Statistical Process Control (SPC) in the food industry – A systematic review and future research agenda

Lim, Sarina Abdul Halim; Antony, Jiju; Albliwi, Saja Ahmed

doi:10.1016/j.tifs.2014.03.010

Cited by 82 publications

(70 citation statements)

References 65 publications

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“…Consider, for instance, several sensors observing some industrial production chain. These (potentially correlated) sensor data streams can be monitored with a statistical process control (SPC) algorithm (Lim et al, 2014;Ge et al, 2013;Lowry and Montgomery, 1995). The basic idea is to raise an alarm as soon as an anomaly according to the SPC is detected, meaning that the production chain is out of control.…”

Section: Introductionmentioning

confidence: 99%

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

et al. 2017

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Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no "gold standard" for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

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Section: Introductionmentioning

confidence: 99%

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

et al. 2017

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“…These factors are prerequisites for the creation of an environment favorable for the SPC use. The CSF conduction failure may lead to issues associated with the implementation, maintenance and continuity of the program Lim et al, 2014;Rohani et al, 2010;Woodall & Montgomery, 1999).…”

Section: Identifying Csf For Spc Implementationmentioning

confidence: 99%

“…The greatest challenge is to eliminate the barriers people face when they deal with such tools. This goal can be achieved through continuous SPC training (Elg et al, 2008;Lim et al, 2014;Mason & Antony, 2000). From an organizational viewpoint, it is quite difficult to have the whole organization committed with the program, but such commitment is accomplished through top management support (Elg et al, 2008;Lim et al, 2014).…”

Section: Identifying Csf For Spc Implementationmentioning

confidence: 99%

Impact analysis of critical success factors on the benefits from statistical process control implementation

Soriano

Oprime

Lizarelli

2017

Prod.

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The Statistical Process Control -SPC is a set of statistical techniques focused on process control, monitoring and analyzing variation causes in the quality characteristics and/or in the parameters used to control and process improvements. Implementing SPC in organizations is a complex task. The reasons for its failure are related to organizational or social factors such as lack of top management commitment and little understanding about its potential benefits. Other aspects concern technical factors such as lack of training on and understanding about the statistical techniques. The main aim of the present article is to understand the interrelations between conditioning factors associated with top management commitment (Support), SPC Training and Application, as well as to understand the relationships between these factors and the benefits associated with the implementation of the program. The Partial Least Squares Structural Equation Modeling (PLS-SEM) was used in the analysis since the main goal is to establish the causal relations. A cross-section survey was used as research method to collect information of samples from Brazilian auto-parts companies, which were selected according to guides from the auto-parts industry associations. A total of 170 companies were contacted by e-mail and by phone in order to be invited to participate in the survey. However, just 93 industries agreed on participating, and only 43 answered the questionnaire. The results showed that the senior management support considerably affects the way companies develop their training programs. In turn, these trainings affect the way companies apply the techniques. Thus, it will reflect on the benefits gotten from implementing the program. It was observed that the managerial and technical aspects are closely connected to each other and that they are represented by the ratio between top management and training support. The technical aspects observed through SPC application directly affect the benefits from the program.

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“…Such plans can directly impact the efficiency and efficacy of the production; if these plans are poorly delineated, then they can compromise product quality and productivity (Duarte & Saraiva, 2008;Hoerl & Snee, 2010;Montgomery, 2013;Lim et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…This may be a result of: i) cultural paradigms; ii) the types of groups used to promote and improve the field of statistical quality control, such as task force and semi-autonomous groups; iii) governance within groups (centralized or decentralized, use of specific methods or routines and monitoring of activities); and iv) understanding and applying new technics and tools in the problem identification stage and solution (Juran & Gryna, 1991;Atkinson, 1994;Chapman & Hyland, 1997;Bond, 1999;Harrison, 2000;Bechet et al, 2000;Jabnoun, 2001;Hyland et al, 2003;Jager et al, 2004;Fryer et al, 2007;Marin-Garcia et al, 2008;Mahanti & Evans, 2012;Rantamäki et al, 2013;Sharma & Kharub;Lim et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of 50 years of worldwide research on statistical process control

et al. 2016

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Resumo: Um crescente número de trabalhos sobre controle estatístico do processo (CEP) emergiu nos últimos cinquentaAbstract: An increasing number of papers on statistical process control (SPC) has emerged in the last fifty years, especially in the last fifteen years. This may be attributed to the increased global competitiveness generated by innovation and the continuous improvement of products and processes. In this sense, SPC has a fundamentally important role in quality and production systems. The research in this paper considers the context of technological improvement and innovation of products and processes to increase corporate competitiveness. There are several other statistical technics and tools for assisting continuous improvement and innovation of products and processes but, despite the limitations in their use in the improvement projects, there is growing concern about the use of SPC. A gap between the SPC technics taught in engineering courses and their practical applications to industrial problems is observed in empirical research; thus, it is important to understand what has been done and identify the trends in SPC research. The bibliometric study in this paper is proposed in this direction and uses the Web of Science (WoS) database. Data analysis indicates that there was a growth rate of more than 90% in the number of publications on SPC after 1990. Our results reveal the countries where these publications have come from, the authors with the highest number of papers and their networks. Main sources of publications are also identified; it is observed that the publications of SPC papers are concentrated in some of the international research journals, not necessarily those with the major high-impact factors. Furthermore, the papers are focused on industrial engineering, operations research and management science fields. The most common term found in the papers was cumulative sum control charts, but new topics have emerged and have been researched in the past ten years, such as multivariate methods for process monitoring and nonparametric methods.

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Statistical Process Control (SPC) in the food industry – A systematic review and future research agenda

Cited by 82 publications

References 65 publications

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

Impact analysis of critical success factors on the benefits from statistical process control implementation

A bibliometric analysis of 50 years of worldwide research on statistical process control

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