The Experts below are selected from a list of 182778 Experts worldwide ranked by ideXlab platform
Muhammad Riaz - One of the best experts on this subject based on the ideXlab platform.
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enhanced cumulative sum charts for monitoring Process dispersion
PLOS ONE, 2015Co-Authors: Mu'azu Ramat Abujiya, Muhammad RiazAbstract:The cumulative sum (CUSUM) control chart is widely used in industry for the detection of small and moderate shifts in Process location and dispersion. For efficient monitoring of Process Variability, we present several CUSUM control charts for monitoring changes in standard deviation of a normal Process. The newly developed control charts based on well-structured sampling techniques - extreme ranked set sampling, extreme double ranked set sampling and double extreme ranked set sampling, have significantly enhanced CUSUM chart ability to detect a wide range of shifts in Process Variability. The relative performances of the proposed CUSUM scale charts are evaluated in terms of the average run length (ARL) and standard deviation of run length, for point shift in Variability. Moreover, for overall performance, we implore the use of the average ratio ARL and average extra quadratic loss. A comparison of the proposed CUSUM control charts with the classical CUSUM R chart, the classical CUSUM S chart, the fast initial response (FIR) CUSUM R chart, the FIR CUSUM S chart, the ranked set sampling (RSS) based CUSUM R chart and the RSS based CUSUM S chart, among others, are presented. An illustrative example using real dataset is given to demonstrate the practicability of the application of the proposed schemes.
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on monitoring Process Variability under double sampling scheme
International Journal of Production Economics, 2013Co-Authors: Muhammad Riaz, Shabbir Ahmad, Saddam Akber AbbasiAbstract:The presence of variation in all manufacturing and measurement Processes is a natural phenomenon and is the key factor which affects the performance of all types of Processes. A better understanding of the causes of Variability in any Processes is necessary to improve the Process. For an efficient monitoring of Process Variability, we have suggested a set of variance type control charts based on auxiliary characteristics and evaluated their performances in terms of Average Time to Signal (ATS) (the performance measure at every point of Variability shift) and Average Extra Quadratic Loss (AEQL) (the performance measure over the whole Process shift range) under normal and gamma Process environments. We have also examined the effects of contaminated environments on the ATS performance of different variance based charting structures. Illustrative examples on some selective variance type control structures are also provided for procedural details. Finally we have closed with concluding remarks about this study.
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a mean deviation based approach to monitor Process Variability
Journal of Statistical Computation and Simulation, 2009Co-Authors: Muhammad Riaz, Aami SaghiAbstract:The study proposes a Shewhart-type control chart, namely an MD chart, based on average absolute deviations taken from the median, for monitoring changes (especially moderate and large changes – a major concern of Shewhart control charts) in Process dispersion assuming normality of the quality characteristic to be monitored. The design structure of the proposed MD chart is developed and its comparison is made with those of two well-known dispersion control charts, namely the R and S charts. Using power curves as a performance measure, it has been observed that the design structure of the proposed MD chart is more powerful than that of the R chart and is very close competitor to that of the S chart, in terms of discriminatory power for detecting shifts in the Process dispersion. The non-normality effect is also examined on design structures of the three charts, and it has been observed that the design structure of the proposed MD chart is least affected by departure from normality.
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A Process Variability control chart
Computational Statistics, 2008Co-Authors: Muhammad Riaz, Ronald J. M. M. DoesAbstract:In this study a Shewhart type control chart namely the V t chart, is proposed for improved monitoring of the Process Variability of a quality characteristic of interest Y. The proposed control chart is based on the ratio type estimator of the variance using a single auxiliary variable X. It is assumed that (Y, X) follows a bivariate normal distribution. The design structure of the V t chart is developed for Phase-I quality control and its comparison is made with those of the S 2 chart (a well-known Shewhart control chart) and the V r chart (a Shewhart type control chart proposed by Riaz (Comput Stat, 2008a) used for the same purpose. It is observed that the proposed V t chart outperforms the S 2 and V r charts, in terms of discriminatory power, for detecting moderate to large shifts in the Process Variability. It is observed that the performance of the V t chart keeps improving with an increase in |ρ yx | , where ρ yx is the correlation between Y and X.
