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This study investigates two moderators of the effects of manufacturers' recovery efforts following a product defect on customers' perceptions of product quality: the severity of the product defect and whether the recovery efforts were covered under warranty or not.

A total of 478 USA customers who purchased a new car from a cooperating manufacturer participated in a survey. Customers reported the most important product defect (if any) the customers had experienced with the customers' vehicle during the past year. Three linear regressions (OLS) were used to test the proposed hypotheses.

Defect severity moderates the effects of recovery efforts on quality perceptions. The well-known recovery effect occurs only for product defects of minor severity. Experiencing a severe product defect damages the customers' perceptions of product quality even if the product defect is completely fixed. Double deviations (failed recovery of a product defect) do not damage quality perceptions for defects of minor severity. Finally, warranty coverage of repairs can attenuate the adverse effects of a failed recovery of severe defects on customers' quality perceptions. Additionally, only non-complainers who have experienced a severe product defect correspond to the prevailing conceptualization of an at-risk customer group.

Despite the pervasiveness of product defects, research on the effects of experiencing product defects on customers' product quality perceptions is scarce. Furthermore, the authors' findings reconcile inconsistent results and provide a more nuanced understanding of the well-known recovery and double-deviation effects. Finally, the role of warranty coverage in the recovery process as a buffer for customers' perceptions of product quality is novel.

The four sections to this article have distinct but inter‐related objectives. Part I introduces the concepts, problems and tensions central to an understanding of the product liability debate. These issues recur throughout the article. Part II outlines the development of product liability law in Europe and assesses the impact of the European Directive on Product Liability. The “product liability crisis” in the United States is discussed in Part III, which looks at the law's development and proposals for reform. In Part IV the United States and European positions are compared and the case is made out for a global uniform product liability law which recognises the social responsibility of the producer towards those injured by his products.

This study explores the challenges of garment suppliers in delivering defect-free products to their buyers and how buyers play a role in overcoming the challenges.

Following a qualitative research approach and a multiple case study method, quantitative and qualitative data were collected from the four garment suppliers and buyer's representatives. Both quantitative and qualitative data analysis techniques were applied to understand the challenges in delivering defect-free products.

The study findings show that garment suppliers' main challenges in delivering defect-free products are unsystematic quality control, informal root cause analysis, limited education and training facilities, dearth of a learning culture, limited quality control capability, lack of cross-functional team, inadequate modern technologies, workers' resistance to change and poor performance evaluation. Moreover, this study demonstrates how buyers can enhance their support to suppliers to receive defect-free products.

Whereas the garment industry has more than four thousand suppliers, this study considers only four suppliers. Therefore, the generalisability of the study may be questioned. Furthermore, as this study considers only a single sewing line in each factory, future studies could incorporate more lines for a holistic understanding.

The findings of this study could help the managers of supplier firms understand how to tackle the hurdles of defect-free garment production and give buyers a guideline about what role they need to play to receive defect-free garments from suppliers.

For the first time, this study presents how garment suppliers and their lead buyers play significant roles in satisfying end consumers' demand by overcoming the challenges of defect-free garment production.

This study investigates the impact of a buyer-assisted quality intervention on suppliers' quality performance and buyer–supplier relational dynamics in the garment industry.

This study employed a multiple-embedded case study following a qualitative research approach. The study used data from buyer-assisted quality interventions in sewing lines of four garment supplier factories. Qualitative data were collected through semi-structured interviews of buyer's representatives at their office and senior managers, line inspectors, supervisors and workers in supplier factories. In addition, data related to product quality was obtained from quality check sheets and observations on the shop floor. Data were analysed using qualitative data analysis techniques.

This study demonstrates that a buyer-assisted quality intervention improves product quality performance by reducing quality defects in targeted garment products resulting in improved buyer–supplier relationships. Moreover, this study identifies the lack of a systematic approach in quality control as a key reason for poor product quality.

The study adds knowledge to the literature on quality improvement and buyer–supplier relationships by analysing buyer-assisted quality interventions in the garment industry in Bangladesh. The study demonstrates that buyer's assistance and adopting a systematic approach in quality control can significantly improve product quality in the garment industry.

This research can help the quality assurance managers in buyer and supplier firms understand the significance of quality interventions and systematic quality control approach to decrease product quality defects and ensure smooth buyer–supplier relationships.

The study adds new knowledge on the link between buyer-assisted quality interventions, systematic quality control and product quality in garments factories in Bangladesh.

The aim of this paper is to highlight the application of six sigma, software engineering techniques and simulation to software development with a view to improving the software process and product.

This paper attempts to integrate six sigma and simulation to define, analyse, measure and predict various elements of software development (such as cost, schedule, defects) that influence software quality, thereby helping the software personnel take necessary measures early in the development process to improve the software processes and remove defects. Simulation results provide qualitative and quantitative suggestions on the ways to change the software process to achieve six sigma quality. The integration of six sigma and CMM and the role of knowledge management in software organisations have been taken into account.

Most software organisations operate between 2.3 and 3 sigma level. This paper presents a framework for definition, measurement, and analysis of important elements of the software product and process using six sigma tools and exploits the use of simulation in bringing six sigma improvements. Case studies have been presented to demonstrate the findings.

