Lot Acceptance Research Articles

Lot acceptance criteria for products following attribute single sampling plan and its impact towards discretization of some continuous lifetime distributions

Lot acceptance criteria for products following attribute single sampling plan and its impact towards discretization of some continuous lifetime distributions

Economic Design of Generalized Multiple Dependent State Sampling Plan Under Exponentiated Half Logistic Distribution

The main objective of this study is to propose an economic design of a generalized multiple dependent state sampling plan based on time truncated life test under exponentiated half logistic distribution for assuring median life. In this study, the optimal plan parameters are determined for various median ratios using the approach of two points on the operating characteristic curve. The performance of the proposed plan is compared with other sampling plans designed under exponentiated half logistic distribution. An investigation on the effect of misspecification of shape parameters on probabilities of lot acceptance is done and the industrial application of the proposed plan is explained with real time data. The results obtained from both conventional as well as economic designing show that the proposed plan outperforms the other existing sampling plans, such as multiple dependent state sampling plan, multiple dependent state repetitive group sampling plan, and single sampling plan.

Enhancing quality in Lot reception: A comparative analysis of innovative attribute acceptance sampling plans

Purpose: This study aims to enhance production quality by applying Quality Control (QC) principles through acceptance sampling, specifically analyzing the efficacy of attribute acceptance sampling plans in final lot receptions.Design/methodology/approach: Through a comprehensive review and critical evaluation of various sampling methodologies found in literature, this paper assesses their efficiency under distinct administrative and operational conditions. It emphasizes the comparison of different attribute acceptance sampling plans by examining variations in parameters and key performance indicators, such as Average Outgoing Quality (AOQ) and inspection time allocation percentage. Furthermore, it proposes a model for Continuous Sampling Plans (CSP) to evaluate these plans' performance in response to operational characteristic variations.Findings: The analysis reveals that the selected methodologies significantly aid in decision-making processes for lot acceptance, utilizing non-conforming rates depicted by the Average Quality Level (AQL). This provides a robust framework for improving Quality Control strategies, demonstrating the potential of these methodologies to optimize production quality through strategic lot acceptance.Practical implications: This paper outlines a practical approach for industry practitioners to enhance decision-making in lot acceptance, offering a method to balance quality control with operational efficiency effectively.Originality/value: By comparing a wide range of attribute acceptance sampling plans and introducing a novel CSP model, this research contributes valuable insights into the optimization of QC strategies. It offers a unique perspective on enhancing production quality, marking a significant advancement in the field of Quality Control and management.

Optimal double acceptance sampling plans based on truncated life tests for Tsallis q-exponential distributions

Optimal double sampling plans for lot acceptance based on truncated life tests are derived by minimizing the expected sample size when the lifetime variable follows a Tsallis q-exponential distribution. The proposed test plans significantly reduce the inspection effort with respect to the best single sampling schemes. Some tables and figures are presented to analyze the behavior of the suggested test plans. The results show that the proposed lot inspection scheme clearly outperforms the standard single sampling plan. A justification is provided using a comparative study for existing double sampling plans. Finally, several applications are provided for illustrative purposes.

Estimation of the lot fraction defective in a finite lot of products with auxiliary quality characteristics

Many companies use sampling plans for the acceptance or rejection of lots of products. The final outcome of this decision-making process is based on the inspection of a sample of products selected from a lot under inspection, where a quality characteristic is observed. An important parameter of interest related to acceptance sampling for attributes is the proportion of defective items. This parameter is unknown for a given lot of products, but it can be estimated from the aforementioned sample information. Additional quality characteristics can be observed at the inspection stage. We propose to use this auxiliary information to obtain more accurate estimators of the lot fraction defective at the estimation stage. Various relevant applications of this process are described. For possible scenarios that may arise in practice, the empirical properties of the suggested estimation methods are investigated using Monte Carlo simulations, and desirable results are obtained when there is a strong relationship between the quality characteristic of interest and the auxiliary quality characteristic.

A Comparison of Board-Level Lot Acceptance Testing Method with Worst-Case Analysis Results

A Comparison of Board-Level Lot Acceptance Testing Method with Worst-Case Analysis Results

The design of the mixed repetitive sampling plans based on the Cpk index

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.

Development of an approach to monitor the manufacturing consistency of HIV rapid diagnostic tests: Panel qualification and potential impact on country programs.

