Considering the lead time control problem of multi-stage manufacturing systems, a new queuing system based on multi-stage manufacturing systems is developed, in which the manufacturing times are exponentially distributed: By using the total probability decomposition and Markov process method, the lead time distribution function is obtained. Then, the lead time control chart is proposed using the time between events control chart, and the center line and control limits for the lead time are achieved by above method. Numerical examples show that the lead time can be well monitored by the lead time control charts.

This article proposes a Cumulative Sum (CUSUM) scheme, called the TX-CUSUM scheme, for the monitoring of a negative event. It is able to check both the time interval (T) between occurrences of the event and the magnitude (X) of each occurrence. In this article, T is assumed to follow an exponential distribution and X is assumed to follow a gamma distribution. Our studies show that the TX-CUSUM scheme is much more effective than the existing Shewhart-type T&X chart for monitoring, so that cost or loss incurred by an event can be reduced by using this scheme. The improvement in performance is achieved because of the use of the CUSUM feature and the simultaneous monitoring of T and X.

This article proposes a Cumulative Sum (CUSUM) scheme, called the TC‐CUSUM scheme, for monitoring a negative or hazardous event. This scheme is developed using a two‐dimensional Markov model. It is able to check both the time interval ( T ) between occurrences of the event and the size ( C ) of each occurrence. For example, a traffic accident may be defined as an event, and the number of injured victims in each case is the event size. Our studies show that the TC‐CUSUM scheme is several times more effective than many existing charts for event monitoring, so that cost or loss incurred by an event can be reduced by using this scheme. Moreover, the TC‐CUSUM scheme performs more uniformly than other charts for detecting both T shift and C shift, as well as the joint shift in T and C . The improvement in the performance is achieved because of the use of the CUSUM feature and the simultaneous monitoring of T and C . The TC‐CUSUM scheme can be applied in manufacturing systems, and especially in non‐manufacturing sectors (e.g. supply chain management, health‐care industry, disaster management, and security control). Copyright © 2009 John Wiley & Sons, Ltd.

This article proposes a CUSUM chart, called the R-CUSUM chart, for the monitoring of a negative event. This chart is developed using a Markov model. It is able to check both the time interval (T) between occurrences of the event and the size (C) of each occurrence. Our studies show that the R-CUSUM chart is more effective than the existing R (=C/T) chart for event monitoring. The improvement in performance is achieved because of the use of the CUSUM feature. The R-CUSUM chart can be applied in manufacturing systems, and especially in non-manufacturing sectors (e.g., health care industry, disaster management, and security control).

Purpose – The purpose of this paper is to analyze the causal relationships among categories in the China Quality Award (CQA) model based on the Malcolm Baldrige National Quality Award model.Design/methodology/approach – The paper identifies seven factors from CQA categories: leadership, strategic planning, human resource focus, process management, customer and market focus, information and analysis, and results. Extending the basic Baldrige theory “Leadership drives the system that creates results,” this paper identifies driver (leadership), direction (strategic planning), foundation (information and analysis), system (human resource focus, process management, and customer and market focus), and results(business results). Structural equation model (SEM) is used to analyze the empirical data and estimate the path coefficients among CQA categories.Findings – First, driver has not only a direct influence on results, but also has an indirect influence on results through system. Leadership has a great influenc...

A traffic accident can be considered as an example of the attribute events, and the number of the injured in each accident is called the event size. Some control charts have been developed for monitoring either the time interval (T) between the occurrences of an event or the event size (C) in each occurrence. This article studies the statistical monitoring of the attribute events in which T and C are monitored simultaneously and C is an integer. Essentially, it integrates a T chart and a C chart, and is therefore referred to as a TC scheme. Our studies show that the new chart is more effective than an individual T chart or C chart for detecting the out-of-control status of the event, in particular for detecting downward shifts (sparse occurrence and/or small size). Another desirable feature of the TC scheme is that its detection effectiveness is more invariable against different types of shifts (i.e. T shift, C shift and joint shift in TC) compared with an individual T or C chart. The improvement in performance is achieved due to the simultaneous monitoring of T and C. The TC scheme can be applied in manufacturing systems and especially in non-manufacturing sectors (e.g. supply chain management, health care industry, disaster management and security control).

This article studies the monitoring of the attribute events based on statistical computation and analyses. The size of an attribute event is an integer rather than a continuous variable. For example, the detection of a product lot containing defectives is an attribute event, the size of which is the number of defectives found in this lot. While many control charts have been developed for monitoring the time interval (T) between the occurrences of an event, many other attribute charts may be employed to examine the size (C) of the event. However, these two types of control charts have been investigated and applied separately with limited syntheses in Statistical Process Control (SPC). This article presents a single SPC chart (called the rate chart for attribute, or rate chart in short) for simultaneously monitoring the time interval T and size Cof an attribute event based on the ratio between C and T. Our studies show that the new chart is more effective for detecting the out-of-control status of the attribute event compared with an individual t chart or an individual c chart, as well as a combined t&c chart. More profound is that the rate chart performs more uniformly than other charts for detecting both T shift and C shift, as well as the joint shift in T and C. The rate chart has demonstrated its potential for both manufacturing systems and non-manufacturing sectors (e.g., supply chain management, office administration and health care industry), especially for the latter.

In this paper, we develop a multi-objective model to optimally control the lead time of a manufacturing system, using goal attainment method. The manufacturing system is modelled as a new queueing system based on the failure rate of products, in which the products may have quality problems and need to be repaired in each service station. It is assumed that the product order arrives according to a Poisson process. In each service station, there is only one server (machines) with exponentially distributed processing time, in which the service rate (capacity) is controllable. The maintenance time of manufacturing and assembly operations is exponentially distributed. By using the total probability decomposition and Markov process method, the Queue Length Distribution, the recursion expression of the equilibrium distribution and the mean waiting time are obtained. Then, we develop a multi-objective optimal control problem, in which the average lead time, the maintenance costs and the operating costs of the system per period are minimized using goal attainment method.

Simulation has a great use in the aspects of designing, analyzing, controlling and evaluating a system. This article explores to do scientific research in the field of statistical quality control with an application of visualized simulation techniques. A computer simulation model is designed in a creative way, which is constructed of several typical manufacturing processes, and then some statistical process control (SPC) points are designed to produce quality characteristics and if necessary to gather these data. The simulation procedures are presented and the results are analyzed and discussed.