Performance evaluation of moving average-based EWMA chart for exponentially distributed process

2020 ◽  
Vol 43 (4) ◽  
pp. 365-372
Author(s):  
Saddam Akber Abbasi ◽  
Muhammad Abid ◽  
Muhammad Riaz ◽  
Hafiz Zafar Nazir
Keyword(s):  
Performance Evaluation ◽  
Moving Average ◽  
Ewma Chart ◽  
Distributed Process

The GLR Chart for Poisson Process with Individual Observations

2012 ◽  
Vol 542-543 ◽  
pp. 42-46
Author(s):  
Li Li
Keyword(s):  
Poisson Process ◽  
Numerical Experiments ◽  
Moving Average ◽  
Design Procedure ◽  
The Other ◽  
Design Parameters ◽  
Ewma Chart ◽  
Comparable Performance ◽  
Distributed Process ◽  
Weighted Moving Average

A GLR (generalized likelihood ratio) chart for Poisson distributed process with individual observations is proposed and the design procedure of the GLR chart is discussed. The performance of the GLR charts is compared to the exponentially weighted moving average (EWMA) chart and the GWMA chart. The numerical experiments show that the GLR chart has comparable performance as the other two charts. However, the GLR chart is much easier to design and implement since there are more design parameters in these two charts.

Moving average EWMA chart for the Weibull distribution

2021 ◽  
pp. 1-10
Author(s):  
Nasrullah Khan ◽  
Muhammad S. Nawaz ◽  
Rehan A. K. Sherwani ◽  
Muhammad Aslam
Keyword(s):  
Weibull Distribution ◽  
Moving Average ◽  
Ewma Chart

Supplier management by distributing orders among new and existing suppliers: the methodology and its application to a fast fashion company

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Betul Acar Alagoz ◽  
Murat Caner Testik ◽  
Derya Dinler
Keyword(s):  
Performance Evaluation ◽  
Business Processes ◽  
Low Cost ◽  
Moving Average ◽  
Holistic Approach ◽  
Supplier Management ◽  
Business Environment ◽  
Content Type ◽  
A Company ◽  
Over Time

PurposeThis study aims to create a reliable, collaborative and sustainable business environment with suppliers of a company for providing high-quality and low-cost products on time. A supplier management system that sustains existing suppliers by sharing work based on systematic performance evaluation while developing the supplier base with potential suppliers is proposed.Design/methodology/approachBuilt on quantitative approaches, supplier management functions are integrated in the designed system. A quantitative strengths, weaknesses, opportunities and threats (SWOT) analysis is adapted for evaluating potential suppliers. A multi-objective integer linear programming (ILP) model is developed for the distribution of orders among selected potential and existing suppliers. A performance evaluation scheme based on an exponentially weighted moving average (EWMA) is proposed to evaluate and monitor suppliers' performance over time.FindingsProposed system develops a supplier base by methodically selecting and approving new suppliers, and a sustainable relationship with both new and existing suppliers is established based on performance over time. Decisions on retaining or removing suppliers from the base are objectively made by quantitative evaluations. Orders are fairly distributed among suppliers under the constraints imposed by the management. Dependence on a certain set of suppliers and its associated risks are reduced while agility in offering goods is enabled.Originality/valueBusiness processes for selecting new suppliers, distributing orders among all suppliers, evaluating and monitoring performance over time are quantitatively integrated to add value in operational decision-making. The proposed system is original in the holistic approach for managing and sustaining multiple suppliers of a company based on performance.

scholarly journals A synthetic exponentially weighted moving average control chart to monitor process median based on ranked set sampling

2021 ◽  
Vol 36 ◽  
pp. 01002
Author(s):  
Jing Wen Ng ◽  
Voon Hee Wong ◽  
Sook Theng Pang
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Length Distribution ◽  
Ranked Set Sampling ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Run Length ◽  
Ewma Chart ◽  
Run Length Distribution ◽  
Weighted Moving Average

