Product Failure Rate Research Articles

Analysis of Quality Control for Furniture Production using Pareto Diagram and Fishbone Diagram at PT. SKM Jati

This research aims to describe Quality Control in Improving the Production Efficiency of Tables and Chairs Using Pareto and Fishbone Diagrams at PK. SKM JATI. The type of research used is qualitative descriptive with secondary and primary data, which is intended to collect data and information about a problem and facts related to the object and the research site as they are during the study. This research was conducted in Madiun Regency by interviewing the owner of PK. SKM Jati. Data collection techniques used in this study include in-depth interviews, observations, and documentation techniques to obtain accurate data for creating Pareto and fishbone diagrams. The research results show that PK. SKM Jati still faces obstacles that hinder production efficiency, with defects such as holes in the wood, moist wood, and varnish failures. The highest production failure rate is varnish failure, followed by holes in the wood, and lastly, moist wood. Through fishbone diagram analysis, four main problems are identified: people, materials, machines, and methods.

Case-Based Reasoning in Achieving Sustainability Targets of New Products

Improving product sustainability is becoming an increasingly significant challenge for modern enterprises. A growing number of manufacturers are interested in enhancing product sustainability throughout the product life cycle. This study is concerned with using case-based reasoning to identify ways of improving product sustainability and select variables for model specification. Parametric models are applied to search for opportunities to improve product sustainability. This can be achieved through changes introduced at the product design stage. Simulations are performed using constraint-satisfaction modeling to identify conditions for achieving the sustainability targets of new products. Constraint-satisfaction modeling provides a suitable framework for finding all possible sustainability-enhancing changes (if any) during the new product development process. These changes may support R&D specialists in identifying opportunities to improve the sustainability of new products. We demonstrate the usefulness of the proposed approach with an example in which our method enabled a reduction in the product failure rate and an increase of battery lifespan for a robot vacuum cleaner line. We analyzed several factors affecting two targets of product sustainability: minimizing the product failure rate and maximizing battery lifespan. Our findings indicate that R&D staff size is the biggest factor in reducing the product failure rate, and that battery capacity is the most significant factor in battery lifespan.

Optimization of a preventive maintenance strategy for a product with limited repair times

This paper proposes a base warranty and replacement warranty strategy for repairable products. It should be emphasized that the product failure rate is low in the early stage of the base warranty. Therefore, in order to reduce the manufacturer’s maintenance cost, the manufacturer does not carry out preventive maintenance in the early stage of the base warranty, and carries out regular preventive maintenance in the later stage of the base warranty. At the same time, if the number of product failures in the base warranty exceeds the specified limit, the manufacturer must replace products for customers free of charge according to the warranty contract. After the product is replaced, if the number of product failures does not exceed the limit, the manufacturer will continue to provide customers with the remaining base warranty period of the original product, and the warranty period will continue to be extended for a period of time. By minimizing the expected cost rate, it is shown that the optimal maintenance policy for warranty products is unique. Finally, we give a numerical example and study the effects of periodic preventive maintenance and other relevant parameters on the optimal solution.

Optimal maintenance policy for imperfect production systems using reliability function and defect rate

AbstractThis study examines how a manufacturer can improve the robustness of an imperfect production system by implementing maintenance activities, which can be measured in terms of system reliability and product failure rate. A two‐dimensional maintenance policy comprising system reliability and the product defect rate is proposed to assess maintenance activity costs. The optimal thresholds of the two dimensions are analyzed to investigate the trade‐off between cost and system quality. A numerical example is provided to verify the proposed model's effectiveness. The results showed that the less stable the system, the greater the total costs incurred; therefore, lower stringent thresholds may be set to prevent frequent maintenance. Moreover, a sensitivity analysis is performed to investigate the essential parameters that significantly affect maintenance decisions. The results showed that the thresholds of the reliability function and defect rate significantly impact total costs. Any inaccurate assessment of system usage could lead to incorrect estimations and a substantial increase in cost.

