•A novel PCAC to measure products with multi-quality characteristic.•Pareto optimality to achieve the performance optimization for CM selection.•A feedback mechanism to advise ineligible CMs on ...quality issues.•A rigid coupling to validate the proposed methodology.
Outsourcing is a crucial strategy of the mechanical manufacturing industry. When outsourcing, it is necessary for companies to carefully inspect the performance of contract manufacturers (CMs) to ensure high-quality products and satisfactory services. This study presents an integrated methodology for the quality performance assessment and optimization of products with multiple quality characteristics for CM selection in the mechanical manufacturing industry. First, we present a novel process capability analysis chart (PCAC) using the 100(1–α)% upper confidence limits of Cpmh, Cpuh, and Cplh to gauge the process capabilities of products with multiple quality characteristics and reduce the influence of sampling errors. Second, we apply Pareto optimality to reexamine eligible CMs under the objectives of maximizing performance in individual quality characteristics and minimizing variance in overall performance. A feedback mechanism is included to advise ineligible CMs on means of improvement. Finally, we present a case study to demonstrate the feasibility and effectiveness of the proposed methodology.
•An easy-to-use method ready to be used by practitioners.•A new approach to simplifying quality control tasks.•A unique chart to assess process capability and process stability.•Not only does it ...simplify, but it also increases the ability to early detect out-of-control signals.•The simplicity of the method allows you to update the chart even manually.
This paper covers a hitherto unresolved problem, the search for a practical method for the Cpk^ control chart. The simplicity of the proposed method facilitates its immediate application. This control chart method reduces the complexity of the current paradigm for capability and quality control studies, while increasing sensitivity to out-of-control signals and improving the early detection aspect, as the ARL results show. The concept of distance implicit in Cpk definition is used to derive a new method based on chi-square distribution. The goodness of fit of the derived expression, which approximates the distribution of the Cpk^ statistic of the proposed method to the reference distribution of the simulated values and to the exact probability density function, was confirmed graphically, through the comparison of the histograms and the probability density functions, and analytically, using the F-test.
The improvement process of six-sigma DMAIC refers to the process that the industry performs to enhance process quality via the following five procedures: (1) define, (2) measure, (3) analyze, (4) ...improve, and (5) control. It is a common tool used in the industry to ameliorate and enhance process quality. In addition, the process capability index is a tool that the industry most frequently uses to measure process quality. Though many studies have discussed six-sigma methods, none of the specific theoretical models could be provided as a reference for the above five DMAIC improvement steps, making it difficult to control the effect. In order to solve this problem, this paper developed the multi-characteristic process capability analysis chart, MPCAC, using the process capability index and applied the method of statistical inference as a tool promoting define, measure, and analyze in the improvement process of six-sigma DMAIC. Additionally, the testing of the orthogonal table in the Taguchi method can efficiently assist process engineers in finding the optimum combination of machining parameters to improve or boost the process quality level of the quality characteristics by reducing the number and the cost of experiments. Therefore, this paper then adopted the testing of the orthogonal table in the Taguchi method as a tool for process improvement in the fourth step. Finally, according to the optimal combination of machining parameters improved in the fourth step, a standard operating procedure for transistor gaskets was established as a tool for process control in the fifth step.
This study addressed concerns related to increased percentages of damaged and re-worked production, heightened demand for factory products, and lack of awareness of the approved Sigma (σ) level ...during manufacturing, and associated deviations in the manufacturing process. The primary research problem was to assess the manufacturing process's stability and capability to consistently produce conical filters that meet required specifications. The study followed a sample-based approach, where twenty samples, each containing four observations, were collected continuously over a period of seven days. For each sample, the mean (X ̅) and range (R) were calculated. The mean X-Double bar of 319.32 and the average range R-bar of 0.848 were obtained through data analysis. The main findings revealed that, on average, the manufacturing process was relatively close to the target value (X-Double bar = 319.32). However, the presence of several data points outside the control limits indicated potential variability in the process. The average range (R-bar = 0.848) highlighted certain variations in the manufacturing process, which might contribute to issues like damaged or re-worked production. The study identified the need for further investigation to determine the root causes of these variations, which could include machine malfunctions, material fluctuations, or operator errors. By addressing these concerns and reducing process variability, the factory can enhance product quality, decrease waste, and improve customer satisfaction. In conclusion, continuous process monitoring and improvement initiatives, such as Six Sigma, are essential for achieving greater process capability in conical filter manufacturing. This research contributes valuable insights into process performance and provides a basis for implementing corrective actions to ensure consistent product quality and meet customer demands.
