{"id":"https://openalex.org/W4408969857","doi":"https://doi.org/10.54660/.ijmrge.2021.2.1.791-799","title":"A Predictive Modeling Approach to Optimizing Business Operations: A Case Study on Reducing Operational Inefficiencies through Machine Learning","display_name":"A Predictive Modeling Approach to Optimizing Business Operations: A Case Study on Reducing Operational Inefficiencies through Machine Learning","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W4408969857","doi":"https://doi.org/10.54660/.ijmrge.2021.2.1.791-799"},"language":"en","primary_location":{"id":"doi:10.54660/.ijmrge.2021.2.1.791-799","doi":"https://doi.org/10.54660/.ijmrge.2021.2.1.791-799","is_oa":true,"landing_page_url":"https://doi.org/10.54660/.ijmrge.2021.2.1.791-799","pdf_url":"https://www.allmultidisciplinaryjournal.com/uploads/archives/20250329162858_MGE-2025-2-153.1.pdf","source":{"id":"https://openalex.org/S4210181739","display_name":"International Journal of Multidisciplinary Research and Growth Evaluation","issn_l":"2582-7138","issn":["2582-7138"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Multidisciplinary Research and Growth Evaluation","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.allmultidisciplinaryjournal.com/uploads/archives/20250329162858_MGE-2025-2-153.1.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116845884","display_name":"Bolaji Iyanu Adekunle","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bolaji Iyanu Adekunle","raw_affiliation_strings":["Federal Ministry of Mines and Steel Development , Nigeria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal Ministry of Mines and Steel Development , Nigeria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116753117","display_name":"Ezinne C Chukwuma-Eke","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ezinne C. Chukwuma-Eke","raw_affiliation_strings":["TotalEnergies Nigeria Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TotalEnergies Nigeria Limited","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116754321","display_name":"Emmanuel Damilare Balogun","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emmanuel Damilare Balogun","raw_affiliation_strings":["Independent Researcher ; USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Researcher ; USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5116754322","display_name":"Kolade Olusola Ogunsola","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kolade Olusola Ogunsola","raw_affiliation_strings":["Independent Researcher ; UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Researcher ; UK","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.5075,"has_fulltext":true,"cited_by_count":51,"citation_normalized_percentile":{"value":0.97384871,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"2","issue":"1","first_page":"791","last_page":"799"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.611299991607666,"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/T11891","display_name":"Big Data and Business Intelligence","score":0.611299991607666,"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/T14260","display_name":"Impact of AI and Big Data on Business and Society","score":0.3756999969482422,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.00559999980032444,"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/predictive-analytics","display_name":"predictive analytics","score":0.8360000252723694},{"id":"https://openalex.org/keywords/operational-inefficiency","display_name":"operational inefficiency","score":0.9750000238418579},{"id":"https://openalex.org/keywords/resource-allocation","display_name":"resource allocation","score":0.7820000052452087},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"demand forecasting","score":0.8500000238418579},{"id":"https://openalex.org/keywords/process-bottlenecks","display_name":"process bottlenecks","score":0.796999990940094},{"id":"https://openalex.org/keywords/workforce-planning","display_name":"workforce planning","score":0.9919999837875366},{"id":"https://openalex.org/keywords/cost-reduction","display_name":"cost reduction","score":0.6330000162124634},{"id":"https://openalex.org/keywords/real-time-analytics","display_name":"real-time analytics","score":0.6880000233650208},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.8320000171661377},{"id":"https://openalex.org/keywords/data-quality","display_name":"data quality","score":0.6460000276565552},{"id":"https://openalex.org/keywords/decision-making","display_name":"decision-making","score":0.35899999737739563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6066677570343018},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3860950469970703},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34611424803733826},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.3245828151702881},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.32202380895614624},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2432686686515808},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22304272651672363}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.54660/.ijmrge.2021.2.1.791-799","doi":"https://doi.org/10.54660/.ijmrge.2021.2.1.791-799","is_oa":true,"landing_page_url":"https://doi.org/10.54660/.ijmrge.2021.2.1.791-799","pdf_url":"https://www.allmultidisciplinaryjournal.com/uploads/archives/20250329162858_MGE-2025-2-153.1.pdf","source":{"id":"https://openalex.org/S4210181739","display_name":"International