DQ-DeepLearn: Data Quality Driven Deep Learning Approach for Enhanced Predictive Maintenance in Smart Manufacturing

In the realm of smart manufacturing, predictive maintenance plays a pivotal role in ensuring equipment reliability, minimizing downtime, optimizing costs, and reducing product failure rate by detecting faulty products in the early stage. However, the efficacy of predictive maintenance hinges on the quality of training data employed for predictive modeling. Inadequate data quality can lead to erroneous predictions and, consequently, ineffective maintenance strategies. This research paper introduces a solution that leverages deep learning and data quality management to enhance predictive maintenance in smart manufacturing. Our proposed methodology encompasses two major components such as data quality management and faulty product detection. Data quality management includes data preprocessing, feature reduction, and data balancing, whereas faulty product detection is done using deep learning techniques. By combining these elements, a predictive model capable of accurately forecasting faulty products in early stages to reduce economic losses is developed. The proposed approach effectively addresses data quality issues and is tested on the SECOM dataset which indicates that it surpasses traditional models with an accuracy of 96.4% and perfect recall. Ultimately, this research contributes to the advancement of predictive maintenance and deep learning in the context of smart manufacturing, benefiting both industrial practitioners and researchers alike.

A 3-D Finite Difference Method for Obtaining the Steady-State Temperature Field of a PCB Circuit

The temperature rise caused by the heat generated by the on-board LED driver module during work will lead to the rise of product failure rate. The thermal analysis of this drive module should not only consider the heat and packaging of the LED itself, but also the influence of other power components. Common commercial thermal simulation software, such as ANSYS ICEPAK or FLOTHEM, usually requires detailed thermal models for such printed circuit board (PCB)-level thermal simulation, and requires a large number of simulation parameter settings and a long simulation time to obtain temperature results that are barely close to the actual. In this article, a 3-D finite difference method is proposed to obtain the steady-state temperature field of PCB circuit. The model can be used to calculate the steady-state temperature field of PCB circuit by setting different component sizes, power, and thermal conductivity. This article uses this method to calculate the temperature field of PCB steady-state working with MATLAB. The experimental measurement of PCB is compared with thermal simulation by ANSYS ICEPAK. It proves that the temperature results obtained by this method are similar to the actual test temperature compared with ANSYS ICEPAK. The time required for calculation is greatly reduced.

Contracting for Product Support Under Information Asymmetry

In after-sales product support, both literature and practice have highlighted the advantages of paying for performance under performance-based contracts (PBC) over the more traditional transaction-based contracts (TBC) that tie supplier payments to each repair incident. Although PBC is believed to better align incentives in the supply chain when the supplier’s private effort is difficult to verify, emerging technologies can make the repair process more transparent, which can eliminate the supplier’s moral hazard problem. Meanwhile, fast growth of service outsourcing for established products makes information asymmetry with regards to product failure rates a new challenge for maintenance, repair, and overhaul (MRO) operations. To analyze this changing environment, we build a stylized adverse selection model and explore which contract is more efficient in dealing with information asymmetry. We assume that equipment failure rates are only known by the customer, whose outside options are type dependent. The uninformed supplier has to design appropriate mechanisms to overcome the disadvantages of this information structure while maximizing profits. We show that the two contracts demonstrate different screening ability, and TBC may be preferred over PBC. Type-dependent outside options can lead to countervailing incentives, making PBC immune to information asymmetry and enabling it to achieve the first-best outcome. When the differential of outside options has a relatively stronger effect compared with the differential of failure rates, TBC can bring the supplier a higher ex ante payoff than PBC. Our paper brings to light a heretofore unknown advantage of TBC, and we demonstrate when these contracts are likely to be observed in the third-party MRO market. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported by the Fishman-Davidson Center at the Wharton School, Erasmus Research Institute of Management, and the National Natural Science Foundation of China [Grant 71801228]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4606 .

