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There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: "covariance matrix" and "multivariate coefficient of variation (MCV)". This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain model to compute the average run length and average time to signal values. We conduct extensive numerical analyses to measure the performance of the proposed scheme in two scenarios of process variability. At last, we present a numerical example by using a real dataset from a healthcare process to illustrate how the scheme can be implemented in practice.

There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: "covariance matrix" and "multivariate coefficient of variation (MCV)". This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain model to compute the average run length and average time to signal values. We conduct extensive numerical analyses to measure the performance of the proposed scheme in two scenarios of process variability. At last, we present a numerical example by using a real dataset from a healthcare process to illustrate how the scheme can be implemented in practice.

There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: "covariance matrix" and "multivariate coefficient of variation (MCV)". This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain model to compute the average run length and average time to signal values. We conduct extensive numerical analyses to measure the performance of the proposed scheme in two scenarios of process variability. At last, we present a numerical example by using a real dataset from a healthcare process to illustrate how the scheme can be implemented in practice.

This article proposes a new np control chart, called the npx chart, that employs an attribute inspection (inspecting whether a unit is conforming or nonconforming) to monitor the mean value of a variable x. The distinctive feature of the npx chart is using the statistical warning limits to replace the specification limits for the classification of conforming or nonconforming units. By optimizing the warning limits, the npx chart usually outperforms the X¯ chart on the basis of same inspection cost. In addition, the npx chart often works as a leading indicator of trouble and allows operators to take corrective action before any defective is actually produced.

This article proposes a new np control chart, called the npx chart, that employs an attribute inspection (inspecting whether a unit is conforming or nonconforming) to monitor the mean value of a variable x. The distinctive feature of the npx chart is using the statistical warning limits to replace the specification limits for the classification of conforming or nonconforming units. By optimizing the warning limits, the npx chart usually outperforms the X¯ chart on the basis of same inspection cost. In addition, the npx chart often works as a leading indicator of trouble and allows operators to take corrective action before any defective is actually produced.

This paper highlights the steps that are required to achieve total continuous process improvement in the production of products in a manufacturing industry. The importance of each step will be discussed and the strategies that need to be taken to achieve the objectives of each of the steps will be mentioned. The aim of this paper is to provide essential guidelines for operators, supervisors, engineers and managers so that they are aware of the various steps that need to be taken in achieving TCPI.