Taguchi Capability Index Research Articles

Optimal designing of skip-lot sampling plan with a provision for reducing the normal inspection using the Taguchi process capability index

A product with a strong track record of quality might have less inspection according to the skip-lot sampling plan. As a result, skip-lot sampling plans are created to cut down the inspection cost with desired protection. The skip-lot sampling method is also reliable, practical, and favorable from an economic standpoint. As a result, this work develops the SkSP-V plan, a novel system of skip-lot sampling plan, based on the Taguchi process capability index. The proposed plan stipulates that any lot is rejected during sampling inspection, normal inspection must resume, however, it also allows for a reduced level of normal inspection in the event that higher product quality is demonstrated. The exact sampling distribution of the estimated Taguchi capability index is used to derive the operating characteristic function of the proposed plan. An optimization model is employed to establish the optimal plan parameters, guaranteeing adherence to the specified quality levels and risk criteria. The performance measures such as probability of acceptance and average sample number of the proposed plan are examined and compared with existing plans. This article also includes real-world example to effectively demonstrate the practical implementation of the proposed SkSP-V plan based on the Taguchi process capability index.

A cost-effective skip-lot sampling scheme using loss-based capability index for product acceptance determination

Acceptance sampling plans are essential for quality control and assurance, empowering businesses to make well-informed decisions regarding lot disposition. Among these plans, the skip-lot sampling plan (SkSP) stands out as a cost-effective strategy for suppliers with consistently excellent product quality. In this article, we present an enhanced version of the SkSP, termed Cpm-based SkSP-RGSP, which incorporates a repetitive group sampling plan (RGSP) using the Taguchi capability index Cpm as its reference plan. We develop an optimization model to ensure that the proposed plan meets a two-point condition in its operating characteristic function and effectively minimizes the average sample number (ASN). Through investigations of two critical performance measures, ASN and discriminatory power, we demonstrate the superiority of our plan over conventional sampling approaches. Detailed plan parameters, including required sample size and lot sentencing criteria, are provided to assist examiners in practical implementation. Additionally, we showcase the plan's applicability and practicality through a case study. This research contributes to the field of quality control by offering an efficient and cost-effective sampling scheme for lot disposition.

A switcher skip lot-sampling plan under resubmitted lots using the Taguchi capability index for building a solid vendor-customer relationship

Variable sampling plans incorporating process capability indices (PCIs) have been introduced to adapt to the changing landscape of modern manufacturing, where the occurrence of defective goods has significantly decreased. Skip-lot sampling plans (SkSP) are particularly effective for batches characterized by consistent and high product quality. Additionally, manufacturers with a strong track record of producing high-quality goods or possessing empirical knowledge about their production processes can benefit from resampling. It has been demonstrated that resubmitted sampling plans outperform traditional single sampling plans. In this study, a novel variant of skip-lot sampling, referred to as SkSP-SR, is proposed. SkSP-SR utilizes two reference plans: one for normal inspection (utilizing SSP) and the other for skip inspection (utilizing RSP). The approach also considers the loss of information associated with deviations from the target by incorporating the Taguchi capability index. Compared to traditional sampling plans, the suggested approach not only reduces sample sizes but also enhances discriminating power. Comprehensive tables presenting plan parameters for commonly used quality and risk criteria are provided, enabling practitioners to readily implement the recommended plan.

Generalized quick-switch sampling systems indexed by Taguchi capability with record traceability

Acceptance sampling plans of variable measurements quantified by process capability index have become compelling because they can validate how well the upstream lots (process) fit the downstream high-yield requirements in a more discriminatory and economic way. Recent research has accomplished two contrasting systems of quick-switch sampling (VQSS) based on the Taguchi capability index, namely acceptance criterion-switching VQSS (Balamurali and Usha, 2019, published in Journal of the Operational Research Society) and sample size-switching (Wang and Wu, 2021, published in Computers & Industrial Engineering) VQSS. Both studies, nonetheless, lacked in discerning the system’s inspection strengths and weaknesses, nor investigated their joint performance regarding sampling efficiency and sensitivity towards abrupt deviation of the process capability. Moreover, for a sequential lot-inspection process, their simplified mathematical formulation has overlooked the importance of lot-and-sample identification in the supply chain. To forward improvements, we develop a generalized VQSS (GVQSS) system with lot-and-sample records traceability. The GVQSS system activates the concurrent changes of sample size and acceptance criterion based on the past result for lot disposition. It can also accommodate two existing VQSS systems. Additionally, an R-Shiny interactive web app is constructed for practitioners to easily acquire the optimal criteria of VQSS systems. These advancements enable the GVQSS to deal with different phases of supplier–buyer partnerships. Finally, we demonstrate how to apply the GVQSS system through a real-world case.

