Application of Classification and Related Methods to SQC Renaissance in Toyota Motor View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1998

AUTHORS

Kakuro Amasaka

ABSTRACT

To. capture the true nature of making products, and in the belief that the best personnel developing is practical research to raise the technological level, we have been engaged in SQC promotion activities under the banner “SQC Renaissance”. The aim of SQC promoted by Toyota is to take up the challenge of solving vital technological. assignments, and to conduct superior QCDS research by employing SQC in a scientific, recursive manner. To this end, we must build Toyota’s technical methods for conducting scientific SQC as a key technology in all stages of the management process, from product planning and development through to manufacturing and sales. Especially, multivariate analysis resolves complex entanglements of cause and effect relationships for both quantitative and qualitative data. A part of application examples are reported below, focusing on the cluster analysis as a representative example. More... »

PAGES

684-695

References to SciGraph publications

  • 1997. Factor analysis for selected observations in STOCHASTIC MODELLING IN INNOVATIVE MANUFACTURING
  • 1997. Latent structures of goodness-of-invention in STOCHASTIC MODELLING IN INNOVATIVE MANUFACTURING
  • 1997. A Construction of SQC Intelligence System for Quick Registration and Retrieval Library — A Visualized SQC Report for Technical Wealth— in STOCHASTIC MODELLING IN INNOVATIVE MANUFACTURING
  • Book

    TITLE

    Data Science, Classification, and Related Methods

    ISBN

    978-4-431-70208-5
    978-4-431-65950-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-4-431-65950-1_75

    DOI

    http://dx.doi.org/10.1007/978-4-431-65950-1_75

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1013069499


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Statistics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "name": [
                "TQM Promotion div.Toyota Motor Corporation, 1, Toyota-cho, Toyota, Aichi 471, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Amasaka", 
            "givenName": "Kakuro", 
            "id": "sg:person.016514760775.17", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016514760775.17"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-59105-1_24", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029411884", 
              "https://doi.org/10.1007/978-3-642-59105-1_24"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-59105-1_24", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029411884", 
              "https://doi.org/10.1007/978-3-642-59105-1_24"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-59105-1_26", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031216779", 
              "https://doi.org/10.1007/978-3-642-59105-1_26"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-59105-1_26", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031216779", 
              "https://doi.org/10.1007/978-3-642-59105-1_26"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-59105-1_27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047606541", 
              "https://doi.org/10.1007/978-3-642-59105-1_27"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-59105-1_27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047606541", 
              "https://doi.org/10.1007/978-3-642-59105-1_27"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1142/s021853939600003x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062979097"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "1998", 
        "datePublishedReg": "1998-01-01", 
        "description": "To. capture the true nature of making products, and in the belief that the best personnel developing is practical research to raise the technological level, we have been engaged in SQC promotion activities under the banner \u201cSQC Renaissance\u201d. The aim of SQC promoted by Toyota is to take up the challenge of solving vital technological. assignments, and to conduct superior QCDS research by employing SQC in a scientific, recursive manner. To this end, we must build Toyota\u2019s technical methods for conducting scientific SQC as a key technology in all stages of the management process, from product planning and development through to manufacturing and sales. Especially, multivariate analysis resolves complex entanglements of cause and effect relationships for both quantitative and qualitative data. A part of application examples are reported below, focusing on the cluster analysis as a representative example.", 
        "editor": [
          {
            "familyName": "Hayashi", 
            "givenName": "Chikio", 
            "type": "Person"
          }, 
          {
            "familyName": "Yajima", 
            "givenName": "Keiji", 
            "type": "Person"
          }, 
          {
            "familyName": "Bock", 
            "givenName": "Hans-Hermann", 
            "type": "Person"
          }, 
          {
            "familyName": "Ohsumi", 
            "givenName": "Noboru", 
            "type": "Person"
          }, 
          {
            "familyName": "Tanaka", 
            "givenName": "Yutaka", 
            "type": "Person"
          }, 
          {
            "familyName": "Baba", 
            "givenName": "Yasumasa", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-4-431-65950-1_75", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-4-431-70208-5", 
            "978-4-431-65950-1"
          ], 
          "name": "Data Science, Classification, and Related Methods", 
          "type": "Book"
        }, 
        "name": "Application of Classification and Related Methods to SQC Renaissance in Toyota Motor", 
        "pagination": "684-695", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-4-431-65950-1_75"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "3e42c7a4fa7b9a0ea41951136ac5651a51c5f3affaf973b196ff86bffccfa1e8"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1013069499"
            ]
          }
        ], 
        "publisher": {
          "location": "Tokyo", 
          "name": "Springer Japan", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-4-431-65950-1_75", 
          "https://app.dimensions.ai/details/publication/pub.1013069499"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T15:19", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000251.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-4-431-65950-1_75"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-4-431-65950-1_75'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-4-431-65950-1_75'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-4-431-65950-1_75'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-4-431-65950-1_75'