Although regulatory bodies have standards that manufacturers of rapid diagnostic tests (RDTs) must meet for market approval, RDTs have no specific sampling and testing standards to monitor ongoing lot production, unlike pharmaceuticals and certain devices. With the importance of accurate diagnosis for improved health outcomes, independent quality assurance testing is key to ensuring the availability of high-quality RDTs, particularly in low-resource settings. This work develops an approach for HIV RDT lot testing, involving qualification of specimens to enable testing across various RDTs (namely Determine HIV-1/2, OraQuick HIV-1/2, Bioline HIV-1/2 3.0, UniGold HIV, and HIV Ag/Ab Combo). A sampling plan and acceptance criteria were developed per lot (approximating sensitivity and specificity) based on ISO 2859-1: 1999, using the test line response to a qualified panel (disease-positive and negative specimens) as the attribute. Based on general performance of HIV RDTs, an average % defective tests allowed per lot (acceptance quality limit) of 0.65% within ISO 2859-1: 1999 was selected, where RDTs are tested with 80 positives (accept 1 / reject 2 defective results) and 80 negatives (accept 1 / reject 2 defective results) per lot. Panel qualification was conducted with 83 positive and 84 negative serum specimens to select specimens that consistently provided expected results when tested in quadruplicate with three lots per product. While all products yielded consistent results with at least 80 negative specimens, only 4 products did the same for positive specimens. With this approach, each of these 4 RDT products can be tested with the qualified 80-positive specimen panel, requiring the other product to be tested with 20 specimens in quadruplicate. Additionally, this approach was adapted to evaluate HIV antibody/antigen combination tests with Ag panel qualification using p24 samples. While panels were qualified to monitor ongoing lot consistency of HIV RDTs, this approach could be mimicked with other types of diagnostics for monitoring manufacturing consistency, field investigation, small-scale stability checks, and proficiency testing.

Gompertz Log-Logistic Distribution: A Case Study of CASP-CUSUM Schemes

Abstract: The acceptance sampling technique is one of the oldest methods of quality control and relates to inspection and decision-making regarding lots of goods. In this way, many optimal techniques were developed to amplify and manage the quality of the products. Consistent with the supposition that the quality characteristic variable is scattered according to certain probability laws. Based on this assumption, we optimized CASP-CUSUM schemes for continuous variables which used a Truncated Gompertz Log-Logistic distribution used in Statistical Quality Control and Reliability analysis. In particular, the distribution is intended to estimate the optimal truncated point and the probability of acceptance of lots. The operating characteristics and average run length values are shown. The results are illustrated with figures

Design of multiple deferred state sampling inspection plan for variables

The statistical product control, also termed as, acceptance sampling, extensively used in enterprises to reach a decision on ruling of lots of manufactured products, is a definite plan that states the sampling rules to be used and the standards for acceptance or dismissal of lots submitted for inspection. In acceptance sampling, the largely used practice to minimize the testing time and cost of inspection of manufactured units is the variable sampling inspection plan, in which the sentencing of a lot of individual items is based on metrical data. One of the special purpose sampling plans, multiple deferred state sampling plan for attributes reviews the quality of current lot as well as forthcoming lots for settlement of a lot. This paper proposes a technique for designing a multiple deferred state sampling plan for variables with the quality characteristics of product following normal distribution. The optimized parameters of the proposed plan are assessed by utilizing two points on the operating characteristic (OC) curve for specific requirement to ensure upon the producer and consumer conservation, when the standard deviation is known and unknown.

Designing Bayesian Two-Sided Group Chain Sampling Plan for Gamma Prior Distribution

Acceptance sampling is used to decide either the whole lot will be accepted or rejected, based on inspection of randomly sampled items from the same lot. As an alternative to traditional sampling plans, it is possible to use Bayesian approaches using previous knowledge on process variation. This study presents a Bayesian two-sided group chain sampling plan (BTSGChSP) by using various combinations of design parameters. In BTSGChSP, inspection is based on preceding as well as succeeding lots. Poisson function is used to derive the probability of lot acceptance based on defective and non-defective products. Gamma distribution is considered as a suitable prior for Poisson distribution. Four quality regions are found, namely: (i) quality decision region (QDR), (ii) probabilistic quality region (PQR), (iii) limiting quality region (LQR) and (iv) indifference quality region (IQR). Producer’s risk and consumer’s risk are considered to estimate the quality regions, where acceptable quality level (AQL) is associated with producer’s risk and limiting quality level (LQL) is associated with consumer’s risk. Moreover, AQL and LQL are used in the selection of design parameters for BTSGChSP. The values based on all possible combinations of design parameters for BTSGChSP are presented and inflection points’ values are found. The finding exposes that BTSGChSP is a better substitute for the existing plan for industrial practitioners.

