TY  - BOOK
AU  - Chen,Tin-Chih Toly
AU  - Wang,Yi-Chi
ED  - SpringerLink (Online service)
TI  - Artificial Intelligence and Lean Manufacturing
T2  - SpringerBriefs in Applied Sciences and Technology,
SN  - 9783031045837
AV  - TS155 
PY  - 2022///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Industrial engineering
KW  - Production engineering
KW  - Engineering design
KW  - Cooperating objects (Computer systems)
KW  - Production management
KW  - Business logistics
KW  - Internet of things
KW  - Industrial and Production Engineering
KW  - Engineering Design
KW  - Cyber-Physical Systems
KW  - Production
KW  - Supply Chain Management
KW  - Internet of Things
KW  - Artificial intelligence -- Industrial applications
KW  - Lean manufacturing
N1  - Chapter 1. Basics in Lean Management -- Chapter 2. AI in Manufacturing -- Chapter 3. AI Applications to Kaizen Management -- Chapter 4. AI Applications to Pull Manufacturing and JIT -- Chapter 5. AI Applications to Production Leveling -- Chapter 6. AI Applications to Shop Floor Management: 5S, Kanban, SMED -- Chapter 7. AI Applications to Value Stream Mapping
N2  - 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
UR  - https://doi.org/10.1007/978-3-031-04583-7
ER  -