. The standard deviation for the work progress review for week two is given by 3.95 with a variance of 15.60. In the course of this week of preparation, a five day preparation was important. Similar to the first week, the exercise took five days covering from Monday to Friday. At 97.6 % confidence level, all the values of the mean falling out side this range are considered to be outside the control limit of the research (Persse, 2008). References Mesbah, M., Cole, B. F., & Lee, M.-L. T. (2002). Statistical methods for quality of life studies: Design, measurements, and analysis. Dordrecht: Kluwer Academic Publishers. Persse, J. R. (2008). Process Improvement Essentials: CMMI, Six Sigma, and ISO 9001. Sebastopol: O'Reilly Media, Inc. Read More