Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/85647 |
Resumo: | The emergence of the Industry 4.0 concept and the profound digital transformation of the industry plays a crucial role in improving organisations' supply chain (SC) performance, consequently achieving a competitive advantage. The order fulfilment process (OFP) consists of one of the key business processes for the organization SC and represents a core process for the operational logistics flow. The dispatch workflow process consists of an integral part of the OFP and is also a crucial process in the SC of cement industry organizations. In this work, we focus on enhancing the order fulfilment process by improving the dispatch workflow process, specifically with respect to the cement loading process. Thus, we proposed a machine learning (ML) approach to predict weighing deviations in the cement loading process. We adopted a realistic and robust rolling window scheme to evaluate six classification models in a real-world case study, from which the random forest (RF) model provides the best predictive performance. We also extracted explainable knowledge from the RF classifier by using the Shapley additive explanations (SHAP) method, demonstrating the influence of each input data attribute used in the prediction process. |
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Predicting weighing deviations in the dispatch workflow process: a case study in a cement industryAnomaly predictiondispatch workflow processMachine learningOrder fulfilment processWeighing systemsBridgesSupply chainsCement industryTransportationRandom forestsPredictive modelsAnomaly detectionScience & TechnologyThe emergence of the Industry 4.0 concept and the profound digital transformation of the industry plays a crucial role in improving organisations' supply chain (SC) performance, consequently achieving a competitive advantage. The order fulfilment process (OFP) consists of one of the key business processes for the organization SC and represents a core process for the operational logistics flow. The dispatch workflow process consists of an integral part of the OFP and is also a crucial process in the SC of cement industry organizations. In this work, we focus on enhancing the order fulfilment process by improving the dispatch workflow process, specifically with respect to the cement loading process. Thus, we proposed a machine learning (ML) approach to predict weighing deviations in the cement loading process. We adopted a realistic and robust rolling window scheme to evaluate six classification models in a real-world case study, from which the random forest (RF) model provides the best predictive performance. We also extracted explainable knowledge from the RF classifier by using the Shapley additive explanations (SHAP) method, demonstrating the influence of each input data attribute used in the prediction process.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitivenessand Internationalization Programme (COMPETE 2020 ) [Project nº 069716; Funding Reference: POCI-01-0247-FEDER-069716].IEEEUniversidade do MinhoBarros, Júlio Dinis LopesCunha, FranciscoMartins, CândidoPedrosa, PauloCortez, Paulo20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85647engJ. Barros, F. Cunha, C. Martins, P. Pedrosa and P. Cortez, "Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry," in IEEE Access, vol. 11, pp. 8119-8135, 2023, doi: 10.1109/ACCESS.2022.3232299.2169-353610.1109/ACCESS.2022.3232299https://ieeexplore.ieee.org/abstract/document/9999237info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-23T01:37:44Zoai:repositorium.sdum.uminho.pt:1822/85647Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:10:07.048641Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry |
title |
Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry |
spellingShingle |
Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry Barros, Júlio Dinis Lopes Anomaly prediction dispatch workflow process Machine learning Order fulfilment process Weighing systems Bridges Supply chains Cement industry Transportation Random forests Predictive models Anomaly detection Science & Technology |
title_short |
Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry |
title_full |
Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry |
title_fullStr |
Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry |
title_full_unstemmed |
Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry |
title_sort |
Predicting weighing deviations in the dispatch workflow process: a case study in a cement industry |
author |
Barros, Júlio Dinis Lopes |
author_facet |
Barros, Júlio Dinis Lopes Cunha, Francisco Martins, Cândido Pedrosa, Paulo Cortez, Paulo |
author_role |
author |
author2 |
Cunha, Francisco Martins, Cândido Pedrosa, Paulo Cortez, Paulo |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Barros, Júlio Dinis Lopes Cunha, Francisco Martins, Cândido Pedrosa, Paulo Cortez, Paulo |
dc.subject.por.fl_str_mv |
Anomaly prediction dispatch workflow process Machine learning Order fulfilment process Weighing systems Bridges Supply chains Cement industry Transportation Random forests Predictive models Anomaly detection Science & Technology |
topic |
Anomaly prediction dispatch workflow process Machine learning Order fulfilment process Weighing systems Bridges Supply chains Cement industry Transportation Random forests Predictive models Anomaly detection Science & Technology |
description |
The emergence of the Industry 4.0 concept and the profound digital transformation of the industry plays a crucial role in improving organisations' supply chain (SC) performance, consequently achieving a competitive advantage. The order fulfilment process (OFP) consists of one of the key business processes for the organization SC and represents a core process for the operational logistics flow. The dispatch workflow process consists of an integral part of the OFP and is also a crucial process in the SC of cement industry organizations. In this work, we focus on enhancing the order fulfilment process by improving the dispatch workflow process, specifically with respect to the cement loading process. Thus, we proposed a machine learning (ML) approach to predict weighing deviations in the cement loading process. We adopted a realistic and robust rolling window scheme to evaluate six classification models in a real-world case study, from which the random forest (RF) model provides the best predictive performance. We also extracted explainable knowledge from the RF classifier by using the Shapley additive explanations (SHAP) method, demonstrating the influence of each input data attribute used in the prediction process. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/85647 |
url |
https://hdl.handle.net/1822/85647 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
J. Barros, F. Cunha, C. Martins, P. Pedrosa and P. Cortez, "Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry," in IEEE Access, vol. 11, pp. 8119-8135, 2023, doi: 10.1109/ACCESS.2022.3232299. 2169-3536 10.1109/ACCESS.2022.3232299 https://ieeexplore.ieee.org/abstract/document/9999237 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133349688639488 |