{"id":"https://openalex.org/W7124316467","doi":"https://doi.org/10.48550/arxiv.2601.08928","title":"DriftGuard: A Hierarchical Framework for Concept Drift Detection and Remediation in Supply Chain Forecasting","display_name":"DriftGuard: A Hierarchical Framework for Concept Drift Detection and Remediation in Supply Chain Forecasting","publication_year":2026,"publication_date":"2026-01-13","ids":{"openalex":"https://openalex.org/W7124316467","doi":"https://doi.org/10.48550/arxiv.2601.08928"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.08928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08928","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.08928","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123131519","display_name":"Shahnawaz Alam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alam, Shahnawaz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123142540","display_name":"Mohammed Abdul Rahman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahman, Mohammed Abdul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123096866","display_name":"Bareera Sadeqa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sadeqa, Bareera","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9599999785423279,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9599999785423279,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.004600000102072954,"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":0.0034000000450760126,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.7005000114440918},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.5210999846458435},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.4447999894618988},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.4269999861717224},{"id":"https://openalex.org/keywords/stockout","display_name":"Stockout","score":0.42559999227523804},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.41280001401901245},{"id":"https://openalex.org/keywords/investment","display_name":"Investment (military)","score":0.38769999146461487},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.36880001425743103},{"id":"https://openalex.org/keywords/supply-chain-management","display_name":"Supply chain management","score":0.3564000129699707}],"concepts":[{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.7005000114440918},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5386999845504761},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.5210999846458435},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.4447999894618988},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C50601946","wikidata":"https://www.wikidata.org/wiki/Q962928","display_name":"Stockout","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.38769999146461487},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.36880001425743103},{"id":"https://openalex.org/C44104985","wikidata":"https://www.wikidata.org/wiki/Q492886","display_name":"Supply chain management","level":3,"score":0.3564000129699707},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3352999985218048},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C147494362","wikidata":"https://www.wikidata.org/wiki/Q2078905","display_name":"Troubleshooting","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.296999990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29600000381469727},{"id":"https://openalex.org/C2778813691","wikidata":"https://www.wikidata.org/wiki/Q1369832","display_name":"Purchasing","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C2781468064","wikidata":"https://www.wikidata.org/wiki/Q1267117","display_name":"Lead time","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2806999981403351},{"id":"https://openalex.org/C2779439359","wikidata":"https://www.wikidata.org/wiki/Q317088","display_name":"Commodity","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2768999934196472},{"id":"https://openalex.org/C2781426361","wikidata":"https://www.wikidata.org/wiki/Q5326940","display_name":"Earnings","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C120330832","wikidata":"https://www.wikidata.org/wiki/Q166656","display_name":"Supply and demand","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C200749887","wikidata":"https://www.wikidata.org/wiki/Q1165574","display_name":"System monitoring","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C117245426","wikidata":"https://www.wikidata.org/wiki/Q235038","display_name":"Technical analysis","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C171078966","wikidata":"https://www.wikidata.org/wiki/Q111029","display_name":"Root (linguistics)","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.08928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08928","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.08928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08928","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Supply":[0],"chain":[1,90],"forecasting":[2],"models":[3],"degrade":[4],"over":[5,186],"time":[6,188],"as":[7,26],"real-world":[8],"conditions":[9],"change.":[10],"Promotions":[11],"shift,":[12],"consumer":[13],"preferences":[14],"evolve,":[15],"and":[16,50,81,107,142,172,203],"supply":[17,89],"disruptions":[18],"alter":[19],"demand":[20],"patterns,":[21],"causing":[22],"what":[23],"is":[24,95],"known":[25],"concept":[27],"drift.":[28],"This":[29],"silent":[30],"degradation":[31],"leads":[32],"to":[33,152,206],"stockouts":[34],"or":[35,79],"excess":[36],"inventory":[37],"without":[38,76],"triggering":[39],"any":[40],"system":[41,98,125],"warnings.":[42],"Current":[43],"industry":[44],"practice":[45],"relies":[46],"on":[47,73,185,209],"manual":[48],"monitoring":[49],"scheduled":[51],"retraining":[52,175],"every":[53],"3-6":[54],"months,":[55],"which":[56],"wastes":[57],"computational":[58],"resources":[59],"during":[60],"stable":[61],"periods":[62],"while":[63],"missing":[64],"rapid":[65],"drift":[66,74,101,122,156],"events.":[67],"Existing":[68],"academic":[69],"methods":[70],"focus":[71],"narrowly":[72],"detection":[75,132,198],"addressing":[77],"diagnosis":[78],"remediation,":[80],"they":[82],"ignore":[83],"the":[84,120,169,180,191],"hierarchical":[85,149],"structure":[86],"inherent":[87],"in":[88],"data.":[91],"What":[92],"retailers":[93],"need":[94],"an":[96,127],"end-to-end":[97],"that":[99,118],"detects":[100],"early,":[102],"explains":[103],"its":[104],"root":[105,170],"causes,":[106,171],"automatically":[108],"corrects":[109],"affected":[110,182],"models.":[111,183],"We":[112],"propose":[113],"DriftGuard,":[114],"a":[115,173],"five-module":[116],"framework":[117],"addresses":[119],"complete":[121],"lifecycle.":[123],"The":[124],"combines":[126],"ensemble":[128],"of":[129],"four":[130],"complementary":[131],"methods,":[133],"namely":[134],"error-based":[135],"monitoring,":[136],"statistical":[137],"tests,":[138],"autoencoder":[139],"anomaly":[140],"detection,":[141],"Cumulative":[143],"Sum":[144],"(CUSUM)":[145],"change-point":[146],"analysis,":[147],"with":[148],"propagation":[150],"analysis":[151,167],"identify":[153],"exactly":[154],"where":[155],"occurs":[157],"across":[158],"product":[159],"lines.":[160],"Once":[161],"detected,":[162],"Shapley":[163],"Additive":[164],"Explanations":[165],"(SHAP)":[166],"diagnoses":[168],"cost-aware":[174],"strategy":[176],"selectively":[177],"updates":[178],"only":[179],"most":[181],"Evaluated":[184],"30,000":[187],"series":[189],"from":[190],"M5":[192],"retail":[193],"dataset,":[194],"DriftGuard":[195],"achieves":[196],"97.8%":[197],"recall":[199],"within":[200],"4.2":[201],"days":[202],"delivers":[204],"up":[205],"417":[207],"return":[208],"investment":[210],"through":[211],"targeted":[212],"remediation.":[213]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-01-16T00:00:00"}