Optimal design of a two-point variables skip-lot sampling plan with Taguchi capability index

The skip-lot sampling plan, an extension of the lot-by-lot based plan, not only retains the original characteristic of every lot inspection, but also skips lots appropriately when lot acceptance criterion is satisfied. Past studies have revealed that the skip-lot sampling plan saves more inspection samples than the traditional single sampling plan. However, most research related to the skip-lot sampling plan for variables lacks the additional loss assessment, when the quality deviated from the target value. Hence, this study proposes a skip-lot sampling plan based on the Taguchi capability index for variables products. The performance of the designed plan is investigated in terms of the operating characteristic curve and average sample number. Additionally, for the purpose of discussing how to select sampling frequency in this approach, a simulation study for average run length is conducted for validation. Finally, one practical case is presented for illustrating how to apply proposed method to resistor manufacturing to verify its applicability.

Developing a variables modified chain sampling plan with Taguchi capability index

AbstractAcceptance sampling plan has been a practical tool in quality management, which provides both the producer and the consumer a general rule for sentencing lots. In recent years, a new strategy, chain sampling plan, considering disposition of historical lots for sentencing the current lot has been discussed. However, reviewing results of preceding lots only happens when the quality of current lot is on the undefined region, which may not be completely utilized. Therefore, in this study, we develop a variables modified‐ChSP (VMChSP) for variable inspection based on the Taguchi capability index to improve above situation. Optimization model of the proposed plan is formulated to solve plan parameters by minimizing the required sample size. Plan parameters under various allowable risks and quality levels are also provided for practical purpose. Finally, further analysis and comparisons between the proposed plan and conventional plans are provided and discussed in this research.

Design and construction of a variables quick switching sampling system based on Taguchi capability index

Quick switching sampling (QSS) system comprises a pair of single sampling plans with switching rules and has been developed for lot disposition. The traditional QSS system for variables data only focused on products’ yield evaluation but ignored the additional loss caused when the quality deviated from the target value. Therefore, the purpose of this research is to develop a variables QSS system based on the loss-based capability index. To determine the designed plan parameters, a nonlinear optimization problem is constructed. The objective function is minimizing the required average sample number, and the constraints are set to satisfy the quality requests and allowable risks demanded by the producers and consumers, simultaneously. The performance of the proposed system is investigated and compared with other existing methods in terms of the operating characteristic, average sample number, and average run length. Finally, a practical case is presented for illustrating the applicability of the proposed plan.

Fuzzy Evaluation Model of Process Improvement Capability with Costs Consideration

The Taguchi capability index, which reflects the expected loss and the yield of a process, is a useful index for evaluating the quality of a process. Several scholars have proposed a process improvement capability index based on the expected value of the Taguchi loss function as well as the corresponding cost of process improvement. There have been a number of studies using the Taguchi capability index to develop suppliers’ process quality evaluation models, whereas models for evaluating suppliers’ process improvement potential have been relatively lacking. Thus, this study applies the process improvement capability index to develop an evaluation model of the supplier’s process improvement capability, which can be provided to the industry for application. Besides, owing to the current need to respond quickly, coupled with cost considerations and the limits of technical capabilities, the sample size for sampling testing is usually not large. Consequently, the evaluation model of the process improvement capability developed in this study adopts a fuzzy testing method based on the confidence interval. This method reduces the risk of misjudgment due to sampling errors and improves the testing accuracy because it can incorporate experts and their accumulated experiences.

