Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36536
Appears in Collections:Management, Work and Organisation Journal Articles
Peer Review Status: Refereed
Title: Integrating spare part inventory management and predictive maintenance as a digital supply chain solution
Author(s): Shokri, Alireza
Toliyat, Seyed Mohammad Hossein
Hu, Shanfeng
Skoumpopoulou, Dimitra
Contact Email: seyed.toliyat@stir.ac.uk
Keywords: Inventory Management
Supply Chain Management
simulation
Procurement
Artificial Intelligence
Predictive Maintenance
Issue Date: 24-Oct-2024
Date Deposited: 24-Nov-2024
Citation: Shokri 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/JM2-05-2024-0131
Abstract: Purpose-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.
DOI Link: 10.1108/JM2-05-2024-0131
Rights: Publisher 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.com
Notes: Deposit 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.
Licence URL(s): http://creativecommons.org/licenses/by-nc/4.0/

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