Journal of Multidisciplinary Research and Growth Evaluation","issn_l":"2582-7138","issn":["2582-7138"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Multidisciplinary Research and Growth Evaluation","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.54660/.ijmrge.2021.2.1.791-799","doi":"https://doi.org/10.54660/.ijmrge.2021.2.1.791-799","is_oa":true,"landing_page_url":"https://doi.org/10.54660/.ijmrge.2021.2.1.791-799","pdf_url":"https://www.allmultidisciplinaryjournal.com/uploads/archives/20250329162858_MGE-2025-2-153.1.pdf","source":{"id":"https://openalex.org/S4210181739","display_name":"International Journal of Multidisciplinary Research and Growth Evaluation","issn_l":"2582-7138","issn":["2582-7138"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Multidisciplinary Research and Growth Evaluation","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.651199996471405,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4408969857.pdf","grobid_xml":"https://content.openalex.org/works/W4408969857.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W3083885319","https://openalex.org/W3109504564"],"related_works":["https://openalex.org/W4404156965","https://openalex.org/W4405089209","https://openalex.org/W4407934990","https://openalex.org/W4402216846","https://openalex.org/W4409343966","https://openalex.org/W4406771208","https://openalex.org/W6968183722","https://openalex.org/W7160354596","https://openalex.org/W7084314727","https://openalex.org/W4406601441"],"abstract_inverted_index":{"Predictive":[0],"modeling":[1,98,122,192],"has":[2],"emerged":[3],"as":[4,36,160],"a":[5,102,211],"powerful":[6],"tool":[7],"for":[8,196,214,221],"optimizing":[9,215],"business":[10,89,104,145,216],"operations":[11,146],"by":[12,126],"leveraging":[13],"machine":[14,50,66,142,206],"learning":[15,51,67,80,143],"techniques":[16,193],"to":[17,106,148,170,180,240],"reduce":[18],"inefficiencies.":[19],"This":[20,63],"study":[21,93,203],"explores":[22],"the":[23,139,172,187,219],"application":[24],"of":[25,88,141,174,189],"predictive":[26,97,121,175,191,208,243],"analytics":[27,209,236],"in":[28,101,144,182],"identifying":[29],"and":[30,40,49,61,71,78,86,112,135,154,165,199,225,237,245],"mitigating":[31],"common":[32],"operational":[33,115,200,246],"inefficiencies":[34],"such":[35,159],"delays,":[37],"resource":[38,108],"misallocation,":[39],"excessive":[41],"costs.":[42],"By":[43],"utilizing":[44],"historical":[45],"data,":[46],"real-time":[47,235],"analytics,":[48],"algorithms,":[52],"businesses":[53,178],"can":[54],"make":[55],"data-driven":[56,185],"decisions":[57],"that":[58,120,205],"enhance":[59,113,242],"efficiency":[60],"productivity.":[62,156],"examines":[64],"key":[65],"methodologies,":[68],"including":[69],"supervised":[70],"unsupervised":[72],"learning,":[73],"regression":[74],"models,":[75],"decision":[76],"trees,":[77],"deep":[79],"techniques,":[81],"which":[82],"enable":[83],"accurate":[84],"forecasting":[85],"optimization":[87],"processes.":[90],"The":[91,202],"case":[92],"approach":[94,213],"demonstrates":[95],"how":[96],"is":[99,194,210],"implemented":[100],"real-world":[103],"environment":[105],"improve":[107],"allocation,":[109],"streamline":[110],"workflows,":[111],"overall":[114],"performance.":[116],"Key":[117],"findings":[118],"indicate":[119],"significantly":[123],"improves":[124],"decision-making":[125,227],"providing":[127],"actionable":[128],"insights":[129],"into":[130],"demand":[131],"patterns,":[132],"process":[133],"bottlenecks,":[134],"workforce":[136],"planning.":[137],"Moreover,":[138],"integration":[140],"leads":[147],"increased":[149],"cost":[150],"savings,":[151],"reduced":[152],"waste,":[153],"enhanced":[155],"However,":[157],"challenges":[158],"data":[161],"quality,":[162],"model":[163],"interpretability,":[164],"scalability":[166],"must":[167],"be":[168],"addressed":[169],"maximize":[171],"benefits":[173],"analytics.":[176],"As":[177],"continue":[179],"evolve":[181],"an":[183],"increasingly":[184],"landscape,":[186],"adoption":[188],"advanced":[190],"essential":[195],"maintaining":[197],"competitiveness":[198],"efficiency.":[201],"concludes":[204],"learning-driven":[207],"transformative":[212],"operations,":[217],"paving":[218],"way":[220],"more":[222],"adaptive,":[223],"intelligent,":[224],"efficient":[226],"frameworks.":[228],"Future":[229],"research":[230],"should":[231],"focus":[232],"on":[233],"integrating":[234],"AI-driven":[238],"automation":[239],"further":[241],"accuracy":[244],"agility":[247],"across":[248],"various":[249],"industries.":[250]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":11}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-03-30T00:00:00"}