Lot acceptance testing using sample mean and extremum with finite qualification samples

In the aerospace composites industry, new material lots are tested to determine if they are suitable for use. It is common to accept or reject the material lot by comparing the sample mean and lower extremum to reference values that are established based on an initial (qualification) sample of material property measurements. Current industry practices assume that the samples are drawn from a normal distribution with known parameters equal to the mean and standard deviation of the qualification sample: this assumption yields a producer’s risk that is too high. This article presents a two-sample method of setting these reference values, considering the sampling distribution of the qualification sample. This new method is validated through simulation which shows that it produces the correct probability of Type I error. Simulation is also used to investigate the statistical power of the new method and it is compared to others commonly used. A case study is presented to demonstrate the use of the new method using composite material data from industry.

Enhanced Lot Acceptance Testing Based on Defect Counts and Posterior Odds Ratios

Optimal defects-per-unit test plans based on posterior odds ratios are developed for the disposition of product lots. The number of nonconformities per unit is modeled by the Conway–Maxwell–Poisson distribution rather than the typical Poisson model. In essence, a submitted batch is conforming if its posterior acceptability is sufficiently large. First, a useful approximation of the optimal test plan is derived in closed form using the asymptotic normality of the log ratio. A mixed-integer nonlinear programming problem is then solved via Monte Carlo simulation to find the smallest number of inspected items per lot and the maximum tolerable posterior odds ratio. The methodology is applied to the manufacturing of paper and glass. The suggested sampling plan for lot sentencing provides the specified protections to both manufacturers and customers and minimizes the needed sample size. In terms of inspection effort and accuracy, the proposed approach is virtually an advantageous extension of the classical frequentist perspective. In many practical cases, it yields more precise assessments of the current consumer and producer risks, as well as more realistic decision rules.

Bayesian Two-Sided Complete Group Chain Sampling Plan for Poisson Distribution with Gamma Prior

For statistical quality assurance based on the inspection of a random sample, acceptance sampling plan help to decide whether the lot should be accepted or rejected. Most traditional plans only focus on minimizing the consumer’s risk, but producer’s risk also should not be ignored in acceptance sampling plan. Therefore, this study focuses on reducing both producer’s and consumer’s risks through the quality region. This study proposes a Bayesian two-sided complete group chain sampling plan (BTSCGChSP) for the average probability of lot acceptance. The Poisson distribution with gamma as prior distribution is used to derive the average probability of lot acceptance. Next, R programing language is used to obtain the average number of defectives according to average probability of acceptance and pre-specified values of design parameters. For selected design parameters in BTSCGChSP, the acceptable quality level (AQL) associated with producer’s risk and limiting quality level (LQL) associated with consumer’s risk are considered to estimate quality regions. In this paper, four quality regions are measured: (i) probabilistic quality region (PQR), (ii) quality decision region (QDR), (iii) limiting quality region (LQR) and (iv) indifference quality region (IQR). Operating characteristic curves (OC) are used for performance comparison with existing Bayesian group chain sampling plan (BGChSP) for the same probability of lot acceptance and other design parameter values. Findings validate that BTSCGChSP provides more ideal OC curve than BGChSP for the same probability of acceptance. For quality regions with the same values of consumer’s and producer’s risks, then the BTSCGChSP region will contain fewer defectives than in the BGChSP region. Hence, the proposed plan is a better substitute for existing BGChSP.

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.

Economical design of multiple deferred (dependent) state repetitive group sampling plan based on truncated life test under exponentiated half-logistic distribution

The design of a multiple deferred (dependent) state repetitive group sampling plan for exponentiated half-logistic distributed percentile life assurance under time-truncated life test is considered in this article. In this research, tables are provided to choose the design parameters of the proposed plan determined for different percentile ratios using the method of two points on the operating characteristic curve with known shape parameter values. We describe the sampling procedure of the proposed multiple deferred (dependent) state repetitive group sampling plan with real-time data and discuss the effect of misspecification of the shape parameter on lot acceptance probabilities. Based on an economical design of the proposed plan, an optimal plan is determined that satisfies both producer and consumer risks with minimum cost. In addition, the performance of the proposed plan is analyzed and compared with other existing sampling plans under an exponentiated half-logistic lifetime distribution. Finally, conclusions are provided.

