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Leveraging Statistical Process Control for continuous improvement of the manufacturing process
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Investigation of the application of Statistical Process Control into Low Volume Manufacturing
RAMOS-DELGADO, DALIA,RUBI (2023) Investigation of the application of Statistical Process Control into Low Volume Manufacturing. Masters thesis, Durham University.
Statistical process control (SPC) into Low Volume Manufacturing environment face a challenge applying SPC techniques. SPC is commonly used for quality control and improvement in the manufacturing sector. In the early 1920s, Dr Walter Shewhart developed the control chart employed to monitor a process over time, where the first data is collected and then plotted on a graph. Moreover, a control chart is composed of a Central Line (CL), the Upper Control Limit (UCL) and the Lower Control Limit (LCL). Parameters and control limits are calculated to analyze the control chart, requiring twenty to twenty-five subgroups of data, with three to five values per subgroup, or at least sixty measurements. However, collect this amount of data is difficult in certain production processes, where the lot size could even be one and it could take weeks or months to accumulate enough data to estimate the process parameters. Statistical process control is a challenge in some scenarios such as startup production, different or individual parts in the same production line, or production of customized products. In these cases, there is not enough amount of data to compute the parameters to monitor the process. Therefore, special techniques and statistical methods are required. Some authors developed self-starting control charts and alternative methods for short-run production, e.g. Q charts, Exponentially Weighted Moving Average (EWMA) and Cumulative sum (CUSUM). This thesis studies the performance of these SPC tools, implementing a Low Volume Statistical Process Control (LV-SPC) model through an Excel spreadsheet, analyzing the production process data from companies that are performing low volume manufacturing. This work provides an interpretation and explanation about statistical process control into low volume manufacturing, analyzing the application of different SPC methods developed for short production runs based on data collected from different companies. Data collected was processed to individual measurements from the process deviation rather than the mean values. Converting the data to individual values the SPC methods for low volume manufacturing are viable to use. Also, performance, effectiveness, and how it can be further implemented were discussed.
| Item Type: | Thesis (Masters) |
|---|---|
| Award: | Master of Science |
| Faculty and Department: | |
| Thesis Date: | 2023 |
| Copyright: | Copyright of this thesis is held by the author |
| Deposited On: | 23 Jan 2023 11:06 |
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- Corpus ID: 126273873
Statistical process control by quantile approach.
- Osama H. Arif
- Published 2000
- Engineering, Mathematics
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136 References
Non‐normality in statistical process control measurements, a statistical control chart for stationary process data, short-run statistical process control: q-chart enhancements and alternative methods, median rankit control chart by the quantile approach, three suggestions for improving control charting procedures, the effects of spc on the target of process quality improvement, transforming non-normal data to normality in statistical process control, controversies and contradictions in statistical process control, statistical process monitoring and feedback adjustment: a discussion, statistical methods in engineering and quality assurance, related papers.
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Research Issues and Ideas in Statistical Process Control
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- https://doi.org/10.1080/00224065.1999.11979944
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An overview is given of current research on control charting methods for process monitoring and improvement. The discussion includes a historical perspective along with ideas for future research. Research topics include, for example, variable sample size and sampling interval methods, economic designs, attribute data methods, charts based on autocorrelated observations, multivariate methods, and nonparametric methods. Recommendations and references are provided to those interested in pursuing research ideas in statistical process control (SPC). Some issues regarding the relevance of SPC research are also discussed.
- Control Charting
- Statistical Process Control
- Statistical Quality Control
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Notes on contributors, william h. woodall.
Dr. Woodall is Russell Professor of Statistics in the Department of Management Science and Statistics. He is a Senior Member of ASQ. His email address is [email protected] .
Douglas C. Montgomery
Dr. Montgomery is a Professor in the Department of Industrial and Management Systems Engineering. He is a Fellow of ASQ.
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Statistical Process Control for the Pharmaceutical Industry
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An overview of Total Quality Management and Statistical Process Control is provided with a goal of focusing on their implementation. The implementation is presented as three distinct areas. These are the philosophical and behavioral requirements, the statistical monitoring, and the statistical design. The first emphasizes the culture of problem solving and continuous improvement, the second focuses on the control charts, and the third on the Taguchi methods. After this overview, a discussion of common mistakes that occur in practice during the implementation of these methods is provided. Again, managerial mistakes are separated from the more specific technical or statistical mistakes. The paper concludes with recommendations concerning applications specific to the pharmaceutical industry.
- Received June 21, 1990.
- Accepted August 23, 1990.
- Copyright © Parenteral Drug Association. All rights reserved.
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An important application of Statistical Process Control (SPC) is the use of control charts to examine the condition and variability of a production process. This visual ... which is the focus of this thesis. The literature on the joint SPC-maintenance comprises several models describing this relation with di erent assumptions. For instance, [6 ...
