Nonconforming Items Research Articles

A serial multistage manufacturing system (SMMS), consisting of multiple consecutive stations, is commonly used in manufacturing to improve production efficiency. In an intelligent manufacturing system, the degradation data of quality-related component (QRC) and the key product characteristic (KPC) data at each stage are conveniently available. Monitoring both QRC degradation and KPC quality is essential for ensuring the reliability of SMMS and the consistency of final products. Thus, this paper proposes a joint monitoring (JM) scheme that considers the interaction between QRC degradation and KPC in SMMS. First, the interaction considering the influence of non-conforming items is modelled. Second, a multivariate directional generalised likelihood ratio (MDGLR) chart is used to monitor the variation of KPC at each stage, and the hazards rate of QRC is assessed through a proportional hazards model (PHM). When the JM scheme triggers an alarm, a diagnostic approach is proposed to identify the stage that requires maintenance. Subsequently, Monte Carlo simulation is conducted to compare the JM scheme with existing schemes, demonstrating its superiority in terms of average time to signal (ATS). Finally, a case study on the shaft sleeve production process in a four-stage manufacturing system validates the practical applicability of the proposed scheme.

Purpose In process monitoring, manufacturers typically employ two main types of control charts: memory-less and memory-type charts. Memory-type charts, such as the Exponentially Weighted Moving Average (EWMA), generally outperform memory-less charts like the Shewhart chart. However, both types have inherent limitations that necessitate various extensions and modifications. With these considerations in mind, this study aimed to develop, present, and validate an optimal scheme for detecting non-conformities within the context of a small lamp and light bulb manufacturer based in the United Arab Emirates. Design/methodology/approach The developed scheme accounts for constraints related to inspection capacity and false alarm rates. Through comparative testing, we demonstrate the relative performance of the proposed optimal weighted Exponentially Weighted Moving Average (wEWMA) chart, which is further validated through a detection effectiveness evaluation. Findings Specifically, our findings indicate that, compared to other EWMA schemes with varying design specifications, the proposed optimal wEWMA control chart outperforms the traditional EWMA chart by 26% and the original wEWMA chart by 17%, based on the Average Number of Defectives (AND). Practical implications The proposed scheme relies solely on data readily available within the case organization, enabling operations managers to swiftly implement corrective actions to eliminate non-conforming items. Furthermore, the organization can integrate non-conformity detection into its broader quality initiatives, allowing the scheme to function as a strategic tool for both quality management and strategy–quality alignment. Originality/value The optimal wEWMA scheme enhances a previously modified EWMA model, which was designed to effectively detect various shifts in the fraction non-conforming (p). To achieve superior overall performance, this study optimizes the sample size (n) and sampling interval (h), factors that were not addressed in earlier research. The proposed optimal wEWMA scheme also holds promise for broader application across other manufacturing sectors, including household consumer goods (e.g. home appliances) and industrial products (e.g. transformers, aluminium tubes, and printed circuit boards). Future research may examine its effectiveness in monitoring multi-attribute characteristics and high-yield processes.

: The purpose of this study is to investigate how consumers might be protected against improper products when purchasing and selling online. Examining the type and extent of legal protections available to consumers and sellers of non-conforming items is the main goal of this study. A clear understanding of the rights and obligations of consumers and sellers in online transactions is very important. This study uses a normative juridical approach. A normative legal research technique, which entails a qualitative examination of the legal norms found in existing laws and regulations, is employed in this study. The obligations of business actors and consumer legal protection against inappropriate products during online transactions are the main topics of this study. The results of the study show that customers are entitled to products that reflect their preferences, and sellers in the market will be held liable for such errors under the Consumer Protection Act if the goods sold do not match. In order to boost transparency and customer trust, e-commerce also plays a significant role in advising manufacturers to make sure that the pictures of the products being promoted appropriately reflect their state and in promoting the usage of actual product photos. Buyers can take legal action, both litigation and non-litigation, if the goods sold do not match the drawings.

