Faulty Items Research Articles

A sustainable fuzzy economic production quantity model (SFEPQM) with sporadic machine failures, inspection and prepayments under carbon tax and cap policy

Goals for sustainability have caused an upcoming change in the emphasis of Economic Production Quantity models, which was previously limited mainly to financial considerations to include aspects of the environment as well. The primary tools used in many nations to reduce emissions are carbon cap and price legislation. However, companies in the production industry may have difficulties with their planning and oversight procedures as they frequently have to cope with faulty products and machinery failures. Previous research has not addressed the comprehensiveness of green investment in machine breakdowns. Considering this problem, we have developed a sustainable economic production quantity model with sporadic malfunctions of machinery and prepayments under carbon tax and cap regulations. Our aim is to minimise the overall cost of faulty items with the investment in green technologies. An approach that combines with the outstanding effectiveness of green strategies to minimise emissions will culminate in a greener production that generates more profit than the traditional system. This study considers two cases i.e. with and without green technology investment (GTI). It also has many predictions, including the execution of inspections afterwards as well as during manufacturing, faulty goods are scrapped while others are sold at discounted price.

Comparative Analysis of Big Data Computing in Industry 4.0 and Industry 5.0: An Experimental Study

A comparison of the use of big data computing in Industry 4.0 and Industry 5.0 was carried out utilizing data collected from the actual world for the purpose of this research. The findings suggest that there has been a 2% drop in the number of faulty items produced in Industry 5.0, coupled with a 1% decrease in the amount of energy used in highly automated companies. According to the findings of the quality control, fault Type B accounts for around 65 percent of the overall defects in Industry 4.0. The results highlight the benefits of Industry 5.0, which capitalizes on human-machine cooperation, data-driven processes, and customized products and services. These insights help to contribute to manufacturing processes that are more efficient, more sustainable, and more quality-driven. Big data computing, Industry 4.0 and 5.0, quality control, and energy efficiency are some of the keywords to look for.

Effect of green investment to reduce carbon emissions in an imperfect production system

Climate change is a serious hazard to human life today, and the greenhouse effect is its key cause. The production sector is one of the biggest contributors to climate change as it accounts for a large part of greenhouse gas emissions. Many nations have created ecological policies and regulations to prevent industries from emitting excessive amounts of carbon emissions (CO2) into the ecosystem. Industry experts believe that green technology might aid in making a more sustainable future as a result of the current climate crisis. Investments in green technologies are rising rapidly which may allow more nations to adopt green technology and transition away from non-renewable resources. This research explores the viability of investing in green technology to achieve a sustainable production scheduling considering an uncertain amount of CO2 during the processes of production, transport, and storage, and thus offers managerial insights for policymakers to reduce both CO2 and overall cost. This study assumes an imperfect production process where a fraction of the items is faulty, and the firm employs a rework approach to rectify the faulty items. The firm invests in green technology to significantly cut CO2 and support innovation to address the climate crisis and benefit communities. The main point of the study is to develop a solution procedure of the problem associated with the amount of CO2 where all the CO2 factors might increase or decrease fuzziness. According to the skills gained by the decision maker we fuzzify all CO2 factors as trapezoidal fuzzy numbers, and we use a signed distance method to defuzzify the model. The results imply that the production sector’s outsize ecological CO2 can be reduced without compromising quality by designing optimal production strategies. The findings also confirm that green investment is the greatest economical method for reducing CO2 and overall costs.

