Defective Items Research Articles

A knowledge graph-based inspection items recommendation method for port state control inspection of LNG carriers

The safe navigation and operation of Liquified natural gas (LNG) carriers is crucial in maritime transportation. The Port State Control (PSC) inspection is the primary method for identifying deficiencies in vessels. Different from other vessels, the PSC inspection of LNG carriers requires knowledge of specialized transport equipment. Therefore, effective management of inspection knowledge and precise targeting of inspection items are particularly crucial. Knowledge graph is an efficient way to manage knowledge in the context of artificial intelligence. In this study, an LNG carrier PSC inspection knowledge graph is constructed for fusing multisource knowledge data from PSC inspection. Additionally, a knowledge graph-based recommendation model, namely the improved knowledge graph convolutional network combined with pretrained translation embedding (PT-KGCN), is developed to recommend inspection items and knowledge. The PT-KGCN model first carries out knowledge graph embedding. It then predicts possible deficiencies on the basis of historical PSC inspection data. Finally, it recommends inspection items by correlating the predicted deficiencies with knowledge from the knowledge graph. The results show that more than 87% of defective items in historical data are correctly predicted. The research findings can provide ideas for the practical application of knowledge data in the inspection fields.

Analysis and implementation of the dmaic six sigma method as an effort to control weaving fabric production defects: Industrial textile case study

Defective items are those that are produced during the manufacturing process but do not meet the imposed quality standards. These items can be repaired economically if certain costs are covered, which must be less than the post-repair selling value of the product. Semi-finished textiles are produced by the research institute. There are still issues with the production of semi-finished cloth. As a result, the researcher attempted to control and evaluate flaws in the production of semi-finished fabrics by combining the DMAIC approach (Define, Measure, Evaluate, Improve, and Control) with the six-sigma method. A million production operations have a 3.3% Defect Per Million Opportunities (DPMO), or a sigma level value of 2,625 or 133,219 probability of damage, according to the study's findings. There are two types of production-related damage: non-process and process. After one month of implementing and evaluating improvements, there has been a gradual improvement in all production processes, with the most recent calculation for June 2024 yielding a sigma value of 3.51.

Determination of Single Sampling Attribute Plans Based Upon Dodge Roaming Model with Application

Bayesian sampling plans for production inspection involve using a sampling method to assess the features of the plan, under the assumption that defect rates fluctuate randomly among different production batches. This results in a likelihood distribution that can be established through experience and the quality information at hand. In this study, the parameters of a single Bayesian sampling plan were determined using the Beta-Binomial distribution, and were subsequently contrasted with parameters from other single sampling plans. Based on research findings, (Ala corporation for soft drinks) oversees the control of product quality. Since the variable fluctuates randomly between manufacturing batches, 120 batches were selected to calculate the defect rate by analyzing batch size and number of defective items. Applying Bayesian and decision-making models can lead to the development of a single sampling inspection procedure that closely approximates the actual quality level. When the decision-making model was used, the researchers discovered that the sample size was smaller and led to lower inspection costs compared to other inspection plans.

Optimal Pricing and Retailing Strategy for an Assembled Product Manufacturing–Remanufacturing Process under Carbon Emission Regulations and Autonomation

Online-to-offline (O2O) retailing offers unique opportunities for customizable assembled products with spare parts. Customers can browse and configure their desired product online, selecting from various components. Imperfect production, where a certain percentage of products have defects, can be amplified in the manufacturing system. Stricter carbon emission regulations put pressure on manufacturers to minimize waste. This creates a tension between discarding imperfect products, generating emissions, and potentially offering them at a discount through the O2O channel, which could raise quality concerns for consumers. In this study, an imperfect single-stage production process is examined, incorporating manufacturing–remanufacturing within a single stage for assembled products containing various spare parts. The study explores an investment scenario aimed at enhancing the environmental sustainability of the product. Additionally, two carbon emissions regulation strategies, specifically carbon cap-and-trade regulation and carbon taxation, are evaluated for their effectiveness in mitigating carbon footprints. The identification of waste, particularly in the form of defective items, is achieved through automated inspection techniques. The demand for spare parts associated with the assembled products is intricately linked to the selling prices set across diverse channels. Finally, the total profit of the manufacturing system is maximized with the optimized value of the selling prices, order quantity, backorder quantity, and investments in autonomated inspection, setup cost, and green technology. Numerical illustrations show that system profit was optimized when the defective rate followed a triangular distribution under carbon cap-and-trade regulation and when green technology investment helped to enhance retailer profit by 18.12%, whereas autonomated inspection increased retailer profit by 10.27%.

