Developing Quality Control Charts for the Control Points of a Food Product

Monitoring the production process is a critical issue for improving the quality of product and for reducing the costs regarding external failures. Quality control charts are often used to visualize measurements on the process during the monitoring activities. This paper presents a case study based on the use of advanced charts, Cumulative Summation (CUSUM) and Estimated Weighted Moving Average (EWMA) charts, for visualizing the control points of a particular chicken product in fast-food industry. Furthermore, GM (1,1) and GM (1,1) Markov models were built to generate predictions to see the trends and future values to maintain a follow-up procedure for the fluctuations in the process performance. In this context, three control points are considered that are weight of the chicken wings, sterilizer temperature, and grid-pan temperature. The findings provide a significant feedback for the efficiency of the corresponding processes. Results show that the methodology selected to develop these charts has an important impact on creating an effective quality control process

A Technique to compare ewmad2 scheme with different sample sizes

Basically the Exponentially Weighted Moving Average (EWMA) charts were introduced for monitoring the process mean in 1959 by Robert. It is much sensitive in detecting small shifts of mean than Shewhart charts. But lately it was felt that monitoring process mean was not enough in some cases. Then it was essential to introduce EWMA chart for monitoring sample variance and such a chart was introduced by Chen and Gan in 1993. These both EWMA charts can be used to monitor process mean and variance independently. But later it was identified monitoring mean and variance is a bivariate problem. In this series Exponentially Weighted Moving Average Distance square scheme (EWMAD2) was introduced with the claim that the control limits of the schemes are independent of sample size. In this research project the effect of sample size in EWMAD2 scheme is analyzed with the simulated samples with the different sample size and Average Run Lengths. In conclusion it is proved that EWMAD2 scheme is independent of sample size in determining the control limits

Online Detection of Outliers and Structural Breaks using Sequential Monte Carlo Methods

Outliers and structural breaks occur quite frequently in time series data. Whereas outliers often contain valuable information about the process under study, they are known to have serious negative impact on statistical data analysis. Most obvious effect is model misspecification and biased parameter estimation which results in wrong conclusions and inaccurate predictions. Structural time series consist of underlying features such as level, slope, cycles or seasonal components. Structural breaks are permanent disruptions of one or more of these components and might be a signal of serious changes in the observed process. Detecting outliers and estimating the location of structural breaks has progressively become monumental both as a theoretical research problem and an essential part of applied data analysis. Among numerous applications include finance, industrial manufacturing, medical informatics, severe weather prediction. Given that these data arrive rather frequently and sequentially in time, fast reliable and accurate detection techniques are required. We propose a model from class of state-space models of the form $y_{t} = f(X_{t}, \psi, v_{t})$ and $X_{t} = g(X_{t-1}, \psi, w_{t})$ where $\big\{ X_{t} \big\}_{t\geq 0}$ is a hidden Markov state process. The inference of $\big\{ X_{t} \big\}_{t\geq 0}$ depends on the observation process $\{y_{t}\}_{t\geq 1}$ and the parameter vector $\psi$, whose elements are usually unknown. The innovations $v_{t}$ and $w_{t}$ are conditionally \textit{Gaussian} given the precision parameter $\lambda$ and auxiliary state $\omega$. We employ sequential Monte Carlo techniques to approximate the joint target distribution $p(X_{0:t}, \psi|y_{1:t})$. The posterior estimates for the auxiliary states $\omega$ will be used to identify outliers and structural breaks. The results prove that the algorithm is comparable to traditional and computationally expensive MCMC and superior to regular techniques such as Exponentially Weighted Moving Average (EWMA), Shewhart, and cumulative sum (CUSUM) control chart

Short Production Run Control Charts to Monitor Process Variances

Control chart is one of the most commonly used statistical tools for quality control and improvement. If the process mean and standard deviation are not given or unknown, most Shewhart control charts require sufficient sample data before the control chart can be established. However, in certain industries or processes, it may not be practical to collect adequate amount of data at the beginning of the manufacturing process to build the trial control chart in Phase I. For quality improvement in such or similar processes, some authors developed self-starting control charts for short-run production, e.g. t chart, Q chart, EWMA t chart/Q chart, CUSUM t chart/Q chart. This thesis studies the performance of some short run control charts for monitoring process variances. Numerical simulations are using in this study. The results of the numerical experiments are extensively tested for different combinations of process lengths and starting points of process shifts

The Mixed CUSUM-EWMA (MCE) control chart as a new alternative in the monitoring of a manufacturing process

Goal: The objective is to conclude, based on a comparative study, if there is a significant difference in sensitivity between the application of MCE and the individual application of the CUSUM or EWMA chart, i.e., greater sensitivity particularly for cases of lesser magnitude of change. Design/Methodology/Approach: These are an applied research and statistical techniques such as statistical control charts are used for monitoring variability. Results: The results show that the MCE chart signals a process out of statistical control, while individual EWMA and CUSUM charts does not detect any situation out of statistical control for the data analyzed. Limitations: This article is dedicated to measurable variables and individual analysis of quality characteristics, without investing in attribute variables. The MCE chart was applied to items that are essential to the productive process development being analysed. Practical Implications: The practical implications of this study can contribute to: the correct choice of more sensitive control charts to detect mainly small changes in the location (mean) of processes; provide clear and accurate information about the fundamental procedures for the implementation of statistical quality control; and encourage the use of this quality improvement tool. Originality/Value: The MCE control chart is a great differential for the improvement of the quality process of the studied company because it goes beyond what CUSUM and EWMA control charts can identify in terms of variability

Design of side-sensitive double sampling control schemes for monitoring the location parameter

Double sampling procedure is adapted from a statistical branch called acceptance sampling. The first Shewhart-type double sampling monitoring scheme was introduced in the statistical process monitoring (SPM) field in 1974. The double sampling monitoring scheme has been proven to effectively decrease the sampling effort and, at the same time, to decrease the time to detect potential out-of-control situations when monitoring the location, variability, joint location and variability using univariate or multivariate techniques. Consequently, an overview is conducted to give a full account of all 76 publications on double sampling monitoring schemes that exist in the SPM literature. Moreover, in the review conducted here, these are categorized and summarized so that any research gaps in the SPM literature can easily be identified. Next, based on the knowledge gained from the literature review about the existing designs for monitoring the process mean, a new type of double sampling design is proposed. The new charting region design lead to a class of a control charts called a side-sensitive double sampling (SSDS) monitoring schemes. In this study, the SSDS scheme is implemented to monitor the process mean when the underlying process parameters are known as well as when they are unknown. A variety of run-length properties (i.e., the 5th, 25th, 50th, 75th, 95th percentiles, the average run-length (), standard deviation of the run-length (), the average sample size () and the average extra quadratic loss () metrics) are used to design and implement the new SSDS scheme. Comparisons with other established monitoring schemes (when parameters are known and unknown) indicate that the proposed SSDS scheme has a better overall performance. Illustrative examples are also given to facilitate the real-life implementation of the proposed SSDS schemes. Finally, a list of possible future research ideas is given with hope that this will stimulate more future research on simple as well as complex double sampling schemes (especially using the newly proposed SSDS design) for monitoring a variety of quality characteristics in the future.StatisticsM. Sc. (Statistics