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PurposeThis study investigates the repetitive mixed sampling (MRS) plan based on the Cpk index that was proposed by Aslam et al. (2013a). They were the first to study the MRS plan, but they did not pay attention to the fact that submitting to the variable inspection a sample that was first submitted to the attribute inspection, truncates the X observations. In addition, they did not work with an accurate expression to calculate the probabilities of the Cpk statistic.Design/methodology/approachThe authors presented the results based on their original sampling plan through Monte Carlo simulation and defined the theoretical results of their plan when the sample submitted to the variable inspection is no longer the same one submitted to the attribute inspection.FindingsThe β risks of the optimum sampling plans presented by Aslam et al. (2013a) are pretty high, exceeding 46%, on average – this same problem was also observed in Saminathan and Mahalingam (2018), Balamurali (2020) and Balamurali et al. (2020), where the β risks of their proposed sampling plans are yet higher.Originality/valueIn terms of originality, the authors can declare the following. It is not a big deal to propose new sampling plans, if one does not know how to obtain their properties. The miscalculations of the sampling plans risks are dangerous; imagine the situation where the acceptance of bad lots exceeds 50% just because the sampling plan was incorrectly designed. Yes, it is a big deal to warn that this type of problem is arising in a growing number of papers. The authors of this study are the pioneers to discover that many studies focusing on the sampling plans need to be urgently revised.

Verification and validation of product quality highly affects the success of product design and manufacturing processes as well as the long-term collaboration between buyers and suppliers. Lifetime testing for acceptance is the main bottleneck in verification and validation and often requires considerable time and expense, especially in the current high-product-yield era. To establish a cost-effective and risk-controllable life testing sampling scheme, we propose a variables-type multiple-dependent-state (MDS) sampling plan using the lifetime performance index under a Weibull distribution with Type-II right censoring. The design parameters of the lifetime-capable MDS sampling plan are formulated as optimization models to minimize the required number of testing failures subject to nonlinear constraints for the desired lifetime capability levels and allowable risks regulated by the supplier and buyer. Compared with variable single sampling plans, the plan proposed herein is more cost-effective sampling and has greater discriminatory power. The applicability of the proposed plan is demonstrated through an example.

Acceptance sampling plans are a cross-functional quality control instrument for benchmarking compliant standards of incoming as well as outgoing lots. The multiple dependent state (MDS) plan has been proposed and proven to advantage the conventional single sampling plan (SSP). However, the MDS plan presents a contradictory situation: Its performance depreciates as the number of previously considered lots increases. This outcome implies that the greater the number of cumulative lot-sentencing results one intends to include is, the greater the sample size is required for inspection, where a loosened criterion for quality acceptance levels, unfavorably, is introduced as well. Therefore, we proposed a modified MDS plan that depends on the loss-based capability index to accommodate an adjustable mechanism, namely, the adjustable multiple dependent state (AMDS) sampling plan. The AMDS plan allows encompassing not only a greater number of preceding lots in the lot disposition decision but also reducing the sample size required for the inspection and raising the lot-sentencing quality standards to stimulate the supplier attaining with persistently reliable submissions for a long-term supplier-consumer relationship. In comparison to the plans’ performance, our proposed AMDS plans demonstrate superior cost-effectiveness, discriminatory powers, and average run lengths than those of the SSP and MDS sampling plans. Furthermore, we tabulated the plan parameters under numerous conditions and delivered a real industry case to demonstrate its applicability.

The present study aims to develop a new variable repetitive group sampling plan using the Exponentially Weighted Moving Average (EWMA) statistic based on the yield index for the submitted lot. The optimal parameters of the proposed plan were determined under three scenarios based on the Average Sample Number (ASN). ASN should be minimized to decrease the inspection time and cost using the optimization problem for the required quality levels and sundry combinations of producer's and consumer's risks. A comparison study was conducted to determine the efficiency of the proposed plan. Furthermore, the proposed plan was presented with an example elaborating its applicability in the industry. The proposed plan was compared with the single sampling plan and repetitive group sampling plan based on the yield index. The upshots were tabulated for different quality levels. The obtained results demonstrated that with respect to performance, the proposed sampling plan was more lucrative than the existing sampling plans in terms of ASN.

It is of great significance to design an economic sampling plan for the electrical products in mega engineering projects. The existing traditional plan normally considers single failure mode of the item being inspected. However, the major products in mega engineering projects usually have different failure modes. To overcome the drawbacks of traditional plans, a truncated sequential sampling plan of electrical products having two failure modes is proposed in this paper. The newly proposed sequential sampling plan takes two different failure modes of an electrical product into consideration, consisting of the sequential sampling scheme for soft-failure mode and the sequential sampling scheme for catastrophic-failure mode. The concept of cut-off line is introduced, limiting the sample size within a certain range. At last, a numerical example is provided. This newly proposed sequential sampling plan provides a reference for the design of economic acceptance sampling plans for expensive electrical products and the products with multi-failure modes.

