Integrating spare part inventory management and predictive maintenance as a digital supply chain solution

Alireza Shokri, Seyed Mohammad Hossein Toliyat, Shanfeng Hu, Dimitra Skoumpopoulou

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
JournalJournal of Modelling in Management
Publication statusAccepted/In press - 24 Sept 2024

Keywords

  • Inventory Management
  • Supply Chain Management
  • Simulation
  • Procurement
  • Artificial Intelligence
  • Predictive Maintenance

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