An Empirical Data Model for Spare Parts Management: Linking Maintenance, Logistics, Inventory, and Equipment Data to Bridge Information Silos and Reduce Data-Gathering Efforts

Effective spare parts management (SPM) is imperative for equipment-intensive organizations to reduce equipment downtime through maintenance. Despite the extensive availability of data-driven SPM methodologies, decision-makers are challenged and tend to rely on tacit knowledge and simple approaches due to extensive data-gathering requirements and fragmented information across multiple organizational IT systems and departmental knowledge silos. A review of 60 academic SPM contributions demonstrated that data remains siloed and that research is limited in integrating data across SPM-relevant knowledge areas. This study proposes an empirical SPM data model to address this gap by consolidating and linking spare parts with maintenance, logistics, inventory, and equipment data, thus forming a coherent database across the identified SPM knowledge areas to bridge data silos and reduce data-gathering requirements. A case study assesses the effects of model implementation for decision-making on 10,843 spare parts and shows that model implementation led to a 15.1% stock value reduction, a 76–91% full-time equivalent resource improvement, a 4–5% decision quality improvement, and enhancement of decision-maker engagement. The data model reduces data-gathering efforts, enhances data accessibility, and improves decision quality and consistency.