{"id":"https://openalex.org/W6990881007","doi":null,"title":"Enhancing Net Sales Forecasting by Applying a Clustering-Based Hierarchical Method","display_name":"Enhancing Net Sales Forecasting by Applying a Clustering-Based Hierarchical Method","publication_year":2024,"publication_date":"2024-12-11","ids":{"openalex":"https://openalex.org/W6990881007"},"language":"en","primary_location":{"id":"pmh:oai:aisel.aisnet.org:itais2024-1023","doi":null,"is_oa":false,"landing_page_url":"https://aisel.aisnet.org/itais2024/29","pdf_url":null,"source":{"id":"https://openalex.org/S30879505","display_name":"Journal of the Association for Information Systems","issn_l":"1536-9323","issn":["1536-9323"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a-star","anvur-scientific","cwts-core","dongbi-a","fnege-1","jufo-3","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310321080","host_organization_name":"Association for Information Systems","host_organization_lineage":["https://openalex.org/P4310321080"],"host_organization_lineage_names":["Association for Information Systems"],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ITAIS 2024 Proceedings","raw_type":"text"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mayer, J\u00f6rg H.","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mayer, J\u00f6rg H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhang, Yanni","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yanni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037716243","display_name":"Christian Hengesbach","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hengesbach, Christian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030788352","display_name":"Markus E\u00dfwein","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Esswein, Markus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5085825092","display_name":"Reiner Quick","orcid":"https://orcid.org/0000-0002-5685-6583","observed_orcids":["https://orcid.org/0000-0002-5685-6583"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quick, Reiner","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.45735603,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hierarchical-forecasting","display_name":"hierarchical forecasting","score":0.8410000205039978},{"id":"https://openalex.org/keywords/clustering","display_name":"clustering","score":0.7549999952316284},{"id":"https://openalex.org/keywords/forecast-accuracy","display_name":"forecast accuracy","score":0.578000009059906},{"id":"https://openalex.org/keywords/seasonal-variation","display_name":"seasonal variation","score":0.6420000195503235}],"concepts":[{"id":"https://openalex.org/C2984642479","wikidata":"https://www.wikidata.org/wiki/Q7404320","display_name":"Sales forecasting","level":2,"score":0.8555999994277954},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7009999752044678},{"id":"https://openalex.org/C139749660","wikidata":"https://www.wikidata.org/wiki/Q5657855","display_name":"Sales management","level":2,"score":0.5867999792098999},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5828999876976013},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.51910001039505},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4381999969482422},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.4377000033855438},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.4374000132083893},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43130001425743103},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4235999882221222},{"id":"https://openalex.org/C161657586","wikidata":"https://www.wikidata.org/wiki/Q1203326","display_name":"Technology forecasting","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.3422999978065491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34150001406669617},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.33709999918937683},{"id":"https://openalex.org/C19351080","wikidata":"https://www.wikidata.org/wiki/Q1395034","display_name":"New product development","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.29490000009536743},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29269999265670776},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C53059260","wikidata":"https://www.wikidata.org/wiki/Q374758","display_name":"Multilevel model","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C98825075","wikidata":"https://www.wikidata.org/wiki/Q485643","display_name":"Customer relationship management","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:aisel.aisnet.org:itais2024-1023","doi":null,"is_oa":false,"landing_page_url":"https://aisel.aisnet.org/itais2024/29","pdf_url":null,"source":{"id":"https://openalex.org/S30879505","display_name":"Journal of the Association for Information Systems","issn_l":"1536-9323","issn":["1536-9323"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a-star","anvur-scientific","cwts-core","dongbi-a","fnege-1","jufo-3","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310321080","host_organization_name":"Association for Information Systems","host_organization_lineage":["https://openalex.org/P4310321080"],"host_organization_lineage_names":["Association for Information Systems"],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ITAIS 2024 Proceedings","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"net":[1],"sales":[2,104],"forecasting":[3,26,123],"is":[4,16],"crucial":[5],"for":[6,18,96],"a":[7,30,62,73],"company\u2019s":[8],"planning.":[9],"At":[10,116],"different":[11,37],"hierarchical":[12,31,38],"levels,":[13],"cluster":[14],"analysis":[15],"useful":[17],"solving":[19],"the":[20,90,94,117,122,128],"trade-off":[21],"between":[22],"bottom-up":[23],"and":[24,68,75,93,134],"top-down":[25],"approaches.":[27],"We":[28,40],"develop":[29],"method":[32],"to":[33,127],"enhance":[34],"accuracy":[35],"at":[36],"levels.":[39],"create":[41],"clusters":[42],"based":[43],"on":[44],"statistical":[45],"time":[46],"series":[47],"features":[48],"rather":[49],"than":[50],"natural":[51],"attributes":[52],"such":[53],"as":[54,78],"geography,":[55],"customer":[56],"type,":[57],"or":[58],"product":[59],"categories.":[60],"Emphasizing":[61],"staged":[63],"research":[64],"process":[65],"with":[66,86,105],"\u201cbuild\u201d":[67],"\u201cevaluate\u201d":[69],"activities,":[70],"we":[71,120],"choose":[72],"science":[74],"technology":[76],"company":[77],"our":[79],"case.":[80],"Our":[81],"approach":[82],"provides":[83],"business":[84],"professionals":[85],"an":[87],"understanding":[88],"of":[89],"applied":[91,138],"algorithm":[92],"potential":[95],"expert":[97],"recommendations.":[98],"Product":[99],"launches,":[100],"withdrawals,":[101],"high":[102],"fluctuation,":[103],"similar":[106],"seasonality,":[107],"upward/downward":[108],"trend":[109],"sales,":[110],"etc.,":[111],"can":[112],"be":[113],"automatically":[114],"clustered.":[115],"case":[118],"company,":[119],"improved":[121],"performance":[124],"significantly":[125],"compared":[126],"linear":[129],"regression,":[130],"seasonal":[131],"na\u00efve,":[132],"SARIMA":[133],"Holt-Winters":[135],"methods":[136],"currently":[137],"without":[139],"clustering.":[140]},"counts_by_year":[],"updated_date":"2026-10-13T10:08:12.033832","created_date":"2025-10-10T00:00:00"}