Effectiveness of carbon policies and multi-period delay in payments in a global supply chain under remanufacturing consideration

Carbon emission regulation is one of the most important priorities for the business sector. The rate of carbon generation increases tremendously due to several issues like transportation, repair, or remanufacture. The generation of faulty or imperfect items increases during transportation. Instead of returning them, the retailer can remanufacture/repair those faulty products in their repair shop and sell them to customers as the new product. Carbon dioxide is emitted from this repair shop continuously. In this study, the retailer remanufacturers faulty items under different carbon emission reduction policies and multi-period trade credit financing to control the shortage. Shortages arise due to imperfect products. Three carbon emission reduction policies are reviewed and compared in the context of different credit financing and investments to find the maximum profit. The adaptation of investment is beneficial to reduce carbon emissions and increase the profit of the system. Nine models are built based on long-term credit financing and carbon regulation policies. Analytical approaches are used to find solutions for complex nonlinear profit functions. Numerical illustrations prove that if cycle time is less than the permitted delay period, the system’s profit is 2.58% and 56.76% higher than the other two cases. Limited carbon regulation provides 0.23% and 0.16% better profit than the other two carbon reduction policies. Green investment increases the system profit by 0.063% and reduces emissions by 112 kg per cycle. Adaptation of trade credit policy increases the profit up to 1.55%. Finally, it is noticeable that limited carbon regulation is the best policy when the permissible delay in payment period is greater than the cycle time with remanufacturing under-investment.

Impact of dual uptime-reducing strategies, postponement, multi-delivery, and rework on a multiproduct fabrication-shipping problem

This study examines the joint impact of outsourcing, overtime, multi-delivery, rework, and postponement on a multiproduct fabrication problem. A growing/clear trend in today’s customer requirements turned into rapid response and desired quality of multi-merchandises and multiple fixed-amount deliveries in equal-interval time. To satisfy customers’ expectations, current manufacturing firms must effectively design/plan their multiproduct production scheme with minimum fabrication-inventory-shipping expenses and under confined capacity. Motivated by assisting manufacturing firms in making the right production decision, this study develops a decision-support delayed-differentiation model considering multi-shipment, rework, and dual uptime-reducing strategies (namely, overtime and outsourcing). Our delayed-differentiation model comprises stage one, which makes all common/standard parts of multi-end-merchandises, and stage two, which produces multiple end merchandise. For cutting making times, the study proposes subcontracting a portion of the common/standard part’s lot size and adopting overtime-making end merchandise in stage two. The screening and reworking tasks identify and repair faulty items to ensure customers’ desired quality. The finished lots of end merchandise are divided into a few equal-amount shipments and distributed to customers in equal-interval time. We employ mathematical derivation and optimization methodology to derive the annual expected fabrication- inventory-shipping expense and the cost-minimized production-shipping policy. A numerical demonstration is presented to exhibit our research scheme’s applicability and exposes the studied problem’s critical managerial insights, which help the management make beneficial decisions.

A hybrid delayed differentiation multiproduct EPQ model with scrap and end-products multi-shipment policy

The present work intends to optimize a hybrid delayed differentiation multiproduct economic production quantity-EPQ model with the scrap and end-products multi-shipment policy. Since the requirements of multi-goods have a standard part in common, our fabrication planning adopts a two-phase delayed differentiation strategy to make the standard components first and produce the finished multi-goods in the second phase. Implementing a partial subcontracting option (with the additional expense) for the standard parts helps us to expedite the required uptime in the first phase. A screening process identifies the faulty items that need to be removed to ensure the in-house production quality. A multi-shipment plan delivers the finished lot of end-products to clients in fixed time intervals. This study optimizes the overall operating expenses of this intra-supply chain system, including fabrication, delivery, and client stock holding, through our proposed modeling, formulation, and optimization procedure. In addition, this study gives a numerical demonstration of the obtained results’ applicability and usefulness to managerial decision-making.

Inventory system with generalized triangular neutrosophic cost pattern incorporating maximum life-time-based deterioration and novel demand through PSO

At the manufacturing plant or while the products are being transferred from one supply layer to another, there is a considerable possibility of receiving damaged or faulty items mixed in with non-defective commodities. This research focuses on the non-defective and defective products that are shipped to retailers by their suppliers. The retailer reworks faulty items to make them non-defective, and the retailer receives a discount on the cost of purchasing defective items. The presented inventory system addresses the uncertainty in inventory costs and also considers the deterioration of items with prioritized maximum product life. In this study, our aim is to minimize the total inventory cost when demand rate as a function of quality and power pattern of time under crisp and generalized triangular neutrosophic environments. Based on the payment deal, interest charges are imposed only when the payment delay has passed a particular allowable time limit. The neutrosophic number, which provides three different types of membership functions representing truth, hesitation, and falseness, is used in the inventory model to handle the cost pattern’s uncertainty. A particle swarm optimization approach is used to analyze the proposed inventory model, and the results are validated using a numerical example and sensitivity analysis for various parameters.

