An Efficient Novel Compensatory Multi-attribute Control Chart for Correlated Multinomial Processes

Sadigh Raissi,Amir Sarabadani,Ahmad Reza Baghestani

doi:10.19026/rjaset.6.3963

Sadigh Raissi, Amir Sarabadani + Show 1 more

Open Access

https://doi.org/10.19026/rjaset.6.3963

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Abstract

Monitoring multi-attribute processes is an important issue in many quality control environments. Almost all the priory proposed control charts utilize equal weights for each Attribute Quality Characteristics (AQCs). In such condition, there is no priority among AQCs. But in real-world, compensatory may exist. Hence due to some applied reasons such as function or efficiency, unequal weights for each AQC are possible. This study proposed a novel efficient control chart for simultaneous monitoring of weighted AQC when data expressed by linguistic terms. Correspondingly a new procedure to interpret out-of-control signals is presented. Performance and comparison advantage of the proposed control chart is measured in terms of Average Run Length (ARL) using a real case which priory was expressed. Consequences displayed that considering weight could efficiently extend the prior research for practical circumstancese.

Highlights

Statistical Process Control (SPC) is an outstanding process monitoring tools, which quantitatively can be used to measure the quality variables
Performance and comparison advantage of the proposed control chart is measured in terms of Average Run Length (ARL) using a real case which priory was expressed
Nowadays SPC is powerful tool for process monitoring and continuous quality improvement. It is a set of several analytical tools on which the control chart as a graphical display on process stability over time is the most important one

Summary

Introduction

Statistical Process Control (SPC) is an outstanding process monitoring tools, which quantitatively can be used to measure the quality variables. Control charts to monitor and detect shifts in a process generally designed based on the nature of data gathered for quantifying one or several quality-related characteristics of the product or service. The early works for time dependent and time independent large samples, proposed by Patel (1973) based on a Hotelling T 2 control chart to monitor multi-binomial or multipoisson process.

Results

Conclusion