Process capability indices (PCIs),
C
p
,
C
a
,
C
pk
,
C
pm
, and
C
pmk
have been developed in certain manufacturing industry as capability measures based on various criteria, including process ...consistency, process departure from a target, process yield, and process loss. It is noted in certain recent quality assurance and capability analysis works that the three indices,
C
pk
,
C
pm
, and
C
pmk
provide the same lower bounds on the process yield. In this paper, we investigate the behavior of the actual process yield, in terms of the number of non-conformities (in ppm), for processes with fixed index values of
C
pk
=
C
pm
=
C
pmk
, possessing different degrees of process centering. We also extend Johnson's 1992. The relationship of
C
pm
to squared error loss. Journal of Quality Technology 24, 211–215 result formulating the relationship between the expected relative squared loss and PCIs. Also a comparison analysis among PCIs is carried out based on various criteria. The result illustrates some advantages of using the index
C
pmk
over the indices
C
pk
and
C
pm
in measuring process capability (yield and loss), since
C
pmk
always provides a better protection for the customers. Additionally, several extensions and applications to real world problem are also discussed. The paper contains some material presented in the Kotz and Johnson 2002. Process capability indices—a review, 1992–2000. Journal of Quality Technology 34(1), 1–19 survey but from a different perspective. It also discusses the more recent developments during the years 2002–2006.
Multivariate process capability indices (MPCIs) have been proposed to measure multivariate process capability in real‐world application over the past three decades. For the practitioner's point of ...view, the intention of this paper is to examine the performances and distributional properties of probability‐based MPCIs. Considering issues of construction of capability indices in multivariate setup and computation with performance, we found that probability‐based MPCIs are a proper generalization of univariate basic process capability indices (PCIs). In the beginning of this decade, computation of probability‐based indices was a difficult and time‐consuming task, but in the computer age statistics, computation of probability‐based MPCIs is simple and quick. Recent work on the performance of MPCI NMCpm and distributional properties of its estimator reasonably recommended this index, for use in practical situations. To study distributional properties of natural estimators of probability‐based MPCIs and recommended index estimator, we conducted simulation study. Though natural estimators of probability‐based indices are negatively biased, they are better with respect to mean, relative bias, mean square error. Probability‐based MPCI MCpm is better as compared with NMCpm with respect to performance and as its estimator quality. Hence, in real‐world practice, we recommend probability‐based MPCIs as a multivariate analogue of basic PCIs.
Process capability analysis is a vital part of an overall quality improvement programme. Numerous techniques and tools have been proposed for process capability analysis. Among these, indices and ...charts of process capability are simple and effective tools and widely used in the manufacturing industry. Many scholars have revealed numerous valuable aspects of previously developed tools and methods. Due to the rising demands of product quality, the current tools and methods are insufficient for enabling managers to make informed decisions. To address this gap, this study proposes a hypothesis testing procedure which determines whether the process capabilities satisfy the target level. Furthermore, this study proposes an integrated quality test chart (IQTC), which can display the process potential and performance for an entire product with smaller-the-better, larger-the-better and nominal-the-best specifications. The proposed procedure and IQTC incorporate the quality-level concept of the Six Sigma model and can be used to quantitate the relationships among the quality level, capability indices and process yield. They can be applied to assist managers in measuring, monitoring, analysing and improving process performance in a timely manner which will help ensure that the quality levels of their products meet customer demands. Finally, an example is provided to illustrate how to use the proposed procedure and IQTC.