Streamlining the External Ventricular Drain and Intracranial Pressure Monitor Procedural Setup: A Quality Improvement Initiative

External ventricular drain (EVD) and intracranial pressure (ICP) monitor placements are among the most common critical care procedures for severe brain injury. Quality improvement initiatives have streamlined similar processes. The aim of the project was to decrease the time to collect supplies for EVD or ICP monitor placement by 25% by April 1,2021. The project followed the define-measure-analyze-improve-control 6 sigma framework. Several quality gaps were identified: equipment stored separately, delays in replacing faulty items, and wasted resources. The team defined the process using the suppliers-inputs-processes-outputs-customers+requirements method, measured time to collect supplies, and analyzed data with an Ishikawa/fishbone diagram. The improve phase included a kaizen burst to generate solutions and an impact/effort grid to evaluate options. The team concluded that the optimal plan was to stock a mobile EVD cart and an ICP monitor pole with disposable go-bags. The average time for nurses to collect EVD placement supplies decreased from 411 to 63seconds (7-1minute), and the average time for nurses to collect ICP monitor placement supplies decreased from 418 to 53seconds (7-<1minute). Residents decreased the time to obtainEVD placement supplies from 330 to 56seconds (6-<1minute) and ICP monitor supplies from 489 to 77seconds (8-1minute). Feedback was overwhelmingly positive and focused on improved process efficiency and reduced waste. The time reduction and enthusiasm are likely associated with the simplicity and comprehensiveness of the intervention design. Including key stakeholders in decision-making and succinct communications reduced resistance to change.

A critical study of product liability and analysis with special reference to India

Consumers have legal redress for any harm brought on by a faulty product under product liability law. According to estimates, faulty items have a detrimental impact on millions of individuals throughout the world, and the sellers or producers wind up shelling out a sizable sum for products-liability insurance as well as damages. A product must satisfy a consumer's reasonable expectations, thus it is the duty of the producers and the buyers to guarantee the product's safety and quality in accordance with its description. A modest way of life and locally produced goods meant that anybody who experienced harm from a faulty product could deal with the maker directly without the need for legal action or government regulation. However, owing to the growth in problems emerging from faulty goods, changes in the instruments of production and consumption, such as the industrial revolution and technical advancements, among others, have led to the establishment of products-liability law. To safeguard the rights of its customers and the general public at large, India is recognising and realising the need of developing and implementing a strong product liability framework. In specifically, the article addresses the importance of product liability and professional indemnity insurance plans in India as well as some significant developments and their impact on liability insurance. The Act's whole chapter devoted to product liability provides insight into the anticipated effect on liability insurance.

Fuzzy acceptance sampling plan for transmuted Weibull distribution

In conventional sampling plans, the rate of the fraction of faulty items is supposed and fixed, but in some practical situations, this value is not fixed but fuzzy. To attain real and flexible value, we used transmuted Weibull distribution in a fuzzy environment. Fuzzy transmuted Weibull distribution is based on fuzzy set theory. According to our investigation, transmuted Weibull is not used in acceptance sampling in a fuzzy environment. The improvement of this research is to evaluate the vague portion of malfunctioning items (tilde{p }) based on the probability distribution function of transmuted Weibull model and attain a fuzzy operating characteristic curve. First-time transmuted Weibull distribution is applied in acceptance sampling plan for cases where the quality of the product is imprecise. Therefore, fuzzy statistics has been used in this design instead of classical statistics. The results are described by numerical examples and an application of real data is considered for illustration.