Research on ESD failure voltage model of the T-shaped wiring structure in GOA products

In the GOA (Gate on Array) circuits, which are commonly employed in LCD (Liquid Crystal Display) products, the metal wire layers have been found to be sensitive to ESD (Electrostatic Discharge) in production, leading to an increase in the failure rate of GOA products, especially in T-shaped wiring structures. In this paper, we conducted a study on the relationship between the ESD failure voltage and structure parameters of T-shaped wiring structures in GOA products to enhance their reliability. We established an ESD failure voltage model for T-shaped wiring structures based on the relevant electrical breakdown theory. And failure voltage and gap breakdown voltage data are required for accurate model parameters fitting. Hence, ESD tests on the T-shaped wiring structure samples were conducted. In addition, ESD simulation of T-shaped wiring structure using CST software was applied to supply gap voltage data unavailable from tests. Based on the results of the tests and simulation, parameters of the ESD failure voltage model were fitted. And we performed the model modification using verification tests for higher accuracy. The results show that our model errors are below 5 %. Guided by the ESD failure voltage model, a more reliable T-shaped structure can be produced in GOA.

Reliability-Based Robust Design Optimization for Maximizing the Output Torque of Brushless Direct Current (BLDC) Motors Considering Manufacturing Uncertainty

In recent years, the deterministic design optimization method has been widely used to improve the output performance of brushless direct current (BLDC) motors. However, it does not contribute to reducing the failure rate and performance variation of products because it cannot determine the manufacturing uncertainty. In this study, we proposed reliability-based robust design optimization to improve the output torque of a BLDC motor while reducing the failure rate and performance variation. We calculated the output torque and vibration response of the BLDC motor using the electromagnetic–structural coupled analysis. We selected the tooth thickness, slot opening width, slot radius, slot depth, tooth width, magnet thickness, and magnet length as the design variables related to the shape of the stator and rotor that affect the output torque. We considered the distribution of design variables with manufacturing tolerances. We performed a reliability analysis of the BLDC motor considering the distribution of design variables with manufacturing tolerances. Using the reliability analysis results, we performed reliability-based robust design optimization (RBRDO) to maximize the output torque; consequently, the output torque increased by 8.8% compared to the initial BLDC motor, the standard deviation in output performance decreased by 46.9% with improved robustness, and the failure rate decreased by 99.2% with enhanced reliability. The proposed reliability-based robust design optimization is considered to be useful in the actual product design field because it can evaluate both the reliability and robustness of the product and improve its performance in the design stage.

Pengendalian Kualitas UMKM Bagus Bakery dengan Mengunakan Metode Seven Tools

Quality is the most important factor for a product. The failure rate in the production process that is not in accordance with predetermined standards is a measure of the quality and effectiveness of production. Likewise for the production of UMKM Bagus Bakery which has a production failure rate or NG product of 184 of the totalproduction of 4610 pieces or 3.99%. Based on fishbone diagram analysis, the causes of production process failure are material composition, unsanitary environment, oil temperature. The test results based on the seven tools method of overcoming bantet bread using fermipan to 10 grams and bread improver 15 grams, cleaning the equipment every time the dough is made, the oil temperature between 165~175 °C produces the smallest production failure value.

Product failure detection for production lines using a data-driven model

For a healthy production line, it is essential to ensure a low failure rate of products. Product quality in production lines can be inspected using several techniques at the end of a production process, including a manual inspection. Different methods are applied to inspect the product quality at the end of the production process and sometimes during the production. This is often done using manual inspection, but this is less efficient, expensive, and time-consuming. Machine learning algorithms have the potential for evaluating and predicting product quality in a production line. In this paper, a novel product failure detection model that applies ANOVA (Analysis of Variance) feature selection method, Min-Max Scaling normalization method, mean imputation technique, Random Forest classification algorithm, a data sampling technique, and Grid Search parameter optimization approach is proposed and validated. For the comparison of the proposed model, several experiments have been performed using five classification algorithms, including RUSBoosted Tree. Experimental results demonstrated that the proposed model using the Random Forest algorithm, ANOVA feature selection, and sampling method achieves the best performance among other models and detects the faulty products effectively. It was also shown that the RUSBoosted Tree algorithm can be considered by practitioners for building the faulty product prediction model when data sampling and feature selection techniques are not integrated into the prediction model.

