{"id":"https://openalex.org/W7225178376","doi":"https://doi.org/10.15837/ijccc.2026.6.7694","title":"A dual-stream multitask deep learning model for supply chain demand forecasting with statistically derived early-warning signals","display_name":"A dual-stream multitask deep learning model for supply chain demand forecasting with statistically derived early-warning signals","publication_year":2026,"publication_date":"2026-11-03","ids":{"openalex":"https://openalex.org/W7225178376","doi":"https://doi.org/10.15837/ijccc.2026.6.7694"},"language":"en","primary_location":{"id":"doi:10.15837/ijccc.2026.6.7694","doi":"https://doi.org/10.15837/ijccc.2026.6.7694","is_oa":true,"landing_page_url":"https://doi.org/10.15837/ijccc.2026.6.7694","pdf_url":"https://univagora.ro/jour/index.php/ijccc/article/download/7694/2630","source":{"id":"https://openalex.org/S147751515","display_name":"International Journal of Computers Communications & Control","issn_l":"1841-9836","issn":["1841-9836","1841-9844"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310314650","host_organization_name":"Agora University","host_organization_lineage":["https://openalex.org/P4310314650"],"host_organization_lineage_names":["Agora University"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://univagora.ro/jour/index.php/ijccc/article/download/7694/2630","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160543289","display_name":"Kyoungjong Park","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I79056234","display_name":"Gwangju University","ror":"https://ror.org/01gcnxt20","country_code":"KR","type":"education","lineage":["https://openalex.org/I79056234"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kyoungjong Park","raw_affiliation_strings":["Department of Business Administration, Gwangju University, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Business Administration, Gwangju University, South Korea","institution_ids":["https://openalex.org/I79056234"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5160543289"],"corresponding_institution_ids":["https://openalex.org/I79056234"],"apc_list":{"value":500,"currency":"EUR","value_usd":539},"apc_paid":{"value":500,"currency":"EUR","value_usd":539},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.86882194,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":"6","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9911999702453613,"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":0.9911999702453613,"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":0.004000000189989805,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.001500000013038516,"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":0.8059999942779541},{"id":"https://openalex.org/keywords/supply-chain-management","display_name":"supply chain management","score":0.9290000200271606},{"id":"https://openalex.org/keywords/early-warning-indicators","display_name":"early warning indicators","score":0.9110000133514404},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"anomaly detection","score":0.8640000224113464},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.6209999918937683},{"id":"https://openalex.org/keywords/transformer-encoder","display_name":"Transformer encoder","score":0.9150000214576721},{"id":"https://openalex.org/keywords/change-point-detection","display_name":"change point detection","score":0.9760000109672546},{"id":"https://openalex.org/keywords/weak-labels","display_name":"weak labels","score":0.7639999985694885},{"id":"https://openalex.org/keywords/replenishment-decisions","display_name":"replenishment decisions","score":0.5809999704360962},{"id":"https://openalex.org/keywords/supply-chain-disruption","display_name":"supply chain