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Jiju Antony - One of the best experts on this subject based on the ideXlab platform.
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a systematic review of Statistical Process Control implementation in the food manufacturing industry
Total Quality Management & Business Excellence, 2017Co-Authors: Sarina Abdul Halim Lim, Jiju Antony, Norin Arshed, Saja Ahmed AlbliwiAbstract:This paper is a systematic review of the literature on Statistical Process Control (SPC) implementation in the food industry. Using systematic searches across three decades of publications, 41 journal articles were selected for the review. Key findings of the review include motivations: to reduce product defects and to follow the food law and regulations (benefits); barriers: high resistance to change and lack of sufficient Statistical knowledge; and (limitations) an absence of Statistical thinking and a dearth of SPC implementation guidelines. Further findings highlight the predominance of publications from the USA and the UK within this topic. Future research directions concerning SPC implementation issues as well as a ready reference of the SPC literature in the food manufacturing industry are also discussed.
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Statistical Process Control readiness in the food industry development of a self assessment tool
Trends in Food Science and Technology, 2016Co-Authors: Sarina Binti Abdul Halim Lim, Jiju AntonyAbstract:Abstract Background The increasing pressure from the customers, governmental regulations and fierce market competition forced food companies to pursue powerful quality improvement technique. Although Statistical Process Control is widely known for its effectiveness in Process Control, many food companies faced difficulties to adopt such technique, where being in the state of not ready has always been the reason. There has been a debate about the importance of deciding the state of readiness of a company to initiate their CI techniques such as SPC towards the successful implementation and sustainability of such technique. Scope and approach This paper emphasises the importance of SPC readiness towards its implementation in the food industry and determines its factors. The SPC readiness factors were identified based on the current literature review and complemented with a three-round Delphi study involving the SPC experts (academics, industry and consultants). Key findings and conclusion The SPC readiness factors identified are top management support, sense of urgency, measurement system, employees involvement and organisational culture readiness. The developed conceptual self-assessment readiness tool enables food practitioners to identify the current state of organisational readiness and facilitate the companies to plan strategic changes and preparation activities for the adoption of SPC in their businesses.
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towards a conceptual roadmap for Statistical Process Control implementation in the food industry
Trends in Food Science and Technology, 2015Co-Authors: Sarina Abdul Halim Lim, Jiju Antony, Jose Arturo Garzareyes, Norin ArshedAbstract:Statistical Process Control (SPC) is one of the most highly used quality Control techniques in the industry. The lack of specific implementation guidelines makes it the least applied quality Control technique in the food industry. This paper presents a five-phase SPC implementation conceptual roadmap in the food industry developed based on a critical review on current literature of various SPC deployment methods. It considers six critical factors for the SPC implementation in the food industry. This paper makes unique contributions by presenting a systematic approach for the managers of this industry to successfully deploy SPC in their organisations.
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Statistical Process Control spc in the food industry a systematic review and future research agenda
Trends in Food Science and Technology, 2014Co-Authors: Sarina Abdul Halim Lim, Jiju Antony, Saja Ahmed AlbliwiAbstract:This paper presents a systematic review on the reported implementation of Statistical Process Control (SPC) in the food industry. The final selection comprehends 41 articles selected and comprehensively analysed to assess SPC development in the food industry through its motivations, benefits, challenges and limitations. Key outputs indicated from the review include: reduced Process variability and conformance to the food regulations are the biggest motivations; resistance to accept SPC is the most cited challenge; lack of Statistical knowledge is the most common limitation and the biggest benefits for implementing SPC in the food industry are improved food safety and reduced Process variation.
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the implementation of Statistical Process Control in the food industry a systematic review
International Conference on Industrial Engineering and Operations Management, 2014Co-Authors: Sarina Abdul, Halim Lim, Jiju AntonyAbstract:This study is to illustrate a systematic review application in investigating common issues emerging from Statistical Process Control (SPC) implementation in the food industry. A total of 41 journal articles were rigorously selected from four databases and reviewed. The most common themes emerge in SPC implementation in the food industry is the benefits while the remaining themes are motivation, barriers and critical success factors (CSF). This review found that the evidence of SPC implementation in the food industry is beneficial; however, a lack of both awareness and guidelines relating to SPC implementation in the food industry has resulted in a slow adoption. This paper also provided a critical review of the existing SPC implementation framework. This systematic review concluded that there is a need for further research into the SPC deployment aspect addressing how to deploy SPC in the food industry in a systematic manner. The development of practical and useful guidelines to assist food manufacturers with the implementation of SPC is suggested able to address such issue.
Todd Pawlicki - One of the best experts on this subject based on the ideXlab platform.