Success and failure rates of new food and non-food products introduced on the market

Theoretical and empirical research reveals that despite implementing new product development and management best practices, many projects fail with new product strategies. But what if failure rates as high as 90% are true? This would mean that high costs of product innovation are incurred by many stakeholders who create a specifi c network of relationships, not just enterprises. The widespread belief that the new product failure rate is 90% is not supported by empirical evidence. The aim of the paper is to present the real market eff ects of new products, success and failure rates, from the point of view of food and non-food companies representing various industries. The research measures in the fi eld of marketing and sales eff ects of new products are also proposed. The method used in this paper is a literature review in the area of new product development and management. The author assumes that the review and conceptual nature of this research is dominant. Practical and social implications of the study, its originality concerns the results that provide the basis for the improvement of enterprises’ eff orts in the fi eld of a new product strategy. The limitations of the study include a complex character of considered theoretical constructs and they also concern the used secondary data sources on which the considerations in the article are based. The values of the paper refl ect the directions of enterprises’ conduct in the new food and nonfood product development process. Failure and success rates are in fact diffi cult to quantify. The research contribution to marketing sciences primarily includes the formulation of a set of real success and failure rates in food and non-food industries.

Reliability estimation of type-II generalized loglogistic distribution

In this article, a lifetime distribution named as Type-II generalized log-logistic distribution (TGLLD) is considered and its failure rate of products with different shape parameters used to find out ageing criteria. An attempt has been made to derive the statistical and reliability properties of TGLLD. Parameters are evaluated using maximum likelihood estimation and obtained the reliability of the distribution. A simulation study also conducted to know the performance of the estimators. The estimates obtained are validated with the use of live data.

Optimization of Preventive Maintenance and Replacement Strategies for Nonrenewing Two-Dimensional Warranty Products Experiencing Degradation and External Shocks

“Vehicle damage insurance” in China, “Protection Plus Mobile Elite” launched by Samsung, “iPhone Apple Care +” offered by Apple, and so on are special warranty services aiming at external shocks. These warranty terms, named accident insurance, exist widely in the marketplace. Considering the effect of external shocks on degradation, a two-dimensional preventive maintenance and replacement strategy for products, sold with a nonrenewing and two-dimensional rectangle warranty service, is proposed in this paper. Under this strategy, preventive maintenance actions are scheduled based on units of age or usage, which occurs first. There is a reduction in the intensity function after a preventive maintenance action. Each shock before the Nth shock causes a failure of the product or an increase in product failure rate. The product is replaced by a new one on the Nth shock if it survives the N − 1 shocks. From the view of manufacturers, the mean warranty servicing cost over the whole warranty region is obtained by using the renewal theory. Based on direct numerical Riemann–Stieltjes integration, an approximation algorithm of the cost is also given. The mean cost is minimized by the optimization of preventive maintenance interval and the number of shocks N. A numerical example is given to illustrate the feasibility of the proposed strategy. The effects of maintenance cost, the arrival rate of the shocks, and other model parameters on the optimal strategy are also investigated numerically.

THE ROLE OF CONSUMER INFLUENCES IN THE NEW PRODUCT DEVELOPMENT OF PACKAGED FOOD AND BEVERAGE VENTURES

AbstractUnderstanding and applying consumers' needs in product development is key to success across various industries. The failure rate of new products, however, remains high because consumer demands are not adequately addressed. To investigate the role of consumers in the product development processes of small ventures, we conducted in-depth, semi-structured interviews of 40 packaged food and beverage small ventures in California and Finland. Our findings showed that consumers rarely drove the design of new products, but rather functioned as a source of validation or feedback for small improvements of existing products. Consumer feedback was often unstructured and concentrated in the latter stages of the product development process. However, even though customer involvement in the small ventures' product development was more restricted than recommended in cocreation literature their influence still brought meaningful changes to the products. Given the small ventures' lack of resources, future research on creative, low-cost ways to co-create is called for.

