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This paper aims to contribute to both theory and practice by describing current problem-solving practices, proposed by various authors, as part of the quality management process of companies in the automotive industry. The paper, based on proposals from different authors, also includes a set of quality tools that can be used in the different steps of the problem-solving process. The results show that there is general agreement on best problem-solving practices, emphasizing root-cause analysis and the development and implementation of corrective actions, which are included in all the proposals analyzed. The analysis also shows that many quality tools can be used in the different steps of this process. In addition, the paper proposes an initial framework developed by the authors that can be used by organizations to improve their problem-solving practices. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence systems in clinical routine is still hampered by the necessity of a medical device certification and/or by the difficulty of implementing these systems in a clinic's quality management system. In this context, the key questions for a user are how to ensure robust model predictions and how to appraise the quality of a model's results on a regular basis. In this paper we discuss some conceptual foundation for a clinical implementation of a machine learning system and argue that both vendors and users should take certain responsibilities, as is already common practice for high-risk medical equipment. We propose the methodology from AAPM Task Group 100 report No. 283 as a conceptual framework for developing risk-driven a quality management program for a clinical process that encompasses a machine learning system. This is illustrated with an example of a clinical workflow. Our analysis shows how the risk evaluation in this framework can accommodate artificial intelligence based systems independently of their robustness evaluation or the user's in–house expertise. In particular, we highlight how the degree of interpretability of a machine learning system can be systematically accounted for within the risk evaluation and in the development of a quality management system. [ABSTRACT FROM AUTHOR]

By integrating data-driven analysis methods into existing procedures, quality and failure management performance can be increased systematically. Linking product and process data with existing failure and quality knowledge offers significant potential for enhancing the continuous improvement of processes and products. Nevertheless, the potential of data-driven analysis still needs to be fully exploited. Despite the apparent advantages of analysis-oriented approaches, many companies are hesitant to implement such transformation projects because quantifying possible benefits is challenging. Thus, there is an economic risk due to the limited availability of resources, which must be utilized purposefully. An approach that allows the systematic identification and prioritization of promising use cases and reduces the risk of introducing data-based approaches is missing. This paper conceptualizes a model to evaluate the potential for introducing data-based analysis in production processes. A maturity assessment, consisting of four dimensions and seventeen sub-dimensions, is presented, which enables the identification of promising use cases for implementing data-based approaches. The conceptualized model is validated using an expert survey. [ABSTRACT FROM AUTHOR]

Maintaining profitability measures while conducting business through environmentally and socially sustainable operations is an optimization challenge for organizations globally and for our society. Aiming to contribute to the research streams on this global challenge, this paper studies the state of the art literature on two management methodologies along with sustainability management from an integration perspective: quality management with its intraorganizational focus and supply chain management with its interorganizational view. The paper establishes key themes, trends and new avenues for research through a structured systematic review. The systematic review undertaken includes both descriptive analysis and thematic synthesis of state of the art quality management, sustainability and supply chain management integration literature. Integration synergies of quality and supply chain management were established including performance improvements and integration increasing the effect of both methodologies. Incorporation of sustainability into quality and supply chain management was identified to be a highly emerging area with multi-dimensional (financial, ecologic and social) approaches highly in need for more sustainable supply chains. Ultimately, a new, emerging research area was revealed: sustainable supply chain quality management. Although, several reviews were conducted on the quality, supply chain and sustainability management practices, this study is one of the very few, undertaken from the perspective of all three approaches and cumulative integration. This contribution provides an initial theoretical framework to guide future theory building on a fruitful research avenue. [ABSTRACT FROM AUTHOR]

This paper presents a new quality- and lifecycle-oriented approach of integrated production engineering in automotive industry. In a first step, current production engineering projects are analyzed and present methodical, information-technical and organizational challenges regarding the project phase of concept planning are depicted. Based on this, existing industrial- and research-oriented solution approaches are illustrated and critically evaluated. Considering the weaknesses of these solutions, this paper introduces the new developed quality- and lifecycle-oriented production engineering approach. As one key issue of this new planning approach, the idea of using a model- and rule-based configuration system is presented. [ABSTRACT FROM AUTHOR]

