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Although the tenn quality does not have a precise and universally accepted definition, its meaning is generally well understood: quality is what makes the difference between success and failure in a competitive world. Given the importance of quality, there is a need for effective quality systems to ensure that the highest quality is achieved within given constraints on human, material or financial resources. This book discusses Intelligent Quality Systems, that is quality systems employing techniques from the field of Artificial Intelligence (AI). The book focuses on two popular AI techniques, expert or knowledge-based systems and neural networks. Expert systems encapsulate human expertise for solving difficult problems. Neural networks have the ability to learn problem solving from examples. The aim of the book is to illustrate applications of these techniques to the design and operation of effective quality systems. The book comprises 8 chapters. Chapter 1 provides an introduction to quality control and a general discussion of possible AI-based quality systems. Chapter 2 gives technical information on the key AI techniques of expert systems and neural networks. The use of these techniques, singly and in a combined hybrid fonn, to realise intelligent Statistical Process Control (SPC) systems for quality improvement is the subject of Chapters 3-5. Chapter 6 covers experimental design and the Taguchi method which is an effective technique for designing quality into a product or process. The application of expert systems and neural networks to facilitate experimental design is described in this chapter.
This book presents recently developed intelligent techniques with applications and theory in the area of quality management. The involved applications of intelligence include techniques such as fuzzy sets, neural networks, genetic algorithms, etc. The book consists of classical quality management topics dealing with intelligent techniques for solving the complex quality management problems. The book will serve as an excellent reference for quality managers, researchers, lecturers and postgraduate students in this area. The authors of the chapters are well-known researchers in the area of quality management.
Quality 4.0 is for all industries, and this book is for anyone who wants to learn how Industry 4.0 and Quality 4.0 can help improve quality and performance in their team or company. This comprehensive guide is the culmination of 25 years of research and practice-exploring, implementing, and critically examining the quality and performance improvement aspects of what we now call Industry 4.0 technologies. Navigate the connected, intelligent, and automated ecosystems of infrastructure, people, objects, machines, and data. Sift through the noise around AI, AR, big data, blockchain, cybersecurity, and other rising technologies and emerging issues to find the signals for your organization. Discover the value proposition of Quality 4.0 and the leading role for Quality professionals to drive successful digital transformation initiatives. The changes ahead are powerful, exciting, and overwhelming-and we can draw on the lessons from past work to mitigate the risks we face today. Connected, Intelligent, Automated provides you with the techniques, philosophies, and broad overall knowledge you need to understand Quality 4.0, and helps you leverage those things for the future success of your enterprise. Chapter 1: Quality 4.0 and the Fourth Industrial Revolution Chapter 2: Connected Ecosystems Chapter 3: Intelligent Agents and Machine Learning Chapter 4: Automation: From Manual Labor to Autonomy Chapter 5: Quality 4.0 Use Cases Across Industries Chapter 6: From Algorithms to Advanced Analytics Chapter 7: Delivering Value and Impact Through Data Science Chapter 8: Data Quality and Data Management Chapter 9: Software Applications & Data Platforms Chapter 10: Blockchain Chapter 11: Performance Excellence Chapter 12: Environment, Health, Safety, Quality (EHSQ) and Cybersecurity Chapter 13: Voice of the Customer (VoC) Chapter 14: Elements of a Quality 4.0 Strategy Chapter 15: Playbook for Transformation
Quality control is changing along with the manufacturing environment. A series of revolutionary changes will occur in management contents, methods, capabilities, and real-time effectiveness and efficiency of management. As an essential factor in intelligent manufacturing, quality control systems require real and comprehensive innovation. Focused on new trends and developments in quality control from a worldwide perspective, this book presents the latest information on novel approaches in quality control. Its thirteen chapters cover three topics: intelligent manufacturing, robust design, and control charts.
"This book "quality of service" in organizations, offering fundamental knowledge on the subject, describing the significance of network management and the integration of knowledge to demonstrate how network management is related to QoS in real applications"--Provided by publisher.
This book addresses emerging issues resulting from the integration of artificial intelligence systems in our daily lives. It focuses on the cognitive, visual, social and analytical aspects of computing and intelligent technologies, highlighting ways to improve the acceptance, effectiveness, and efficiency of said technologies. Topics such as responsibility, integration and training are discussed throughout. The book also reports on the latest advances in systems engineering, with a focus on societal challenges and next-generation systems and applications for meeting them. The book is based on two AHFE 2019 Affiliated Conferences – on Artificial Intelligence and Social Computing, and on Service, Software, and Systems Engineering –, which were jointly held on July 24–28, 2019, in Washington, DC, USA.