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Monitoring Process Variability using auxiliary information
Computational Statistics, 2008Co-Authors: Muhammad RiazAbstract:In this study a Shewhart type control chart namely V _ r chart is proposed for improved monitoring of Process Variability (targeting large shifts) of a quality characteristic of interest Y . The proposed control chart is based on regression type estimator of variance using a single auxiliary variable X . It is assumed that ( Y, X ) follow a bivariate normal distribution. The design structure of V _ r chart is developed and its comparison is made with the well-known Shewhart control chart namely S ^2 chart used for the same purpose. Using power curves as a performance measure it is observed that V _ r chart outperforms the S ^2 chart for detecting moderate to large shifts, which is main target of Shewhart type control charts, in Process Variability under certain conditions on ρ_ yx . These efficiency conditions on ρ_ yx are also obtained for V _ r chart in this study.
Roberto Da Costa Quinino - One of the best experts on this subject based on the ideXlab platform.
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gs2 an optimized attribute control chart to monitor Process Variability
International Journal of Production Economics, 2018Co-Authors: Erica Leandro Bezerra, Linda Lee Ho, Roberto Da Costa QuininoAbstract:Abstract Precise measurement of quality characteristics is expensive and time-consuming and requires instrument calibration. Furthermore, in destructive experiments, the sampled units are damaged and must be discarded. In these cases, an alternative is the classification of each sampled unit into a group using a device such as gauge rings. Operationally, this method is faster, and no measurement is taken on the sampled unit. In this paper, a new attribute control chart is proposed to monitor Process Variability. The statistic G S 2 is calculated, and the chart signals whenever G S 2 > C L G , where C L G is the control limit that is determined to satisfy a desired value of A R L 0 and to minimize A R L 1 . The performance (fixed A R L 1 and A R L 0 ) of the proposed control chart can be economically compared with the traditional S 2 control chart depending on the magnitude of the cost of the evaluation per measurement in relation to the cost of inspection by attributes.
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combining attribute and variable data to monitor Process Variability mix s 2 control chart
The International Journal of Advanced Manufacturing Technology, 2016Co-Authors: Linda Lee Ho, Roberto Da Costa QuininoAbstract:The necessity to monitor Process Variability to maintain similar, but more economical, performance to that of the S 2 control chart in terms of ARL1 is the motivation of this paper. In general, a sample of four to six units is used to build an S 2 control chart. Reducing the sample size to two or three units is a possibility, but the performance will be poor (in terms of ARL1), and the project specification may not be met. Additional cost would be required to remove the nonconforming units before shipment to the customers. This would compromise the quality of products in an environment of high worldwide competitiveness. The aim of this paper is to present the MIX S 2 control chart, which employs a two-phase control chart to monitor the Variability. At every interval of h hours, each unit is sequentially classified as approved or rejected by employing a Go/No Go ring gauge. A unit is approved if its dimension lies in [LDL; UDL], where L(U)DL is the lower(upper) discriminant limit; otherwise, it is rejected. If A 1-approved items are first observed, then the production continues. However, if B 1-rejected items are first observed, then n (n < 5) complementary units are taken, and the quality characteristics are measured to calculate the sample variance S 2. If S 2 is greater than the control limit, then the Process is stopped for adjustment; otherwise, the production continues. The MIX S 2 chart employs mixed control charts (attribute and variable) with the aim to find optimized parameters—LDL, UDL, A 1, B 1, and CL—such that similar values to ARL1 (and average sample size) are provided at a lower cost than the S 2 control chart.
Fredrik Milani - One of the best experts on this subject based on the ideXlab platform.