Quality Management is considered to be suitable as support for the integration of sustainability considerations in areas such as product development. The purpose of this paper is to review research in which Quality Management methods, tools or practices have been used in conjunction with sustainable development initiatives. We have identified four themes that synthesize the research on Quality Management and its support to approaches for sustainable development: (I) supporting sustainability through integration of management systems, (II) Quality Management as support to the implementation of Environmental Management Systems and to the management of sustainability, (III) supporting integration of sustainability considerations in daily work, and (IV) supporting stakeholder management and customer focus. By far the most research has been conducted within the first two themes. This paper also contributes with proposals for future research, such as the need to move beyond existing standards and management systems to enable more radical improvements, and the need for empirical evidence of the effect of integrated management systems on environmental performance. We also highlight the point that Quality Management practices and tools must be developed and adapted in order to support sustainability considerations. [ABSTRACT FROM AUTHOR]

Though the idea of Zero Defect Manufacturing is not new, it remains a disruptive concept that is able to entirely reshape the manufacturing ideology. Existing literature suggests that Zero Defect Manufacturing can be implemented in two different approaches – namely product- (defective parts) and / or process-oriented (defective equipment) approaches. The recent onset of Industry 4.0 presents organizations with a plethora of technologies that promise to further enhance the quality of both products and processes, but also adds a third dimension to Zero Defect Manufacturing - people. Therefore, in this paper, we add the people-oriented approach as a third dimension to Zero Defect Manufacturing and draw on practical insights to present a framework for digitally enhanced quality management. [ABSTRACT FROM AUTHOR]

Quality inspection planning is an integral part of the production planning process. It is often derived from the experience of the planner. Despite the increasing availability of data during the manufacturing phase and the emergence of data analytics tools to transform these data into valuable information, there is a lack of revising existing inspection activities as part of quality control. In order to adopt the quality inspection strategies with regard to specific intermediate product characteristics, this paper enables the systematic planning of quality inspection strategies based on the current state of individual products within the manufacturing system. The generated strategies are evaluated based on the expected value of strategy-specific costs. To integrate conditional probabilities of arising uncertainties Bayesian Networks are utilized. [ABSTRACT FROM AUTHOR]

A skilled workforce is essential for producing high quality construction work, efficiently and effectively. This paper is formulated to improve the understanding and knowledge surrounding the skill shortage currently being faced within the UK, and the significant impact it is having on quality management. It surrounds around each main area, and the impacts of recent years: Brexit, Covid-19 and ageing workforce. A mixed method approach was applied to collect data from the quantitative and qualitative sources. The quantitative survey data was collected through snowball sampling from the 130 construction professionals working the UK construction industry. Moreover, a case study has been conducted on a large-scale construction project located in the Northeast of England where the outcome of these issues can be seen in all aspects of the work. Training and development are the fundamental issues highlighted during the literature review and then subsequently expressed by participants of the surveys. In addition, the moderation analysis also provides the important role of training and development in reducing the negative impacts of COVID-19 and Brexit towards quality management. However, it was also revealed that the training and development are not useful for the aging workforce in combating the skilled shortage challenges towards quality management system in the construction industry. This research will allow the industry to understand the need for a skilled workforce and how it benefits the quality management elements of a construction project. [ABSTRACT FROM AUTHOR]

Quality management practices are widely implemented by companies, as they constitute a competitive advantage. Nowadays it is almost mandatory to follow quality standards, in order to make a product available on the market. However, facing new production paradigms, such as Industry 4.0, questions arise about how quality management processes could benefit and adapt in the era of digital technologies. Following a literature review approach, this paper lead to the development of a table that links the relationship between quality management practices and Industry 4.0 technologies that improve quality, as it aimed. [ABSTRACT FROM AUTHOR]

Contemporary companies tend to avoid quality testing activities as they are regarded as non-value-adding efforts. Nevertheless, certain quality tests are mandatory to ensure the product quality to the customer. In order to reduce financial efforts and avoid testing overhead, quality testing activities need to be planned and harmonised throughout the entire product life cycle. The following paper focuses the development of a digital configuration system for quality testing activities throughout the product life cycle. To identify all important inspection processes, an overview of life cycle-orientated quality testing activities is presented. Thereafter, a development concept for the digital configuration system is introduced. The scope of the paper includes the digitalised process of planning quality testing activities in context of product life cycle management applied within developing and producing companies running volume production. [ABSTRACT FROM AUTHOR]

