Analyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0

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dc.contributor.authorPara, Jesus
dc.contributor.authorDel Ser, Javier
dc.contributor.authorNebro, Antonio J.
dc.contributor.authorZurutuza, Urko
dc.contributor.authorHerrera, Francisco
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T12:07:38Z
dc.date.available2024-07-24T12:07:38Z
dc.date.issued2019-06
dc.descriptionPublisher Copyright: © 2019 Elsevier Ltd
dc.description.abstractIndustry 4.0 is revolutionizing decision making processes within the manufacturing industry. Among the technological portfolio enabling this revolution, the late literature has capitalized on the potential of data analytics for improving the production cycle at different stages, from resource provisioning to planning, delivery and storage. However, such a promising role of data analytics has been so far explored without a proper, quantitative inspection of the cost-improvement trade-off, nor has the process of acquiring sensors and extracting valuable information from their captured data formalized in a series of methodological steps. This paper introduces the Analyze, Sense, Preprocess, Predict, Implement and Deploy (ASPPID) methodology, an iterative decision workflow that spans from the acquisition of sensing equipment to the quantitative assessment of the contribution of their captured data to enhance the production step under focus. By placing the data scientist at the core of the workflow, this methodology helps improvement teams make informed decisions about which parts of the process need to be sensed, and how to exploit this information towards a verifiable improvement of the production cycle. The implementation of this methodology is exemplified in a real use case within the automotive industry, where the detection of defects in an annealing process can be modeled as a classification problem over a highly imbalanced dataset. Results obtained after applying the proposed ASPPID methodology show that the scrap ratio is reduced by sensing the correct part of the process at minimal investment costs, thus highlighting the crucial role of the data scientist in the management team of manufacturing plants.en
dc.description.sponsorshipThe real case could not be possible without the participation of Fagor Ederlan S.Coop. and its R&D center, Edertek. Javier Del Ser receives funding support from the ELKARTEK and EMAITEK programs of the Basque Government, Spain . Antonio J. Nebro is supported by Grants TIN2014-58304 and ECO2014-56397-P ( Spanish Ministry of Economy and Competitiveness ), and P11-TIC-7529 and P12-TIC-1519 ( Plan Andaluz I+D+I, Spain ). The real case could not be possible without the participation of Fagor Ederlan S.Coop. and its R&D center, Edertek. Javier Del Ser receives funding support from the ELKARTEK and EMAITEK programs of the Basque Government, Spain. Antonio J. Nebro is supported by Grants TIN2014-58304 and ECO2014-56397-P (Spanish Ministry of Economy and Competitiveness), and P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz I+D+I, Spain).
dc.description.statusPeer reviewed
dc.format.extent14
dc.identifier.citationPara , J , Del Ser , J , Nebro , A J , Zurutuza , U & Herrera , F 2019 , ' Analyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID) : An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0 ' , Engineering Applications of Artificial Intelligence , vol. 82 , pp. 30-43 . https://doi.org/10.1016/j.engappai.2019.03.022
dc.identifier.doi10.1016/j.engappai.2019.03.022
dc.identifier.issn0952-1976
dc.identifier.urihttps://hdl.handle.net/11556/3789
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85063401850&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.relation.projectIDPlan Andaluz I+D+I
dc.relation.projectIDSpanish Ministry of Economy and Competitiveness, P12-TIC-1519-P11-TIC-7529
dc.relation.projectIDEusko Jaurlaritza
dc.relation.projectIDMinisterio de Economía y Competitividad, MEC, TIN2014-58304-ECO2014-56397-P
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsCost efficiency
dc.subject.keywordsImbalanced learning
dc.subject.keywordsIndustry 4.0
dc.subject.keywordsMethodological data analytics
dc.subject.keywordsProcess monitoring
dc.subject.keywordsControl and Systems Engineering
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsElectrical and Electronic Engineering
dc.subject.keywordsSDG 9 - Industry, Innovation, and Infrastructure
dc.titleAnalyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0en
dc.typejournal article
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