Process capability analysis (PCA) is a completely effective statistical tool for ability of a process to meet predetermined specification limits (SLs). Unfortunately, especially the real case ...problems include many uncertainties, it is one of the critical necessities to define the parameters of PCIs by using crisp numbers. So, the results obtained may be incorrect, if the PCIs are calculated without taking into account the uncertainty. To overcome this problem, the fuzzy set theory (FST) has been successfully used to design of PCA. We also know that fuzzy set extensions have an important role in modelling the case that include uncertainty, incomplete and inconsistent information and they are more powerful than traditional FST to model uncertainty. Defining of main parameters of PCIs such as SLs, mean (µ) and variance (σ2) by using the flexible of fuzzy set extensions rather than precise values due to uncertainty, time, cost, inspectors hesitancy and the results based on fuzzy sets for PCIs contain more, flexible and sensitive information. In this study, two of well-known PCIs called Cp and Cpk have been re-designed at the first time by using one of fuzzy set extensions named Pythagorean fuzzy sets (PFSs). Defining PCIs with more than one membership function instead of an only one membership function is enabling to evaluate the process more broadly more flexibility. For this aim, the main parameters of PCIs have been defined and analyzed by using PFSs. Finally, four new PCIs based on PFSs such as Csp, Cspk, Cfp and Cfpk have been derived. The proposed new PCIs based on PFSs have been also applied on manufacturing process and capability for gears have been analyzed. It is shown that the flexibility of the PFSs on PCIs enables the PCA to give more realistic, more sensitive, and more comprehensive results.
Purpose: This study examines Clements’ Approach (CA), Box-Cox transformation (BCT), and Johnson transformation (JT) methods for process capability assessments through Weibull-distributed data with ...different parameters to figure out the effects of the tail behaviours on process capability and compares their estimation performances in terms of accuracy and precision.
Design/methodology/approach: Usage of process performance index (PPI) Ppu is handled for process capability analysis (PCA) because the comparison issues are performed through generating Weibull data without subgroups. Box plots, descriptive statistics, the root-mean-square deviation (RMSD), which is used as a measure of error, and a radar chart are utilized all together for evaluating the performances of the methods. In addition, the bias of the estimated values is important as the efficiency measured by the mean square error. In this regard, Relative Bias (RB) and the Relative Root Mean Square Error (RRMSE) are also considered.
Findings: The results reveal that the performance of a method is dependent on its capability to fit the tail behavior of the Weibull distribution and on targeted values of the PPIs. It is observed that the effect of tail behavior is more significant when the process is more capable.
Research limitations/implications: Some other methods such as Weighted Variance method, which also give good results, were also conducted. However, we later realized that it would be confusing in terms of comparison issues between the methods for consistent interpretations.
Practical implications: Weibull distribution covers a wide class of non-normal processes due to its capability to yield a variety of distinct curves based on its parameters. Weibull distributions are known to have significantly different tail behaviors, which greatly affects the process capability. In quality and reliability applications, they are widely used for the analyses of failure data in order to understand how items are failing or failures being occurred. Many academicians prefer the estimation of long term variation for process capability calculations although Process Capability Indices (PCIs) Cp and Cpk are widely used in literature. On the other hand, in industry, especially in automotive industry, the PPIs Pp and Ppk are used for the second type of estimations.
Originality/value: Performance comparisons are performed through generating Weibull data without subgroups and for this reason, process performance indices (PPIs) are executed for computing process capability rather than process capability indices (PCIs). Box plots, descriptive statistics, the root-mean-square deviation (RMSD), which is used as a measure of error, and a radar chart are utilized all together for evaluating the performances of the methods. In addition, the bias of the estimated values is important as the efficiency measured by the mean square error. In this regard, Relative Bias (RB) and the Relative Root Mean Square Error (RRMSE) are also considered. To the best of our knowledge, all these issues including of execution of PPIs are performed all together for the first time in the literature.
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