PENGENDALIAN KUALITAS TWISTED CABLE DENGAN METODE SEVEN TOOLS DAN QUALITY CONTROL CIRCLE (QCC) DI PT VOKSEL ELECTRIC Tbk

Production planning increases every month, with the increase in planning it isnecessary to improve quality. PT Voksel Electric Tbk is a manufacturing industryengaged in the production of power cables, telecommunications, and fiber optics. The company tries to control the quality of its products to maintain quality in accordance with the company's vision, which is to become a world-class cable solution provider in Indonesia. The purpose of this study was to determine how to control the quality of Twisted Cable at PT Voksel electric Tbk using the seven tools and QCC methods. The increased production planning does not match the actual production. This is due to the level of production failure. In this study, the method used in quality control is seven tools that are used to analyze the causes of product failure rates. By using the PDCA cycle (plan, do, check, action) and by using seven tools as a tool to determine the cause and effect of the failure of the production process. The causes obtained are from gram dust, from the crust on visual insulation, and from powder materials. By controlling the quality using the seven tools method, it can reduce production process time, production costs, and increase production capacity from 700 km to 800 km. an increase in production capacity of 100 km provides a benefit ofRp. 470,000,000,00,-

GMP production of 6-[18F]Fluoro-l-DOPA for PET/CT imaging by different synthetic routes: a three center experience

BackgroundThe radiofluorinated levodopa analogue 6-[18F]F-l-DOPA (3,4-dihydroxy-6-18F-l-phenylalanine) is a commonly employed radiotracer for PET/CT imaging of multiple oncological and neurological indications. An unusually large number of different radiosyntheses have been published to the point where two different Ph. Eur. monographs exist depending on whether the chemistry relies on electrophilic or nucleophilic radiosubstitution of appropriate chemical precursors. For new PET imaging sites wishing to adopt [18F]FDOPA into clinical practice, selecting the appropriate production process may be difficult and dependent on the clinical needs of the site.MethodsData from four years of [18F]FDOPA production at three different clinical sites are collected and compared. These three sites, Aarhus University Hospital (AUH), Odense University Hospital (OUH), and Herlev University Hospital (HUH), produce the radiotracer by different radiosynthetic routes with AUH adopting an electrophilic strategy, while OUH and HUH employ two different nucleophilic approaches. Production failure rates, radiochemical yields, and molar activities are compared across sites and time. Additionally, the clinical use of the radiotracer over the time period considered at the different sites are presented and discussed.ResultsThe electrophilic substitution route suffers from being demanding in terms of cyclotron operation and maintenance. This challenge, however, was found to be compensated by a production failure rate significantly below that of both nucleophilic approaches; a result of simpler chemistry. The five-step nucleophilic approach employed at HUH produces superior radiochemical yields compared to the three-step approach adopted at OUH but suffers from the need for more comprehensive synthesis equipment given the multi-step nature of the procedure, including HPLC purification. While the procedure at OUH furnishes the lowest radiochemical yield of the synthetic routes considered, it produces the highest molar activity. This is of importance across the clinical applications of the tracer discussed here, including dopamine synthesis in striatum of subjects with schizophrenia and congenital hyperinsulinism in infants.ConclusionFor most sites either of the two nucleophilic substitution strategies should be favored. However, which of the two will depend on whether a given site wishes to optimize the radiochemical yield or the ease of the use.

