Enhancing The Performance Of A Short Run Multivariate Control Chart For The Process Mean

Abstract In recent years, the manufacturing industry has tended to reduce mass production and produce in small quantities, which is called “Short Run Production”. In such a situation, the course of the production process is short, usually, the number of productions is less than 50. Therefore, a control chart for the short run production process is required. This paper discusses the comparison between multivariate control chart for short run production (V control chart) and T2 Hotelling control chart applied to sunergy glass data. Furthermore, a simulation of Average Run Length (ARL) was carried out to determine the performance of the two control charts. The results obtained are that the production process has not been statistically controlled using either the V control chart or the T2 Hotelling control chart. The number of out-of-control on the control chart V using the the EWMA test is more than the T2 Hotelling control chart. Based on the ARL value, it shows that the V control chart is more sensitive than the T2 Hotelling control chart.

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A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

Quality and Reliability Engineering International ◽

10.1002/qre.2621 ◽

2020 ◽

Vol 36 (4) ◽

pp. 1161-1196 ◽

Cited By ~ 5

Author(s):

Hamed Sabahno ◽

Philippe Castagliola ◽

Amirhossein Amiri

Keyword(s):

Control Chart ◽

Measurement Errors ◽

Process Mean ◽

Variable Parameters ◽

Multivariate Control Chart ◽

Multivariate Control

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Design multivariate statistical process control procedure in the case of Ethio cement

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-07-2021-0227 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Daniel Ashagrie Tegegne ◽

Daniel Kitaw Azene ◽

Eshetie Berhan Atanaw

Keyword(s):

Neural Network ◽

Production Process ◽

Control Chart ◽

Cement Industry ◽

Time Interval ◽

Control Procedure ◽

Sufficient Information ◽

Content Type ◽

Multivariate Control Chart ◽

Multivariate Control

PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.Design/methodology/approachMultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.FindingsThe finding of this paper is that the points and lines in the classic Hotelling T2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.Originality/valueModifying the classical Hotelling T2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

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