Matoteng M. Ncube

Multivariate CUSUM Quality- Control Procedures

It is a common practice to use, simultaneously, several one-sided or two-sided CUSUM procedures of the type proposed by Page (1954). In this article, this method of control is considered to be a single multivariate CUSUM (MCUSUM) procedure. Methods are given for approximating parameters of the distribution of the minimum of the run lengths of the univariate CUSUM charts. Using a new method of comparing multivariate control charts, it is shown that an MCUSUM procedure is often preferable to Hotellings TZ procedure for the case in which the quality characteristics are bivariate normal random variables.

A Comparison of dispersion quality control charts

The problem of detecting shifts in the process variability has not received as much attention as that of detecting shifts in the process mean of continuous production processes, even though it is important in the context of quality control. We examine and compare shewhart s (1931) and Page s (1963) procedures for detecting shifts in variability based on the sample range (R-charts), with procedures based on the sample standard deviation (S-charts). The underlying process control variables are assumed to be normally distributed. We also compare Cumulative Sum (CUSUM) procedures using sample ranges and sample standard deviations. We shall show by average run length (ARL) comparisons that procedures based on the sample standard deviation detect shifts from target value more quickly than procedures based on the sample range.

A Combined Shewhart-Cumulative Score Quality Control Chart

The proposed Shewhart–cumulative score quality control scheme combines the features of the Shewhart (1931) chart and the cumulative score procedure of Munford (1980) for monitoring the mean of a continuous production process. Simple methods are presented for determining the average run lengths of one‐ and two‐sided versions of the procedure. The proposed scheme is considerably more sensitive to shifts in the process mean that either the Shewhart or the cumulative score procedures.

A longitudinal analysis of data breaches

Purpose – The purpose of this research is to provide companies and consumers with information about the potential connections between data breach types and institutions. This study also aims to add to the body of knowledge about data breaches.Design/methodology/approach – This study analyzes a chronology of five years of data breaches. The data were classified and analyzed by breach and institution type, record size, and state. Multiple statistical tests were performed.Findings – Breach types stolen and exposed are statistically more likely to occur. Educational institutions are more likely to have a breach and it is more probable that educational breaches will be of type hacker or exposed. The proportion of insider incidents is smaller than the other breach types. The number of records breached is independent of institution and breach type.Research limitations/implications – Only those breaches with a specified number of records are included. The information used may have been updated after our analysis,...

Variable sampling interval combined shewhart-cumulative score quality control procedure

A standard combined Shewhart–cumulative score quality control scheme for controlling the process mean takes samples from the process at fixed length sampling intervals. This paper proposes a modification which varies the time intervals between samples depending on what is being observed from the data. The properties of the proposed variable sampling interval combined Shewhart–cumulative score scheme are evaluated using a Markov chain approach. Results show that the scheme proposed is considerably more efficient than either the standard combined Shewhart–cumulative score chart or the standard cumulative sum chart.

An Exponentially Weighted Moving Average Combined Shewhart Cumulative Score Quality Control Procedure

The proposed exponentially weighted moving average combined Shewhart cumulative score (EWMA‐CUSCORE) procedure for controlling the process mean cumulate scores of ‐1, 0, 1 or 2h assigned to each moving average of the current and past sample mean values depending on a preassigned interval in which its value falls. It will be shown by average run length (ARL) comparisons that the proposed scheme performs better than the Shewhart type schemes, the combined Shewhart cumulative score type schemes, the cusum type schemes and the standard EWMA type schemes for detecting shifts in the process mean when the underlying process control variable is normal.

An ewma-cuscore quality control procedure for process variability

Combined Shewhart-cumulative score (cuscore) quality control schemes are available for controlling the mean of a continuous production process. In many industrial applications, it is important to control the process variability as well. The proposed exponentially weighted moving average combined Shewhart-cumulative score ewma-cuscore quality control procedure for detecting shifts in process variability uses the procedures developed by Ncube and Woodall [1] to monitor shifts in the process mean of continuous production processes. It is shown, in the one-sided case, by Average Run Length (ARL) comparisons, that the proposed scheme performs significantly and uniformly better than comparative standard cuscore procedures for shifts in the process standard deviation values considered.

Cumulative Score Quality Control Procedures for Process Variability

Combined Shewhart‐cumulative score (cuscore) quality control schemes are available for controlling the mean of a continuous production process. In many industrial applications, it is important to control the process variability as well. The proposed combined Shewhart‐cumulative‐score (cuscore) procedure for detecting shifts in process variability uses the procedures developed by Ncube and Woodall (1984) to monitor shifts in the process mean of continuous production processes. It is shown, in the one‐sided case, by average run length comparisons, that the proposed schemes perform significantly better than comparative Shewhart procedures and in some cases even better than cusum schemes when using some process variability quality characteristics.

A Comparison of Cusum‐Cuscore and Ewma‐Cuscore Quality Control Procedures

Proposes cusum‐cuscore procedures that consider equal weight past sample mean values and past sample score values. The objective is to compare the performance of the cusum‐cuscore and the ewma‐cuscore and in particular to investigate the number of past sample mean values needed to make a significant impact on the performance of the schemes. It will be shown by average run length calculations that the two schemes complement each other very well and that they perform significantly better than standard Shewhart, cuscore and cusum procedures.

A cumulative score quality control scheme

Ncube and Woodall (1984) developed combined Shewhartcumulative score (cuscore) quality control procedures for detecting shifts in the process mean of continuous production processes. These procedures only considered the past history of the scores but not that of the values of the quality characteristic, the sample mean. The proposed scheme takes into consideration the past history of the values of the sample mean quality characteristic and the past history of the scores. It will be shown, by average run length comparisons, that the proposed scheme performs significantly better than the standard cuscore procedures, the Shewhart procedures and cusum procedures for shifts in the process mean considered