disruption","score":0.38999998569488525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7544999718666077},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.5601000189781189},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5407000184059143},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4876999855041504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46639999747276306},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4602999985218048},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.44620001316070557},{"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/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.39089998602867126},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.37070000171661377},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3614000082015991},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.35910001397132874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3589000105857849},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3440000116825104},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.33980000019073486},{"id":"https://openalex.org/C2781468064","wikidata":"https://www.wikidata.org/wiki/Q1267117","display_name":"Lead time","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.30239999294281006},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.259799987077713},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.15837/ijccc.2026.6.7694","doi":"https://doi.org/10.15837/ijccc.2026.6.7694","is_oa":true,"landing_page_url":"https://doi.org/10.15837/ijccc.2026.6.7694","pdf_url":"https://univagora.ro/jour/index.php/ijccc/article/download/7694/2630","source":{"id":"https://openalex.org/S147751515","display_name":"International Journal of Computers Communications & Control","issn_l":"1841-9836","issn":["1841-9836","1841-9844"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310314650","host_organization_name":"Agora University","host_organization_lineage":["https://openalex.org/P4310314650"],"host_organization_lineage_names":["Agora University"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.15837/ijccc.2026.6.7694","doi":"https://doi.org/10.15837/ijccc.2026.6.7694","is_oa":true,"landing_page_url":"https://doi.org/10.15837/ijccc.2026.6.7694","pdf_url":"https://univagora.ro/jour/index.php/ijccc/article/download/7694/2630","source":{"id":"https://openalex.org/S147751515","display_name":"International Journal of Computers Communications & Control","issn_l":"1841-9836","issn":["1841-9836","1841-9844"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310314650","host_organization_name":"Agora University","host_organization_lineage":["https://openalex.org/P4310314650"],"host_organization_lineage_names":["Agora University"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321330","display_name":"Gwangju University","ror":"https://ror.org/01gcnxt20"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225178376.pdf","grobid_xml":"https://content.openalex.org/works/W7225178376.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1981780459","https://openalex.org/W2064675550","https://openalex.org/W2116512828","https://openalex.org/W2122646361","https://openalex.org/W2183341477","https://openalex.org/W2295598076","https://openalex.org/W2328176404","https://openalex.org/W2474046725","https://openalex.org/W2607045400","https://openalex.org/W2619383789","https://openalex.org/W2626778328","https://openalex.org/W2626967530","https://openalex.org/W2786827964","https://openalex.org/W2794778778","https://openalex.org/W2907492528","https://openalex.org/W2909693411","https://openalex.org/W2963351448","https://openalex.org/W2963677766","https://openalex.org/W2966327746","https://openalex.org/W2981873476","https://openalex.org/W2996331899","https://openalex.org/W3022643593","https://openalex.org/W3111082827","https://openalex.org/W4206173445","https://openalex.org/W4296052713","https://openalex.org/W4322734923","https://openalex.org/W4382203079","https://openalex.org/W4386619143","https://openalex.org/W4387567845","https://openalex.org/W4402455114","https://openalex.org/W4402564686","https://openalex.org/W4402858974","https://openalex.org/W4409292605","https://openalex.org/W4412736375","https://openalex.org/W4414701243","https://openalex.org/W4417250501"],"related_works":["https://openalex.org/W4416764158","https://openalex.org/W3111082827","https://openalex.org/W7213544863","https://openalex.org/W7046471369","https://openalex.org/W7125422760","https://openalex.org/W7223928802","https://openalex.org/W7140293750","https://openalex.org/W7084157526","https://openalex.org/W7162537856","https://openalex.org/W4415002956"],"abstract_inverted_index":{"Accurate":[0],"demand":[1,31,59,130],"forecasting":[2,60],"is":[