Quality control testing under acceptance sampling plans involves inspecting a representative sample of products or materials from a larger lot or batch to determine whether the lot meets predetermined quality standards. In this research, the modified Lindley distribution is used as a model for lifetime study. When a life test is amputated at a pre-appropriated time to decide on the admission or refusal of the submitted batches, the problems of the single, double, and multiple (three and four stages) acceptance sampling strategies are introduced. The optimal sample sizes are computed for single, double, and multiple acceptance sampling plans to ensure that the veritable mean life is greater than the prescribed mean life at the stipulated consumer’s risk. The operating characteristic functions are investigated at diverse quality levels. For single, double, and multiple acceptance sampling plans, the minimal ratios of the veritable mean life to the prescribed mean life at the established percent of the producer’s risk are obtained. To demonstrate the uses of single, double, and multiple, some numerical experiments are presented.

When a life test is terminated at a predetermined time to decide whether to accept or refuse the submitted batches, the types of group sampling inspection plans (single, two, and multiple stages) are introduced. The tables in this study give the optimal number of groups for various confidence levels, examination limits, and values of the ratio of the determined experiment time to the fixed percentile life. At various quality levels, the operating characteristic functions and accompanying producer's risk are derived for various types of group sampling inspection plans. At the determined producer's risk, the optimal ratios of real percentile life to a fixed percentile life are obtained. Three case studies are provided to illustrate the processes described here. Comparisons of single-stage and iterative group sampling plans are introduced. The first, second, and third sample minimums must be used to guarantee that the product's stipulated mean and median lifetimes are reached at a certain degree of customer trust. The suggested sample plans' operational characteristic values and the producer's risk are given. In order to show how the suggested approaches based on the mean life span and median life span of the product may function in reality, certain reallife examples are examined.

Tightened-normal-tightened (TNT) sampling scheme is one of the most frequently used sampling schemes for making decisions about the finished product lots by examining certain samples from the lots. TNT sampling scheme includes two attribute sampling plans, one for tightened inspection and other for normal inspection along with switching rules. This paper introduces a procedure for TNT by incorporating two single sampling plans (SSP) under the conditions of intervened Poisson distribution (IPD) for the lots which may have a possibility of some intervention during the production process. The paper also assesses the performance of the proposed scheme procedure through its operating characteristic curves. Also, the unity value table is provided for certain parameters of specified producer’s risk and consumer’s risk for shop floor conditions. Further, the efficiency of proposed TNT scheme over the individual SSP under the conditions of IPD is demonstrated with illustrations.

The study proposes a new decision theoretic sampling plan (DSP) for Type-I and Type-I hybrid censored samples when the lifetimes of individual items are exponentially distributed with a scale parameter. The DSP is based on an estimator of the scale parameter which always exists, unlike the MLE which may not always exist. Using a quadratic loss function and a decision function based on the proposed estimator, a DSP is derived. To obtain the optimum DSP, a finite algorithm is used. Numerical results demonstrate that in terms of the Bayes risk, the optimum DSP is as good as the Bayesian sampling plan (BSP) proposed by Lin et al. (2002) and Liang and Yang (2013). The proposed DSP performs better than the sampling plan of Lam (1994) andLinetal.(2008a) in terms of Bayes risks. The main advantage of the proposed DSP is that for higher degree polynomial and non-polynomial loss functions, it can be easily obtained as compared to the BSP.

Traditional multiple deferred state sampling plan by attribute is an inspection with crisp parameter. In this plan, in addition to using the current lot information, the future successive lots information can also be utilized on sentencing the current lot. In this study, a fuzzy multiple deferred state (FMDS) sampling plan by attribute is proposed when the proportion of defective items ( p ) is uncertain. So, the fuzzy probability theory is applied to construct the operating characteristic curve of mentioned plan. Then, we concentrate more on a special feature of FMDS(0,1,2) plan. We show that the proposed plan is well-defined because it converts to classic case when p is not imprecise. In order to be fully informed, some examples will also be discussed. The comparisons between the FMDS(0,1,2) plan and the existing fuzzy single sampling plan (FSSP) with zero and one acceptance number show that the proposed plan is more powerful than the FSSP in distinguishing good and bad quality of the lot. It also enjoys the least sum of the consumer and producer risks.