Data driven supplier selection as a circular economy enabler: A Taguchi capability index for manufactured products with asymmetric tolerances

Prior studies have failed to account for the effect of product quality on the sustainability of supply chain networks. To address this gap, this study develops a Taguchi capability index that considers the economic, environmental, and social losses incurred due to poor product quality that leads to process losses. These product quality measures give a foundation for the operationalization of the circular economy concept, which is now receiving a growing level of attention from both firms and governments throughout the world. Sustainable supply chain management models that integrate upstream and downstream operations are increasing in popularity due to globalized competition, where the quality of components from each supplier affects the quality of the final product. One practical challenge is that many quality characteristics possess asymmetric tolerances. This study develops a comprehensive supplier selection model based on process quality, using a Taguchi capability index with asymmetric tolerances as an instrument for data driven supplier selection. Mathematical programming is used to obtain the confidence intervals of the indices to construct fuzzy membership functions. A fuzzy hypothesis testing method is then used to select suppliers taking into account uncertainty associated with measurement data. A case study illustrates the effectiveness of the methodology to assist firms in selecting the suppliers, and thus improve the sustainability of supply chain networks as a circular economy enabler.

Fuzzy testing decision-making model for intelligent manufacturing process with Taguchi capability index

Fuzzy testing decision-making model for intelligent manufacturing process with Taguchi capability index

Construction and fuzzy hypothesis testing of Taguchi Six Sigma quality index

By taking process targeting as well as process variability into consideration, the Taguchi capability index gives a reasonable indication of process loss. This makes it an ideal tool for practical applications that depend on the evaluation of process quality. The ability of Six Sigma quality management to reduce process defect rates has led a number of researchers to investigate the relationship between and Six Sigma quality levels. Unfortunately, previous efforts indicate the quality level using only a range, rather than a specific value. Our objective in this study was to develop a Taguchi Six Sigma quality index that retains the advantages of in the assessment of process performance, while providing a specific value for the quality level associated with the process in question. To ensure rigorous quality assessments, we employed the upper confidence limit of in the design of a testing model for use by manufacturers. Fuzziness and stochastic uncertainty are unavoidable aspects of data collection. We, therefore, adopted a right half triangular-shaped fuzzy number for to deal with imprecise data. We also developed a method of the fuzzy hypothesis testing for to make reliable decisions for process quality assessment.

Determination of an efficient variables sampling system based on the Taguchi process capability index

Process capability indices are the important tools used in most of the manufacturing industries to check whether the manufactured products meet their quality specifications or not. These indices are the numerical measures used to judge the precision, accuracy, and performance of the manufacturing process. Acceptance sampling plan is another important tool used to check the quality of procured products. So, the sampling plan with combination of process capability indices will be a powerful tool for improving and maintaining the quality of products. In this paper, we propose a variables sampling system based on one of the most efficient process capability indices called Taguchi capability index. The proposed system can be applied when the quality characteristic under investigation has double specification limits and follows a normal distribution. Advantages of the proposed sampling system over the existing sampling plans are also discussed. Tables are also constructed to determine the optimal parameters by formulating the problem as a non-linear programming in which the average sample number is minimised by satisfying the producer and consumer risks simultaneously. A sensitivity analysis is provided in order to show the efficiency of the proposed sampling system.

Assessing Nanofabrication Performance Based on the Estimated Taguchi Capability Index with Subsamples

The Taguchi capability index C_(pm) has been proposed to the manufacturing industry for measuring manufacturing capability. Investigations of the estimated Taguchi capability index based on subsamples have been proposed and arrested substantial research attention. In this paper, investigation based on subsamples is considered for normally distributed processes from the traditional distribution frequency approach. A reliable inferential procedure based on subsamples of the estimated Taguchi capability index is proposed. Computational programs using Matlab software are proposed to calculate p-values and critical values required to ensure the process reaching a certain desirable level of the time. A practical example is provided to demonstrate how the proposed procedure may be applied for judging whether the process runs under the desirable quality requirement.

Process performance evaluation based on Taguchi capability index with the consideration of measurement errors

Most research works related to process capability analysis are carried out under the assumption of no gauge measurement errors. Unfortunately, such an assumption does not adequately accommodate real-world situations even with modern, highly sophisticated measuring instruments and devices. Estimating and testing process capability without considering gauge measurement errors may often lead to unreliable decisions. Thus, how to accurately measure process performance in the presence of measurement errors becomes a critical and essential task. This article applies the concept of generalised confidence intervals to evaluate process performance based on Taguchi capability index Cpm with the consideration of measurement errors. To investigate the performance of the proposed method, a series of simulations was undertaken. A sensitivity study on the effects of ignoring measurement errors was also carried out. The results reveal that the proposed method performs very well for evaluating process performance when measurement errors are present or not.