     

    This table displays all metadata directly associated to this object as RDF triples.

    104 TRIPLES      23 PREDICATES      31 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-4-431-65950-1_75 schema:about anzsrc-for:01
    2 anzsrc-for:0104
    3 schema:author Nc8d8717266d44602a5e3ba58a06438c3
    4 schema:citation sg:pub.10.1007/978-3-642-59105-1_24
    5 sg:pub.10.1007/978-3-642-59105-1_26
    6 sg:pub.10.1007/978-3-642-59105-1_27
    7 https://doi.org/10.1142/s021853939600003x
    8 schema:datePublished 1998
    9 schema:datePublishedReg 1998-01-01
    10 schema:description To. capture the true nature of making products, and in the belief that the best personnel developing is practical research to raise the technological level, we have been engaged in SQC promotion activities under the banner “SQC Renaissance”. The aim of SQC promoted by Toyota is to take up the challenge of solving vital technological. assignments, and to conduct superior QCDS research by employing SQC in a scientific, recursive manner. To this end, we must build Toyota’s technical methods for conducting scientific SQC as a key technology in all stages of the management process, from product planning and development through to manufacturing and sales. Especially, multivariate analysis resolves complex entanglements of cause and effect relationships for both quantitative and qualitative data. A part of application examples are reported below, focusing on the cluster analysis as a representative example.
    11 schema:editor Ncc6ca65ada894abb874731419b9c5001
    12 schema:genre chapter
    13 schema:inLanguage en
    14 schema:isAccessibleForFree false
    15 schema:isPartOf N8f5cc32dcca140aaae993ce26d0bd54d
    16 schema:name Application of Classification and Related Methods to SQC Renaissance in Toyota Motor
    17 schema:pagination 684-695
    18 schema:productId N1d7a565424264b3b832c86fc6df8f89f
    19 Ne15e6820b6f04eb3aefa03e7c3899cb9
    20 Ne7e9b27422e347f9bafc8bcfa9e68847
    21 schema:publisher N5c9b360efb5c490794ff42a046f2ae5b
    22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013069499
    23 https://doi.org/10.1007/978-4-431-65950-1_75
    24 schema:sdDatePublished 2019-04-15T15:19
    25 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    26 schema:sdPublisher N13118509edca445e98ea5ebacc793174
    27 schema:url http://link.springer.com/10.1007/978-4-431-65950-1_75
    28 sgo:license sg:explorer/license/
    29 sgo:sdDataset chapters
    30 rdf:type schema:Chapter
    31 N13118509edca445e98ea5ebacc793174 schema:name Springer Nature - SN SciGraph project
    32 rdf:type schema:Organization
    33 N1d7a565424264b3b832c86fc6df8f89f schema:name doi
    34 schema:value 10.1007/978-4-431-65950-1_75
    35 rdf:type schema:PropertyValue
    36 N2e045f53a78f49b19c04c5939cef296c rdf:first Nbf0fc5bf2ddd436789a4603de35037b6
    37 rdf:rest N36d3cc9ca3cc4180bd28b086c835f77f
    38 N2f0e3c4a8b9c45d2b7bcc5057dd819cf rdf:first N907163d3c5314f9e8be28c70c82ace13
    39 rdf:rest Nc3db7c27a67f4253a93e6b587bc22141
    40 N36d3cc9ca3cc4180bd28b086c835f77f rdf:first N6a3cab60fe7d423a879869fe6edced5a
    41 rdf:rest rdf:nil
    42 N446f27fa0bd149c5a3c6191619e24d8e schema:familyName Hayashi
    43 schema:givenName Chikio
    44 rdf:type schema:Person