Exponentially Weighted Moving Average (EWMA) control charts yield insights into data in a way more comprehensible to the practitioners and researchers because of its capability in discovering small to moderate process mean shifts. EWMA control chart is incorporated with conforming run length (CRL) chart, named synthetic EWMA chart, to enhance the performance of the chart in detecting the out-of-control signal. Synthetic EWMA chart based on ranked set sampling (RSS) for monitoring process mean has been proposed as it advanced the detection of chart over a series of mean shifts. With the situation that normality assumption is scarcely attain in practice, we proposed synthetic EWMA median chart based on RSS. Rather than select average run length (ARL) as sole performance evaluating tool, the median and percentiles of run-length distribution are used to examine the performance of the proposed chart as it provides more information on the entire run-length distribution. Near-optimal parameters of the proposed chart will be acquired by setting the incontrol ARL at a designated value. The run length performances of the proposed chart are then compared with the existing charts such as EWMA median chart based on RSS.

ZERO INFLATED POISSON EWMA CONTROL CHART FOR MONITORING RARE HEALTH-RELATED EVENTS

2012 ◽  
Vol 12 (04) ◽  
pp. 1250065 ◽  
Author(s):  
AMIR AFSHIN FATAHI ◽  
RASSOUL NOOROSSANA ◽  
PERSHANG DOKOUHAKI ◽  
BABAK FARHANG MOGHADDAM
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Random Variable ◽  
Performance Measure ◽  
Performance Criteria ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Health Related ◽  
Run Lengths ◽  
Approach Zero

Recently, rare health events issue has motivated many researches in the field of control charting. Various methods such as g-type control chart, g-type CUSUM control chart, sets method, CUSCORE method, SHDA method and the Bernoulli CUSUM have been developed in this regard, in which each of them has a specific approach to the problem. As a relatively new approach, zero inflation in Poisson distribution, named ZIP distribution can be applied. In this paper, an exponentially weighted moving average (EWMA) control chart is developed for the ZIP random variable to monitor rare health-related events with a predefined performance measure value. Since the ZIP-EWMA plotted data are dependent, Markov chain approach is applied to calculate average run lengths (ARLs) as the control chart performance criteria. Based on the ARL measure, the ZIP-EWMA chart performs better in comparison with the methods available in the literature. As the main contribution of this paper is the development a control chart which performs better than the previously proposed charts. Also, a motivating real case study related to monitoring needle-stick rare occurrences in a hospital is investigated to show the applicability of the developed chart.

scholarly journals HYBRID EXPONENTIALLY WEIGHTED MOVING AVERAGE (HEWMA) CONTROL CHART BASED ON EXPONENTIAL TYPE ESTIMATOR OF MEAN

2019 ◽  
pp. 187-198 ◽  
Author(s):  
Syed Muhammad Muslim Raza ◽  
Maqbool Hussain Sial ◽  
Muhammad Haider ◽  
Muhammad Moeen Butt
Keyword(s):  
Control Chart ◽  
Exponential Type ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Ewma Chart ◽  
Weighted Moving Average ◽  
Better Than ◽  
Exponentially Weighted

In this paper, we have proposed a Hybrid Exponentially Weighted Moving Average (HEWMA) control chart. The proposed control chart is based on the exponential type estimator for mean using two auxiliary variables (cf. Noor-ul-Amin and Hanif, 2012). We call it an EHEWMA control chart because it is based on the exponential estimator of the mean. From this study, the fact is revealed that E-HEWMA control chart shows more efficient results as compared to traditional/simple EWMA chart and DS.EWMA control chart (cf. Raza and Butt, 2018). The comparison of the E-HEWMA control chart is also performed with the DS-EWMA chart. The proposed chart also outperforms the other control chartsin comparison. The E-HEWMA chart can be used for efficient monitoring of the production process in manufacturing industries.A simulated example has been used to compare the proposed and traditional/simple EWMA charts and DS.EWMA control chart. The control charts' performance is measured using the average run length-out of control (ARL1). It is observed that the proposed chart performs better than existing EWMA control charts.  

scholarly journals SACF of the Errors of Stationary Time Series Models in the Presence of a Large Additive Outlier

