This book applies artificial intelligence to lean production and shows how to practically combine the advantages of these two disciplines. Lean manufacturing originated in Japan and is a well-known tool for improving manufacturers' competitiveness. Prevalent tools for lean manufacturing include Kanban, Pacemaker, Value Stream Map, 5s, Just-in-Time and Pull Manufacturing. Lean Manufacturing and the Toyota Manufacturing System has been successfully applied to various factories and supply chains around the world. A lean manufacturing system can not only reduce wastes and inventory, but also respond to customer needs more immediately. Artificial intelligence is a subject that has attracted much attention recently. Many researchers and practical developers are working hard to apply artificial intelligence to our daily lives, including in factories. For example, fuzzy rules have been established to optimize machine settings. Bionic algorithms have been proposed to solve production sequencing and scheduling problems. Machine learning technologies are applied to detect possible product quality problems and diagnose the health of a machine. This book will be of interest to production engineers, managers, as well as students and researchers in manufacturing engineering.

This book provides a comprehensive overview of the latest developments in Explainable AI (XAI) and its applications in manufacturing. It covers the various methods, tools, and technologies that are being used to make AI more understandable and communicable for factory workers. With the increasing use of AI in manufacturing, there is a growing need to address the limitations of advanced AI methods that are difficult to understand or explain to those without a background in AI. This book addresses this need by providing a systematic review of the latest research and advancements in XAI specifically tailored for the manufacturing industry. The book includes real-world case studies and examples to illustrate the practical applications of XAI in manufacturing. It is a valuable resource for researchers, engineers, and practitioners working in the field of AI and manufacturing.

The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities. Most books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. Artificial Intelligence takes a more holistic approach, addressing AI from the view of systems engineering. The book centers on the people-process-technology triad that is critical to successful development of AI products and services. Development starts with an AI design, based on the AI system architecture, and culminates with successful deployment of the AI capabilities. Directed toward AI developers and operational users, this accessibly written volume of the MIT Lincoln Laboratory Series can also serve as a text for undergraduate seniors and graduate-level students and as a reference book. Key features: In-depth look at modern computing technologies Systems engineering description and means to successfully undertake an AI product or service development through deployment Existing methods for applying machine learning operations (MLOps) AI system architecture including a description of each of the AI pipeline building blocks Challenges and approaches to attend to responsible AI in practice Tools to develop a strategic roadmap and techniques to foster an innovative team environment Multiple use cases that stem from the authors’ MIT classes, as well as from AI practitioners, AI project managers, early-career AI team leaders, technical executives, and entrepreneurs Exercises and Jupyter notebook examples

This book constitutes the refereed proceedings of the 7th European Lean Educator Conference ELEC 2021, hosted in Trondheim, Norway, in October 2021 and sponsored by IFIP WG 5.7. The conference was held virtually. The 42 full papers presented were carefully reviewed and selected from 82 submissions. They are organized in the following thematic sections: Learning Lean; Teaching Lean in the Digital Era; Lean and Digital; Lean 4.0; Lean Management; Lean Coaching and Mentoring; Skills and Knowledge Management; Productivity and Performance Improvement; New Perspectives of Lean.

This book highlights the importance of data-driven technologies and artificial intelligence in supply chain management. It covers important concepts such as enabling technologies in Industry 4.0, the impact of artificial intelligence, and data-driven technologies in lean manufacturing. "Provides solutions to solve complex supply chain management issues using artificial intelligence and data-driven technologies" Emphasizes the impact of a data-driven supply chain on quality management "Discusses applications of artificial intelligence, and data-driven technologies in the service industry, and lean manufacturing" Highlights the barriers to implementing artificial intelligence in small and medium enterprises Presents a better understanding of different risks such as procurement risks, process risks, demand risks, transportation risks, and operational risks The book comprehensively discusses the applications of artificial intelligence and data-driven technologies in supply chain management for diverse fields such as service industries, manufacturing industries, and healthcare. It further covers the impact of artificial intelligence and data-driven technologies in managing the FMGC supply chain. It will be a valuable resource for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, industrial engineering, manufacturing engineering, production engineering, and computer engineering.

This book comprises the proceedings of the 12th International Symposium on Intelligent Manufacturing and Service Systems 2023. The contents of this volume focus on recent technological advances in the field of artificial intelligence in manufacturing & service systems including machine learning, autonomous control, bioinformatics, human-artificial intelligence interaction, digital twin, robotic systems, sybersecurity, etc. This volume will prove a valuable resource for those in academia and industry.