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020 _a9783031045837
024 7 _a10.1007/978-3-031-04583-7
_2doi
040 _aTR-AnTOB
_beng
_erda
_cTR-AnTOB
041 _aeng
050 4 _aTS155
072 7 _aTGP
_2bicssc
072 7 _aTEC009060
_2bisacsh
072 7 _aTGP
_2thema
090 _aTS155EBK
100 1 _aChen, Tin-Chih Toly.
_eauthor.
_0(orcid)0000-0002-5608-5176
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aArtificial Intelligence and Lean Manufacturing
_h[electronic resource] /
_cby Tin-Chih Toly Chen, Yi-Chi Wang.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
505 0 _aChapter 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 _aThis 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.
650 0 _aIndustrial engineering.
650 0 _aProduction engineering.
650 0 _aEngineering design.
650 0 _aCooperating objects (Computer systems).
650 0 _aProduction management.
650 0 _aBusiness logistics.
650 0 _aInternet of things.
650 1 4 _aIndustrial and Production Engineering.
650 2 4 _aEngineering Design.
650 2 4 _aCyber-Physical Systems.
650 2 4 _aProduction .
650 2 4 _aSupply Chain Management.
650 2 4 _aInternet of Things.
653 0 _aArtificial intelligence -- Industrial applications
653 0 _aLean manufacturing
700 1 _aWang, Yi-Chi.
_eauthor.
_0(orcid)0000-0003-0861-7526
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
856 4 0 _uhttps://doi.org/10.1007/978-3-031-04583-7
_3Springer eBooks
_zOnline access link to the resource
942 _2lcc
_cEBK