{"id":"https://openalex.org/W4206932478","doi":"https://doi.org/10.1109/ssci50451.2021.9659992","title":"Data-Driven Fuzzy Demand Forecasting Models for Resilient Supply Chains","display_name":"Data-Driven Fuzzy Demand Forecasting Models for Resilient Supply Chains","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4206932478","doi":"https://doi.org/10.1109/ssci50451.2021.9659992"},"language":"en","primary_location":{"id":"doi:10.1109/ssci50451.2021.9659992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci50451.2021.9659992","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5043885890","display_name":"A. Thavaneswaran","orcid":"https://orcid.org/0000-0002-3211-8308"},"institutions":[{"id":"https://openalex.org/I46247651","display_name":"University of Manitoba","ror":"https://ror.org/02gfys938","country_code":"CA","type":"education","lineage":["https://openalex.org/I46247651"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Aerambamoorthy Thavaneswaran","raw_affiliation_strings":["University of Manitoba, Winnipeg, Canada"],"affiliations":[{"raw_affiliation_string":"University of Manitoba, Winnipeg, Canada","institution_ids":["https://openalex.org/I46247651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013006175","display_name":"Ruppa K. Thulasiram","orcid":"https://orcid.org/0000-0002-6519-3929"},"institutions":[{"id":"https://openalex.org/I46247651","display_name":"University of Manitoba","ror":"https://ror.org/02gfys938","country_code":"CA","type":"education","lineage":["https://openalex.org/I46247651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ruppa K. Thulasiram","raw_affiliation_strings":["University of Manitoba, Winnipeg, Canada"],"affiliations":[{"raw_affiliation_string":"University of Manitoba, Winnipeg, Canada","institution_ids":["https://openalex.org/I46247651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001499957","display_name":"Md. Erfanul Hoque","orcid":null},"institutions":[{"id":"https://openalex.org/I46247651","display_name":"University of Manitoba","ror":"https://ror.org/02gfys938","country_code":"CA","type":"education","lineage":["https://openalex.org/I46247651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Md. Erfanul Hoque","raw_affiliation_strings":["University of Manitoba, Winnipeg, Canada"],"affiliations":[{"raw_affiliation_string":"University of Manitoba, Winnipeg, Canada","institution_ids":["https://openalex.org/I46247651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048266821","display_name":"S.S. Appadoo","orcid":"https://orcid.org/0000-0001-5117-4535"},"institutions":[{"id":"https://openalex.org/I46247651","display_name":"University of Manitoba","ror":"https://ror.org/02gfys938","country_code":"CA","type":"education","lineage":["https://openalex.org/I46247651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Srimantoorao S. Appadoo","raw_affiliation_strings":["University of Manitoba, Winnipeg, Canada"],"affiliations":[{"raw_affiliation_string":"University of Manitoba, Winnipeg, Canada","institution_ids":["https://openalex.org/I46247651"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043885890"],"corresponding_institution_ids":["https://openalex.org/I46247651"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.28286385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"56","issue":null,"first_page":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10328","display_name":"Supply Chain and Inventory Management","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10328","display_name":"Supply Chain and Inventory Management","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9937999844551086,"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/T10539","display_name":"Sustainable Supply Chain Management","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/1408","display_name":"Strategy and Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.7327272295951843},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.710628092288971},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.677778959274292},{"id":"https://openalex.org/keywords/bullwhip-effect","display_name":"Bullwhip effect","score":0.6735392212867737},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.6045005917549133},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6034252643585205},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5395239591598511},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44281578063964844},{"id":"https://openalex.org/keywords/supply-and-demand","display_name":"Supply and demand","score":0.4148520827293396},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3648803234100342},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3608085811138153},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.3569644093513489},{"id":"https://openalex.org/keywords/supply-chain-management","display_name":"Supply chain management","score":0.32607945799827576},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14231139421463013},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1282280683517456},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12051641941070557},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.08544307947158813}],"concepts":[{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.7327272295951843},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.710628092288971},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.677778959274292},{"id":"https://openalex.org/C178595485","wikidata":"https://www.wikidata.org/wiki/Q1343909","display_name":"Bullwhip