{"id":"https://openalex.org/W7119026713","doi":"https://doi.org/10.1109/indin64977.2025.11279159","title":"GL-MHSA:A Demand Forecasting Method for Related Products in Parts Supply Chain System","display_name":"GL-MHSA:A Demand Forecasting Method for Related Products in Parts Supply Chain System","publication_year":2025,"publication_date":"2025-07-12","ids":{"openalex":"https://openalex.org/W7119026713","doi":"https://doi.org/10.1109/indin64977.2025.11279159"},"language":null,"primary_location":{"id":"doi:10.1109/indin64977.2025.11279159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin64977.2025.11279159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 23rd International Conference on Industrial Informatics (INDIN)","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/A5122079660","display_name":"Jing Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["Beihang University,School of Automation Science and Electrical Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Automation Science and Electrical Engineering,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028426177","display_name":"Lei Ren","orcid":"https://orcid.org/0000-0001-6346-6930"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Ren","raw_affiliation_strings":["Beihang University,School of Automation Science and Electrical Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Automation Science and Electrical Engineering,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122175212","display_name":"Jin Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin Cui","raw_affiliation_strings":["School of Automation Science and Electrical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122175825","display_name":"Jiajie Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajie Wu","raw_affiliation_strings":["Beihang University,School of Automation Science and Electrical Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Automation Science and Electrical Engineering,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122241213","display_name":"Yuqing Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqing Wang","raw_affiliation_strings":["Beihang University,School of Automation Science and Electrical Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Automation Science and Electrical Engineering,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062426863","display_name":"Haiteng Wang","orcid":"https://orcid.org/0000-0002-7316-3607"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiteng Wang","raw_affiliation_strings":["Beihang University,School of Automation Science and Electrical Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Automation Science and Electrical Engineering,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115691252","display_name":"Zuo-Jun Max Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zuo-Jun Max Shen","raw_affiliation_strings":["University of Hong Kong,Hong Kong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Hong Kong,Hong Kong,China","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68732092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.8704000115394592,"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":0.8704000115394592,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.02319999970495701,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.012799999676644802,"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/demand-forecasting","display_name":"Demand forecasting","score":0.6998000144958496},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.5525000095367432},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47679999470710754},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45680001378059387},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4449999928474426},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.4226999878883362},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.38499999046325684},{"id":"https://openalex.org/keywords/demand-patterns","display_name":"Demand patterns","score":0.3734000027179718}],"concepts":[{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.6998000144958496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6622999906539917},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.5525000095367432},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5198000073432922},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47679999470710754},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45680001378059387},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4449999928474426},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.38499999046325684},{"id":"https://openalex.org/C32597650","wikidata":"https://www.wikidata.org/wiki/Q5255044","display_name":"Demand patterns","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34549999237060547},{"id":"https://openalex.org/C2778397037","wikidata":"https://www.wikidata.org/wiki/Q17083947","display_name":"Supply chain network","level":4,"score":0.336899995803833},{"id":"https://openalex.org/C120330832","wikidata":"https://www.wikidata.org/wiki/Q166656","display_name":"Supply and demand","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.28200000524520874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2777000069618225},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.27320000529289246},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C44104985","wikidata":"https://www.wikidata.org/wiki/Q492886","display_name":"Supply chain management","level":3,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/indin64977.2025.11279159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin64977.2025.11279159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 23rd International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4147151708602905,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2116341502","https://openalex.org/W2136132422","https://openalex.org/W2946794439","https://openalex.org/W2962946486","https://openalex.org/W2996740073","https://openalex.org/W3015940208","https://openalex.org/W4383899300","https://openalex.org/W4388673069","https://openalex.org/W4388776014","https://openalex.org/W4394820861","https://openalex.org/W4401742364","https://openalex.org/W4402209859","https://openalex.org/W4402738086","https://openalex.org/W4402916480","https://openalex.org/W4405303930","https://openalex.org/W4406274288","https://openalex.org/W4408324113"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"demand":[1,45,114,147,172],"forecasting":[2,39,46,173],"for":[3,14,146],"products":[4],"in":[5,77,112],"the":[6,24,34,78,109,113,143,158],"parts":[7],"supply":[8],"chain":[9],"system":[10],"(PSCS)":[11],"is":[12,94,135],"critical":[13],"enterprises":[15],"to":[16,80,97,127,141,176],"optimize":[17],"production":[18],"and":[19,33,53,70,84,169],"inventory":[20],"operations.":[21],"To":[22],"address":[23],"challenges":[25],"of":[26,29,37,166],"inadequate":[27],"modeling":[28,165],"complex":[30],"inter-product":[31],"relationships":[32],"limited":[35],"accuracy":[36],"existing":[38,177],"methods,":[40],"this":[41],"paper":[42],"proposes":[43],"a":[44,50,122,129],"method":[47,62],"based":[48],"on":[49,151],"Graph":[51,90],"Convolutional":[52,91],"LSTM":[54,106],"Network":[55,92],"with":[56,137],"Embedded":[57],"Multi-Head":[58,123],"Self-Attention":[59,124],"(GL-MHSA).":[60],"The":[61,116],"first":[63],"extracts":[64],"hybrid":[65],"distance":[66],"features,":[67],"including":[68],"Euclidean":[69],"pattern":[71],"distances,":[72],"from":[73],"product":[74,82,167],"sales":[75],"data":[76],"PSCS":[79,154],"mine":[81],"associations":[83,168],"construct":[85],"graph-structured":[86],"relational":[87],"data.":[88],"A":[89],"(GCN)":[93],"then":[95],"used":[96],"capture":[98],"structural":[99],"association":[100],"features":[101,118,140],"among":[102],"products,":[103],"while":[104],"an":[105,152],"network":[107],"models":[108],"temporal":[110],"dependencies":[111],"sequences.":[115],"extracted":[117],"are":[119],"fused":[120],"through":[121],"(MHSA)":[125],"mechanism":[126],"obtain":[128],"comprehensive":[130],"feature":[131],"representation.":[132],"This":[133],"representation":[134],"concatenated":[136],"other":[138],"auxiliary":[139],"form":[142],"final":[144],"input":[145],"prediction.":[148],"Experimental":[149],"results":[150],"automotive":[153],"dataset":[155],"show":[156],"that":[157],"proposed":[159],"GL-MHSA":[160],"model":[161],"achieves":[162],"more":[163],"accurate":[164],"significantly":[170],"improves":[171],"performance":[174],"compared":[175],"approaches.":[178]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-08T00:00:00"}
