Statistical Process Control In Industry Research Articles

The usage of Statistical Process Control (SPC) in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the usage of Statistical Process Control (SPC) approach in Industry 4.0 conditions. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of Statistical Process Control (SPC) with Industry 4.0 signifies a transformative shift in quality management, elevating SPC from a conventional monitoring tool to a proactive force in contemporary manufacturing. Originally employed for ensuring consistent quality through process monitoring, SPC's role has been redefined in the Industry 4.0 era, utilizing data analytics, real-time monitoring, and connectivity to offer a comprehensive understanding of the manufacturing ecosystem. Enabled by the Internet of Things (IoT), SPC gains real-time insights into production processes, crucial for swift anomaly identification and issue resolution. Advanced analytics and artificial intelligence enhance SPC's predictive capabilities, enabling proactive measures to maintain product quality. This publication underscores the significance of SPC in Industry 4.0 conditions, emphasizing its roots in statistical principles, systematic process control, and the distinction between common and special cause variations. The integration of SPC with Industry 4.0 and Quality 4.0 leverages technologies for enhanced quality management, emphasizing real-time data monitoring and collaborative, data-driven approaches. While Table 2 outlines specific aspects of SPC integration, highlighting its versatility, Table 3 enumerates the advantages, emphasizing improved visibility and predictive quality management. However, challenges such as data security concerns and technology integration complexity, outlined in Table 4, necessitate strategic solutions. In conclusion, this integration represents a pivotal advancement, positioning SPC as an indispensable asset for organizations seeking quality excellence, operational efficiency, and resilience in the dynamic landscape of modern manufacturing. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of Statistical process Control in Industry 4.0 conditions. Keywords: Industry 4.0; Quality 4.0, quality management; quality methods, SPC, Statistical Process Control. Category of the paper: literature review.

Asymmetric control limits for range chart with simple robust estimator under the non-normal distributed process

This paper aims to modify Shewhart, the weighted variance and skewness correction methods in industrial statistical process control. The robust and asymmetric control limits of range chart are constructed to use in contaminated and skewed distributed process. The way of construction of control limits is simple and corresponds to three methods in which sample range estimator is replaced with the robust interquartile range. These three modified methods are evaluated in terms of their type I risks and average run length by using simulation study. The performance of the proposed range charts is assessed when the Phases I and II data are uncontaminated and contaminated. The Weibull, gamma and lognormal distributions are chosen since they can represent a wide variety of shapes from nearly symmetric to highly skewed.

Integrated and objective-oriented statistical process control

Global competition and increased environmental concern have emphasized the significance of process quality for an economic success. Statistical Process Control (SPC) is considered as one of the major tools for better quality. However, there are many opinions about the nature, the role and the results of SPC in industry. If SPC is understood and implemented correctly, then it should not only lead to "a reduction of waste, shorter throughput times and greater reliability of supply, but also in a better understanding of the processes and their variation" as Does, Roes and Trip formulated it. SPC should ultimately lead to "an operational management of the continuous improvement of processes." The aim of an industrial production process is to make profit, thus quality should be measured by process profitability. From a more operational point of view highest quality, i.e. highest profitability, is reached by producing with high conformance to an appropriately selected target value, and quality improvement means further reduction of variation around the target value. Thus, understanding the inherent variation represents the key for controlling "profitability" and for being able to implement a system of continuous "quality improvement". The inherent variations reflect the complex relations between everything concerning the production process and results in uncertainty about the future development. Exactly at this point stochastics, the science of uncertainty, enters the stage. For controlling profitability the relations and the resulting variations have to be taken appropriately into account by an integrated and objective-oriented stochastic approach.

Variable-Selection-Based Epidemic Disease Diagnosis

Epidemic surveillance in a community involves monitoring infection trend, triggering alarms before outbreaks, and identifying sources and paths of disease transmission. Algorithms for outbreak detection that are derived from industrial statistical process control (SPC) and scan statistics have been reported in the literature, but there are relatively few methods reported for identifying transmission paths. In this work, we propose an expanded spatial-temporal (EST) model for identifying infection sources. Three dimensional information, subject, location, and time, are expanded into a two-dimensional space by dividing the time horizon into segments and multiplying each segment by the locations. Based on the EST model, we further propose a variable-selection algorithm to identify potential location/time combinations as sources of infection, and thus achieve diagnosis. Numerical simulations show that the proposed scheme is effective in locating infection sources.

