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The origins of quality engineering are in manufacturing, where quality engineers apply basic statistical methodologies to improve the quality and productivity of products and processes. In the past decade, people have discovered that these methodologies are effective for improving almost any type of system or process, such as financial, health care, and supply chains. This paper begins with a review of key advances and trends within quality engineering over the past decade. The second part uses the first part as a foundation to outline new application areas for the field. It also discusses how quality engineering needs to evolve in order to make significant contributions to these new areas. © 2015 The Authors Quality and Reliability Engineering International Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

The article demonstrates how to characterize the behavior of a chemical manufacturing process using a time series model to control and adjust the manufacturing process. The authors discuss a different interpretation of the word control, and consider the situation where the process is adjusted whenever data coming from the process indicates that the process is too far from a given target value. They describe the idea behind time series control of a process and proceed more intuitively and use the chemical process as an illustrative example.

The article focuses on the importance of time series analysis on modern quality monitoring and control. It discusses the effect of autocorrelation on statistical process control procedures based on a number of modern feedback control concepts and algorithms. A detailed analysis of a time series example, the Series C, from the seminal book on time series by G. E. Box and G. M. Jenkins is provided. Several charts are also supplied to illustrate how a time series plot can be used as a functional tool for determining stationarity.

Modern products frequently feature monitors designed to detect actual or impending malfunctions. False alarms (Type I errors) or excessive delays in detecting real malfunctions (Type II errors) can seriously reduce monitor utility. Sound engineering practice includes physical evaluation of error rates. Type II error rates are relatively easy to evaluate empirically. However, adequate evaluation of a low Type I error rate is difficult without using accelerated testing concepts, inducing false alarms using artificially low thresholds and then selecting production thresholds by appropriate extrapolation, as outlined here. This acceleration methodology allows for informed determination of detection thresholds and confidence in monitor performance with substantial reductions over current alternatives in time and cost required for monitor development. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article analyzes the effect of a drastic change to the regular behavior of a process that interrupts the quality engineers from analyzing the input-output relationships. This change occurs when operators make significant changes while operating a process. This phenomenon is illustrated by observing changes in the temperature profile of a ceramic furnace. These changing patterns are explained by graphical patterns in scatter plots that are necessary to understand the process behavior.

Describes an assessment of delayed dynamic relationships between input and output in quality control processes using the theory originally developed by Box and Jenkins. Gas furnace used by Box and Jenkins to assess the relationship between an input and an output in process control; Overview of the estimated cross-correlation function between the input gas feed rate and the output carbon dioxide; Steps involved in prewhitening the input.

Split-plot experiments are commonly used in industry for product and process improvement. Recent articles on designing split-plot experiments concentrate on minimum aberration as the design criterion. Minimum aberration has been criticized as a design criterion for completely randomized fractional factorial design and alternative criteria, such as the maximum number of clear two-factor interactions, are suggested (Wu and Hamada (2000)). The need for alternatives to minimum aberration is even more acute for split-plot designs. In a standard split-plot design, there are several types of two-factor interactions, not all of them equally interesting. However, minimum aberration is not designed to distinguish among the different types of two-factor interactions. It should be noted that this criticism is valid not only for the minimum aberration but also for any other design criteria originally proposed for completely randomized designs. Consequently, we provide a modified version of the maximum number of clear two-factor interactions design criterion to be used for split-plot designs. [ABSTRACT FROM AUTHOR]

Describes the use of variogram in statistical quality control process. Temperature readings from a ceramic furnace; Periodicities in stationary process.

Computers are increasingly employed to monitor the performance of complex systems. An important issue is how to evaluate the performance of such monitors. In this article we introduce a three-dimensional representation that we call a “waterfall chart” of the probability of an alarm as a function of time and the condition of the system. It combines and shows the conceptual relationship between the cumulative distribution function of the run length and the power function. The value of this tool is illustrated with an application to Page's one-sided Cusum algorithm. However, it can be applied in general for any monitoring system. [ABSTRACT FROM AUTHOR]