Integrated QSS-RS plans based on the process yield index for lot acceptance determination

Acceptance sampling plans based on the process yield indices have been adopted in the inspection of outgoing and incoming lots when the required fraction defective is very low. To achieve this aim and increase their overall efficiency and flexibility, various sampling plans have been constructed for different purposes using numerous methods. In practice, resubmitted sampling (RS) plan encourages suppliers to work harder to produce better quality products that pass the first inspection. Besides, the concept of switching inspection rules, which can provide a flexible sampling procedure and reducing the required sample size for inspection, is especially useful when the inspection is costly or destructive. This paper proposes integrated sampling plans by considering the merits of the process yield index Spk , RS, and switching inspection rules for controlling lot fraction nonconforming when the quality characteristic is normally distributed with two specification limits. The performance of the proposed plans is investigated and the results indicate a satisfactory performance of the proposed plans. Besides, the advantages of the proposed plans over the existing plans are discussed. Finally, real data from the spark plug industry are used to illustrate the implementation.

Optimal allocation of shared parking spaces for hospital parkers considering parking choice behavior under bounded rationality

ABSTRACT Lack of parking spaces at downtown hospitals causes urban problems like patient access delays and additional carbon emissions. This paper proposes a cumulative prospect theory-based shared parking space allocation model (CPT-SPSA model) to alleviate the parking difficulties. The CPT-SPSA model aims to maximize the profit of the shared parking platform under time window constraints considering the parking choice behavior of hospital parkers. To determine the parking choice behavior, cumulative prospect theory (CPT) is utilized to model parkers’ evaluation of parking lots considering individual heterogeneity. Numerical experiments indicate that the profit of the platform and the utilization rate of the shared parking spaces are improved significantly and more successful matches of shared parking supply and demand are achieved under the CPT-SPSA model. Those with different parking purposes vary in acceptance of each parking lot. The platform can earn more profit by reasonably adjusting the parking fees of the shared parking spaces.

New Group Chain Sampling Plan (NGChSP-1) for Generalized Exponential Distribution

Acceptance criteria are the conditions imposed on any sampling plan to determine whether the lot is accepted or rejected. Group chain sampling plan (GChSP-1) was constructed according to the 5 acceptance criteria; modified group chain sampling plan (MGChSP-1) was derived with 3 acceptance criteria; later new group chain sampling plan (NGChSP-1) was introduced with 4 acceptance criteria where the NGChSP-1 balances the acceptance criteria between the GChSP-1 and MGChSP-1. Producers favor a sampling plan with more acceptance criteria because it reduces the probability of rejecting a good lot (producer risk), whereas consumers may prefer a sampling plan with fewer acceptance criteria as it reduces the probability of accepting a bad lot (consumer risk). The disparity in acceptance criteria creates a conflict between the two main stakeholders in acceptance sampling. In the literature, there are numerous methods available for developing sampling plans. To date, NGChSP-1 was developed using the minimum angle method. In this paper, NGChSP-1 was constructed with the minimizing consumer's risk method for generalized exponential distribution where mean product lifetime is used as quality parameter. There are six phases involved to develop the NGChSP-1 for different design parameters. Result shows the minimum number of groups decrease when the value of design parameters increases. The results of the performance comparison show that the NGChSP-1 is a better sampling plan than the GChSP-1 because it has a smaller number of groups and lower probability of lot acceptance than the GChSP-1. NGChSP-1 should offer better alternatives to industrial practitioners in sectors involving product life test.

Construction of multiple dependent state sampling plan for variables based on logistic distribution

Acceptance sampling plans are specific rules used in statistical quality control to reach a decision on acceptance or rejection of lots of manufactured products submitted for inspection subject to the quality requirements of the sampled units. In order to reduce the inspection time and cost, the variable sampling plans are used as it needs comparatively small sample size. The multiple dependent state sampling plan is one of the conditional sampling plans which acquires the sample information from present lot as well as the prior lots for disposition of a lot. The MDS plan is applicable in the situations where lots are submitted for inspection in sequence of production from a process having a constant proportion non-conforming. MDS plan under the assumption of normality are found in literature. In this article, a multiple dependent state sampling plan is developed for sentencing lots of products whose quality characteristics follows a Logistic distribution. The proposed plan is designed by considering the two points on the OC curve, by formulating it as a non-linear optimization problem, when standard deviation is known and unknown. The tables are constructed for the selection of the proposed plan parameters. The proposed plan is compared with the single variable sampling plan based on Logistic distribution.