Control Charts - Research Paper Example

BusinessControl chartsControl charts are used to check a process to determine whether the procedure is in control or not. Attribute data is always collected to assess the quality of the product when it is not possible to determine the quality of the product with the data that is continuous. The mainly use charts to control the process control fall under two categories; P charts and C charts (Min Xie, Goh & Vellaisamy, 2002.P chartsThe P charts or the proportional control charts in a population can be described as the ratio of the number of defective or non-conforming items in the population to the number of items that are in that particular population.

The items that are being considered may have one or more quality characteristics that should be inspected at the same time. The item is considered non –conforming or flawed if one of the characteristics under assessment does not match or correspond the laid down standards.The proportional charts can be said to track the proportional of defects and are used when evaluating a product with regard to a certain standard and hence classifying it as being defective or otherwise. These charts plot the proportion of defective items per subgroup.

The data collected are the number of flawed items and is assumed to follow a binomial distribution with an unidentified proportional parameter P.C chartsThe C chart is applicable when a product is known to have various defects, which are controllable. C charts tracks the number of flaws per unit sampled. C charts are usually used when dealing with an intricate product or service.This kind of chart is occasionally used watch the total number of events occurring in any given time. The C chart requires the following assumptions in order be effectively utilised: it assumes that the likelihood of non-conformity is small at any given location; secondly, the C charts assume that the probable locations for conformity are very huge.

The C charts plots the number of defects per unit and the data composed is the total number of defects in every subgroup, which is known to follow poisons distribution with an unknown mean number of defects in each subgroup.Examples of C chart use can include; monitoring the number of voids when inspecting injection-moulding process; to monitor daily returns on product.Control limitsControl limits for control charts are basically in the control phase of a six sigma project. These charts should be sensitive enough be able to quickly give a signal when a special cause exists.

Assessment of sensitivity is achieved by calculating the number of subgroups needed to signal a special cause. A good control charts should never give a false alarm when the process is controllable. Assessment of false alarm is achieved by calculating the percentage of subgroup that is known be out of control when the process is in control. The formula for calculating control limits for the C charts is as shown below. Where is the estimate for the long term process mean.One important thing to note is the sensitivity of the P chart due to the underlying assumptions generated by using control limits derived from a binomial distribution instead of an observed sample variance.

The assumptions frequently make P charts be used incorrectly. The formula for calculating control limits for P charts is as follows. Where is the estimate for long-term process mean.ReferencesMin Xie, Goh & Vellaisamy, 2002, ‘Statistical models and control charts for high- quality processes’. London: Springer.Edward.1997 ‘Control charts: an introduction to statistical quality control’, McGraw-Hill: New Jersey.Leonard J. & Leonard Kazmier, 2009, ‘Schaum's Outline of Business Statistics, Fourth Edition’, New Jersey: McGraw-Hill Professional, Black, 2009,’ Business Statistics: Contemporary Decision Making’.

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