Adaptive EWMA Control Charts with a Time Varying Smoothing Parameter

It is known that time-weighted charts like EWMA or CUSUM are designed to be optimal to detect a specific shift. If they are designed to detect, for instance, a very small shift, they can be inefficient to detect moderate or large shifts. In the literature, several alternatives have been proposed to circumvent this limitation, like the use of control charts with variable parameters or adaptive control charts. This paper has as main goal to propose some adaptive EWMA control charts (AEWMA) based on the assessment of a potential misadjustment, which is translated into a time-varying smoothing parameter. The resulting control charts can be seen as a smooth combination between Shewhart and EWMA control charts that can be efficient for a wide range of shifts. Markov chain procedures are established to analyze and design the proposed charts. Comparisons with other adaptive and traditional control charts show the advantages of the proposals.Acknowledgements: financial support from the Spanish Ministry of Education and Science, research project ECO2012-38442

On the constrained economic design of control charts: a literature review

The economic design is an appealing approach to settle the design parameters of a control chart. Unfortunately, the economic models to design control charts have been scarcely implemented by quality practitioners due to the simplifying assumptions when representing the multifaceted complexity and constraints present within manufacturing and transactional environments. Although there has been an increasing scepticism about the economic models usefulness in practice, some recent studies proposed in literature face the problem of the control charts economic design from a new point of view: the objective is to achieve a well balanced trade-off between the operational and the statistical aspects. Under this perspective, the economic design problem can be intended in a broader sense as the constrained design of a SPC inspection procedure. This paper presents a discussion of some recent trends in the economic design stream of research and outlines the importance of considering the constraints related to SPC resources availability and modelling the occurrence of random shifts

Comparison of automatic monitoring systems in automatic forecasting

Forecasting Techniques;mathematische statistiek

Optimal Designs Of The Double Sampling X Chart Based On Parameter Estimation

Control charts, viewed as the most powerful and simplest tool in Statistical Process Control (SPC), are widely used in manufacturing and service industries. The double sampling (DS) X chart detects small to moderate process mean shifts effectively, while reduces the sample size. The conventional application of the DS X chart is usually investigated assuming that the process parameters are known. Nevertheless, the process parameters are usually unknown in practical applications; thus, they are estimated from an in-control Phase-I dataset. In this thesis, the effects of parameter estimation on the DS X chart’s performance are examined. By taking into consideration of the parameter estimation, the run length properties of the DS X chart are derived. Since the shape and the skewness of the run length distribution change with the magnitude of the process mean shift, the number of Phase-I samples and sample size, the widely applicable performance measure, i.e. the average run length (ARL) should not be used as a sole measure of a chart’s performance. For this reason, the ARL, the standard deviation of the run length (SDRL), the median run length (MRL), the percentiles of the run length distributions and the average sample size (ASS) are recommended to effectively evaluate the proposed DS X chart with estimated parameters

Optimal Designs Of The Double Sampling X Chart Based On Parameter Estimation

Control charts, viewed as the most powerful and simplest tool in Statistical Process Control (SPC), are widely used in manufacturing and service industries. The double sampling (DS) X chart detects small to moderate process mean shifts effectively, while reduces the sample size. The conventional application of the DS X chart is usually investigated assuming that the process parameters are known. Nevertheless, the process parameters are usually unknown in practical applications; thus, they are estimated from an in-control Phase-I dataset. In this thesis, the effects of parameter estimation on the DS X chart’s performance are examined. By taking into consideration of the parameter estimation, the run length properties of the DS X chart are derived. Since the shape and the skewness of the run length distribution change with the magnitude of the process mean shift, the number of Phase-I samples and sample size, the widely applicable performance measure, i.e. the average run length (ARL) should not be used as a sole measure of a chart’s performance. For this reason, the ARL, the standard deviation of the run length (SDRL), the median run length (MRL), the percentiles of the run length distributions and the average sample size (ASS) are recommended to effectively evaluate the proposed DS X chart with estimated parameters

Control Charts With Estimated Process Parameters And A Proposed Coefficient Of Variation Chart