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business Process Variability modeling a survey
ACM Computing Surveys, 2017Co-Authors: Marcello La Rosa, Wil M P Van Der Aalst, Marlon Dumas, Fredrik MilaniAbstract:It is common for organizations to maintain multiple variants of a given business Process, such as multiple sales Processes for different products or multiple bookkeeping Processes for different countries. Conventional business Process modeling languages do not explicitly support the representation of such families of Process variants. This gap triggered significant research efforts over the past decade, leading to an array of approaches to business Process Variability modeling. In general, each of these approaches extends a conventional Process modeling language with constructs to capture customizable Process models. A customizable Process model represents a family of Process variants in a way that a model of each variant can be derived by adding or deleting fragments according to customization options or according to a domain model. This survey draws up a systematic inventory of approaches to customizable Process modeling and provides a comparative evaluation with the aim of identifying common and differentiating modeling features, providing criteria for selecting among multiple approaches, and identifying gaps in the state of the art. The survey puts into evidence an abundance of customizable Process-modeling languages, which contrasts with a relative scarcity of available tool support and empirical comparative evaluations.
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business Process Variability modeling a survey
Institute for Future Environments; Science & Engineering Faculty, 2017Co-Authors: Marcello La Rosa, Marlon Dumas, Wil M P Van Der Aalst, Fredrik MilaniAbstract:It is common for organizations to maintain multiple variants of a given business Process, such as multiple sales Processes for different products or multiple bookkeeping Processes for different countries. Conventional business Process modeling languages do not explicitly support the representation of such families of Process variants. This gap triggered significant research efforts over the past decade leading to an array of approaches to business Process Variability modeling. This survey examines existing approaches in this field based on a common set of criteria and illustrates their key concepts using a running example. The analysis shows that existing approaches are characterized by the fact that they extend a conventional Process mod- eling language with constructs that make it able to capture customizable Process models. A customizable Process model represents a family of Process variants in a way that each variant can be derived by adding or deleting fragments according to configuration parameters or according to a domain model. The survey puts into evidence an abundance of customizable Process modeling languages, embodying a diverse set of con- structs. In contrast, there is comparatively little tool support for analyzing and constructing customizable Process models, as well as a scarcity of empirical evaluations of languages in the field.
Lorenz T Biegler - One of the best experts on this subject based on the ideXlab platform.
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optimal Process design with model parameter uncertainty and Process Variability
Aiche Journal, 2003Co-Authors: William C Rooney, Lorenz T BieglerAbstract:Optimal design under unknown information is a key task in Process systems engineering. This study considers formulations that incorporate two types of unknown input parameters, uncertain model parameters, and variable Process parameters. In the former case, a Process must be designed that is feasible over the entire domain of uncertain parameters, while in the latter case, control variables can be adjusted during Process operation to compensate for variable Process parameters. To address this problem we extend the two-stage formulation for design under uncertainty and derive new formulations for the multiperiod and feasibility problems. Moreover, to simplify the feasibility problem in the two-stage algorithm, we also introduce a KS constraint aggregation function and derive a single, smooth nonlinear program that approximates the feasibility problem. Three case studies are presented to demonstrate the proposed approach.
Ronald J. M. M. Does - One of the best experts on this subject based on the ideXlab platform.
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A Process Variability control chart
Computational Statistics, 2008Co-Authors: Muhammad Riaz, Ronald J. M. M. DoesAbstract:In this study a Shewhart type control chart namely the V t chart, is proposed for improved monitoring of the Process Variability of a quality characteristic of interest Y. The proposed control chart is based on the ratio type estimator of the variance using a single auxiliary variable X. It is assumed that (Y, X) follows a bivariate normal distribution. The design structure of the V t chart is developed for Phase-I quality control and its comparison is made with those of the S 2 chart (a well-known Shewhart control chart) and the V r chart (a Shewhart type control chart proposed by Riaz (Comput Stat, 2008a) used for the same purpose. It is observed that the proposed V t chart outperforms the S 2 and V r charts, in terms of discriminatory power, for detecting moderate to large shifts in the Process Variability. It is observed that the performance of the V t chart keeps improving with an increase in |ρ yx | , where ρ yx is the correlation between Y and X.
