Spare Hardware Management Via Automation

Abstract

This research focuses on how Industry 4.0 technologies can be applied to manage spare hardware inventory to overcome various challenges associated with the conventional techniques. These include stock discrepancies, slow procurement, and bad forecasting that leads to time wastage and increased costs. A literature-based methodology was employed, analysing peer-reviewed studies and technical papers to examine technologies like IoT, Artificial Intelligence (AI), image processing, and microcomputing (e.g., Raspberry Pi). The study consolidates six major themes: traditional inventory constraints, automation and smart solutions, inventory optimization methods (EOQ, ABC, FSN), condition-based maintenance, cost-efficient automation for SMBs, and integration of ERP systems with AI. Research also shows that the use of automated systems helps in increasing inventory accuracy, decreasing delays in the operation and making accurate decisions as data is captured and analysed in real-time. A conceptual framework of real-time tracking, AI forecasting, and ERP-WMS integration is proposed for an efficient inventory solution. The research also found that automation deployment has beneficial impacts on efficiency, sustainability and competitiveness but that there are still challenges to deployment, including cost and readiness of workforce. As such, this study adds to the existing literature and practice of digital transformation in supply chain and offers specific recommendations to industries that are seeking to digitally transform their inventory management systems to suit the tenets of Industry 4.0.