A sustainable manufacturing–remanufacturing inventory model with price and green sensitive demand for defective and usable items

A sustainable manufacturing–remanufacturing inventory model with two production phases is presented in this study. In this two phases model, the first phase is used for manufacturing whereas the second phase is used for both manufacturing and remanufacturing. Keeping in mind about environment and price of products, it is presumed that market demand rate is influenced by the greenness of item and unit selling price simultaneously. Remanufacturing is performed for non-serviceable items i.e., defective items which are produced from imperfect production and used items that are purchased from the market. But the products for which remanufacturing cost is more than manufacturing cost are scraped off. In the production process carbon releases from setting up, manufacture and holding of the perfect items. To mitigate the carbon emission carbon tax regulation is imposed. Next, concavity of the profit function is driven through the pseudo concavity. Profit is maximized by minimizing the economic, environmental, and social effect. The models are illustrated by a numerical example and a sensitivity analysis is also elaborated for fixed parameters. Special cases are provided to compare the result with previous literature. The results shows that manufacturer wishes to engage in remanufacturing as remanufacturing cost continuously increases with respect time. Another important finding is that the manufacturer can choose remanufacturing as the cost is lower than manufacturing for saving environment. Finally, mathematical findings are listed and managerial implications are recorded.

Economic production quantity model with defective items, imperfect rework process, and lost sales

Economic production quantity model with defective items, imperfect rework process, and lost sales

Shewhart-EWMA chart for monitoring binomial data subject to shifts of random amounts

Attribute charts are used widely for monitoring binary events in manufacturing, service, and healthcare processes. While Shewhart type charts are efficacious in detecting large or sudden shifts in a nonconforming rate p, exponentially weighted moving average (EWMA) type charts tend to be more powerful for detecting smaller or gradual shifts. Since many processes can shift by various amounts, this paper proposes a combined optimized Shewhart-EWMA dual chart that combines the strengths of both charts to quickly detect shifts of any random magnitude within a defined range. The performance of this combined Shewhart-EWMA chart is optimized by allocating total detection power between the Shewhart chart element and EWMA chart element in a manner that yields the best overall effectiveness while maintaining the false alarm rate at a specified desired level. In numeric analysis under a range of settings, the detection speed of this combined Shewhart-EWMA chart is found to outperform individual Shewhart and EWMA charts, with up to 499% and 31% fewer average number of defective items respectively until process shifts of random sizes are detected. It also demonstrates enhanced overall efficacy when compared to synthetic, cumulative sum, combined Shewhart-synthetic, and combined Shewhart-CUSUM charts.

A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model’s consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model.

Impact of Learning on Ordering Policy for Deteriorating Imperfect Quality Items with Promotion Dependent Demand, Partial Trade-Credit and Shortages

Objective: The objective of the developed inventory model is to find the optimal solution for the retailer, while performing inspection for the defectives, and also to offer partial credit periods, where shortages are allowed. Methods: To derive the required solution which benefits the retailer, the partial derivatives method for the decision variables where used and to solve the complicated differential equations the necessary and sufficient conditions has been derived and the theorem and its proofs are given. Findings: In this research findings, the optimal credit period policy and promotional effort dependent demand pattern that offers more profit to the retailer, provided with inspection policy for finding defective items in the lot. Shortages are allowed and completely backordered. Numerical examples and sensitivity analysis are performed to verify the reliability of the developed model. Novelty: This study presents a new model wherein the retailer offers credit period and with shortages that will apt the real life situation as the model developed here is for deteriorating items, which is inevitable to maintain the perfect system. Keywords: Multi­Echelon Inventory Model, Sustainability, Shortages, Credit Period Offers, Learning In Inspection

A Sustainable Supply Chain Model with Low Carbon Emissions for Deteriorating Imperfect-Quality Items under Learning Fuzzy Theory

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller’s responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain.