This article presents a stage of a quality management model in higher education institutions based on business process modelling. The paper points out the importance of graduates’ satisfaction in assessing quality in universities and looks at a key development, that have shaped the idea of correlating the graduates’ requirements regarding the developed specific and transversal competencies during the study with the required competences in the labor market. On the basis of the existing literature evidence and on the performed results, the paper proposes a business process management model by outlining the importance of understanding graduates’ requirements, their needs and expectations. [ABSTRACT FROM AUTHOR]

This paper presents the core of a quality management methodology for production chain optimization, applicable from early design stages of production systems to the production start-up, as well as its application in lithium-ion cell production. The entire methodology was presented before [1]. The results of the methodology's first step, which is based on process Failure Mode and Effects Analysis (FMEA), are briefly described since the collected correlations are the input for the further steps. This paper describes how these correlations are transferred into correlation matrices and then propagated over the entire production chain by means of multiple domain matrices (MDM). The resulting production chain MDM provides parameters with direct or indirect influences on the quality parameters of the final product. Furthermore, relevant parameters are selected by an iterative application of MDM based production chain analyses in combination with Pareto analysis and experimental investigations. Proceeding steps quantify the identified influences and derive set point values for the individual processes by means of a multivariate optimization of the final cell quality considering the entire production chain. [ABSTRACT FROM AUTHOR]

Storage technologies such as lithium-ion batteries (LIB) are a key technology to enable emerging transportation as well as sustainable energy policies. The manufacturing of LIB cells is characterized by high scrap rates of up to 40 % in the industry and a high energy demand, leading to a high environmental impact and high costs. Furthermore, the LIB cells themselves and their manufacturing are highly complex systems with various quality parameters and complex material flows. However, with the rising demand for energy storage technologies, the production of LIB cells needs to become more environmentally friendly and more cost-effective. To achieve this, a better understanding and a higher transparency of the material and energy flows in the context of quality parameters is needed. This paper presents a gate-to-gate methodology for the evaluation of material and energy efficiency in context of quality parameters of LIB cell manufacturing. The implementation of the presented concept is based on data acquisition and a tracking and tracing system and tested at the pilot production line of the Battery LabFactory Braunschweig (BLB). The results of this assessment can be used as inputs for further sensitivity analysis in system assessment methodologies such as Life Cycle Assessment (LCA) or Life Cycle Costing (LCC) respectively Total Cost of Ownership (TCO). [ABSTRACT FROM AUTHOR]

Process quality has reached a high level on mass production, utilizing well known methods like the DoE. The drawback of the underlying statistical methods is the need for tests under real production conditions, which cause high costs due to the lost output. Research over the last decade let to methods for correcting a process by using in-situ data to correct the process parameters, but still a lot of pre-production is necessary to get this working. This paper presents a new approach in improving the product quality in process chains by using context data – which in part are gathered by using Industry 4.0 devices – to reduce the necessary pre-production. [ABSTRACT FROM AUTHOR]

From the operational perspective on manufacturing systems, disturbances related to product property (e.g. quality) can be seen as errors leading to longer production cycles, additional quality inspection and handling efforts, resulting in inefficiencies. Identification and analyzing the impact of different error types (e.g. pseudo errors) at an early process step have a significant influence on the overall process chain. Therefore, within this paper, a framework for a simulation-based assessment of quality inspection strategies and effect analysis of error classification on the overall manufacturing system with regard to selected key figures is developed. Moreover, the framework is applied to a use case from the electronic production with regard to different scenarios. Based on the simulation results, decision support is given to increase the overall manufacturing system performance. [ABSTRACT FROM AUTHOR]

Deviations of individual parts, assemblies, modules, processes, and equipment interact in assembly and can lead to rework or rejects of the finished product. In general, deviations cannot be avoided but must be managed. There are many analysis methods, however only few abstract methods concentrate on optimization. Thus, there is a lack of concrete, easy and feasible optimization methods for deviation reduction. This paper presents a novel method for assembly optimization by combining tolerance chains and a developed optimization tree, which gives a holistic overview of practical improvement measures. The method is validated on an automated robot process in engine production. [ABSTRACT FROM AUTHOR]

The degree of process quality during production ramp-up is critical to the success of a product's life cycle. If the required process quality is not achieved, investments must be arranged in rework. This contributes to further costs and reduces the profit margin of the product. This research paper provides an artificial intelligence based decision model that gives recommendations for the configuration of the quality-relevant process parameters in order to ensure process quality in production ramp-up. Within a manufacturing simulation, the decision model is validated. It is shown that the required process quality is achieved by applying the decision model to accompany the production ramp-up. [ABSTRACT FROM AUTHOR]