2021 ◽  
pp. 31-40
Author(s):  
R. Suresh
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Stationary Time Series ◽  
Time Series Models ◽  
White Noise Process ◽  
First Order ◽  
Stationary Time ◽  
Additive Outlier ◽  
Distributed Process ◽  
Limiting Behaviour

In this paper, the limiting behaviour of the Sample Autocorrelation Function(SACF) of the errors {et} of First-Order Autoregressive (AR(1)), First-Order Moving Average (MA(1)) and First Order Autoregressive First-Order Moving Average (ARMA(1,1)) stationary time series models in the presence of a large Additive Outlier(AO) is discussed. It is found that the errors which are supposed to be uncorrelated due to either white noise process or normally distributed process are not so in the presence of a large additive outlier. The SACF of the errors follows a particular pattern based on the time series model. In the case of AR(1) model, at lag 1, the contaminated errors {et} are correlated, whereas at higher lags, they are uncorrelated. But in the MA(1) and ARMA(1,1) models, the contaminated errors {et} are correlated at all the lags. Furthermore it is observed that the intensity of correlations depends on the parameters of the respective models.

An Optimal Design of the Synthetic Coefficient of Variation Chart Based on the Median Run Length

2020 ◽  
pp. 2150018
Author(s):  
Wai Chung Yeong ◽  
Sok Li Lim ◽  
Michael Boon Chong Khoo ◽  
Khai Wah Khaw ◽  
Peh Sang Ng
Keyword(s):  
Coefficient Of Variation ◽  
Average Run Length ◽  
Moving Average ◽  
Small Sample ◽  
False Alarms ◽  
Small Shift ◽  
Run Length ◽  
Ewma Chart ◽  
Synthetic Chart ◽  
Run Lengths

The synthetic coefficient of variation (CV) chart is currently evaluated based only on the average run length (ARL), but this paper evaluates the chart based on different percentiles of the run length, which shows that false alarms frequently happen earlier than that shown by the in-control ARL (ARL[Formula: see text], and for small sample sizes and shift sizes, the out-of-control condition is frequently detected before what is shown by the out-of-control ARL (ARL[Formula: see text]. Furthermore, the run lengths show large variations. Hence, the chart’s performance could not be interpreted only in terms of the ARL. This paper proposes the median run length (MRL)-based design for the synthetic CV chart, which is not available in the literature. The MRL-based design shows larger MRL0 and ARL0, smaller MRL1 and ARL1, and less variation in the out-of-control run lengths compared to existing ARL-based designs. However, the in-control run lengths show more variation. Comparisons show that the synthetic chart outperforms the VSS and Shewhart charts, while comparison with the Exponentially Weighted Moving Average (EWMA) chart shows that although it outperforms the synthetic chart based on the ARL for small shift sizes, the synthetic chart shows better performance in terms of the MRL. The MRL-based synthetic chart is then implemented on an industrial example.

Time Series Analysis and Statistical Process Control for Anomaly Detection: An Application to the Situation of PM 10 in Northern Thailand

2017 ◽  
Vol 866 ◽  
pp. 379-382
Author(s):  
Unchalee Tonggumnead ◽  
Kittipong Klinjan
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Moving Average ◽  
Correlated Data ◽  
Cusum Chart ◽  
Statistical Process ◽  
Shewhart Chart ◽  
Ewma Chart ◽  
Pm 10 ◽  
Weighted Moving Average

The monitoring of processes is a vital mechanism for ensuring that such processes remain safe and under control. The present research aims to solve problems associated with correlated data by applying the Box-Jenkins method integrated with statistical process control (SPC) tools, namely the Shewhart chart, the moving average chart, the cumulative sum (CUSUM) chart, and the exponentially weighted moving-average (EWMA) chart. The efficiency of the four SPC tools was also compared in terms of the false alarm rate (FAR) and the missed detection rate (MDR). The findings indicated that the EWMA chart was the most effective in detecting anomaly, the Shewhart chart and the moving average chart produced high MDR, and the CUSUM chart suffered the highest FAR.

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