Comments on ‘Some methodological issues in biosurveillance’

We greatly appreciate the opportunity to comment on this interesting and thought-provoking paper. Dr Fricker has brought up many important issues with respect to biosurveillance, primarily in the very challenging context of syndromic surveillance and other types of public health surveillance with many input data streams. Similar to Dr Fricker, we have backgrounds in industrial statistical process control (SPC). Our study of public health surveillance and health-care monitoring issues began roughly 6 years ago. Some of what we learned was summarized in two review papers, Woodall [1] and Tsui et al. [2], aimed primarily toward industrial SPC researchers and practitioners. We have studied several public health surveillance topics, including sets methods [3], temporal scan approaches [4--6], and spatiotemporal methods [7--10] [Tsui et al. 2010; submitted]. Although there are no fundamental points of disagreement between Dr Fricker’s views and ours, we would like to elaborate on a few topics and share our experience with respect to industrial SPC methods and ideas and how they relate to public health surveillance.

Comments on ‘Some methodological issues in biosurveillance’

Rarely does one get to be a participant in the frontier of a new discipline or area of research. But since 9/11 the field of syndromic surveillance has provided public health researchers and practitioners just such an opportunity. Some of the $5 billion in funds awarded by the Centers for Disease Control and Prevention (CDC) during fiscal years 2002– 2007 to states, territories, and several large local jurisdictions to enhance public health capacities to detect, respond to, and recover from bioterrorism events have been allocated to expanding or improving surveillance systems [1]. These expansions have included developing public health capabilities in the area of biosurveillance and its sub-field, syndromic surveillance. The aim of these new systems is to use near ‘real-time’ data and automated tools to detect and characterize outbreaks (natural or intentional) before conventional methods. Several systems have become popular for use by state and local health jurisdictions in conducting these syndromic surveillance activities, including the Early Aberration Reporting System (EARS), BioSense, and the Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE II) [2--4]. In the initial years of implementing syndromic surveillance systems, state and local health departments used the applications primarily to detect bioterrorism-related events [5]. However, due to developing needs, the public health community has applied biosurveillance more broadly to other situations, including influenza and fire-related illness surveillance, as well as to affirm the absence of outbreaks after a disaster [6]. In Monterey County, California, we have had similar needs and particularly wanted a program that we could use for local purposes. We began using EARS in 2005 because of its flexibility for developing syndrome definitions and applied it to a variety of situations, from daily ongoing surveillance for influenza-like illness to emerging situations such as respiratory syndromes potentially associated with an aerial spraying of pesticide (Monterey County, unpublished data). The field is young, and Fricker (this issue) is timely in his advocacy for the standardization of terms and methods used for biosurveillance and particularly syndromic surveillance. Public health practitioners should support such standardization and have outlined areas for further research and evaluation [7]. An equally pressing issue is the statistical methods that are currently used for biosurveillance. Fricker does an admirable job of summarizing how methodologies from the field of industrial statistical process control have been adapted for use in detecting attacks by terrorists on the health of a population. A comprehensive understanding of bioterrorism is relatively recent in the literature and, for research purposes, the number of modern bioterrorist events that resulted in actual cases have been few [8]. However, there has been an apparent recent increase in the use of biological agents with 40 of 56 confirmed criminal cases and 19 of 27 confirmed terrorist cases in the 20th century occurring in the 1990s [8]. While these provide a relative paucity of events for modeling purposes and for developing effective syndromic surveillance methods, they underscore the necessity of surveillance methods that can be used to improve time to event detection and to help ensure that a bioterrorist attack is detected. This is likely why there appears to be general support in public health for an expansion of syndromic and other public health surveillance systems. The majority of public health jurisdictions surveyed in the United States use some form of syndromic surveillance [5]. Internationally, the World Health Organization revised the International Health Regulations in 2007 to include the concept of syndromic surveillance as part of an expanded traditional disease notification system. May et al. [9] provide a review of its uses in developing nations. There is a national recognition of the potential usefulness and importance for developing syndromic surveillance systems. In December 2009, proposed regulations were released by the Centers for Medicare and Medicaid Services in the United States defining ‘meaningful use’ of electronic health records (EHR). In addition, the Office of the National Coordinator for Health Information Technology released an interim final rule describing the required certification standards

