Quality Management Tools and Techniques - Essay Example

? Quality Management Tools and Techniques Quality management tools and techniques Quality management tools and techniques used in data analysis. The X-bar and R (range) chart is a pair of control charts that are utilized in processes that have a subgroup size of two or more. In the standard chart for variables data, R charts and X-bar charts help to determine if a particular process is stable and predictable. According to functionality, the X-bar chart shows how the averages or mean changes over time while the R chart outlines how a range of subgroups changes over time. In addition, it is also used in the monitoring of the effects of process improvement theories. Consequently, as the standard the X-bar and R chart will only work in place of X-bar and s or the median and R chart. In order to create an X-bar and R chart you can use CHARTrunner and SQCpack software. The X-bar is used to show the mean or average of every subgroup. It also used to analyze central location. On the other hand the R-chart is used to depict how data is spread and study system variability. We can actually utilize the R charts and X-bar for any of the processes that with a subgroup size greater than one. Usually it is used when the size of the subgroup falls within two and ten. However, the s charts and X-bar charts are used those subgroups of eleven and more. The X-bar and R charts are only utilized; if you need to assess stability of the system, the data is in variable form, if the data is collected in such subgroups that are larger than one but are less than eleven. So as to ensure the best of the results, before calculating the control limits should collect as many subgroups as possible. This is because with the small amount of data the variability of the entire system may not be represented by the X-bar and R chart. Therefore, the more subgroups utilized in the calculation of limits usually 20-25 the more reliable the results (Waite, 2010). As in the case of Scott and Larraine the utilization of 30 sub groups is actually recommended. Since Scott said that he noticed that the number of complaints seem to have significantly increased since the new system was installed, it can actually be diagnosed that the problems may be emanating from the system thus the need of checking if there is any variability in the system. But since the errors increased in the last third of the month it is also substantiated that the system has been in place close to a month. The X-bar and R charts can be of help if you commence to improve the system and later use them to assess the systems stability. After assessment of the system’s stability, should determine if there is need to stratify the data. This is because you may actually come across variability in the results should collect the data and enter it such way that lets you to stratify it by location, symptom, lots, time and operator. Moreover, since the hotel was continuously receiving complaints the X-bar and R charts can also be used to analyze the results of improvements of the process. This would curb down an increased trend of complaints of the inflated bills from the hotel staff. Finally, the X-bar and R charts can be used for standardization. This means the data should continue to be collected and analyzed through the process of operation. If changes have been made to the system that can make the collection of data to stop, then you can only have the perception and opinion that they improved the system (Waite, 2010). An X-bar monitors the average value of particular process overtime. This means that for every subgroup the x-bar value is plotted. The lower and upper control limits are the ones that define the range of inherent variation in the means of the subgroups when the process is in control. However, the R chart is used to monitor the process if the variable of interest is a quantitative measure. To find the upper and lower limits we use the formulae (Woodwall, 2011). UCL = ?+ 3??vn and UCL ?-3??vn To commence with, the R chart is constructed and if it validates that the process is in statistical control then the X-bar is then constructed. In constructing the R chart find the successive subgroups whereby k value is at least 20, finding the range of each subgroup, finding the centerline, the UCL and finally the LCL. UCL= x+ A(2)RBAR and LCL= x+ A(2)RBAR The process in the billing errors of the San Antonio hotel is actually out of control considering that there is an inherent variation from the data in that particular month. With a variation of 0.30%, there many factors that have led to the errors in the billing process. P charts are actually utilized as control chart where the data collected in subgroups is of varying sizes. This is because the subgroup size can vary, it shows that a proportion on the nonconforming items rather than the actual count. Moreover, P-charts show how the whole process changes over time. The process characteristic is always described in a yes/no, pass/fail, and go/no. It is used to a certain whether the process is predictable, stable, and monitoring the effects of process improvements theories. The p chart is used if you want to assess the stability of the systems, if the data is count of items that are nonconforming per subgroup, the counts can be converted to proportions, they are only two outcomes for any given check, and finally if the subgroups are preserved. This can actually be linked to the San Antonio hotel case therefore come a conclusion that the billing problems may actually not grossly emanated from the computer systems but to the people who were involved in the collection of the data from the customers. According to Woodwall (2010) he asserts that just like the x-bar, c chart and the R chart, the p charts also involves collection of as many subgroups as possible to before calculation of the control limits for more reliable analysis, used for assessing system stability, stratification of data, and finally can be used for standardization something which the Scott and Larraine can embrace in alleviation of billing errors in the hotels billing process. On the other hand, the c chart can be utilized as a control chart that is used with data that is collected in subgroups that are the same size. Mostly, c-charts illustrate how the process measured by the number of nonconformities per item or a group of items, changes over time. Non-conformities are those occurrences that are found in the sampled subgroup. They can actually be described as any characteristic or attribute that is present but should not be, or any characteristic that is not there but should be. For example a dent, virus, missing buttons on the computer, and a tear would all be nonconformities. In addition, c-charts are used to establish if the process is actually stable and predictable, and to monitor the effects of process improvement theories (PQSytems, 2012). An example of a p-chart Figure 1: Proportion of the weekly incomplete insurance claim forms of those non-conforming units in subgroups of varying sizes (Woodwall, 2011) Larraine as being in the management of San Antonio hotel, where they were having a large turn out of visitors in a particular period, the process used to be compile the details of the guest was actually very complex thus the errors exhibited in the billing process. The use of the c chart can actually erase this problem as it particularly useful if the items are to complex to be perceived as either conforming or nonconforming. This is because the computer may be having come defects yet they are not considered as defective. A c chart is used if; you need to assess the stability of the system, there is data on the number of conformities per subgroup, if the subgroups are of the same size or not, the possible non-conformities identified prior to collection of data, and finally if the time orders of the subgroups are preserved. Figure 2: Number of no-conformities in subgroups of equal size The 3-sigma limit is a statistical calculation that refers to data that is within three standard deviations from a mean. Three-sigma limits are used to set both the lower and upper control limits in statistical quality control charts. This is because control charts are used to determine the limits for a business process that is in a state of statistical control (Beri, 2010). The control charts are actually based on the theory; even in perfectly designed processes a certain amount of variability in output measurements is inherent. The variations in process quality that arise due to random causes are referred to as being in-control. The processes that are out-of-control include both the random and special causes of variation. Since the control charts are intended to establish the presence of special causes, Shewart set 3 standard deviation limits as "a rational and economic guide to minimum economic loss." The three-sigma limits set a range for the process parameter at 0.27% control limits. Therefore, the billing process at the San Antonio hotel is said to be out of control with a parameter of 0.30%. If the standard deviation indicates low values, it indicates that the data points fall close to the mean while high values also indicate that the data is widespread thus not close to the mean (Investopedia, 2013). Reference List Beri. (2010). Business Statistics. Belmont: Tata McGraw Hill Education. Investopedia. (2013, January). Retrieved July 17, 2013, from Three Sigma Limits: http://www.investopedia.com/terms/t/three-sigma-limits PQ Sytems. (2012, August). PQ Sysytems. Retrieved March 17, 2013, from C Chart: http://www.pqsystems.com/qualityadvisor/DataAnalysisToo Waite, J. (2010, October 14). Retrieved March 17, 2013, from Xbar and R charts: http://www.freequality.org/documents/knowledge/x-bar%20R%20Charts.pdf Woodwall, W. H. (2011). The Statistical Design of Quality Control Charts. The Statistician , 2 (1), 155-165. Read More