This paper presents a method to reduce manual planning effort for introducing and initializing process monitoring on existing assembly lines. The focus is set onto the reduction of manual initialization processes by automatically identifying devices and their process abilities, as well as relevant process data. Individual assembly processes often change due to new product variants, and therefore the entire planning of a process monitoring has to be manually adapted. Combining existing expert knowledge of assembly processes and requirements with modern communication interfaces and intelligent data processing, automated installation effort for monitoring processes promises high potential. [ABSTRACT FROM AUTHOR]

Deviations in assembly processes lead to rework or rejects and create a high risk to miss customer requirements. Therefore deviations must be monitored and planned. With increasing product complexity the sole reduction of deviations is not expedient. Process development must accompany product development. One approach for the systematic process development and technology management is the use of tolerance management methods. This paper presents a novel method for systematic analysis in combination with the Key Characteristics Flowdown and a technology radar to derive improvement concepts. The methodology is validated on an automated robotic cell in engine manufacturing. [ABSTRACT FROM AUTHOR]

The development of lithium-ion batteries (LIBs) is facing challenges due to the high level of uncertainty in cause-effect-relationships (CERs) in the manufacturing process. This process is characterized as an interlinked chain of input, intermediate and quality properties, which determine the quality of LIBs. In order to reduce the ramp-up time for LIB production plants, an understanding of influencing properties must be established. Existing methods to investigate these properties are usually expert-based or assess individual process steps. Thus, this paper presents a method, which utilizes multivariate process capability indices for the identification of CERs and quality assurance in the field of LIB production. This data-driven approach was applied to and evaluated on the production data of the prototype line for prismatic LIBs at the BMW Group in Munich. [ABSTRACT FROM AUTHOR]

Abstract Data mining methods are used to analyze and improve production processes in a lithium-ion cell manufacturing line. The CRISP-DM methodology is applied to the data captured during the manufacturing processes. Key goals include the identification of process dependencies and key quality drivers as well as the prediction of the product quality before the cumbersome and costly formation and aging procedure. Several Data mining methods, such as Generalized Linear Model (GLM), Artificial Neural Networks (ANN), Support Vector Regression (SVR), Decision Trees (DT), Random Forest (RF), and Gradient Boosted Trees (GBT) are compared and evaluated. Best results are yielded by an application of GLM, RF, and GBT for prediction of battery cell capacity before the expensive formation process. Key quality drivers identified are the electrode fabrication processes, as well as the electrolyte filling process during cell assembly. This is, to our knowledge, the first time data from a real battery production line has been systematically processed and analyzed along the whole process chain. The results of this paper can assist to manufacture better batteries and to reduce costs of lithium-ion cells by providing a systematic procedure for data acquisition and by lowering scrap rates during production. Graphical abstract Image 1 Highlights • Data mining approaches were applied to a real battery production line. • A systematic procedure for data acquisition, processing, and analysis is given. • Electrode fabrication and electrolyte filling are identified as key quality drivers. • The results can help to decrease battery production cost by reducing scrap rates. [ABSTRACT FROM AUTHOR]

Abstract This study is focused on the investigation of the main sustainability drivers that may improve production performances for Asian firms. The main objective of the paper is to unfold the potential tangible and intangible sustainability resources that can act as strategic assets in improving production performances, with the Resource-Based View as the main framework used in the study. As a further investigation, the research explores how these resources may vary under the contingency perspective by considering two different stages of implementation of Quality Management Systems within the respondents. These issues are examined through a wide survey of textile firms in India and Pakistan, using Structural Equation Modelling as the main tool of investigation. Some of the main outcomes show that Environmental Management and Safety Management result in the strategic use of resources to improve production performances. It emerges from the contingency analysis that increasing importance is given to intangible assets by those firms that are at an advanced stage of implementation of their Quality Management System. Highlights • We realize an extensive survey on the core resources that may impact on production performances for Asian firms. • A contingency analysis is made through Structural Equation Modeling, with Quality Management System as discriminant factor. • Environmental Management, Safety Management and Sustainability Awareness have a key role within a QMS. • Firms at different stages of implementing a QMS do not share the same core resources. • Social Issues and Sustainability Awareness are core resources for firms with an advanced stage of QMS implementation. [ABSTRACT FROM AUTHOR]

