{"id":"https://openalex.org/W2968576950","doi":"https://doi.org/10.1145/3341069.3342975","title":"Inventory Management of Automobile After-sales Parts Based on Data Mining","display_name":"Inventory Management of Automobile After-sales Parts Based on Data Mining","publication_year":2019,"publication_date":"2019-06-22","ids":{"openalex":"https://openalex.org/W2968576950","doi":"https://doi.org/10.1145/3341069.3342975","mag":"2968576950"},"language":"en","primary_location":{"id":"doi:10.1145/3341069.3342975","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341069.3342975","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100426167","display_name":"Qun Liu","orcid":"https://orcid.org/0000-0002-6329-3096"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qun Liu","raw_affiliation_strings":["Department of Automation, Xiamen University, Xiamen, P R China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Xiamen, P R China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061054286","display_name":"Kehua Miao","orcid":"https://orcid.org/0000-0003-2267-480X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kehua Miao","raw_affiliation_strings":["Department of Automation, Xiamen University, Xiamen, P R China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Xiamen, P R China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027971353","display_name":"Kaihong Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaihong Lin","raw_affiliation_strings":["Department of Automation, Xiamen University, Xiamen, P R China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Xiamen, P R China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2058,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.59251166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"195","last_page":"199"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9498000144958496,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9498000144958496,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.7553151249885559},{"id":"https://openalex.org/keywords/inventory-management","display_name":"Inventory management","score":0.6363259553909302},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6116635799407959},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5785619020462036},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5391907691955566},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45477601885795593},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4383643865585327},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43557772040367126},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24407362937927246},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23386812210083008},{"id":"https://openalex.org/keywords/operations-management","display_name":"Operations management","score":0.20859551429748535},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15722882747650146}],"concepts":[{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.7553151249885559},{"id":"https://openalex.org/C3018434026","wikidata":"https://www.wikidata.org/wiki/Q3761396","display_name":"Inventory management","level":2,"score":0.6363259553909302},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6116635799407959},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5785619020462036},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5391907691955566},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45477601885795593},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4383643865585327},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43557772040367126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24407362937927246},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23386812210083008},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.20859551429748535},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15722882747650146},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3341069.3342975","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341069.3342975","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1593155594","https://openalex.org/W2174932498","https://openalex.org/W2515296665","https://openalex.org/W2761649170","https://openalex.org/W2802772536","https://openalex.org/W2902297500"],"related_works":["https://openalex.org/W2341492732","https://openalex.org/W3187193180","https://openalex.org/W106542691","https://openalex.org/W1699080303","https://openalex.org/W4297799326","https://openalex.org/W3116064965","https://openalex.org/W4287027380","https://openalex.org/W3193760048","https://openalex.org/W4285822516","https://openalex.org/W2505261959"],"abstract_inverted_index":{"The":[0,93,108],"inventory":[1,90,104],"management":[2],"of":[3,8,15,21,26,29,32,56,142],"automotive":[4],"aftermarket":[5],"parts":[6,58,84,89,116],"is":[7,38,106],"great":[9],"significance":[10],"to":[11,40,45,79,99,134],"the":[12,19,27,52,60,74,81,88,102,113,118,126,131,136,143],"after-sales":[13,34,83],"activities":[14],"automobile":[16,33,82],"dealers":[17],"and":[18,48,66,73,87,123,139],"reduction":[20],"operating":[22],"costs.":[23],"In":[24],"view":[25],"problem":[28],"insufficient":[30],"utilization":[31],"service":[35],"data,":[36],"it":[37],"necessary":[39],"introduce":[41],"data":[42,55],"mining":[43,61],"methods":[44],"further":[46],"analyze":[47,100],"mine":[49],"data.":[50],"Taking":[51],"historical":[53],"sales":[54],"auto":[57,132,144],"as":[59],"object,":[62],"K-means":[63],"clustering":[64],"algorithm":[65],"LSTM":[67],"recurrent":[68],"neural":[69],"network":[70],"were":[71],"applied,":[72],"Python":[75],"tool":[76],"was":[77],"used":[78,98],"develop":[80],"classification":[85,94,122],"model":[86],"prediction":[91,124],"model.":[92],"results":[95,110],"can":[96,111],"be":[97],"whether":[101],"dealer's":[103],"structure":[105,138,141],"reasonable.":[107],"forecast":[109],"predict":[112],"demand":[114],"for":[115,130],"in":[117],"next":[119],"stage.":[120],"Comprehensive":[121],"results,":[125],"study":[127],"provides":[128],"reference":[129],"dealer":[133],"determine":[135],"variety":[137],"quantity":[140],"parts.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