3,123,267,283,306],"crucial":[4],"in":[5,178],"supply":[6,12],"chain":[7],"management":[8],"(SCM)":[9],"as":[10,309,317],"global":[11,241],"networks":[13],"become":[14],"increasingly":[15],"complex":[16],"and":[17,38,61,89,100,117,138,183,206,211,237,288,290,294,302,340,358],"exposed":[18],"to":[19,126,170],"disruptions.":[20],"Traditional":[21],"models":[22,96],"that":[23,57,81,95,107,148,348],"rely":[24],"mainly":[25],"on":[26,147,153],"historical":[27,86],"sales":[28,87,109,167],"may":[29],"miss":[30],"shifts":[32],"driven":[33],"by":[34,203,208,269],"heterogeneous":[35],"auxiliary":[36,98],"signals":[37,104],"often":[39],"do":[40],"not":[41],"provide":[42],"explicit":[43],"early":[44,360],"warning":[45],"of":[46,134,141,216,276,300],"abnormal":[47],"events.":[48],"This":[49],"study":[50],"proposes":[51],"a":[52,68,74,91,142,194,230,233,240,246,297,310,318,323,349],"dual-stream":[53],"multitask":[54,351],"deep-learning":[55],"model":[56,72,200,227,236,330,352],"integrates":[58],"statistically":[62,101,143],"derived":[63,102,187,272],"earlywarning":[64],"signal":[65,314],"estimation":[66],"within":[67],"unified":[69],"framework.":[70],"The":[71,120],"adopts":[73],"dual-input":[75],"architecture:":[76],"(i)":[77],"an":[78,222],"LSTM":[79,196],"branch":[80,94],"captures":[82],"temporal":[83],"dynamics":[84],"from":[85,106,168,188,273],"sequences":[88],"(ii)":[90],"Transformer":[92],"encoder":[93],"supplementary":[97],"features":[99],"anomaly":[103,145,185,304,320],"computed":[105],"same":[108,278],"series,":[110],"including":[111],"STL":[112],"residual\u2013based":[113],"indicators,":[114],"change-point":[115],"flags,":[116],"calendar":[118],"variables.":[119],"fused":[121],"representation":[122],"jointly":[124],"optimized":[125],"predict":[127],"both":[128,217,355],"the":[129,135,139,154,173,198,212,226,264,274,277,280,303,328,332,337],"two":[131],"days":[132,177],"ahead":[133],"input":[136],"window":[137],"probability":[140],"defined":[144],"event":[146],"day.":[149],"Experiments":[150],"are":[151,259],"conducted":[152],"public":[155],"Kaggle":[156],"Store":[157],"Item":[158],"Demand":[159],"Forecasting":[160],"dataset":[161],"(10":[162],"stores,":[163],"50":[164],"items,":[165],"daily":[166],"2013":[169],"2017),":[171],"using":[172],"most":[174],"recent":[175],"90":[176],"each":[179],"series":[180],"for":[181,261],"validation":[182],"synthetic":[184],"labels":[186,258,271],"statistical":[189],"residual":[190],"analysis.":[191],"Compared":[192],"with":[193],"unimodal":[195],"baseline,":[197],"proposed":[199,329],"reduces":[201],"RMSE":[202],"approximately":[204,209],"7.23%":[205],"MAE":[207],"6.86%,":[210],"bootstrap":[213],"confidence":[214],"intervals":[215],"gaps":[218],"exclude":[219],"zero.":[220],"Under":[221,322],"identical":[223],"evaluation":[224],"protocol":[225],"also":[228],"outperforms":[229],"naive":[231,235],"forecast,":[232],"seasonal":[234],"AutoARIMA,":[238],"whereas":[239],"gradient-boosted":[242],"tree":[243],"baseline":[244],"attains":[245,331],"slightly":[247],"lower":[248],"point":[249],"error":[250],"(RMSE":[251],"0.5574":[252],"versus":[253],"0.5798).":[254],"Because":[255],"ground-truth":[256],"incident":[257],"unavailable":[260],"this":[262],"dataset,":[263],"classification":[265],"task":[266],"supervised":[268],"weak":[270],"residuals":[275],"series;":[279],"resulting":[281],"discrimination":[282],"limited":[284],"(ROC\u2013AUC":[285],"between":[286,292],"0.578":[287],"0.589":[289],"PR\u2013AUC":[291],"0.126":[293],"0.141":[295],"against":[296],"positive":[298],"rate":[299],"0.100),":[301],"head":[305],"therefore":[307],"presented":[308],"calibrated,":[311],"cost-aware":[312],"screening":[313],"rather":[315],"than":[316],"validated":[319],"detector.":[321],"5:1":[324],"false-negative-to-false-positive":[325],"cost":[326,335],"ratio":[327],"lowest":[333],"expected":[334],"among":[336],"configurations":[338],"compared,":[339],"temperature":[341],"scaling":[342],"yields":[343],"well-calibrated":[344],"alert":[345],"probabilities,":[346],"so":[347],"single":[350],"can":[353],"support":[354],"replenishment":[356],"decisions":[357],"screening-level":[359],"warning.":[361]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2026-11-04T00:00:00"}