Highlights • To obtain knowledge from automotive quality problem-solving data through data mining. • To extract the relationship matrix between the components and faults. • Ontology library provides a common language between different departments. • Intelligent Quality Problem Solving System improves the efficiency of problem-solving. • Digital Fishbone Diagram reduces problem analysis time and costs. Abstract In the current automotive industry, quality management, especially quality problem-solving (QPS), plays an important role in fulfilling the expectations of demanding customers who seek high-quality products at low-cost. During the problem-solving process, various real-time and historical quality data are often not fully used, yet these data are of high value. This paper provides a comprehensive quality data mining process and method, as well as an intelligent quality problem-solving system (IQPSS). First, based on original quality problem data, an ontology library is constructed using the ontology generating module (OGM). Second, based on the generated ontology and the textual data of the original quality problem, this study builds a quality problem-solving knowledge base (QPSKB) by employing relevant algorithms in the knowledge transformation module (KTM). The component and fault relational matrix mining (CFRMM) algorithm is designed to extract the relationship matrix between the components and faults. The semi-supervised classification algorithm based on the K-nearest neighbor algorithm (KNN) is used to classify the immediate measures, causes and long-term measures into the corresponding ontology and express the ontology as their knowledge. Furthermore, the binary tree-based support vector machine (SVM) approach is applied to classify the cause texts into the factors of Man, Machine, Material, Method, and Environment (4M1E), which are the five factors in a fishbone diagram. In particular, the digital fishbone diagram is a brand-new type of fishbone diagram that subverts the traditional method of fishbone diagram analysis through brainstorming. A pilot run of the IQPSS has been undertaken in an automotive manufacturing company to demonstrate how quality management employees obtain this knowledge by searching in the IQPSS. The results show that the IQPSS contributes appreciably to the quality problem-solving in the manufacturing industry. [ABSTRACT FROM AUTHOR]

The aim of this paper is to explore the joint effects of quality and environmental management on firms’ innovation by addressing the role of knowledge transfer. While previous research has focused on the direct contribution of quality and environmental management to performance outcomes, to the best of our knowledge there has been no research aimed at understanding their joint effect on innovation. Furthermore, we have included knowledge transfer in order to better explain this impact. From 136 questionnaires completed by companies operating in Tunisia, interesting results were generated through structural equation modelling. Both quality and environmental management, which are related, positively influence knowledge transfer and innovation. In addition, knowledge transfer contributes positively to innovation. Overall, our findings show that knowledge transfer has a positive role in the combined impact of quality and environmental management on innovation. [ABSTRACT FROM AUTHOR]

The most effective way of achieving superior business performance is related to the understanding of the process and subsequent improvement. This article focuses on the various management tools of quality those interconnection conditions in a railway environment together generate positive synergies. The following philosophy as Kaizen, Total Quality Management, Six Sigma or usage of dynamic models in terms of the railways belong to an innovative approach to improving the quality of services. Their thought of process optimization, as well as continuously improving the expertise of its staff, makes it easier to cope with global changes but also a service disruption and extraordinary circumstances. Within these tools, it is also essential to have a detailed knowledge of customer requirements, the use of not only subjective, but particularly objective data and especially the use of statistical analysis to achieve success in a transport company. Effective measurement of services quality cannot be bound only to a particular point in time, therefore it is necessary to ensure continuous monitoring of quality throughout the process of services provision. The connection of Six Sigma and dynamic models, which are shown in this paper, represent a modern trend in quality management. Dynamic models follow the procedural character of the provided services that are unique, unrepeatable and constantly changing. [ABSTRACT FROM AUTHOR]

Based on the natural resource-based view (NRBV), this paper analyzes the relationship between internal low carbon integration and external low carbon integration and firm performance, as well as the moderating role of quality management within low carbon supply chain, by researching 12 business departments of machinery, electronic, and transportation components industries in China. The results of this empirical study show that internal low carbon integration helps to improve firm's environmental performance, while hinder firm's financial performance. External low carbon integration improves both environmental performance and financial performance. In the meanwhile, quality management strengthens positive relationships that found in this study and weakens the negative relationship between internal low carbon integration and financial performance. [ABSTRACT FROM AUTHOR]