Coordinating a supplier–retailer JELS model considering product quality assessment and green retailing

Environmental awareness among the people is constantly increasing, which has resulted in massive pressure on companies from various stakeholders including the government and consumers to mitigate the detrimental effects on the environment. This work deals with a joint economic lot size (JELS) model to enhance the greening efforts of a product that flows along a two-level supply chain (supplier–retailer). Both selling price and greening effort level of the product influence the market demand. Here we assume that every individual lot shipped to the retailer carries some random defective items. Each lot received by the retailer goes through an error-free sub-lot sampled inspection process to remove the defective items. A fraction of faulty items are classified as usable and are sold at a salvage value and the rest items are disposed of. The retailer has to bear per item penalty cost for selling the uninspected defective items. We develop the profit function under three decision-making scenarios: centralized, decentralized, and coordinated. Coordination is made based on a trade-credit scheme under which the retailer changes his/her optimal decisions according to a centralized policy. We obtain a minimum and maximum credit period duration which encourages both the retailer and the supplier to follow the coordinated decision-making policy. The coordinated model suggests that more emphasis should be given to the greening effort level for higher profit. It is observed that, in many cases, a sub-lot inspection gives better results compared to a full lot inspection.

Mathematical modeling for a multiproduct manufacturing system featuring postponement, external suppliers, overtime, and scrap

Manufacturing firms operating in today’s competitive global markets must continuously find the appropriate manufacturing scheme and strategies to effectively meet customer needs for various types of quality of merchandise under the constraints of short order lead-time and limited in-house capacity. Inspired by the offering of a decision-making model to aid smooth manufacturers’ operations, this study builds an analytical model to expose the influence of the outsourcing of common parts, postponement policies, overtime options, and random scrapped items on the optimal replenishment decision and various crucial system performance indices of the multiproduct problem. A two-stage fabrication scheme is presented to handle the products’ commonality and the uptime-reduced strategies to satisfy the short amount of time before the due dates of customers’ orders. A screening process helps identify and remove faulty items to ensure the finished lot’s anticipated quality. Mathematical derivation assists us in finding the manufacturing relevant total cost function. The differential calculus helps optimize the cost function and determine the optimal stock-replenishing rotation cycle policy. Lastly, a simulated numerical illustration helps validate our research result’s applicability and demonstrate the model’s capability to disclose the crucial managerial insights and facilitate manufacturing-relevant decision making.

An Adversarial Risk Analysis Framework for Batch Acceptance Problems

We provide an adversarial risk analysis framework for batch acceptance problems in which a decision maker relies exclusively on the size of the batch to accept or reject its admission to a system, albeit being aware of the presence of an opponent. The adversary acts as a data-fiddler attacker perturbing the observations perceived by the decision maker through injecting faulty items and/or modifying the existing items to faulty ones. We develop optimal policies against this combined attack strategy and illustrate the methodology with a review spam example.

Two-echelon imperfect production fuzzy supply chain model for reliability dependent demand with probabilistic deterioration and rework

This paper develops a fuzzy two-layer supply chain for manufacturer and retailer with defective and non-defective types of products. The manufacturer produces up to a specific time, including faulty and non-defective items, and after the screening, the non-defective item sends to the retailer. The retailer’s strategy is to do the screening of items received from the manufacturer; subsequently, the perfect quality items are used to fulfill the customer’s demand, and the defective items are reworked. The retailer considers that customer demand is time and reliability dependent. The supply chain considers probabilistic deterioration for the manufacturer and retailers along with the strategies such as production rate, unit production cost, cost of idle time of manufacturer, screening, rework, etc. The optimum average profit of the integrated model is evaluated for both the cases crisp and fuzzy environments. Managerial insights and the effect of changes in the parameters’ values on the optimal inventory policy under fuzziness are presented.