This study investigates the impact of measurement error on variable sampling schemes indexed by Acceptance Quality Limit (AQL) and Average Outgoing Quality Level (AOQL) while considering a known Coefficient of Variation (CV). We present procedures and tables for selecting appropriate variable sampling plans based on specified AQL and AOQL values. In our approach, rejected lots undergo 100% inspection to replace non-conforming items. The operating characteristic (OC) function is analyzed for various CV values, highlighting how measurement error influences the classification of product quality. Our findings emphasize the importance of understanding the relationship between measurement error, CV, AQL, and AOQL in quality control processes, ultimately aiming to enhance product quality and optimize inspection resources.

Accurate parameter estimation is a critical component of effective process control using g charts. While traditional methods like maximum likelihood and Bayesian estimation are widely used, th ey may exhibit limitations in small sample size scenarios, leading to inaccurate parameter estimates. To address these challenges, minimum variance unbiased (MVU) estimators have been developed. For specific conditions, such as limited data and no nonconforming items, bootstrap-based Bayesian estimators offer a computational alternative. However, these estimators may struggle to detect significant process shifts, particularly in the presence of large deviations. This research introduces a novel Bayesian fast double bootstrap approach for parameter estimation in -charts. By efficiently handling small sample sizes and effectively detecting large process shifts, this method aims to significantly enhance the accuracy and reliability of process monitoring. The proposed approach leverages the strengths of both bootstrap and double bootstrap techniques, while addressing their limitations through a computationally efficient algorithm. This advancement is expected to contribute to improved process control and quality assurance in various industrial applications. Key points:•A Bayesian fast double bootstrap (BFDB) approach was developed for parameter estimation in process monitoring, particularly for small sample sizes. Comparative analysis with minimum variance unbiased (MVU) estimators demonstrated the superior sensitivity and computational efficiency of BFDB for process monitoring•A comparative analysis of BFDB and MVU parameter estimation methods revealed that BFDB consistently outperformed MVU in high-quality process monitoring scenarios.

This paper addresses the issues related to inaccurate inspections and high costs in incoming quality control. Incoming quality control refers to the initial inspection process that verifies whether externally provided products, materials, or services comply with specified quality requirements. Traditional methods inspect each item in sequence for a given part and terminate the inspection upon detecting a non-conforming item before proceeding to the next part. To reduce inspection times, we propose a novel approach termed ‘selection of minimal inspection items’, which formulates the selection of inspection items for a batch of parts as decision variables. This approach ensures that all non-conforming parts are detected while minimizing the total number of inspection items. We identify all the inspection items in the initial batch that cover all the non-conforming parts, then develop a set-covering approach to select the minimum inspection items that cover all non-conforming parts. Subsequently, the next batch of parts is inspected using the selected inspection items to identify as many non-conforming parts as possible. Compared to traditional inspection techniques, this approach demonstrates greater cost-effectiveness. Furthermore, we conduct experiments under scenarios with varying numbers of parts and inspection items across different batches to achieve zero-defect inspection, which ensures all non-conforming parts are identified and eliminated through systematic quality control procedures. Algorithms and programs are developed to implement the reported approach. The experimental results show that the proposed approach significantly reduces inspection times while maintaining high quality.

ABSTRACTThe Poisson distribution is often employed to model count data, but it may not accurately represent a dataset with frequent occurrences of zero counts. This limitation often arises in high‐quality processes where the production of nonconforming items is minimal. To address this issue, modified forms of existing distributions such as the zero‐inflated geometric (ZIG) distributions, zero‐inflated Poisson (ZIP), and zero‐inflated negative binomial (ZINB) have been developed to more accurately capture the zero‐inflated (ZI) count data. Control charts under ZIP distribution are effective for monitoring processes with zero defects. However, determining whether the data exhibit over‐dispersion or under‐dispersion is often challenging. To address this challenge and accommodate various dispersion patterns in zero‐defect datasets, a flexible distribution called the ZI Conway–Maxwell–Poisson (ZICOMP) distribution is developed in the literature. This distribution is capable of modeling datasets that are over‐dispersed, under‐dispersed, or equi‐dispersed. In this study, the ZICOMP distribution is integrated with the exponentially weighted moving average (EWMA) control charting structure to efficiently monitor the processes involving ZI count data, regardless of the dispersion level. Extensive Monte Carlo simulations are performed to evaluate the performance of the proposed chart under different parameter settings. Additionally, two real‐life applications are provided to demonstrate the practical implementation and effectiveness of the proposed chart for both over‐dispersion and under‐dispersion scenarios.