An efficient inspection scheme for variables based on Taguchi capability index

Acceptance sampling has been one of practical tools for quality assurance applications, which provide a general rule to the producer and the consumer for product acceptance determination. It has been shown that variables sampling plans requires less sampling compared with attributes sampling plans. Thus, variables sampling plans become more attractive and desirable especially when the required quality level is very high or the allowable fraction non-conforming is very small. This paper attempts to develop an efficient and economic sampling scheme, variables repetitive group sampling plan, by incorporating the concept of Taguchi loss function. The OC curve of the proposed plan is derived based on the exact sampling distribution and the plan parameters are determined by minimizing the average sample number with two constraints specified by the producer and the consumer. The efficiency of the proposed variables RGS is examined and also compared with the existing variables single sampling plan in terms of the sample size required for inspection. In addition, tables of the plan parameters for various combinations of entry parameters are provided and an example is presented for illustration.

Sequential testing of process capability indices

A Process Capability Index (PCI) is a numeric summary that compares the behaviour of a product or process characteristics with engineering specifications. We propose a sequential procedure for testing whether two processes are equally capable by using the PCI (C pm). We employ a non-sequential Wald-type statistic and provide its sequential version by Brownian motion approximations. We point out that, as a byproduct, the non-sequential Wald-type statistic used here provides an easily computable alternative to Boyels’ approximate F-test [Boyels, The Taguchi Capability Index, J. Quality Technol. 23 (1991), pp. 17–26]. We give an algorithm for conducting the sequential test and we examine its performance by using Monte Carlo simulations. Finally, we illustrate the method by testing capability improvement of an industrial process before and after calibration based on published data.

A Bayesian Approach to Obtain a Lower Bound for the Cpm Capability Index

AbstractThe Taguchi capability index Cpm, which incorporates the departure of the process mean from the target value, has been proposed to the manufacturing industry for measuring manufacturing capability. A Bayesian procedure has been considered for testing process performance assuming $\mu =T$, which was generalized without assuming $\mu = T$. Statistical properties of the natural estimator of the index Cpm for normal processes have been investigated extensively. However, the investigation was restricted to processes with symmetric tolerances. Recently, a generalized Cpm, referred to as $C''_{{\rm p}m}$, was proposed to cover processes with asymmetric tolerances. Under the normality assumption, the statistical properties of the estimated $C''_{{\rm p}m}$ including the exact sampling distribution, the rth moment, expected value, variance, and the mean‐squared error were obtained. In this paper, we use a Bayesian approach to obtain the interval estimation for the generalized Taguchi capability index $C''_{{\rm p}m}$. Consequently, the manufacturing capability testing can be performed for quality assurance. Copyright © 2005 John Wiley & Sons, Ltd.

Sample size determination for lower confidence limits for estimating process capability indices

Until recently, estimates of process indices have been point estimates. However, a more appropriate estimate would be provided by a confidence interval. In this case, a one sided interval is necessary, since the real interest is in how small the process index can be, and is called a lower confidence limit. Recent work by Chou et al. (Lower confidence limits on process capability indices. Journal of Quality Technology 1990;22(3):223–29), Kushler and Hurley (Confidence bounds for capability indices. Journal of Quality Technology 1992;24(4):188–95), and Franklin and Wasserman (A note on the conservative nature of the tables of lower confidence limits for C pk with a suggested correction. Communications in Statistics: Simulation and Computation 1992;21(4):1165–69) have provided exact and approximate formulas to determine these lower confidence limits for C p and C pk . In addition, recent work by Boyles (The Taguchi capability index. Journal of Quality Technology 1991;23(1):17–26) has provided approximate formulas to determine the lower confidence limits for C pm . This paper provides equations to estimate the approximate sample size necessary to achieve a desired confidence limit with specified confidence level. These equations are based on these formulas and are presented for C p , C pk , and C pm . In addition, some observations and recommendations are made.