A delayed differentiation multiproduct model with the outsourcing of common parts, overtime strategy for end products, and quality reassurance

Production planners today must simultaneously face with the time and quality demands of various goods externally and meet limited capacity internally. This study presents a two-stage delayed- differentiation multiproduct model that considers the outsourcing options for common parts, overtime strategy for end products, and quality reassurance to assist in making fabrication runtime decisions that are cost-effective. Stage one produces all necessary common intermediate components for end products. To reduce stage one’s utilization/uptime, this study adopts a partial outsourcing option. Stage two uses an overtime strategy to fabricate end products that further shorten the uptime. The production processes in both phases are assumed to be imperfect. This study employs the reworking/scrapping of random faulty items to reassure product quality. The researchers build a model to depict the proposed problem’s characteristics and used the mathematical modeling, analysis, and optimization approach to determine the best rotation cycle length that minimizes the system’s expenses. Further, in this study, the researchers provide sensitivity analyses and a numerical illustration, which validate the result’s applicability and exhibit its capability. This result contributes to practical multiproduct-fabrication by (1) deriving the optimal manufacturing policy for a delayed-differentiation multiproduct system with dual uptime reduction policies and quality reassurance; and (2) offering a decisional model that allows production planners to explore the collective/separate effect of a quality-ensured and dual uptime reduction strategy on a problem’s operating policy and crucial system performance indicators, which assists in cost-effective decision-making.

PREVISION AND OPTIMISATION OF REPAIRABLE SPARE PARTS: A CASE STUDY IN THE PETROLEUM INDUSTRY

This study was motivated by the high costs incurred by an energy company for repairable spare parts for faulty mission-critical items, particularly with those that operate until failure. The objective of this paper is to develop and apply a method for repairable spare part inventory management of run-to-failure equipment. To achieve a robust method that incorporates the data collected from previous failures, such as environmental factors and operating conditions, we propose an optimisation approach based on an accelerated failure time model. Accelerated failure time is used as a reliability regression model with covariates to describe different operational conditions. An algorithm is also developed to consider the repairable nature of the equipment, predicting the number of spare parts based on the expected number of failures in the period and the equipment repair cycle. The proposed method is applied using data from three different power units of electrical submersible pumps, a mission-critical item in oil production. The results show an average reduction of 60.6 per cent in the required number of spare parts, considering an average fill rate of 95.33 per cent. This reduction implies an estimated annual savings of around US$664,720 in inventory costs, considering the analysed units.

Mathematical and analytical approach for the management of defective items in a multi-stage production system

Reworking defective items any in multi-stage production systems have become a common issue for production industries. Due to random defective rate of production, it may contain several imperfect items as wastes. The reduction of those wastes through reworking in each stage of a cycle or at the end of stage of a cycle by an identifying opportunity design technique. A design is developed for making such type multi-stage production system. For this, imperfect products may be reworked within each cycle while avoiding any shortages or reworked after end cycle with shortages in each cycle. The aim of the model is to diminish wastes by reworking strategy for minimizing the total cost. For this purpose, the proposed study develops a multi-stage production model for an optimum production batch policy with random production of imperfect products. Two-stage inspection is considered for detecting those faulty products and ensuring that there are no faulty items anymore. As it is a long-run production system, setup cost plays an important role, an attempt at setup cost reduction has been made to reduce it by a discrete investment for each stage along with a budget constraint. The Lagrangian method is employed to solve the model and the global minimum cost for optimal decision variables is obtained. Ten lemmas are established to obtain the global optimum solution. The model shows that a large investment to reduce the setup cost is better than a constant setup cost. It is found that the imperfect items should be reworked after the end cycle. There are eight numerical examples, eight special cases, a sensitivity analysis of the key parameters, and sixteen graphical representations to demonstrate the proposed model.