Purpose Monitoring attributes is crucial in industrial settings where quality features are not directly measurable. This study introduces a modified Cumulative Sum (CUSUM) scheme, named wCUSUM, to enhance the efficiency of monitoring attribute characteristics. Design/methodology/approach The wCUSUM scheme improves detection capability by elevating the difference between the actual and in-control numbers of nonconforming items to an exponent w. The charting parameters of the wCUSUM scheme are optimized to minimize the Average Number of Defectives (AND) across out-of-control scenarios while satisfying the constraint of the in-control Average Time to Signal (ATS0). A comparative analysis evaluates the wCUSUM scheme’s performance against the semi-optimal wCUSUM chart proposed by Wu et al. (2008). Sensitivity analysis is conducted to examine the impact of design parameters and inertia on the performance of the wCUSUM chart. A solar panel manufacturing case study demonstrates the proposed chart’s superiority. Findings The optimal wCUSUM outperforms the semi-optimal wCUSUM1 (w = 1) by 13.21%, wCUSUM1.5 (w = 1.5) by 10.58% and wCUSUM2 (w = 2) by 20.93% in terms of AND under different settings. The wCUSUM enhances detection speed by 19% and 51%, assuming uniform and Rayleigh distributions of the shift, respectively, in comparison to its traditional counterpart. In addition, the wCUSUM outperforms the traditional scheme by 21% in terms of the Expected value of the out-of-control Average Number of Observations to Signal (EANOS). The case study shows a 65% detection efficacy improvement over the semi-optimal wCUSUM. Research limitations/implications The proposed wCUSUM chart offers practitioners a robust, economic and efficient tool for monitoring attribute processes. Although its current design aims to monitor a single attribute characteristic, future work could explore its application in multi-attribute scenarios. Originality/value This study introduces a novel wCUSUM scheme, tailoring the exponent w to achieve optimal performance. This approach supports customized control chart designs, enhancing adaptability to diverse process monitoring needs.

ABSTRACT This paper presents a methodology for the inventory planning of a two-machine series system, wherein the first and second machines are designated as the upstream and downstream machines, respectively. The upstream machine commences production in the in-control state, and after a period of time, it may transition into an out-of-control state. In such circumstances, the rate of non-conforming items rises significantly. In consideration of the mechanical nature of the process, the time-to-shift variable is presumed to follow a Weibull distribution. Therefore, the upstream machine needs to be overhauled after a set time. During the implementation of overhaul activity, the upstream machine ceases to operate. A delayed buffering-monitoring policy is developed with three goals: (1) The creation of an intermediate buffer between two machines serves to prevent the interruption of the production line utilized by the downstream machine; (2) the reduction of the manufacturing costs, particularly the buffer inventory holding cost; and (3) the shift detection in the upstream machine through successive inspections. Furthermore, a restoration activity is implemented with the objective of returning the upstream machine condition to an in-control state. To do this, one spare part is consumed, which can be purchased under an incremental discount policy.

Optimizing P-Chart Performance Using a Percentage-Based Framework: Application to Nonconforming Items in the Manufacturing Sector

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats.

Multivariate process capability analysis with decision-maker preferences

Assessing the economic-statistical performance of an attribute SVSSI-np control chart based on genetic algorithms

Monitoring the parameter of discrete distributions is common in industrial production. Also, it is often crucial to monitor the parameter of geometric distribution, which is often regarded as the nonconforming item rate. To enhance the detection of nonconforming item, we designed an exponentially weighted moving average (EWMA) scheme based on the weighted likelihood ratio test (WLRT) method, and this scheme is denoted as the EWLRT scheme, specifically designed for monitoring the increase of the parameter in geometric distribution. Moreover, the optimal statistical design of the EWLRT scheme is presented when the shift is known. Results from numerical comparisons through Monte Carlo simulations indicates that the EWLRT scheme performs better than the competing schemes in some scenarios. Additionally, the designed scheme is characterized by its simplicity and ease of use, making it ideally suited for scenarios involving single observation. An example is illustrated to demonstrate the effectiveness of the EWLRT scheme.

