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Predicting the probability of occupational accidents occurrence in a Portuguese retail company

2025Journal article Open access
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datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
datacite.subject.fosCiências Naturais::Outras Ciências Naturais
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
dc.contributor.authorSena, Inês
dc.contributor.authorSilva, Felipe Gustavo Soares da
dc.contributor.authorBraga, Ana Cristina
dc.contributor.authorFernandes, Florbela P.
dc.contributor.authorVaz, Clara B.
dc.contributor.authorPacheco, Maria F.
dc.contributor.authorNovais, Paulo
dc.contributor.authorLima, José
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2025-12-12T12:21:04Z
dc.date.available2025-12-12T12:21:04Z
dc.date.issued2025
dc.description.abstractWorkplace accidents are a global problem impacting companies and society, as employee well-being and productivity/profit can be affected. Portugal ranks fifth among European Union countries despite efforts to reduce their frequency. Predictive solutions have demonstrated promising results in several economic sectors, but the retail sector, the country's third-largest in accident records, remains unexplored. This study proposes a predictive model based on the Multilayer Perceptron (MLP) algorithm to calculate the probability of risk situations occurring in a retail company. Ten databases provided by the company were analyzed and combined into a single dataset using impact scores. The predictive model was developed to predict risk situations in all the company's stores throughout two working days, the current and the next, and the four working shifts. The predictive model was implemented and tested in an integrated system for nine months and achieved 92% accuracy and a 29% precision rate in identifying risk situations. It is concluded that this approach provides a practical solution to assist companies and occupational health and safety teams prevent and minimize workplace accidents, contributing to increased safety and well-being.eng
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI UID/05757 (DOI: 10.54499/UIDB/05757/2020) and SusTEC LA/P/0007/2021 (DOI: 10.54499/LA/P/0007/2020). This work has been supported by NORTE-01-0247-FEDER-072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector. Inês Sena was supported by FCT , Portugal PhD grant UI/BD/153348/2022. Also, thanks to the Mountains Research Collaborative Laboratory (MORE CoLAB) for letting us test the algorithm in the intelligent system iSafety developed for them.
dc.identifier.citationSena, Inês; Silva, Felipe G.;Braga, Ana Cristina; Fernandes, Florbela P.; Vaz, Clara B.; Pacheco, Maria F.;Novais, Paulo; Lima, José, Pereira, Ana I. (2025). Predicting the probability of occupational accidents occurrence in a Portuguese retail company. Safety Science. ISSN 0925-7535. 192
dc.identifier.doi10.1016/j.ssci.2025.106975
dc.identifier.issn0925-7535
dc.identifier.urihttp://hdl.handle.net/10198/35214
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relationIntelligent system for occupational safety in retail sector
dc.relation.ispartofSafety Science
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectData mining
dc.subjectMachine learning
dc.subjectOccupational accidents
dc.subjectPredictive analysis
dc.subjectRetail sector
dc.titlePredicting the probability of occupational accidents occurrence in a Portuguese retail companyeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardTitleIntelligent system for occupational safety in retail sector
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F153348%2F2022/PT
oaire.citation.titleSafety Science
oaire.citation.volume192
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamOE
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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