Performance Metrics for Surveillance Schemes

ABSTRACT We review various statistical performance metrics that have been used with prospective surveillance schemes. We consider scenarios under which the metrics are most useful and discuss some of their advantages and disadvantages. A contrast is made between the approaches and metrics used in industrial process monitoring and those used in public health surveillance. The in-control average time between signal events (ATBSE) and the in-control average signal event length (ASEL) are introduced as performance metrics that are useful when a monitoring procedure is not reset to its initial state after a signal. We give particular attention to the recurrence interval, defined as the fixed length of time (measured in number of time periods) for which the expected number of false alarms is one. The recurrence interval is used in public health surveillance whereas in-control time-to-signal measures are used in industrial statistical process control. We compare the recurrence interval and measures based on the time-to-signal properties for the temporal monitoring case using exponentially weighted moving average (EWMA) charts, cumulative sum (CUSUM) charts, and Markov dependent signaling processes. We recommend that measures based on the time-to-signal properties be used when possible to evaluate the performance of surveillance schemes for ongoing monitoring.

The Use of Control Charts in Health-Care and Public-Health Surveillance

There are many applications of control charts in health-care monitoring and in public-health surveillance. We introduce these applications to industrial practitioners and discuss some of the ideas that arise that may be applicable in industrial monitoring. The advantages and disadvantages of the charting methods proposed in the health-care and public-health areas are considered. Some additional contributions in the industrial statistical process control literature relevant to this area are given. There are many application and research opportunities available in the use of control charts for health-related monitoring.

Application of latent variable methods to process control and multivariate statistical process control in industry

AbstractMultivariate monitoring and control schemes based on latent variable methods have been receiving increasing attention by industrial practitioners in the last 15 years. Several companies have enthusiastically adopted the methods and have reported many success stories. Applications have been reported where multivariate statistical process control, fault detection and diagnosis is achieved by utilizing the latent variable space, for continuous and batch processes, as well as, for process transitions as for example start ups and re‐starts. This paper gives an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes. It provides a critical review of the methodology and describes how it is transferred to the industrial environment. Recent applications of latent variable methods to process control as well as to image analysis for monitoring and feedback control are discussed. Finally it is emphasized that the multivariate nature of the data should be preserved when data compression and data preprocessing is applied. It is shown that univariate data compression and reconstruction may hinder the validity of multivariate analysis by introducing spurious correlations. Copyright © 2005 John Wiley & Sons, Ltd.

Teaching How to Calibrate a Process Using Experimental Design and Analysis: The Ballistat

As part of the University of Newcastle's Total Quality Management (TQM) course, students study Experimental Design (ED) and Statistical Process Control (SPC) within the framework of the scientific approach to process improvement. A sufficient balance of theory and application is required to keep Business and Management students, most with a largely non-quantitative background, interested and aware of the need and method to correctly implement ED and SPC in industry. Tools to facilitate a basic understanding of the importance of the 3Rs, namely, Randomization, Replication, and Blocking, as well as highlighting the potential for mistakes or inefficient calibration techniques are essential in the learning process. This paper describes the use of a particular tool, called the “Ballistat,” to illustrate TQM concepts, which enables students to obtain the hands-on experience needed to control processes in industry.

A Robust Smoothing Approach to Statistical Process Control

It has previously been shown that smoothing algorithms can provide the basis for methods to detect nuclear material losses and moreover can also provide a general approach to industrial statistical process control. The present paper extends this result by showing that a set of robust smoothers also produces methods that can be used in statistical process control. Further, it is shown that these smoothers are somewhat more sensitive to out of control points than those methods previously studied. The methods are successfully illustrated on chemical process data.

The Use of Robust Smoothers in Nuclear Material Safeguards

It has previously been shown that smoothing algorithms can provide the basis for a method to detect nuclear material diversions and losses and moreover can also provide a general approach to industrial statistical process control. The present paper extends this result by showing that a set of robust smoothers also produces equivalent methods that can be used in nuclear material safeguards algorithms. Further, it is shown that these smoothers are somewhat more sensitive to loss points than the previously studied smoothers. The method is illustrated on real data.