Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36536
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dc.contributor.authorShokri, Alirezaen_UK
dc.contributor.authorToliyat, Seyed Mohammad Hosseinen_UK
dc.contributor.authorHu, Shanfengen_UK
dc.contributor.authorSkoumpopoulou, Dimitraen_UK
dc.date.accessioned2024-12-07T01:02:24Z-
dc.date.available2024-12-07T01:02:24Z-
dc.date.issued2024-10-24en_UK
dc.identifier.urihttp://hdl.handle.net/1893/36536-
dc.description.abstractPurpose-The present study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint the barriers and identify economic values for such integration within the supply chain (SC). Design/methodology/approach-A two-staged embedded multiple case study with multi-method data collection and a combined discrete/continuous simulation were conducted to diagnose obstacles and recommend a potential solution. Findings-Several major organisational, infrastructure and cultural obstacles were revealed and an optimum scenario for the integration of spare part inventory management with PdM was recommended. Practical implications-The proposed solution can significantly decrease the inventory and SC costs as well as machinery downtimes through minimising unplanned maintenance and address shortage of spare parts. Originality-This is the first study with the best of our knowledge that offers further insights for practitioners in the Industry 4.0 (I4.0) era looking into embarking on digital integration of PdM and spare part inventory management as an efficient and resilient SC practice for the automotive sector by providing empirical evidence.en_UK
dc.language.isoenen_UK
dc.publisherEmeralden_UK
dc.relationShokri A, Toliyat SMH, Hu S & Skoumpopoulou D (2024) Integrating spare part inventory management and predictive maintenance as a digital supply chain solution. <i>Journal of Modelling in Management</i>. https://doi.org/10.1108/JM2-05-2024-0131en_UK
dc.rightsPublisher policy allows this work to be made available in this repository. Published in Journal of Modelling in Management by Emerald. Shokri, A., Toliyat, S.M.H., Hu, S. and Skoumpopoulou, D. (2024), "Integrating spare part inventory management and predictive maintenance as a digital supply chain solution", Journal of Modelling in Management, Vol. ahead-of-print No. ahead-of-print. The original publication is available at: https://doi.org/10.1108/JM2-05-2024-0131. This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.comen_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en_UK
dc.subjectInventory Managementen_UK
dc.subjectSupply Chain Managementen_UK
dc.subjectsimulationen_UK
dc.subjectProcurementen_UK
dc.subjectArtificial Intelligenceen_UK
dc.subjectPredictive Maintenanceen_UK
dc.titleIntegrating spare part inventory management and predictive maintenance as a digital supply chain solutionen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1108/JM2-05-2024-0131en_UK
dc.citation.jtitleJournal of Modelling in Managementen_UK
dc.citation.issn1746-5664en_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderInnovate UKen_UK
dc.author.emailseyed.toliyat@stir.ac.uken_UK
dc.citation.date24/10/2024en_UK
dc.description.notesDeposit licences Emerald allows authors to deposit their AAM under the Creative Commons Attribution Non-commercial International Licence 4.0 (CC BY-NC 4.0). To do this, the deposit must clearly state that the AAM is deposited under this licence and that any reuse is allowed in accordance with the terms outlined by the licence. To reuse the AAM for commercial purposes, permission should be sought by contacting permissions@emerald.com.en_UK
dc.contributor.affiliationNorthumbria Universityen_UK
dc.contributor.affiliationManagement, Work and Organisationen_UK
dc.contributor.affiliationNorthumbria Universityen_UK
dc.contributor.affiliationNorthumbria Universityen_UK
dc.identifier.isiWOS:001337831200001en_UK
dc.identifier.scopusid2-s2.0-85207173071en_UK
dc.identifier.wtid2050593en_UK
dc.contributor.orcid0000-0002-7673-9210en_UK
dc.date.accepted2024-09-24en_UK
dcterms.dateAccepted2024-09-24en_UK
dc.date.filedepositdate2024-11-24en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.versionAMen_UK
local.rioxx.authorShokri, Alireza|en_UK
local.rioxx.authorToliyat, Seyed Mohammad Hossein|0000-0002-7673-9210en_UK
local.rioxx.authorHu, Shanfeng|en_UK
local.rioxx.authorSkoumpopoulou, Dimitra|en_UK
local.rioxx.projectProject ID unknown|Innovate UK|http://dx.doi.org/10.13039/501100006041en_UK
local.rioxx.freetoreaddate2024-12-06en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by-nc/4.0/|2024-12-06|en_UK
local.rioxx.filenameJM2.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1746-5664en_UK
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