In order to optimize the quality-related costs, the quality assurance within the production must be designed in terms of economical criteria. This design is time-consuming and cost-intensive. However, due to the increasing individualization up to lot size one, the quality assurance must be adapted in increasingly shorter cycles in order to achieve an economical optimal quality assurance at any time. The realization of an adaptive quality assurance within the production enables manufacturing companies to achieve a minimum of quality-related costs at any time despite an increasing individualization up to lot size one. Due to their high degree of swiftness regarding data acquisition, data processing and output of data in real-time, and furthermore, their capability to control physical elements with computer-based algorithms in an intertwined way, cyber-physical systems (CPS) are predestined to perform an adaptive quality assurance within the production. But, no approach towards an adaptive quality assurance, which is performed by a cyber-physical system in order to achieve a minimum of quality-related costs at any time despite an increasing individualization of manufactured products up to lot size one, has been described in literature yet. This paper fills the gap by showing an approach towards an adaptive quality assurance within the production, which is performed by a cyber-physical system. [ABSTRACT FROM AUTHOR]

The study aims to identify the antecedents responsible for sustaining quality control in the context of under-studied agriculture value chains. There is a pressing need to understand the dynamics of technology implementation in an emerging economy context. The conceptual model is designed from complexity, and collective action theory perspectives. An empirical study validates the model and considers the role of traceability, collective action, big data predictive capability, and behavioral uncertainties in sustaining quality in agriculture value chains (AVCs). The paper addresses the call for in-depth deliberations on exploring the factors of AVC quality. The empirical validation of the theoretical model shows a significant positive relationship among the antecedents. The path coefficients and effect size have been derived from understanding the regression equations of cause and effect groups. It also identifies the avenues for improving quality in value chains. This is a pioneering study to design a new operation model for sustaining the quality of AVCs. It also adds a new theoretical debate to AVC technology deployment in the context of an emerging economy. The study is limited to recognizing a few antecedents from the literature in the Indian context. The deliberations contribute significantly to minimizing agricultural waste in developing countries through quality control. [ABSTRACT FROM AUTHOR]

Recent scandals in pollutant emissions by combustion engines have once more raised awareness of the relevance of shifting individual mobility to electrically driven vehicles powered by renewable energies. High costs and large quality fluctuations during the production of high-energy batteries are considered to be among the main impediments of electric cars to succeed on the consumer market. In order to reduce costs and improve the quality of lithium-ion batteries, a comprehensive quality management concept is proposed in this paper. Goal is the definition of standards for battery production regardless of cell format, production processes and technology. A well-structured procedure is suggested for identification and handling of fluctuations in the quality of intermediate products, leading to a reduction of scrap rates by detecting deviations in early process stages and, additionally, offering the possibility for process control and feedback. Based on a definition of internal and external requirements for processes and intermediate products, the relevant parameters are derived and requirements for measurement equipment are identified. By establishing internal decision points (quality gates), measurement steps can be aggregated, minimizing effort for quality control and summarizing information on relevant quality parameters of intermediate products. Hence, recommendations for process control and the definition of rejection criterions and classifications for intermediate products are provided. [ABSTRACT FROM AUTHOR]

In the customized production more complex processes are required. Companies are challenged by monitoring these complex processes which compared to mass production show a lower degree of standardization and are therefore characterized by higher instabilities. Quality management has developed various techniques to deal with instabilities such as error analysis and process monitoring, which are implemented successfully in mass production. These techniques are based on the principle of causality and are effective in identifying, monitoring and adjusting the main cause of error in isolated effect chains. Within the customized production the elimination of the main cause of error does not lead to a sustained improvement of production quality since causes of error differ due to varied products to be manufactured. Furthermore, processes in customized production increasingly imply immanent interdependencies. The emergence of quality along the value chain is thus getting more complex and can less be explained by an effect chain using the principle of causality. The data-based quality regulation is therefore developed in order to achieve high quality in complex production. This paper outlines the data-based quality regulation as well as its need for research. Afterwards, an approach based on a virtual production model to validate suitable data mining methods for the data-based quality regulation is provided. [ABSTRACT FROM AUTHOR]

Since the beginning of the 20th century flow assembly lines are a global standard for manufacturing companies to produce serial products. The standardization and the allocation of assembly jobs in certain cycle times along the assembly line ensure productivity. All work operations in each cycle are allocated ideally in such a way that every operator uses the complete cycle time for the assembly jobs. One disadvantage of these flow assembly lines is the lack of time for possible rework operations. If a human error occurs, there is hardly any time for its correction. The alternative is to stop the line until the rework is done. These situations cause an interruption of work for the whole line, which is unproductive. Consequently most errors become evident at the end of the line and cause rework, which diminishes productivity as well. This paper helps to examine the feasibility of rework in flow assembly lines with regard to technical and organizational restrictions. [ABSTRACT FROM AUTHOR]