The book "Accelerating Software Quality: Machine Learning and Artificial Intelligence in the Age of DevOps" is a complete asset for software developers, testers, and managers that are on their journey to a more mature DevOps workflow, and struggle with better automation and data-driven decision making. DevOps is a mature process across the entire market, however, with existing Non-AI/ML technologies and models, it comes short in expediting release cycle, identifying productivity gaps and addressing them. This book, that was implemented by myself with the help of leaders from the DevOps and test automation space, is covering topics from basic introduction to AI and ML in software development and testing, implications of AI and ML on existing apps, processes, and tools, practical tips in applying commercial and open-source AI/ML tools within existing tool chain, chat-bots testing, visual based testing using AI, automated security scanning for vulnerabilities, automated code reviews, API testing and management using AI/ML, reducing effort and time through test impact analysis (TIA), robotic process automation (RPA), AIOps for smarter code deployments and production defects prevention, and many more.When properly leveraging such tools, DevOps teams can benefit from greater code quality and functional and non-functional test automation coverage. This increases their release cycle velocity, reduces noise and software waste, and enhances their app quality.The book is divided into 3 main sections: *Section 1 covers the fundamentals of AI and ML in software development and testing. It includes introductions, definitions, 101 for testing AI-Based applications, classifications of AI/ML and defects that are tied to AI/ML, and more.*Section 2 focuses on practical advises and recommendations for using AI/ML based solutions within software development activities. This section includes topics like visual AI test automation, AI in test management, testing conversational AI applications, RPA benefits, API testing and much more.*Section 3 covers the more advanced and future-looking angles of AI and ML with projections and unique use cases. Among the topics in this section are AI and ML in logs observability, AIOps benefits to an entire DevOps teams, how to maintain AI/ML test automation, Test impact analysis with AI, and more.The book is packed with many proven best practices, real life examples, and many other open source and commercial solution recommendations that are set to shape the future of DevOps together with ML/AI
This book analyses the role of Enterprise Resource Planning (ERP) and Business Intelligence (BI) systems in improving information quality through an empirical analysis carried out in Italy. The study begins with a detailed examination of ERP features that highlights the advantages and disadvantages of ERP adoption. Critical success factors for ERP implementation and post-implementation are then discussed, along with the capabilities of ERP in driving the alignment between management accounting and financial accounting information.The study goes on to illustrate the features of BI systems and to summarize companies’ needs for BI. Critical success factors for BI implementation are then presented, along with the BI maturity model and lifecycle. The focus of the research entails a detailed empirical analysis in the Italian setting designed to investigate the role played by ERP and BI systems in reducing information overload/underload and improving information quality by influencing the features of information flow. The practical and theoretical implications of the study are discussed and future avenues of research are suggested. This book will be of value for all those who have an interest in the capacities of ERP and BI systems to enhance business information quality.
This book focuses on the latest scientific and technological advancements in the field of railway turnout engineering. It offers a holistic approach to the scientific investigation of the factors and mechanisms determining performance degradation of railway switches and crossings (S&Cs), and the consequent development of condition monitoring systems that will enable infrastructure managers to transition towards the implementation of predictive maintenance. The book is divided into three distinct parts. Part I discusses the modelling of railway infrastructure, including switch and crossing systems, while Part II focuses on metallurgical characterization. This includes the microstructure of in-field loaded railway steel and an analysis of rail screw failures. In turn, the third and final part discusses condition monitoring and asset management. Given its scope, the book is of interest to both academics and industrial practitioners, helping them learn about the various challenges characterizing this engineering domain and the latest solutions to properly address them.
"The first-of-its-kind book presents state-of-the-art traffic monitoring and analysis methods, helping professionals make the most of their data collection and assessment efforts. This unique resource offers a hands-on understanding of the latest sensors, processors, and communication links for everything from vehicle counts to urban congestion measurement. Moreover, practitioners learn statistical techniques for quantifying data accuracy and reducing uncertainly in both current system state assessments and future system slate forecasts."--BOOK JACKET.