MODERNIZATION OF BUSHES OF WELLS ON THE LATE STAGE OF DEVELOPMENT

Many oil deposits of Russia quickly become mature production fields having an adverse effect on the economic feasibility of their development. This fact makes the oil companies improve methods and recovery techniques of these fields to lower the costs of oil reservoir engineering. Large oil companies apply for well clusters modernization to decrease well service costs. With time the extracted oil becomes more and more viscous therefore there is a problem of decreasing this viscosity. The article considers the transition from the scheme of one-line crude oil gathering system to separate gathering. The modernized cluster has a special water feed station providing a decrease in oil viscosity up to the values comparable to water viscosity by injecting purified and treated water from water-injection pumping station. The price of the equipment after modernization is more expensive due to a large number of expensive motor operated valves, but it is justified by metering units’ maintenance with non-stop production. Also, the maintainability of the equipment is improved, because the most of the installations are on the surface now. The repair teams do not need to remove faulty items from the ground for troubleshooting. The corrosion protection of the equipment is also improved because the modernized cluster is supplied by inhibiting corrosion protection of pipelines made by chemical injection skid. Thus, the optimization of oil and gas well clusters organization could become an expensive enterprise, but it is quite justified as in the long term it is favourable.

Evaluating alerting systems from descriptions

Decisions in almost all domains of life receive support from automation in the form of alerts, binary cues, recommendations, etc. People often use automation or decision aids without having experience with the system, because the system may be new or because they rarely use it. When such experience is unavailable, people will base their use of the system on information they may have received about it and on descriptions, often given as probabilities or proportions. Examples are the sensitivity and specificity of a diagnostic procedure in medicine or the True Positive and False Positive rates of a detector. People use these descriptions to decide to what extent they can rely on the information. So far, it is unclear which aspects of the information about a system determine people’s evaluation of the system from a description. These evaluations will determine the trust they put in the indications from the system and the adjustment of system properties, such as thresholds. To gain some insights into this issue, we conducted an experiment. We developed descriptions of 12 systems in a quality control setting, in which participants had to detect faulty items in a production process. We used Signal Detection Theory (Green &amp; Swets, 1966) to determine the system properties. The systems differed in d’ (1.5 or 2.5), the threshold setting lnβ (-1, 0 or 1) and the prior probability for a signal pS (.05 or .2). Half of the participants saw diagnostic values, receiving descriptions in terms of the probabilities of Hit and False Alarms, while the other half saw descriptions as predictive values, receiving the Positive Predictive Value (PPV) and the Negative Predictive Value (NPV) of each system. In the past, we have shown that people adjust system thresholds better when they see predictive values (Botzer, Meyer, Bak, &amp; Parmet, 2010). Fifty-six students evaluated the systems in a classroom setting on a scale between 0 (completely useless) and 10 (perfect). In addition to the d’ and lnβ, which we specified when we designed the systems, we also computed for each system the Probability of Correct Indication (pCorrect), the Expected Value (given the costs and benefits in the description), and the transmitted information according to Information Theory. We analyzed the results with multivariate analyses of variance and by computing the correlations between the evaluations and system properties. The results showed that participants’ responses were mainly correlated with d’. The effects of the threshold setting lnβ and of pS were small, compared to the effects of d’. The correlations with the Expected Value and the transmitted information were smaller and could be explained through d’. Thus, people evaluated a system in terms of its ability to differentiate between signal and noise. They did not evaluate the system according to the economic value it provided or the transmitted information. In addition, participants evaluated systems with different thresholds (lnβ) similarly. This means that in our experiment participants did not differentiate between more and less appropriate threshold settings. The ability to identify better or worse settings is important, because these settings are often the main system parameter users can adjust. These findings, in addition to the inherent problems that already exist in user adjustments of systems (Meyer &amp; Sheridan, 2017), make it unlikely that people can adjust system settings correctly.

Sampling control according to a quantitative attribute

The distribution of a qualitative characteristic of an item in sampling control according to a quantitative attribute belongs to a two-parametrical family that depends on unknown parameters of shift and scale. Uniformly more powerful (u.m.p.) invariant criteria are considered in order to verify a hypothesis regarding the proportion of faulty items in a lot. An asymptotic approach is proposed, and a random volume of sampling is used.