This paper describes a new approach specifically developed for ergonomic assessments and redesign of common areas with free circulation of people: Ergonomic Tridimensional Analysis, ETdA. In these environments, the total quality management philosophy is focused not only in workforce satisfaction, but also in clients’ wellbeing. As a consequence of this interaction, clients and professionals can equally be exposed to the same ergonomic risk factors. From the management point of view, the ongoing concern with the clients requires a continuous improvement in several organizational keys-areas such as quality, occupational safety and health, environment protection, and cost of products and services. Results show that ETdA model will increase the population awareness for ergonomics and creates a co-production responsibility in the changes to be implemented. [ABSTRACT FROM AUTHOR]

In the present paper the roadmaps leading to quality are presented. They refer both to the general model of Total Quality Management as well as to the quality gurus Deming, Juran, and Crosby. Views of Greek teachers are analyzed according to EFQM model. Common points and differences of these views are stressed and valuable conclusions are drawn in the field of Total Quality Management in Greek Education. [ABSTRACT FROM AUTHOR]

Stockpile blending is always considered to be an effective method of controlling the quality and maintaining the grade consistency of delivered bulk material, such as iron ore. However, major challenges remain to predict the quality of a stockpile during blending (stacking and reclaiming) operations, because the chemical composition of the ore body is not always available during blending. Consequently, the performance of current stockpile management systems is relatively poorer than expectations. This paper details an innovative modelling approach to estimate the quality of a stockpile during the blending process. The geometric model created from laser scanning data is capable of recording the dynamic shapes of the stockpile using mathematical equations. Therefore, the quality of the stockpile is calculated with a high degree of accuracy when the chemical analysis is completed. Thus, a quality embedded geometric model is created. Furthermore, this geometric model is associated with the kinematic model of a Bucket Wheel Reclaimer (BWR) to achieve precise auto-control in reclaiming operations. The link between these two models also supports the quality of every single cut accomplished by the BWR, to be calculated in advance. Using the calculation results in conjunction with precise machine control, the output grade can be predicted, planned and controlled. This will optimise the efficiency and effectiveness of stockpile blending. [ABSTRACT FROM AUTHOR]

Gathering information that is capable to explain customers’ needs is usually seen as a quite straightforward part of the traditional construction process: a customer should be able to tell all relevant needs in the first stage so that a building could be designed and built according to the gained information. But the process is lacking of service abilities if a customer wants to modify the given information due to a change in circumstances, albeit such a change is easily caused due turbulent economic situations and long spans in real-estate development projects. Hence the customer perspective regarding the construction management (CM) process should be accommodated better. In this paper, the case studies of the four premises improvement projects are reported upon, where the CM process was altered to include and apply the concepts of continuous improvement and co-creation. The process documentation covered the impacts of the case project on the usability of the premises, the indoor climate conditions (carbon dioxide and temperature) metering, the time lapse cameras and the on-line user feedback system. The documentation consists of the minutes of the meetings, the financial reporting and the time tables.Both the processes and the results of the projects are analysed. Based on the key findings, some suggestions are put forth upon how to improve the CM process to better serve customer interests and quality improvement in the future. [ABSTRACT FROM AUTHOR]

Nowadays in every field the main aim is to produce corresponding quality products at competitive prices for buyers. In addition to the multiple possibilities to reduce the brewing production cost, one of the most significant ways is to reduce the production cost by recovering the carbon dioxide (CO2) resulting from the fermentation of beer, taking into account the fact that the CO2 thus resulting is completely natural. This scientific paper presents a certain strategy based on costs, strategy which relies on the experience of the authors, on the economic impact of CO2 recovery resulting from the fermentation of beer and which reflects itself in the production cost and implicitly in the final cost of the product beer. [ABSTRACT FROM AUTHOR]

This paper presents a study to determine customer perception in a public institution of the level of performance by the implementation of the Quality Management System. The method used to achieve this report was a case study. For data collection and analysis were used quantitative and qualitative research techniques, by studying researches and documentation, application of questionnaires and by observation. This study intends to identify the perception clients of public institutions on performance officials after the implementation of the Quality Management System. The benefits of implementing a system to improve service quality, are in support of managers, employees and secondly by the customers. [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]