Digitisation of industrial data with a view to improving decision making leading to increased efficiency

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dc.contributor.advisorBruton, Kenen
dc.contributor.authorClancy, Rose
dc.date.accessioned2022-09-28T14:40:42Z
dc.date.available2022-09-28T14:40:42Z
dc.date.issued2022-05
dc.date.submitted2022-05
dc.description.abstractIndustry 4.0 is the fourth stage of the industrial revolution which involves the interconnectedness of products and services, brought about by their digitisation. Industry 4.0 has been criticised regarding its definition and steps for implementation. This research proposes that digitalisation initiatives are conducted to pave the way for organisations in their transition to Industry 4.0. The DMAIC and CRISP-DM methodologies were integrated together in a case study with the purpose of digitising a manufacturing process to enable data-driven decision making leading to process improvement. However, upon implementation of these existing methodologies, there was a lack of tools focused on digitisation specifically. Therefore, the HyDAPI framework was developed, integrating CRISP-DM and DMAIC along with specific tools focused on digitisation, to help managers embark on their digital transformation journey. In line with this, quality management practitioners have yet to reach the potential of digitalisation. One of the objectives of this research was to provide a framework to guide quality practitioners with the implementation of digitalisation in their existing practices. Therefore, the Hybrid Digitalisation Approach to Process Improvement (HyDAPI) framework was proposed to address the emergent need for a digital strategy framework providing a versatile, practical approach for practitioners to follow in implementing digitalisation. The implementation of the proposed HyDAPI framework in an industrial case study was shown to increase efficiency, reduce waste, standardise work and enable the root causation of non-conforming products. The case studies as part of this research focused on the foundry value stream which had a scrap rate of approximately 20% across the year 2020. Analysis was conducted to determine if a relationship existed between manufacturing process parameters and the number of defective parts. The findings from this analysis highlighted that the level of metal ingress scrap was reduced from an average of 0.12 units per batch when SiO2 was at the higher level (average 28.63%) in comparison to the mean of 0.03 units of metal ingress scrap per batch when the SiO2 was at the lower level (average 26.28%). Implementing the HyDAPI framework to digitalise a quality review process resulted in the elimination of 1.9 hours to 3.7 hours per week spent manually gathering data. This research also highlights the requirements for digitalisation found in literature, including advanced skills, organisation structure and organisation agility along with the major barriers to the implementation of digitalisation. The HyDAPI framework was demonstrated to aid organisations in overcoming the barriers to digitalisation. This research also demonstrated how Lean Six Sigma practices can effectively be incorporated to aid the successful implementation of digitalisation and adoption of digital technologies as organisations migrate towards Industry 4.0.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationClancy, R. 2022. Digitisation of industrial data with a view to improving decision making leading to increased efficiency. PhD Thesis, University College Cork.en
dc.identifier.endpage257en
dc.identifier.urihttps://hdl.handle.net/10468/13691
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2022, Rose Clancy.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectDigitisationen
dc.subjectDigitalisationen
dc.subjectDataen
dc.subjectQuality managementen
dc.titleDigitisation of industrial data with a view to improving decision making leading to increased efficiencyen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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