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Statistical Process Control analysis for patient specific imrt and vmat qa
Journal of Radiation Research, 2013Co-Authors: Sivalee Suriyapee, Taweap Sanghangthum, Somyo Srisati, Todd PawlickiAbstract:This work applied Statistical Process Control to establish the Control limits of the % gamma pass of patient-specific intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) quality assurance (QA), and to evaluate the efficiency of the QA Process by using the Process capability index (Cpml). A total of 278 IMRT QA plans in nasopharyngeal carcinoma were measured with MapCHECK, while 159 VMAT QA plans were undertaken with ArcCHECK. Six megavolts with nine fields were used for the IMRT plan and 2.5 arcs were used to generate the VMAT plans. The gamma (3%/3 mm) criteria were used to evaluate the QA plans. The % gamma passes were plotted on a Control chart. The first 50 data points were employed to calculate the Control limits. The Cpml was calculated to evaluate the capability of the IMRT/VMAT QA Process. The results showed higher systematic errors in IMRT QA than VMAT QA due to the more complicated setup used in IMRT QA. The variation of random errors was also larger in IMRT QA than VMAT QA because the VMAT plan has more continuity of dose distribution. The average % gamma pass was 93.7% ± 3.7% for IMRT and 96.7% ± 2.2% for VMAT. The Cpml value of IMRT QA was 1.60 and VMAT QA was 1.99, which implied that the VMAT QA Process was more accurate than the IMRT QA Process. Our lower Control limit for % gamma pass of IMRT is 85.0%, while the limit for VMAT is 90%. Both the IMRT and VMAT QA Processes are good quality because Cpml values are higher than 1.0.
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Statistical Process Control for radiotherapy quality assurance
Medical Physics, 2005Co-Authors: Todd Pawlicki, Matthew L Whitaker, A L BoyerAbstract:Every quality assurance Process uncovers random and systematic errors. These errors typically consist of many small random errors and a very few number of large errors that dominate the result. Quality assurance practices in radiotherapy do not adequately differentiate between these two sources of error. The ability to separate these types of errors would allow the dominant source(s) of error to be efficiently detected and addressed. In this work, Statistical Process Control is applied to quality assurance in radiotherapy for the purpose of setting action thresholds that differentiate between random and systematic errors. The theoretical development and implementation of Process behavior charts are described. We report on a pilot project is which these techniques are applied to daily output and flatness/symmetry quality assurance for a 10 MV photon beam in our department. This clinical case was followed over 52 days. As part of our investigation, we found that action thresholds set using Process behavior charts were able to identify systematic changes in our daily quality assurance Process. This is in contrast to action thresholds set using the standard deviation, which did not identify the same systematic changes in the Process. The Process behavior thresholds calculated from a subset of themore » data detected a 2% change in the Process whereas with a standard deviation calculation, no change was detected. Medical physicists must make decisions on quality assurance data as it is acquired. Process behavior charts help decide when to take action and when to acquire more data before making a change in the Process.« less
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Statistical Process Control for radiotherapy quality assurance
Medical Physics, 2005Co-Authors: Todd Pawlicki, Matthew L Whitake, A L OyeAbstract:Every quality assurance Process uncovers random and systematic errors. These errors typically consist of many small random errors and a very few number of large errors that dominate the result. Quality assurance practices in radiotherapy do not adequately differentiate between these two sources of error. The ability to separate these types of errors would allow the dominant source(s) of error to be efficiently detected and addressed. In this work, Statistical Process Control is applied to quality assurance in radiotherapy for the purpose of setting action thresholds that differentiate between random and systematic errors. The theoretical development and implementation of Process behavior charts are described. We report on a pilot project is which these techniques are applied to daily output and flatness/symmetry quality assurance for a 10 MV photon beam in our department. This clinical case was followed over 52 days. As part of our investigation, we found that action thresholds set using Process behavior charts were able to identify systematic changes in our daily quality assurance Process. This is in contrast to action thresholds set using the standard deviation, which did not identify the same systematic changes in the Process. The Process behavior thresholds calculated from a subset of themore » data detected a 2% change in the Process whereas with a standard deviation calculation, no change was detected. Medical physicists must make decisions on quality assurance data as it is acquired. Process behavior charts help decide when to take action and when to acquire more data before making a change in the Process.« less
Giuseppe Visaggio - One of the best experts on this subject based on the ideXlab platform.
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managing software Process improvement spi through Statistical Process Control spc
Lecture Notes in Computer Science, 2004Co-Authors: Teresa Baldassarre, Nicola Boffoli, Danilo Caivano, Giuseppe VisaggioAbstract:Measurement based software Process improvement is nowadays a mandatory activity. This implies continuous Process monitoring in order to predict its behavior, highlight its performance variations and, if necessary, quickly react to them. Process variations are due to common causes or assignable ones. The former are part of the Process itself while the latter are due to exceptional events that result in an unstable Process behavior and thus in less predictability. Statistical Process Control (SPC) is a Statistical based approach able to determine whether a Process is stable or not by discriminating between the presence of common cause variation and assignable cause variation. It is a well-established technique, which has shown to be effective in manufacturing Processes but not yet in software Process contexts. Here experience in using SPC is not mature yet. Therefore a clear understanding of the SPC outcomes still lacks. Although many authors have used it in software, they have not considered the primary differences between manufacturing and software Process characteristics. Due to such differences the authors sustain that SPC cannot be adopted as is but must be tailored. In this sense, we propose an SPC-based approach that reinterprets SPC, and applies it from a Software Process point of view. The paper validates the approach on industrial project data and shows how it can be successfully used as a decision support tool in software Process improvement.
