{"id":"https://openalex.org/W4388530686","doi":"https://doi.org/10.1287/mnsc.2023.4969","title":"Calibrating Sales Forecasts in a Pandemic Using Competitive Online Nonparametric Regression","display_name":"Calibrating Sales Forecasts in a Pandemic Using Competitive Online Nonparametric Regression","publication_year":2023,"publication_date":"2023-11-09","ids":{"openalex":"https://openalex.org/W4388530686","doi":"https://doi.org/10.1287/mnsc.2023.4969"},"language":"en","primary_location":{"id":"doi:10.1287/mnsc.2023.4969","is_oa":false,"landing_page_url":"https://doi.org/10.1287/mnsc.2023.4969","pdf_url":null,"source":{"id":"https://openalex.org/S33323087","display_name":"Management Science","issn_l":"0025-1909","issn":["0025-1909","1526-5501"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315699","host_organization_name":"Institute for Operations Research and the Management Sciences","host_organization_lineage":["https://openalex.org/P4310315699"],"host_organization_lineage_names":["Institute for Operations Research and the Management Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Management Science","raw_type":"journal-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/A5112431388","display_name":"David Simchi\u2010Levi","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Simchi-Levi","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Cambridge, Massachusetts 02139;","Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;","Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;","Department of Civil and Environmental Engineering, Cambridge, Massachusetts 02139;; Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;; Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;"],"raw_orcid":"https://orcid.org/0000-0002-4650-1519","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Cambridge, Massachusetts 02139;","institution_ids":[]},{"raw_affiliation_string":"Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;","institution_ids":["https://openalex.org/I63966007"]},{"raw_affiliation_string":"Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;","institution_ids":["https://openalex.org/I63966007"]},{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Cambridge, Massachusetts 02139;; Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;; Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052718688","display_name":"Rui Sun","orcid":"https://orcid.org/0000-0001-6273-6898"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Sun","raw_affiliation_strings":["Amazon, Seattle, Washington 98109;"],"raw_orcid":"https://orcid.org/0000-0001-6273-6898","affiliations":[{"raw_affiliation_string":"Amazon, Seattle, Washington 98109;","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074523366","display_name":"Michelle Xiao Wu","orcid":"https://orcid.org/0000-0001-6894-7469"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michelle Xiao Wu","raw_affiliation_strings":["Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;"],"raw_orcid":"https://orcid.org/0000-0001-6894-7469","affiliations":[{"raw_affiliation_string":"Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038115830","display_name":"Ruihao Zhu","orcid":"https://orcid.org/0000-0003-1463-1308"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I4210151850","display_name":"SC Johnson (United States)","ror":"https://ror.org/05j1xsk75","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151850"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruihao Zhu","raw_affiliation_strings":["SC Johnson College of Business, Cornell University, Ithaca, New York 14853"],"raw_orcid":"https://orcid.org/0000-0003-1463-1308","affiliations":[{"raw_affiliation_string":"SC Johnson College of Business, Cornell University, Ithaca, New York 14853","institution_ids":["https://openalex.org/I205783295","https://openalex.org/I4210151850"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2221,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.88655107,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"70","issue":"10","first_page":"6502","last_page":"6518"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9995999932289124,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9995999932289124,"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/T10410","display_name":"COVID-19 epidemiological studies","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"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/computer-science","display_name":"Computer science","score":0.5652715563774109},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5604755878448486},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"Nonparametric regression","score":0.5221065282821655},{"id":"https://openalex.org/keywords/hindsight-bias","display_name":"Hindsight bias","score":0.5041066408157349},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.48513302206993103},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.44337189197540283},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3583149313926697},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.336396187543869},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.3357771635055542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3302098214626312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2863214313983917},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2725535035133362}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5652715563774109},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5604755878448486},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.5221065282821655},{"id":"https://openalex.org/C10347200","wikidata":"https://www.wikidata.org/wiki/Q1960297","display_name":"Hindsight