Reliability improvement incentive strategies in the manufacturing industry

PurposeCompanies face the critical reliability problem of products due to the development of outsourcing. This study intends to provide some feasible solutions for a company to improve the reliability level of products.Design/methodology/approachThe paper considers the reward and reliability decisions regarding a product made with two complementary components from two different suppliers: high-capable and low-capable. Two kinds of reliability improvement incentives (normal incentive and cost-sharing incentive) through which a manufacturer provides a reward and shares the reliability improvement cost with a supplier are discussed. As the Stackelberg leader, the manufacturer determines the strategy, while the suppliers are responsible for determining its reliability. Using a game-theoretic framework, four different contract scenarios are addressed. We develop analytical methods to better understand how the manufacturer decides the incentive mechanism to be used for the suppliers.FindingsThe results show that cost-sharing contracts do not always lead to a higher reliability level and more enormous profits. Setting a target reliability level is better for the manufacturer. The cost-sharing contract is beneficial for a high-capable supplier even though it does not directly participate in that kind of mechanism. A low-capable supplier gains more profit when the manufacturer provides incentive mechanisms that do not specify a target reliability level.Originality/valueThis paper investigates the reliability improvement mechanism used for complementary products and focuses on identifying the optimal decisions when demand is influenced by the gap between the product's failure rate and the standard failure rate.

Firm-level innovation and efficiency in the food sector: Insights from a literature-based innovation output indicator

ABSTRACT Innovation activity is commonly regarded as one prerequisite for the competitiveness of food manufacturers. However, new product introductions typically face a high failure rate, which frequently deprives food manufacturers of returns on new product developments. Accordingly, existing empirical evidence on the effect of innovation activity on firm performance is not unanimous in the food sector. In this study, we explore contemporaneous and lagged effects of product and process innovation on production efficiency of dairy processors in the German market. Endogeneity of innovation variables and production inputs is accounted for by instrumentation using a System GMM approach. While we find no positive effect of patents or the number of new product launches on efficiency, we find a consistent positive effect of successful new products on the dairy processors’ technical efficiency. On the methodological side, our results confirm the relevance of the literature-based innovation output indicator as a differentiated measure of product innovation suitable for measuring the innovation effect on firm performance. Regarding innovation management in the food sector, the results highlight the challenge related to high failure rates of new products in the food sector and emphasize the importance of new product quality for business performance.

A Bi-Objective Simulation-Based Optimization Approach for Optimizing Price, Warranty, and Spare Part Production Decisions Under Imperfect Repair

This paper proposes an efficient methodology based on the Monte-Carlo simulation-based bi-objective optimization, to determine base-warranty (BW) and extended warranty (EW) parameters based on the product lifecycle. The first objective, which is from the manufacturer’s perspective, maximizes the profit while the second objective minimizes the expected number of failures that occurred during the out-of-warranty period. The manufacturer can rectify failed products via minimal repair, imperfect repair and perfect repair. The optimization model has decision variables including the product price, BW length, EW length, EW price, product failure rate, imperfect repair level, and spare part production rate in each time interval. The structure of the model admits the design of a hybrid method based on the multi-objective optimization search algorithm, Monte-Carlo simulation and an exact Out-Of-Kilter (OOK) algorithm. The nondominated sorting genetic algorithm (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) algorithm are used as search algorithms. The proposed approach consists of three stages, where in the first stage, product price, BW length, EW length, EW price, product failure rate, imperfect repair level are set by NSGA-II/MOPSO. In the second stage, the number of failed products is calculated by the Monte-Carlo simulation and in the third stage, we show that the spare part inventory control sub problem can be transformed to a minimum cost network flow problem which is optimized by the OOK algorithm to attain a unified solution. A Taguchi approach is used to find the optimum level of parameters. The performance of algorithms is compared based on three different metrics. Results on a real-world problem demonstrate that the NSGA-II-OOK algorithm is more effective than the MOPSO-OOK algorithm. Through a sensitivity analysis, we analyze how various levels of planning horizon can affect Pareto-set which indicates valuable managerial insight.