Carta kawalan menerima perhatian besar dalam Kawalan Proses Berstatistik (SPC) sebagai alat yang paling berguna dalam pengesanan anjakan proses supaya tindakan pembetulan boleh diambil untuk mengenal pasti dan menyingkirkan sebab-sebab terumpukkan yang hadir. Objektif utama penyelidikan ini adalah untuk menangani masalah anggaran parameter proses bagi carta larian kumpulan dengan kepekaan sisi (carta SSGR) dan carta pensampelan ganda dua sintetik (carta SDS). Kebanyakan carta kawalan memerlukan andaian bahawa nilai sasaran parameter proses dalam kawalan, iaitu min dan sisihan piawai adalah diketahui untuk pengiraan had-had kawalan serta statistik carta kawalan. Malangnya, parameter proses lazimnya tidak diketahui dalam keadaan sebenar, yang mana nilainya dianggarkan daripada set data Fasa-I dalam kawalan. Dalam penyelidikan ini, prestasi carta-carta SSGR dan SDS dengan parameter proses yang dianggarkan dibandingkan dengan prestasi carta-carta yang sepadan apabila parameter proses diketahui. Control charts receive great attention in Statistical Process Control (SPC) as the most useful tool in detecting process shifts so that corrective actions can be taken to identify and eliminate the assignable causes that are present. The main objective of this research is to address the problems of estimation of process parameters for the side sensitive group runs (SSGR) chart and synthetic double sampling (SDS) chart. Most control charts require the assumption that the target values of the in-control process parameters, i.e. the mean and standard deviation are known for the computation of the control charts’ control limits and statistics. Unfortunately, process parameters are usually unknown in real situations, where they are estimated from an in-control Phase-I dataset. In this research, the performances of the SSGR and SDS charts with estimated process parameters are compared with that of their known process parameters counterparts

Proposed Synthetic And Group Runs Control Charts Based On Runs Rules And Double Sampling Np Methods X

Carta kawalan adalah alat yang penting untuk memantau satu atau lebih cirian kualiti yang diminati dalam proses pengeluaran. Carta kawalan X Shewhart yang klasik adalah carta kawalan pembolehubah yang paling luas digunakan dalam industri pembuatan dan perkhidmatan untuk memantau min sesuatu proses dengan data selanjar kerana kesenangannya kepada pekerja-pekerja industri. Carta kawalan X Shewhart adalah sangat berkesan untuk mengesan anjakan besar dalam min proses. Walau bagaimanapun, carta X Shewhart adalah kurang peka terhadap anjakan min yang kecil dan sederhana. Ini merupakan kelemahan utama carta X Shewhart. Petua-petua larian biasanya digunakan untuk meningkatkan kepekaan carta X Shewhart yang klasik bagi pengesanan anjakan min proses yang kecil dan sederhana. Suatu petua larian yang lebih baru dan berkesan ialah skema petua larian m-daripada-k yang disemak semula ( R − / mk) untuk data selanjar. Sebaliknya, dalam pemantauan proses yang melibatkan data atribut, carta kawalan np pensampelan ganda dua (DS) adalah carta yang berkesan untuk mengesan anjakan kecil hingga sederhana dalam ketidakpatuhan pecahan barangan daripada sesuatu proses. A control chart is an important tool to monitor one or more quality characteristics of interest in a production process. The classical Shewhart X control chart is the most widely used variables control chart in manufacturing and service industries to monitor the mean of a process with continuous data, due to its simplicity to shop floor personnel. The Shewhart X control chart is very effective for detecting large shifts in the process mean. However, the Shewhart X chart is insensitive to small and moderate mean shifts. This is a major disadvantage of the Shewhart X chart. Runs rules are commonly used to increase the sensitivity of the classical Shewhart X chart for detecting small and moderate process mean shifts. A more recent and efficient runs rule is the revised m-of-k ( / − R mk) runs rule scheme for continuous data. On the other hand, in process monitoring involving attribute data, the double sampling (DS) np control chart is an effective chart to detect small and moderate shifts in the fraction of nonconforming items from a process

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