Murat Kulahci - One of the best experts on this subject based on the ideXlab platform.
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impact of autocorrelation on principal components and their use in Statistical Process Control
Quality and Reliability Engineering International, 2016Co-Authors: Erik Vanhatalo, Murat KulahciAbstract:A basic assumption when using principal component analysis (PCA) for inferential purposes, such as in Statistical Process Control (SPC), is that the data are independent in time. In many industrial Processes, frequent sampling and Process dynamics make this assumption unrealistic rendering sampled data autocorrelated (serially dependent). PCA can be used to reduce data dimensionality and to simplify multivariate SPC. Although there have been some attempts in the literature to deal with autocorrelated data in PCA, we argue that the impact of autocorrelation on PCA and PCA-based SPC is neither well understood nor properly documented. This article illustrates through simulations the impact of autocorrelation on the descriptive ability of PCA and on the monitoring performance using PCA-based SPC when autocorrelation is ignored. In the simulations, cross-correlated and autocorrelated data are generated using a stationary first-order vector autoregressive model. The results show that the descriptive ability of PCA may be seriously affected by autocorrelation causing a need to incorporate additional principal components to maintain the model's explanatory ability. When all variables have equal coefficients in a diagonal autoregressive coefficient matrix, the descriptive ability is intact, while a significant impact occurs when the variables have different degrees of autocorrelation. We also illustrate that autocorrelation may impact PCA-based SPC and cause lower false alarm rates and delayed shift detection, especially for negative autocorrelation. However, for larger shifts, the impact of autocorrelation seems rather small. © 2015 The Authors. Quality and Reliability Engineering International published by John Wiley & Sons Ltd.
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quality quandaries the effect of autocorrelation on Statistical Process Control procedures
Quality Engineering, 2005Co-Authors: Soren Bisgaard, Murat KulahciAbstract:Quality Quandaries : The Effect of Autocorrelation on Statistical Process Control Procedures
Sarina Abdul Halim Lim - One of the best experts on this subject based on the ideXlab platform.
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a systematic review of Statistical Process Control implementation in the food manufacturing industry
Total Quality Management & Business Excellence, 2017Co-Authors: Sarina Abdul Halim Lim, Jiju Antony, Norin Arshed, Saja Ahmed AlbliwiAbstract:This paper is a systematic review of the literature on Statistical Process Control (SPC) implementation in the food industry. Using systematic searches across three decades of publications, 41 journal articles were selected for the review. Key findings of the review include motivations: to reduce product defects and to follow the food law and regulations (benefits); barriers: high resistance to change and lack of sufficient Statistical knowledge; and (limitations) an absence of Statistical thinking and a dearth of SPC implementation guidelines. Further findings highlight the predominance of publications from the USA and the UK within this topic. Future research directions concerning SPC implementation issues as well as a ready reference of the SPC literature in the food manufacturing industry are also discussed.
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towards a conceptual roadmap for Statistical Process Control implementation in the food industry
Trends in Food Science and Technology, 2015Co-Authors: Sarina Abdul Halim Lim, Jiju Antony, Jose Arturo Garzareyes, Norin ArshedAbstract:Statistical Process Control (SPC) is one of the most highly used quality Control techniques in the industry. The lack of specific implementation guidelines makes it the least applied quality Control technique in the food industry. This paper presents a five-phase SPC implementation conceptual roadmap in the food industry developed based on a critical review on current literature of various SPC deployment methods. It considers six critical factors for the SPC implementation in the food industry. This paper makes unique contributions by presenting a systematic approach for the managers of this industry to successfully deploy SPC in their organisations.
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Statistical Process Control spc in the food industry a systematic review and future research agenda
Trends in Food Science and Technology, 2014Co-Authors: Sarina Abdul Halim Lim, Jiju Antony, Saja Ahmed AlbliwiAbstract:This paper presents a systematic review on the reported implementation of Statistical Process Control (SPC) in the food industry. The final selection comprehends 41 articles selected and comprehensively analysed to assess SPC development in the food industry through its motivations, benefits, challenges and limitations. Key outputs indicated from the review include: reduced Process variability and conformance to the food regulations are the biggest motivations; resistance to accept SPC is the most cited challenge; lack of Statistical knowledge is the most common limitation and the biggest benefits for implementing SPC in the food industry are improved food safety and reduced Process variation.