bias","level":2,"score":0.5041066408157349},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.48513302206993103},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.44337189197540283},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3583149313926697},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.336396187543869},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3357771635055542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3302098214626312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2863214313983917},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2725535035133362},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1287/mnsc.2023.4969","is_oa":false,"landing_page_url":"https://doi.org/10.1287/mnsc.2023.4969","pdf_url":null,"source":{"id":"https://openalex.org/S33323087","display_name":"Management Science","issn_l":"0025-1909","issn":["0025-1909","1526-5501"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315699","host_organization_name":"Institute for Operations Research and the Management Sciences","host_organization_lineage":["https://openalex.org/P4310315699"],"host_organization_lineage_names":["Institute for Operations Research and the Management Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Management Science","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:inm:ormnsc:v:70:y:2024:i:10:p:6502-6518","is_oa":false,"landing_page_url":"http://doi.org/10.1287/mnsc.2023.4969","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1570963478","https://openalex.org/W1964213467","https://openalex.org/W1983440945","https://openalex.org/W2016511206","https://openalex.org/W2033062730","https://openalex.org/W2050831530","https://openalex.org/W2069317438","https://openalex.org/W2093825590","https://openalex.org/W2129160848","https://openalex.org/W2148301044","https://openalex.org/W2159058260","https://openalex.org/W2586593278","https://openalex.org/W2752028569","https://openalex.org/W2898812230","https://openalex.org/W2950929549","https://openalex.org/W2974637350","https://openalex.org/W3008443627","https://openalex.org/W3122167207","https://openalex.org/W3123962485","https://openalex.org/W3125554828","https://openalex.org/W3175901868","https://openalex.org/W4246219036","https://openalex.org/W4289256748"],"related_works":["https://openalex.org/W1541412963","https://openalex.org/W2184572292","https://openalex.org/W1509119367","https://openalex.org/W4321367829","https://openalex.org/W4309301076","https://openalex.org/W2041704562","https://openalex.org/W2288767749","https://openalex.org/W4313160815","https://openalex.org/W2181913714","https://openalex.org/W2949120947"],"abstract_inverted_index":{"Motivated":[0],"by":[1,82,144,161,167,180,275,286,298],"our":[2,98,142,220,252],"collaboration":[3],"with":[4,158,177],"Anheuser-Busch":[5],"InBev":[6],"(AB":[7],"InBev),":[8],"a":[9,41,110,194,237],"consumer":[10],"packaged":[11],"goods":[12],"(CPG)":[13],"company,":[14],"we":[15,90,140],"consider":[16],"the":[17,23,49,52,62,72,77,87,102,105,114,124,128,149,152,162,171,181,189,200,208,217,258,280,299],"problem":[18],"of":[19,74,107,109,117,132,207,219,230,256,263,302],"forecasting":[20],"sales":[21,79],"under":[22],"coronavirus":[24],"disease":[25],"2019":[26],"(COVID-19)":[27],"pandemic.":[28],"Our":[29],"approach":[30],"combines":[31],"nonparametric":[32,45],"regression,":[33],"game":[34],"theory,":[35],"and":[36,70,123,164,170,188,212,225,270,307,314],"pandemic":[37],"modeling":[38],"to":[39,236,242],"develop":[40,193],"data-driven":[42],"competitive":[43],"online":[44,94],"regression":[46],"method.":[47],"Specifically,":[48],"method":[50,253],"takes":[51],"future":[53,133],"COVID-19":[54],"case":[55],"estimates,":[56],"which":[57,147],"can":[58],"be":[59],"simulated":[60],"via":[61],"susceptible-infectious-removed":[63],"(SIR)":[64],"epidemic":[65,130],"model":[66],"as":[67],"an":[68,93,168],"input,":[69],"outputs":[71],"level":[73,106],"calibration":[75,88],"for":[76,279],"baseline":[78],"forecast":[80,259],"generated":[81,160,166,179],"AB":[83,226,246],"InBev.":[84],"In":[85],"generating":[86],"level,":[89],"focus":[91],"on":[92,222],"learning":[95],"setting":[96],"where":[97],"algorithm":[99,143,163,197,221],"sequentially":[100],"predicts":[101],"label":[103],"(i.e.,":[104,113,127],"calibration)":[108],"random":[111],"covariate":[112],"current":[115],"number":[116],"active":[118],"cases)":[119],"given":[120],"past":[121],"observations":[122],"generative":[125],"process":[126],"SIR":[129],"model)":[131],"covariates.":[134],"To":[135],"provide":[136],"robust":[137],"performance":[138],"guarantee,":[139],"derive":[141],"minimizing":[145],"regret,":[146],"is":[148,254],"difference":[150],"between":[151],"squared":[153,172,272],"[Formula:":[154,173],"see":[155,174],"text]-norm":[156,175],"associated":[157,176],"labels":[159,165,178,209],"adversary":[169],"best":[182],"isotonic":[183],"(nondecreasing)":[184],"function":[185],"in":[186,261],"hindsight":[187],"adversarial":[190],"labels.":[191],"We":[192,215],"computationally":[195],"efficient":[196],"that":[198,251],"attains":[199],"minimax-optimal":[201],"regret":[202],"over":[203],"all":[204],"possible":[205],"choices":[206],"(possibly":[210],"non-i.i.d.":[211],"even":[213],"adversarial).":[214],"demonstrate":[216],"performances":[218],"both":[223],"synthetic":[224],"InBev\u2019s":[227,247],"data":[228,290,315],"sets":[229],"three":[231],"different":[232],"markets":[233],"(each":[234],"corresponds":[235],"country)":[238],"from":[239],"March":[240,243],"2020":[241],"2021.":[244],"The":[245,312],"numerical":[248],"experiments":[249],"show":[250],"capable":[255],"reducing":[257],"error":[260,268,273],"terms":[262],"weighted":[264],"mean":[265,271],"absolute":[266],"percentage":[267],"(WMAPE)":[269],"(MSE)":[274],"more":[276],"than":[277],"37%":[278],"company.":[281],"This":[282,293],"paper":[283],"was":[284,295],"accepted":[285],"J.":[287],"George":[288],"Shanthikumar,":[289],"science.":[291],"Funding:":[292],"work":[294],"partially":[296],"supported":[297],"Massachusetts":[300],"Institute":[301],"Technology":[303],"Data":[304],"Science":[305],"Lab":[306],"AB-InBev":[308],"Corporation.":[309],"Supplemental":[310],"Material:":[311],"e-companion":[313],"are":[316],"available":[317],"at":[318],"https://doi.org/10.1287/mnsc.2023.4969":[319],".":[320]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}