Application of the techniques presented in this paper would definitely improve software organisations' processes and product.

The adoption of methodologies outlined in this paper in software companies would enable them to attain improvements in terms of cost, schedule and quality.

The integration of simulation with six sigma applied to software development is novel in this paper. This paper will be valuable for quality professionals and management personnel in software organisations.

This paper aims to present the development of an automated inspection system (AIS) using an image-based analysis mechanism, called i-AIS. The development process of i-AIS used the Design Six Sigma (DSS) methodology. The steps of define, measure, analyze, design and verify (DMADV) are applied and integrated with specific analyses techniques of the quality function deployment (QFD), design failure mode effect analysis (DFMEA) and theory of inventive problem solving (TRIZ). The production process of adhesive tape is the focused case study in this research project, motivated by the high product defect rate complained by customers.

The development process of i-AIS was divided into five standard steps based on the DSS methodologies of DMADV. One of the key processes in this development was to systematically identify the right and intended features of i-AIS. This was carried out based on the application of the QFD technique. Another important process was to further investigate the possible causes of i-AIS failure, to function as intended. This investigative process was carried out based on the DFMEA technique, while the solution to minimize the risk of the identified failures was obtained from the TRIZ method. The final prototype of i-AIS was then presented in the design step.

Verification of the i-AIS prototype revealed its operation at an optimally intended mode that fulfilled the requirements of internal customers. Verification results also revealed that the sigma level has improved from 3.87 to 4.33. Meanwhile, the defect reduction rate is improved to 74.4% and downtime rate also recorded a significant improvement at 80.7% of reduction.

The presented research work is carried out based on a customized case study. Although the proposed methodology can be applied to others cases towards design-based solution, some modifications maybe required based on to the unique features of the case study under consideration.

The presented research project indicated that the proposed methodology was successful to facilitate a structured and systematic process towards defect identification, classification, evaluation and generation of a solution.

The paper presents the development process of an AIS by considering comprehensive managerial aspects that are currently absent in the literature. An integrated DSS structure is proposed to systematically guide the development of i-AIS. The related managerial aspects such as identification of critical defects problem, customer requirement mapping, prototype design analysis and comparison measurements before and after i-AIS installation are considered in this research project.

Defective parts in manufacturing is a serious issue faced by every manufacturer. Even after proper care in design, material selection and manufacturing of product, there exists a defective part. The purpose of this paper is to explore the quality of the manufacturing, and find the use of effective quality tools to reduce the part defect rate in an electrical parts manufacturing unit, thereby, reducing the replaced cost of defective parts.

With the help of quality initiatives, like total quality management (TQM) and Lean Six Sigma (LSS), the firms can produce quality product in each stage of production. The paper focuses on the primary data collected from the XYZ electric manufacturer.

The main finding of this case analysis is that by the effective use of quality tools, the defective part return rate can be reduced, because of which the firm can observe reduction in replaced cost of almost INR24 lakh. In addition, 10A switch part contributes more in replacement cost. Further, it adds to the 35 percent of the overall part rejection.

The study is more focused on particular type of switch product and can extend to other types of products. In addition, the analysis reveals the results of only 88 percent of the defective products.

The study provides results of the improved quality by effective use of quality tools and discusses the different types of defects in the electrical parts manufacturing. Introducing TQM and LSS to manufacturing can reduce the customer return rate to 1,300 parts per million (PPM) and even to 1,000 PPM in future.

The paper discusses the quality issues in the electrical manufacturer. Moreover, the case analysis briefs effective ways to improve the product quality and reduce the rejection rate.

To avoid the high cost of poor quality (COPQ), there is a constant need for minimizing the formation of defects during manufacturing through defect detection and process parameters optimization. This research aims to develop, design and test a smart system that detects defects, categorizes them and uses this knowledge to enhance the quality of subsequent parts.

The proposed system integrates data collected from the deep learning module with the machine learning module to develop and improve two regression models. One determines if set process parameters would yield a defective product while the second model optimizes them. The deep learning model utilizes final product images to categorize the part as defective or not and determines the type of defect based on image analysis. The developed framework of the system was applied to the forging process to determine its feasibility during actual manufacturing.

Results reveal that implementation of such a smart process would lead to significant contributions in enhancing manufacturing processes through higher production rates of acceptable products and lower scrap rates or rework. The role of machine learning is evident due to numerous benefits which include improving the accuracy of the regression model prediction. This artificial intelligent system enhances itself by learning which process parameters could lead to a defective product and uses this knowledge to adjust the process parameters accordingly overriding any manual setting.

The proposed system was applied only to the forging process but could be extended to other manufacturing processes.

This paper studies how an artificial intelligent (AI) system can be developed and used to enhance the yield of good products.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

This paper examines the impact of team factors in software development, such as the domain and language experience of the team members and the personnel capability of the team, on the costs and quality of the software products. The measure of the quality of the software products is based on the number of unique field problems that customers reported. The analysis, based on data collected on 37 software projects from a leading firm in the packaged software industry, indicates that software teams with higher levels of personnel capability exhibit significantly higher productivity and quality in the software products they deliver. A case study of one of the most successful package software development efforts at this firm highlights the important aspects of team dynamics in a highly successful software project.