effect","level":4,"score":0.6735392212867737},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.6045005917549133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6034252643585205},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5395239591598511},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44281578063964844},{"id":"https://openalex.org/C120330832","wikidata":"https://www.wikidata.org/wiki/Q166656","display_name":"Supply and demand","level":2,"score":0.4148520827293396},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3648803234100342},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3608085811138153},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3569644093513489},{"id":"https://openalex.org/C44104985","wikidata":"https://www.wikidata.org/wiki/Q492886","display_name":"Supply chain management","level":3,"score":0.32607945799827576},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14231139421463013},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1282280683517456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12051641941070557},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.08544307947158813},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci50451.2021.9659992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci50451.2021.9659992","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","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":20,"referenced_works":["https://openalex.org/W610451383","https://openalex.org/W1583802019","https://openalex.org/W1977895070","https://openalex.org/W1980684601","https://openalex.org/W1984253589","https://openalex.org/W1985008713","https://openalex.org/W2166304961","https://openalex.org/W2178204454","https://openalex.org/W2251147127","https://openalex.org/W2418281354","https://openalex.org/W2945232411","https://openalex.org/W2957325811","https://openalex.org/W3082291161","https://openalex.org/W3087959594","https://openalex.org/W3115774918","https://openalex.org/W3192954507","https://openalex.org/W3201442926","https://openalex.org/W3201482372","https://openalex.org/W4211007335","https://openalex.org/W4247435554"],"related_works":["https://openalex.org/W2377447009","https://openalex.org/W2378532152","https://openalex.org/W2391761644","https://openalex.org/W2050023151","https://openalex.org/W3161556095","https://openalex.org/W2510483584","https://openalex.org/W2376467364","https://openalex.org/W4313057686","https://openalex.org/W2353698110","https://openalex.org/W2501030226"],"abstract_inverted_index":{"Uncertainty":[0],"in":[1,38,205,234],"supply":[2,76,206],"chain":[3],"leads":[4],"to":[5,87,107],"what":[6,26],"is":[7,27,73,85,106,128,164,213],"known":[8],"as":[9,18,114,116,133,143,145,197,199,243,245],"bullwhip":[10],"effect":[11],"(BE),":[12],"which":[13,166],"causes":[14],"multiple":[15],"inefficiencies":[16],"such":[17],"higher":[19],"costs":[20],"of":[21,43,46,68,95,101,149,161,174,194,202,210,236,248],"production":[22],"(of":[23],"more":[24],"than":[25],"needed),":[28],"wastage":[29],"and":[30,221],"logistics.":[31],"Though":[32],"there":[33],"are":[34],"many":[35],"studies":[36],"reported":[37],"the":[39,41,44,50,69,89,96,103,140,146,150,162,195,200,203,229,246,249],"literature,":[40],"impact":[42],"quality":[45],"dynamic":[47,157,190],"forecasts":[48,67,94,113,142,148],"on":[49,187],"BE":[51],"has":[52],"not":[53],"received":[54],"sufficient":[55],"coverage.":[56],"In":[57,153],"this":[58],"paper,":[59],"a":[60,129,155,189],"fuzzy":[61,90,104,123,158,183,191,238],"data-driven":[62,79,182],"weighted":[63,81],"moving":[64],"average":[65],"(DDWMA)":[66],"future":[70],"demand":[71,112,141,151,163,196,204,223,242],"strategy":[72],"proposed":[74,122,165,181,211,230],"for":[75,110,138,241],"chain.":[77,207],"Also,":[78],"random":[80],"volatility":[82,147,201,247],"forecasting":[83,159,172,184,192,239],"model":[84],"used":[86],"study":[88],"extended":[91,117,124],"Bollinger":[92,118,125],"bands":[93,119,126,240],"demand.":[97,250],"The":[98,121,180,208,225],"main":[99],"reason":[100],"using":[102,218],"approach":[105,185],"provide":[108],"\u03b1-cuts":[109],"DDWMA":[111],"well":[115,144,198,233,244],"forecasts.":[120],"forecast":[127],"two":[130],"steps":[131,173],"procedure":[132],"it":[134],"uses":[135],"optimal":[136],"weights":[137],"both":[139],"process.":[152],"particular,":[154],"novel":[156],"algorithm":[160],"bypasses":[167],"complexities":[168],"associated":[169],"with":[170],"traditional":[171],"fitting":[175],"any":[176],"time":[177],"series":[178],"model.":[179],"focuses":[186],"defining":[188],"intervals":[193],"performance":[209],"approaches":[212],"evaluated":[214],"through":[215],"numerical":[216],"experiments":[217],"simulated":[219],"data":[220],"weekly":[222],"data.":[224],"results":[226],"show":[227],"that":[228],"methods":[231],"perform":[232],"terms":[235],"narrower":[237]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
