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Artificial intelligence and lean manufacturing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence and lean manufacturingby Tin-Chih Toly Chen, Yi-Chi Wang.
Author:	Chen, Tin-Chih Toly.
other author:	Wang, Yi-Chi.
Published:	Cham :Springer International Publishing :2022.
Description:	vi, 90 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligenceIndustrial applications.
Online resource:	https://doi.org/10.1007/978-3-031-04583-7
ISBN:	9783031045837$q(electronic bk.)

Artificial intelligence and lean manufacturing
Chen, Tin-Chih Toly.

Artificial intelligence and lean manufacturing[electronic resource] /by Tin-Chih Toly Chen, Yi-Chi Wang. - Cham :Springer International Publishing :2022. - vi, 90 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-5318. - SpringerBriefs in applied sciences and technology..

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.

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.

ISBN: 9783031045837$q(electronic bk.)

Standard No.: 10.1007/978-3-031-04583-7doiSubjects--Topical Terms:

524554
Artificial intelligence
--Industrial applications.

LC Class. No.: TS155 / .C44 2022

Dewey Class. No.: 658.50028563

Artificial intelligence and lean manufacturing
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505 0 $a 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.
520 $a 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.
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