This thesis focuses on application of statistical process control (SPC) and control charts in a high volume machining center at Waters Corporation. The company has a need of a real time inspection and control methodology so that new hires as well as experienced operators can produce machined parts with high quality and low scrap rates.
Statistical process control (SPC) and Engineering process control (EPC) or Automatic process control (APC). Statistical process control and Engineering process control, are two techniques relied on for quality improvement, which have developed independently. Box and Kramer (1992) provide an excellent comparison of SPC, which they refer to as ...
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This is where statistical process control (SPC) tools comes in to help in the decision-making and determining if the process is operating at an acceptable level [6]. ... India [4] Chinyemba N. Research Thesis, “Development of Cybernetics Engineering Strategies for the Manufacturing to Enhance Productivity in Zimbabwe†, 2008. [5 ...
Introduction to Statistical Process Control Statistical process control (SPC) consists of methods for understanding, moni-toring and improving process performance over time. In this section we will give an introduction to statistical process control. The focus will be on presenting the relevant background for the papers in the thesis.
Statistical Process Control (SPC) principles, primarily used to monitor process variations in manufacturing, to develop an inventory management technique and assesses its impact in reducing the Bullwhip effect. This thesis uses research and modeling to address the question as to whether the
Continued Process Verification (CPV) is a Food and Drug Administration requirement that requires biopharmaceutical companies, such as Amgen, Inc., to demonstrate control of commercial manufacturing processes. Furthermore, the Food and Drug Administration's guidance on CPV specifically calls for the use of SPC. This thesis suggests including the ...
Statistical process control is a challenge in some scenarios such as startup production, different or individual parts in the same production line, or production of customized products. ... This thesis studies the performance of these SPC tools, implementing a Low Volume Statistical Process Control (LV-SPC) model through an Excel spreadsheet ...
This thesis provides a quantile approach to deal with non-normal distributions, in order to construct median rankit control chart, and will also be used to calculate process capability index, average run length (ARL), multivariate control chart and control chart for mixture distribution for non- normal situations. Most quality control and quality improvement procedures involve making ...
Principal Component Analysis: Application to Statistical Process Control. 1.1. Introduction Principal component analysis (PCA) is an exploratory statistical method for graphicaldescriptionof the informationpresent in large datasets. In most applications, PCA consists of studying pvariables measured on nindividuals.
This thesis is the result of four years of scienti c research at the department of Econometrics of the University of Groningen. It was a pleasure for me to ... is Statistical Process Control (SPC), that aims at quality improvement through reduction of variation. One of the primary techniques of SPC is
a process is vital, because it will determine the necessary inputs and the output. In this paper, I present, based on a practical application, the statistical process control in pharmaceutical industry, based on some data received from a big national pharmaceutical company. Statistical process control is not only a tool,
1.3 Multivariate Quality Control 6 1.4 Thesis Objectives 7 2. Literature Review 2.1 Introduction 9 2.2 Adjusting Control Limits Based on The Number of Subgroups or ... stimulated interest in statistical process control (SPC). Whilst traditional SPC techniques are well suited to the mass production industries usefiilnes i, theins shorr t run or ...
The best proposed tool for better quality product is Statistical Process Control (SPC). SPC tools are required to. control and im prove the process. SPC is useful in identifying the reasons f or ...
For a process to be effectively analyzed and improved, hard data and facts about the process are required. SPCs address this need to collect and correlate data. D. STATISTICAL PROCESS CONTROLS For the purpose of this thesis, the term "Statistical Process Controls" collectively refers to statisitically based
Statistical Process Control (SPC) is a very powerful quality tool used to predict and control a process. There are three key components to this tool. ... Created by the author of this thesis. A control chart, as depicted in Figure 5, is constructed using the same premise of the probabilities towards the outer limits of the distribution. One now ...
A. Scope Statistical process control (SPC) is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures help to monitor process behaviour, discover issues in internal systems, and find solutions for production issues. B. Limitations SPC emphasizes early detection and prevention of problems ...
acceptance control charts, and Shewhart control charts based on grouped data are developed. These forms of statistical process control have broad application and are in use widely. The design and implementation methodology is derived assuming either a normal or Weibull process, but is easily adapted to any other underlying distribution. A
An overview is given of current research on control charting methods for process monitoring and improvement. The discussion includes a historical perspective along with ideas for future research. Research topics include, for example, variable sample size and sampling interval methods, economic designs, attribute data methods, charts based on ...
An overview of Total Quality Management and Statistical Process Control is provided with a goal of focusing on their implementation. The implementation is presented as three distinct areas. These are the philosophical and behavioral requirements, the statistical monitoring, and the statistical design. The first emphasizes the culture of problem solving and continuous improvement, the second ...
The third part of the thesis is devoted to the fault diagnosis in latent-based multivariate statistical process control (Lb-MSPC). The rationale of the most extended methods for fault diagnosis in supervised Lb-MSPC is described and one of our proposals, the Fingerprints contribution plots (FCP) is introduced.