Process capability indices for Weibull distributions and upper specification limits

2009 ◽  
Vol 25 (3) ◽  
pp. 317-334 ◽  
Author(s):  
Malin Albing
2020 ◽  
Author(s):  
Alexis Oliva ◽  
Matías Llabrés

Different control charts in combination with the process capability indices, Cp, Cpm and Cpk, as part of the control strategy, were evaluated, since both are key elements in determining whether the method or process is reliable for its purpose. All these aspects were analyzed using real data from unitary processes and analytical methods. The traditional x-chart and moving range chart confirmed both analytical method and process are in control and stable and therefore, the process capability indices can be computed. We applied different criteria to establish the specification limits (i.e., analyst/customer requirements) for fixed method or process performance (i.e., process or method requirements). The unitary process does not satisfy the minimum capability requirements for Cp and Cpk indices when the specification limit and control limits are equal in breath. Therefore, the process needs to be revised; especially, a greater control in the process variation is necessary. For the analytical method, the Cpm and Cpk indices were computed. The obtained results were similar in both cases. For example, if the specification limits are set at ±3% of the target value, the method is considered “satisfactory” (1.22<Cpm<1.50) and no further stringent precision control is required.


2016 ◽  
Vol 34 (4) ◽  
Author(s):  
Abbas Parchami ◽  
Mashaallah Mashinchi ◽  
Ali Reza Yavari ◽  
Hamid Reza Maleki

Most of the traditional methods for assessing the capability of manufacturing processes are dealing with crisp quality. In this paper we discuss the fuzzy quality and introduce fuzzy process capability indices, where instead of precise quality we have two membership functions for specification limits. These indices are necessary when the specification limits are fuzzy and they are helpful for comparing manufacturing processes with fuzzy specification limits. Some interesting relations among the introduced indices are obtained. Numerical examples are given to clarify the method.


2017 ◽  
Vol 6 (3) ◽  
pp. 74-104 ◽  
Author(s):  
Zainab Abbasi Ganji ◽  
Bahram Sadeghpour Gildeh

Process capability indices are used to evaluate the performance of the manufacturing process. When the specification limits and the target value are not precise, the authors cannot use the traditional methods to assess the capability of the process. For the processes with asymmetric tolerance intervals, some fuzzy process capability indices have been introduced such as and . In some cases, these indices may fail to account the process performance. To overcome the problem with them, the authors propose two new fuzzy indices in the case that the specification limits and the target value are fuzzy while the data are crisp. Also, the authors present an application example to demonstrate effectiveness and performance of the proposed indices.


2015 ◽  
Vol 33 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Jeh-Nan Pan ◽  
Chung-I Li ◽  
Wei-Chen Shih

Purpose – In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions. Design/methodology/approach – In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC p , RNMC pm and RNMC pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC p and NMC pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case. Findings – A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices. Practical implications – Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system. Originality/value – Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.


2002 ◽  
Vol 27 (1) ◽  
pp. 55-68
Author(s):  
Satish Y Deodhar ◽  
Devanath Tirupati

Indian Food Specialties Limited (IFS) introduced tools of food quality management in May 2000 in response to changing market conditions and poor profitability. Spoilage in the production process was very high and the company had incurred losses for three successive years starting from 1996-97. The company addressed quality concerns by introducing management tools such as quality control charts and process capability indices, and was considering implementation of a food safety system called Hazard Analysis and Critical Control Points (HACCP). The case describes the changing market conditions and the company's response to improving quality, and provides a learning exercise on quality control charts, process capability indices, and HACCP.


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