Fitting Generalized Gaussian Distributions for Process Capability Index

2018 ◽  
pp. 169-176
Author(s):  
Theo G. J. Beelen ◽  
Jos J. Dohmen ◽  
E. Jan W. ter Maten ◽  
Bratislav Tasić
Keyword(s):  
Process Capability ◽  
Process Capability Index ◽  
Gaussian Distributions ◽  
Capability Index ◽  
Generalized Gaussian Distributions

A mixed control chart using process capability index

2017 ◽  
Vol 36 (2) ◽  
pp. 278-289 ◽  
Author(s):  
Muhammad Aslam ◽  
N. Khan ◽  
Liaquat Ahmad ◽  
Chi-Hyuck Jun ◽  
Jaffer Hussain
Keyword(s):  
Control Chart ◽  
Process Capability ◽  
Process Capability Index ◽  
Capability Index ◽  
Mixed Control

Bootstrap Confidence Intervals of the Modified Process Capability Index for Weibull distribution

2017 ◽  
Vol 42 (11) ◽  
pp. 4565-4573 ◽  
Author(s):  
Muhammad Kashif ◽  
Muhammad Aslam ◽  
G. Srinivasa Rao ◽  
Ali Hussein AL-Marshadi ◽  
Chi-Hyuck Jun
Keyword(s):  
Weibull Distribution ◽  
Confidence Intervals ◽  
Process Capability ◽  
Process Capability Index ◽  
Bootstrap Confidence Intervals ◽  
Capability Index

Process capability index for Poisson regression profile based on the Spmk index

2018 ◽  
Vol 31 (3) ◽  
pp. 430-438 ◽  
Author(s):  
Vasileios Alevizakos ◽  
Christos Koukouvinos ◽  
Philippe Castagliola
Keyword(s):  
Poisson Regression ◽  
Process Capability ◽  
Process Capability Index ◽  
Capability Index

scholarly journals Measuring capabilities of zero-inflated processes

2021 ◽  
Vol 13 (3) ◽  
pp. 37-48
Author(s):  
Surajit Pal ◽  
Susanta Kumar Gauri
Keyword(s):  
Negative Binomial ◽  
Process Capability ◽  
Process Capability Index ◽  
High Quality ◽  
New Approach ◽  
Capability Index ◽  
Count Distribution ◽  
Set Up

The high-quality processes usually have more count of zeros than are expected under chance variation of its underlying Poisson or other count distribution. Therefore, these processes are usually referred to as zero-inflated processes. The zeroinflated processes are commonly modelled by zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) distribution. In a manufacturing set up, the evaluation of process capability index of a zero-inflated process can be useful in many ways, e.g. i) predicting how well the process will hold the specifications, ii) selecting between competing vendors, and iii) assisting product developers/designers in modifying the process, etc. However, researchers have given very little attentions on this aspect of zero-inflated processes. Only one such attempt is reported in literature. But, it does not always represent the true capabilities of zero-inflated processes, and sometimes it may give very misleading impression about the capability of the concerned process. In this article, the concept of Borges and Ho (2001) is applied to zero-inflated processes and a new approach for computation of process capability index of zero-inflated processes is developed. The proposed method reveals the true capabilities of zero-inflated processes consistently. Application of the proposed approach and its effectiveness are illustrated using two datasets published by past researchers.

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