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 flexible planning approach for integrated lot sizing and rework planning with random proportion of defective products

We consider a stochastic capacitated lot sizing problem with in-line rework. Both defective and defect-free products can be the result of an imperfect production process in real-world production systems. However, the proportion of defective items in a production lot is subject to uncertainty. For economic and environmental reasons, the possibility to rework defective products appears reasonable since these products usually have considerable value. In this paper, a nonlinear model formulation for integrated lot sizing and rework planning is proposed. We use a sample average approach to approximate the generic nonlinear model formulation. To cope with the randomness of the proportion of defective products, we apply a flexible planning approach that allows the adjustment of production and rework quantities. We conduct extensive numerical investigations to evaluate the performance of the proposed planning approach.

Unified strategy of production, distribution and pricing in a dual-channel supply chain using leasing option

The surge in e-commerce has significantly altered consumer shopping habits, shifting towards online purchases. This change renders the traditional single-channel supply chain insufficient, often resulting in lost profits. Consequently, many businesses now incorporate an online channel alongside their physical stores. This shift has led to the adoption of dual-channel supply chains by manufacturers, aiming to attract a broader customer base and expand market share. Leasing options are becoming increasingly popular, offering a novel method for consumers to acquire products. However, studies on dual-channel supply chains that include leasing options are scarce and typically focus on single-period pricing. This paper explores a realistic, centralised dual-channel supply chain model that encompasses manufacturing, remanufacturing, refurbishing, distribution, and storage, along with selling, leasing, and repair services. A mathematical model is developed for a multi-period environment. This model simultaneously optimises manufacturing, remanufacturing, distribution strategies, storage costs, service offerings, and pricing for both sold and leased products, considering repair services for defective items. The paper presents numerous numerical analyses to demonstrate how integrating leasing services impacts the supply chain's profitability, the effect of customer preferences on pricing strategies, and how the cost of collecting used products influences remanufacturing decisions over a set time frame.

Impact of Inspection Error of an Economic Production Quantity Model with Acceptance Sampling, Defective Items, and Rework

Abstract: In this model, the manufacturer needs to inspect and rework defective items due to the flawed manufacturing process. When checking for defective products, two types of errors, namely Type 1 error and Type 2 error, may occur at the inspection stage. If a lot of defective materials are rejected during the inspection, a non-destructive screening process separates the material into reworkable and non-defective categories. This study includes inspection rate insufficiency (IRI) and inspection rate sufficiency (IRS) based on the relationship between inspection and production rates. Rework Priority Policy (RPP) is followed in every scenario. This paper aims to minimize the inspection error and optimize the total profit. Numerical examples help demonstrate the value of this well-established model. A sensitivity analysis of this study was carried out to check its accuracy.

Unequal shipment policy in a sustainable production inventory system considering defective items and multi-level price breaks

In this time and age, sustainability is a global phenomenon. This article represents a vendor–buyer integrated inventory system considering defective products. A multi-level quantity flexibility contract with unequal shipment policy is used here to achieve better productivity. Carbon emission for inventory holding and transportation is also considered to make the model sustainable. The vendor provides a multi-level quantity flexibility scheme to encourage the buyer to order additional quantities and deliver it in several unequal sized shipments. There are some imperfect products in each lot due to the unreliable production process, and those products are rejected after the inspection at the buyer's end. An algorithm is proposed to optimise the overall expense of the proposed system. We examine several shipment policies and determine the optimal shipment policy to minimise the system expenditure. Through detailed numerical analysis, we have validated our findings and the soundness of our model. The aim of this study is to bridge the gap between the existing literature and provide insights to industrial practitioners and academic researchers alike. We obtain the optimal order quantity, shipment policy and shipment frequency that minimise overall expenses considering carbon emission costs.