    45 N5c9b360efb5c490794ff42a046f2ae5b schema:location Tokyo
    46 schema:name Springer Japan
    47 rdf:type schema:Organisation
    48 N6a3cab60fe7d423a879869fe6edced5a schema:familyName Baba
    49 schema:givenName Yasumasa
    50 rdf:type schema:Person
    51 N76b3cbf137f94b639af6dd92925ec18d schema:familyName Bock
    52 schema:givenName Hans-Hermann
    53 rdf:type schema:Person
    54 N8c3aa26290a44e579abd94022f68d91e schema:name TQM Promotion div.Toyota Motor Corporation, 1, Toyota-cho, Toyota, Aichi 471, Japan
    55 rdf:type schema:Organization
    56 N8f5cc32dcca140aaae993ce26d0bd54d schema:isbn 978-4-431-65950-1
    57 978-4-431-70208-5
    58 schema:name Data Science, Classification, and Related Methods
    59 rdf:type schema:Book
    60 N907163d3c5314f9e8be28c70c82ace13 schema:familyName Yajima
    61 schema:givenName Keiji
    62 rdf:type schema:Person
    63 Nbf0fc5bf2ddd436789a4603de35037b6 schema:familyName Tanaka
    64 schema:givenName Yutaka
    65 rdf:type schema:Person
    66 Nc3db7c27a67f4253a93e6b587bc22141 rdf:first N76b3cbf137f94b639af6dd92925ec18d
    67 rdf:rest Ne1a8a1b20c674efd9837d61e12d48448
    68 Nc8d8717266d44602a5e3ba58a06438c3 rdf:first sg:person.016514760775.17
    69 rdf:rest rdf:nil
    70 Ncc6ca65ada894abb874731419b9c5001 rdf:first N446f27fa0bd149c5a3c6191619e24d8e
    71 rdf:rest N2f0e3c4a8b9c45d2b7bcc5057dd819cf
    72 Ne15e6820b6f04eb3aefa03e7c3899cb9 schema:name dimensions_id
    73 schema:value pub.1013069499
    74 rdf:type schema:PropertyValue
    75 Ne1a8a1b20c674efd9837d61e12d48448 rdf:first Nf6872985ca8e4873b1684584bebe21d5
    76 rdf:rest N2e045f53a78f49b19c04c5939cef296c
    77 Ne7e9b27422e347f9bafc8bcfa9e68847 schema:name readcube_id
    78 schema:value 3e42c7a4fa7b9a0ea41951136ac5651a51c5f3affaf973b196ff86bffccfa1e8
    79 rdf:type schema:PropertyValue
    80 Nf6872985ca8e4873b1684584bebe21d5 schema:familyName Ohsumi
    81 schema:givenName Noboru
    82 rdf:type schema:Person
    83 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    84 schema:name Mathematical Sciences
    85 rdf:type schema:DefinedTerm
    86 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
    87 schema:name Statistics
    88 rdf:type schema:DefinedTerm
    89 sg:person.016514760775.17 schema:affiliation N8c3aa26290a44e579abd94022f68d91e
    90 schema:familyName Amasaka
    91 schema:givenName Kakuro
    92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016514760775.17
    93 rdf:type schema:Person
    94 sg:pub.10.1007/978-3-642-59105-1_24 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029411884
    95 https://doi.org/10.1007/978-3-642-59105-1_24
    96 rdf:type schema:CreativeWork
    97 sg:pub.10.1007/978-3-642-59105-1_26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031216779
    98 https://doi.org/10.1007/978-3-642-59105-1_26
    99 rdf:type schema:CreativeWork
    100 sg:pub.10.1007/978-3-642-59105-1_27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047606541
    101 https://doi.org/10.1007/978-3-642-59105-1_27
    102 rdf:type schema:CreativeWork
    103 https://doi.org/10.1142/s021853939600003x schema:sameAs https://app.dimensions.ai/details/publication/pub.1062979097
    104 rdf:type schema:CreativeWork
     




    Preview window. Press ESC to close (or click here)


    ...