The Economic Production Quantity (EPQ) plays a crucial role in the manufacturing process. The traditional EPQ model operates under the assumption that Non-Conforming (NC) items are not produced, and the process remains under control. However, real-world scenarios often involve the production of NC products, leading to process deviations. This study introduces a model that optimizes the design of a control chart to incorporate NC products into the EPQ model. The model integrates inventory and quality-related costs to minimize the expected total cost by determining EPQ, sample size, and control limit. The solution methodology employs direct search techniques to solve this problem. The sensitivity analysis results demonstrate the significant cost reduction achieved by the proposed model compared to the classical EPQ model. The numerical examples presented in the study show an average cost reduction of approximately 21 percent, highlighting the potential effectiveness of the model in cost reduction efforts.

A design methodology based on two dimensional fuzzy linguistic variables for attribute control charts with real case applications

Abstract This paper derives an inspection policy for an economic production quantity (EPQ) model under the assumption that a process may produce non-conforming (NC) items. In various stages of a production process, a department receiving an order uses a single sampling inspection policy to detect NC items. Under such a policy, a lot is accepted if the number of NC items in the inspected sample is equal to or less than the acceptance number. The proposed model considers both EPQ- and quality-related costs. Moreover, economic production order quantity, sample size and acceptance number are considered decision variables. A numerical example is presented, and a set of sensitivity analysis are provided to highlight the effectiveness of the proposed model. The results reveal that when the inspection cost is high, the classical EPQ model achieves a lower expected total cost for the production system compared with the EPQ model with the inspection. In contrast, when the NC cost is high, the EPQ model with the inspection policy outperforms the classical EPQ model, which can significantly decrease the expected total cost.

Rapid technological advancement and implementation of automation and computerization in today's manufacturing set up resulted in many high quality processes, where defects are rarely observed. There are many high quality manufacturing processes where two or more types of defects may be generated from different types of equipment/process problems. The zeroinflated defects data containing two types of defects are commonly modeled by bivariate zero-inflated (BZI) Poisson distribution. Pal and Gauri (2022a) proposed a methodology for measuring capability of a BZI Poisson process. However, they ignored the count of zero defect (ZD) products produced in a BZI process. Because of that, Pal and Gauri (2022a) proposed approach fails to discriminate the BZI processes which produces different proportions of ZD units but having almost the same proportion of nonconforming items with respect to the USL of combined number of defects or USLs of individual defect types. In this paper, a new measure of process capability for BZI processes is proposed that can truly discriminate different BZI processes taking into account the USL of combined number of defects (or USLs of individual defect types) as well as the proportion of ZD units produced in these processes. The proposed methodology is illustrated using two case studies. The results of the case studies show that the proposed index well represents the true capability of BZI processes.

The situation analysis of hot dry rock geothermal energy development in China-based on structural equation modeling

A new sampling plan by integrating the resampling concept in a skip-lot sampling plan of type SkSP-3 with a single sampling plan as the reference plan is proposed and designated as SkSP-3-R plan. The performance metrics are derived by using the Markov chain formulation. A designing methodology using two points on the operating characteristic curve approach is proposed to determine the plan parameters of the SkSP-3-R plan involving minimum the average sample number. The execution of the proposed SkSP-3-R plan is discussed with an illustrative example. The comparative study shows that the proposed plan has better performance where the inspection is involved in terms of nonconforming items rather than nonconformities. In addition, it demonstrates that the proposed SkSP-3-R plan is more efficient than SkSP-2 plan, SkSP-3 plan, SkSP-2-R plan, and SSP. Based on average run length, the SkSP-3-R plan's characteristic of response-to-change in quality is also investigated.