when a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. however, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always l...

one of the most important problems of the designs proposed by traditional economic-statistical approaches of control charts is inefficiency in the face of uncertainty. uncertainty in the parameters of economic-statistical models may lead to failure in rapidly detecting changes in processes and impose greater costs to the organization. monitoring the machining process in an automotive industry e...

The control chart based on geometric distribution (geometric chart) has been shown to be competitive to por npcharts for monitoring proportion nonconforming, especially for applications in high quality manufacturing environment. However, implementing a geometric chart often assumes the process parameter to be known or accurately estimated. For a high quality process, an accurate parameter estim...

When a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. Upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. However, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always l...

The setting of the control limits to utilize on a control chart supposes the assumption of the normality. However, in many situations, this condition does not hold. There are numerous studies on the control charts when the underlying distribution is non-normal. This paper examines the effects of non-normality as measured by skewness and provides an alternative method of designing individuals co...

Usually, there are two phases in constructing a multivariate control chart. Phase I is to estimate the in-control process parameters and to establish control limits using historical data. Phase II is when the control limits are used to monitor the process. This paper focuses on determining the optimal number of samples in Phase I since number of samples affect the cost of quality control in pra...

Decision procedures for monitoring industrial processes can be based on application of control charts. The commonly used p-chart and np-chart are unsatisfactory for monitoring high-quality processes with a low fraction nonconforming. To overcome this difficulty, one may develop models based on the number of items inspected until r (P 1) nonconforming items are observed. The cumulative count con...

Control charts are widely used in industry as a tool to monitor process characteristics. Deviations from process targets can be detected based on the evidence of statistical significance. The control chart helps to take decisions such as the need for machine or technology replacement to monitor the process with categorical observations. Fuzzy logic is used to cram the uncertainty and vagueness ...

Control charts are effective tools for signal detection in both manufacturing processes and service processes. Much of the data in service industries comes from processes having nonnormal or unknown distributions. The commonly used Shewhart variable control charts, which depend heavily on the normality assumption, are not appropriately used here. We propose a new variance control chart based on...