The Loan and its Provisions in Islam

Understanding loans is crucial for Muslims to manage their daily lives effectively. This study aims to examine and comprehend the subject through the lens of the Noble Qur’an, noble hadiths, and the sayings of honorable companions. The loan categories, components, and terms were also addressed. This research demonstrates the permissibility of loans in Islamic law, as supported by evidence from the book and Sunnah of the Prophet. The loan process involves various components, such as the formula, two contracts, and the borrowed funds. Additionally, research has demonstrated this. Loans are subject to various conditions and controls that determine their validity and legitimacy. These conditions may include requirements such as a mortgage, a guarantor, or a written agreement. However, it is considered usury to stipulate an increase in the consideration or to require a gift to be presented to the borrower, as these conditions are deemed corrupt. It is impermissible to stipulate the exchange of a defective item for a valid one or to stipulate the sale of one's house, among other things.

A Novel Model for Economic Recycle Quantity with Two-Level Piecewise Constant Demand and Shortages

This paper focuses on the production systems that may produce a proportion of recyclable defective products. The developed model is called an Economic Recycle Quantity (ERQ) model with the assumption of a full recovery of defective items. The defective parts are collected during the production-off time and can be used during the next production cycle of the same category. The demand rate of the non-defective items is a two-level piecewise factor—one during the production-run time and another during the production-off time. The developed model aims to optimize the total inventory cost, the order quantity, and the amount of recyclable defective items that represent the ERQ. The mathematical formulations of the model are deduced theoretically. The model was solved analytically, and the optimal results are illustrated. Sensitivity analysis is carried out to investigate the effect of varying system parameters and validate the proposed model. Results of the sensitivity analysis show that the consideration of defective part recycling reduces the total inventory cost where the raw material is reduced. The cost reduction is about 1%; of course, the environmental impact is more appreciated. Furthermore, the managerial implications are described, and the future perspectives are discussed.

Control charts using half-normal and half-exponential power distributions using repetitive sampling

This manuscript presents the development of an attribute control chart (ACC) designed to monitor the number of defective items in manufacturing processes. The charts are specifically tailored using time-truncated life test (TTLT) for two lifetime data distributions: the half-normal distribution (HND) and the half-exponential power distribution (HEPD) under a repetitive sampling scheme (RSS). To assess the effectiveness of the proposed control charts, both in-control (IC) and out-of-control (OOC) scenarios are considered by deriving the average run length (ARL). Various factors, including sample sizes, control coefficients, and truncated constants for shifted phases, are taken into account to evaluate the performance of the charts in terms of ARL. The behavior of ARLs is analyzed in the shifted process by introducing shifts in its parameters. The superiority of the HEPD-based chart is highlighted by comparing it with both the HND-based ACC and the ACC based on the Exponential distribution (ED) under TTLT using RSS. The results showcase the superior performance of the proposed HEPD-based chart, indicated by smaller ARL values. Additionally, the benefits of another proposed ACC using HND are compared with the ED-based ACC under RSS, further confirming the effectiveness of the HND-based approach through smaller ARLs Finally, the proposed control charts are evaluated through simulation testing and real-life implementation, emphasizing their practical applicability in real-world manufacturing settings.

Analyzing EOQ model for imperfect items with time dependent demand under substitution of two products

The inventory model is based on the quantity of order and the time at which orders for particular goods are to be placed. In this paper, models with defective items have been discussed and the EOQ approach has been shown. One-way substitution of item is used in case of scarcity of the first item. The demand for the items is based on the Heaviside function. It is noticed that the presence of defective items hampers the demand of the two items differently. To establish the virtue of our model, a numerical example and sensitivity analysis is imparted.

Designing Of Double Sampling Plan Indexed Through Six Sigma Quality Level – 1 Using Truncated Binomial Distribution

Among the various probability distributions utilized to characterize situations where an observational apparatus becomes active only upon the occurrence of at least one event, the Zero Truncated Poisson Distribution (ZTPD) stands out. Shanmugam (1985) demonstrated the applicability of the ZTPD in modeling scenarios such as second quality lots, where there exists the possibility of at least one defective item in the sample. In this paper, a systematic procedure for constructing Double Sampling Plans, indexed through Six Sigma Quality Level – 1 (SSQL - 1), is presented. The methodology employs the Truncated Binomial Distribution (TBD) as the baseline distribution. Furthermore, to facilitate the selection of these plans, tables generated using an Excel packages are provided, ensuring ease of use and accessibility.